CN111466063A - Energy storage management and control method, system, computer equipment and storage medium - Google Patents
Energy storage management and control method, system, computer equipment and storage medium Download PDFInfo
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Abstract
The application provides an energy storage management method, an energy storage control method, systems, computer equipment and a storage medium. The energy storage management method is used for managing an energy storage device for providing reserve electric energy for a power consumer, and comprises the following steps: acquiring a power supply prediction sequence available for the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer; and generating an energy sequence of the energy storage device in the power utilization period based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions and the power supply prediction sequence and the power consumption prediction sequence in the power utilization period, so that the energy storage device is managed based on the energy sequence. The energy sequence is based on this application is right energy memory manages, and then realizes the minimum purpose of power consumption total price.
Description
The present application relates to the field of industrial control technologies, and in particular, to an energy storage management method, an energy storage control method, and systems, a computer device, and a storage medium.
Nowadays, along with energy memory cost reduction, begin to set up energy memory in some industrial and mining enterprises, enterprise garden, the enterprise utilizes energy memory to carry out the energy storage operation during low price of electricity, carries out the power supply operation during high price of electricity, reduces the cost of purchasing the electricity to the electric wire netting.
In addition, the power supply cost is calculated more timely by the power grid, and the power supply cost and the electricity price are combined more closely in some places aiming at electricity utilization scenes such as industrial electricity utilization and the like, so that the electricity charge is collected for the electricity utilization party by utilizing a floating electricity price mode. The floating electricity price means that the electricity price purchased by the electricity consumers changes along with the change of time. With the addition of the floating electricity price to the existing electricity price mechanism, how to better utilize the energy storage device to reduce the electricity cost becomes an urgent problem to be solved.
Disclosure of Invention
In view of the above, an object of the present application is to provide an energy storage management method, an energy storage control method, and systems, a computer device, and a storage medium, which are used to solve the problem of how to reduce the electricity cost by using an energy storage device in the prior art.
To achieve the above and other related objects, a first aspect of the present application provides an energy storage management method for managing an energy storage device that provides a consumer with reserve electric energy, the energy storage management method comprising the steps of: acquiring a power supply prediction sequence available for the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer; and generating an energy sequence of the energy storage device in the power utilization period based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions and the power supply prediction sequence and the power consumption prediction sequence in the power utilization period, so that the energy storage device is managed based on the energy sequence.
In certain embodiments of the first aspect of the present application, the power supply prediction sequence includes a power rate prediction sequence, and the step of obtaining the power rate prediction sequence in a power utilization cycle includes any one of: acquiring a power price prediction sequence in the power utilization period; predicting an electricity price prediction sequence in the electricity utilization period available for the electricity utilization party based on the obtained historical electricity price prediction sequence and the deviation between the corresponding historical actual electricity prices; and predicting a power rate prediction sequence in the power utilization cycle based on the acquired power rate related information.
In certain embodiments of the first aspect of the present application, the power supply prediction sequence includes a self power supply prediction sequence from a power supply system, and the step of obtaining the self power supply prediction sequence in a power utilization cycle includes: predicting a self-powered prediction sequence within the power usage cycle based on the obtained power generation related information from the power supply system.
In certain embodiments of the first aspect of the present application, the step of obtaining a sequence of predictions of power usage by the power consumer comprises: acquiring power utilization related information according to the power utilization factors in the power utilization period; and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
In certain embodiments of the first aspect of the present application, the step of generating the energy sequence of the energy storage device in the power utilization cycle based on the energy storage parameters of the energy storage device obtained under the preset obtaining condition, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization cycle comprises: under at least one constraint condition, generating an energy sequence of the energy storage device in the power utilization period by taking the total price of the power utilization in the power utilization period as an optimization target; wherein the constraints comprise constraints determined based on the energy storage parameters.
In certain embodiments of the first aspect of the present application, the generating an energy sequence of the energy storage device during the power utilization cycle with an optimization goal of total price of power utilization during the power utilization cycle being low under at least one constraint condition comprises: generating one or more candidate energy sequences within the power usage cycle under at least one constraint; and under at least one constraint condition and with the total price of electricity consumption in the electricity consumption period as an optimization target, optimizing the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage device in the electricity consumption period.
In certain embodiments of the first aspect of the present application, the step of performing optimization processing on the generated one or more candidate energy sequences comprises: determining a candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for an optimization goal of total price reduction of electricity in the electricity utilization period, and taking the candidate energy sequence as the energy sequence; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
In certain embodiments of the first aspect of the present application, the energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.
In certain embodiments of the first aspect of the present application, the obtaining conditions comprise at least one of: updating events of the power supply prediction sequence, events of the power consumption prediction sequence and an updating period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power usage prediction sequence.
In certain embodiments of the first aspect of the present application, the energy storage management method further comprises the step of displaying at least one of the energy sequence, the power supply forecast sequence, and the power usage forecast sequence.
A second aspect of the present application provides an energy storage control method for controlling an energy storage device that supplies reserve electric energy to a consumer, the energy storage control method including the steps of: acquiring an energy sequence of the energy storage device in a power utilization period generated by the energy storage management method; and determining control information used for controlling the operation of the energy storage device in the operation time interval of the energy storage device based on the energy value corresponding to the operation time interval in the acquired energy sequence.
In certain embodiments of the second aspect of the present application, the energy storage control method further comprises the step of controlling the operation of the energy storage device within respective operation time intervals based on the control information.
In certain embodiments of the second aspect of the present application, the energy storage control method further comprises: and acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
In certain embodiments of the second aspect of the present application, the energy storage control method further comprises the step of updating the control information based on a newly generated energy sequence.
In certain embodiments of the second aspect of the present application, the control information comprises at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the operation time interval.
A third aspect of the present application provides an energy storage management system for managing an energy storage device that provides a power consumer with reserve electric energy, comprising: the acquiring module is used for acquiring a power supply prediction sequence which can be used by the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer; and the generating module is used for generating an energy sequence of the energy storage device in the power utilization cycle based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization cycle, so that the energy storage device is managed based on the energy sequence.
In certain embodiments of the third aspect of the present application, the power supply prediction sequence includes a power rate prediction sequence, and the obtaining module includes at least one of: the first acquisition unit is used for acquiring the electricity price prediction sequence in the electricity utilization period; a second obtaining unit configured to predict an electricity price prediction sequence within the electricity usage cycle available to the electricity consumer based on a deviation between the obtained historical electricity price prediction sequence and a corresponding historical actual electricity price; a third obtaining unit, configured to predict an electricity price prediction sequence in the electricity usage cycle based on the obtained electricity price related information.
In certain embodiments of the third aspect of the present application, the power supply prediction sequence includes a self-power supply prediction sequence from a power supply system, and the obtaining module includes a fourth obtaining unit configured to predict the self-power supply prediction sequence in the power utilization cycle based on the obtained power generation related information of the self-power supply system.
In certain embodiments of the third aspect of the present application, the obtaining module includes a fifth obtaining unit, configured to obtain the electricity consumption related information according to the electricity consumption factor in the electricity consumption cycle; and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
In certain embodiments of the third aspect of the present application, the generating module comprises: the generating unit is used for generating an energy sequence of the energy storage device in the power utilization period by taking the total price of the power utilization in the power utilization period as an optimization target under at least one constraint condition; wherein the constraints comprise constraints determined based on the energy storage parameters.
In certain embodiments of the third aspect of the present application, the generating unit is configured to generate one or more candidate energy sequences within the power usage cycle under at least one constraint; and under at least one constraint condition and with the total price of electricity in the electricity utilization period as an optimization target, optimizing the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage device in the electricity utilization period.
In certain embodiments of the third aspect of the present application, the generating unit is configured to determine one candidate energy sequence from the one or more candidate energy sequences as the energy sequence according to a cutoff condition set with a total price of electricity in the electricity utilization period as an optimization goal; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
In certain embodiments of the third aspect of the present application, the energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.
In certain embodiments of the third aspect of the present application, the obtaining conditions comprise at least one of: updating events of the power supply prediction sequence, events of the power consumption prediction sequence and an updating period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power usage prediction sequence.
In certain embodiments of the third aspect of the present application, the energy storage management system further comprises an output module for outputting for display at least one of the energy sequence, the power supply forecast sequence, and the power usage forecast sequence.
A fourth aspect of the present application provides an energy storage control system for controlling an energy storage device that provides reserve electric energy for a consumer, comprising: an obtaining module, configured to obtain an energy sequence of the energy storage device in a power utilization cycle generated by the energy storage management system; and the determining module is used for determining control information used for controlling the operation of the energy storage device in the operation time interval of the energy storage device based on the energy value corresponding to the operation time interval in the acquired energy sequence.
In certain embodiments of the fourth aspect of the present application, the energy storage control system further comprises a control module for controlling the operation of the energy storage device during respective operation time intervals based on the control information.
In certain embodiments of the fourth aspect of the present application, the energy storage control system further comprises: and the display module is used for acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
In certain embodiments of the fourth aspect of the present application, the energy storage control system further comprises: and the updating module is used for updating the control information based on the latest generated energy sequence.
In certain embodiments of the fourth aspect of the present application, the control information comprises at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the prediction time interval.
A fifth aspect of the present application provides a server, including: the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer; a storage unit for storing at least one program; and the processing unit is used for calling the at least one program to coordinate the interface unit and the storage unit to execute the energy storage management method.
A sixth aspect of the present application provides a computer device comprising: the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer; a storage unit for storing at least one program; and the processing unit is used for calling the at least one program to coordinate the interface unit and the storage unit to execute the energy storage control method.
A seventh aspect of the present application provides a computer-readable storage medium storing at least one program which, when invoked, performs the energy storage management method as described above.
An eighth aspect of the present application provides a computer-readable storage medium storing at least one program that, when invoked, performs the energy storage control method as described above.
A ninth aspect of the present application provides an energy storage control system, comprising: a server as described above and a computer device as described above.
As described above, the energy storage management method, the energy storage control method, the systems, the computer device, and the storage medium according to the present application have the following advantages: and generating an energy sequence of the energy storage device in a power utilization period based on the acquired power supply prediction sequence, the power consumption prediction sequence and the energy storage parameters of the energy storage device, so that the energy storage device can be managed based on the energy sequence, and the purpose of lowest total power utilization price is further realized.
Fig. 1 is a schematic diagram showing the power transmission relationship among a power generation system, a self-powered system, a power utilization system and an energy storage device.
Fig. 2 is a schematic structural diagram of a server according to an embodiment of the present application.
Fig. 3 is a flowchart illustrating an energy storage management method according to the present application.
Fig. 4a to 4d are schematic diagrams respectively illustrating an electricity price prediction sequence, a self-powered quantity prediction sequence, a power consumption prediction sequence and an energy sequence of an energy storage device in a power utilization period based on the energy storage management method of the present application.
Fig. 5 is a graph illustrating a total electricity consumption rate obtained by an electricity consumer based on the energy storage management method of the present application and a total electricity consumption rate without an energy storage device.
Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Fig. 7 is a flowchart illustrating an energy storage control method according to the present application.
Fig. 8 is a schematic structural diagram of an energy storage management system operated by a server according to an embodiment of the present disclosure.
Fig. 9 is a schematic structural diagram of an energy storage control system operated by a computer device according to an embodiment of the present invention.
Fig. 10 is a schematic diagram illustrating a network architecture of an energy storage control system according to an embodiment of the present disclosure.
The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.
Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.
Please refer to fig. 1, which is a schematic diagram illustrating a power transmission relationship among a power generation system, a self-powered system, a power utilization system and an energy storage device. The power generation system is managed by a power supplier, the self-power supply system, the power utilization system and the energy storage device are located on one side of power utilization parties such as power utilization enterprises, parks and buildings, the power generation system provides power for the power utilization system and the energy storage device through a power grid, and the self-power supply system is used for providing power for the power utilization system. The self-powered system comprises a solar power generation system, a wind power generation system, a transduction power generation system and the like. Furthermore, energy storage devices such as chemical energy storage devices and the like. For the existing two-section electricity price mechanism, the control mode of the energy storage device is easy to design, so that the energy storage device can store electric energy when the electricity price is low and release the electric energy when the electricity price is high, and the purpose of reducing the electricity charge is realized. However, when the electricity rate mechanism changes from two-segment sectional electricity rate to a time-floating charging method such as multi-segment sectional electricity rate, the control of the energy storage device becomes very complicated.
In order to improve the utilization rate of the energy storage device under the condition that information such as electricity price and total electricity consumption is in a change state so as to effectively reduce the electricity consumption cost, the application provides an energy storage management method for managing the energy storage device which provides stored electric energy for the electricity consumers. The energy storage management method is mainly executed by an energy storage management system. The energy storage management system can be a software system configured at a server, and executes a corresponding program by using hardware of the configured server to provide an energy sequence of the energy storage device in a power utilization period to be predicted for a power consumer, so that the power consumer can manage the energy storage device based on the energy sequence. Wherein, the power utilization cycle is exemplified by natural day, natural month and the like. The energy sequence refers to a set of a plurality of energy values of the energy storage device to be managed in time sequence in the power utilization period. The generated energy sequence of the energy storage device can be used for helping a power consumer manage the energy storage device, so that the aim of reducing the power consumption cost as low as possible in each power consumption period is fulfilled by managing the energy storage device.
Therefore, the application provides an energy storage management method. The energy storage management method is mainly executed by a server side. Here, the server includes, but is not limited to, a single server, a server cluster, a distributed server group, a cloud server, and the like. The Cloud Service end comprises a Public Cloud (Public Cloud) Service end and a Private Cloud (Private Cloud) Service end, wherein the Public or Private Cloud Service end comprises Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure as a Service (IaaS), and Infrastructure as a Service (IaaS). The private cloud service end is used for example for an Aliskian cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform and the like.
Here, the service end is in communication connection with an electricity price issuing system of an electricity supplier, an energy storage control system of an energy storage device, an electricity utilization control system of an electricity consumer, a management system of production activities, a self-powered system and the like, and may even be in data connection with a third party system, and acquires internet data and the like related to electricity utilization of the electricity consumer in the internet by using a crawler technology. Wherein the electricity price distribution system is a system in which an electricity supplier (or an electricity market manager such as a government department) distributes electricity prices. For example, the electricity rate distribution system distributes the electricity rate prediction sequence every 30 minutes for 24 hours thereafter. The energy storage control system includes, but is not limited to: the energy storage device comprises a detection device for detecting the energy stored by the energy storage device, a charge and discharge control system of the energy storage device and the like. The electricity utilization control system includes but is not limited to: metering devices (e.g., electrical meters), electrical equipment control systems, etc. installed within an enterprise. The management system of the production activity includes but is not limited to: a Manufacturing Execution System (MES), an Enterprise Resource Planning System (ERP), and the like. The self-powered systems include, but are not limited to: a detection device for detecting the amount of power generation from the power supply system, a power generation control system of the self-powered system, and the like. Examples of the third-party system include a self-owned server for storing historical electricity consumption data, a self-owned server for storing historical electricity price data, a self-owned WEB server for acquiring an enterprise electricity consumption plan and the like. Examples of the internet data include weather forecast data, which may be predicted based on historical contemporaneous weather data obtained from the internet, or weather forecast data obtained directly from a weather website or other websites.
Referring to fig. 2, which is a schematic structural diagram of a server according to an embodiment of the present disclosure, as shown in the figure, the server includes an interface unit 11, a storage unit 12, and a processing unit 13. The storage unit 12 includes a nonvolatile memory, a storage server, and the like. The nonvolatile memory is, for example, a solid state disk or a usb disk. The storage server is used for storing the acquired various electricity utilization related information and power supply related information. The interface unit 11 includes a network interface, a data line interface, and the like. Wherein the network interfaces include, but are not limited to: network interface devices based on ethernet, network interface devices based on mobile networks (3G, 4G, 5G, etc.), network interface devices based on near field communication (WiFi, bluetooth, etc.), and the like. The data line interface includes, but is not limited to: USB interface, RS232, etc. The interface unit is connected with data such as each system of a power supply party, each system of a power utilization party, a third party system, the Internet and the like. The processing unit 13 is connected to the interface unit 11 and the storage unit 12, and includes: a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor. The processing unit 13 also includes memories, registers, etc. for temporarily storing data.
Please refer to fig. 3, which is a flowchart illustrating the energy storage management method. The energy storage management method is mainly executed by a processing unit 13 in a server, and data interaction is performed by reading at least one program stored in a storage unit 12 by the processing unit and according to hardware connection between the processing unit and hardware units such as the storage unit and an interface unit. In some implementations, the processing unit may perform the following steps at the beginning of the electricity price change to obtain the energy sequence provided by the energy storage management method to manage the energy storage device during the current electricity utilization period. In other practical applications, the energy stored in the energy storage device needs to be adaptively adjusted in time due to the continuous change of the actual power consumption of the power consumers, so that the power consumption cost of the power consumers in the whole electricity settlement period is as low as possible. For this purpose, the processing unit will repeatedly perform the following steps in order to adjust the energy in the energy storage device in time.
In step S110, a power supply prediction and a power consumption prediction sequence of the power consumers are acquired, which are available to the power consumers in a power utilization cycle. The power utilization cycle is the power utilization cycle to be predicted, and may be a pre-agreed power utilization cycle or a power utilization cycle set according to an available floating power price change cycle. Wherein, the floating electricity price change period refers to the time interval of electricity price change. For example, the floating electricity price change period is a time period during which a single electricity price is maintained. As another example, the floating electricity price change period is an update duration of a floating electricity price sequence. The power supply prediction sequence includes a set of a plurality of power supply amounts that a power supplier, an own power supply system, or a third party predicts in time order within a power consumption cycle. The third party comprises a set of a plurality of power supply amounts predicted in time sequence in a power utilization period, wherein the set is obtained by simulating power supply related parameter data, historical power supply data and the like acquired from power utilization parties.
In some embodiments, where the power available to the power consumer comprises a purchase from a power supplier, the power supply prediction sequence includes a power rate prediction sequence. Accordingly, the step of acquiring the power supply prediction sequence in the electricity usage period in step S110 includes the step of acquiring the power rate prediction sequence in the electricity usage period. Wherein, the electricity price forecasting sequence refers to a set of a plurality of electricity prices forecasted in time sequence in the electricity utilization period.
In actual applications, there are cases where a third party (e.g., a separate electricity rate prediction system, an electricity provider, or a separate electricity rate pricing system) provides the electricity rate prediction sequence. Based on this, in one embodiment, the step of obtaining the power rate prediction sequence in a power utilization cycle may include directly obtaining the power rate prediction sequence from the third party as the power supply prediction sequence in step S110. Here, it should be noted that the electricity price prediction sequence provided by the third party is usually an electricity price prediction sequence spanning a certain time period, and therefore, the electricity usage period may also be set based on the time period spanned by the electricity price prediction sequence provided by the third party, for example, in the case where the time period spanned by the electricity price prediction sequence provided by the third party is 12 hours, the electricity usage period may be set to 12 hours or less than 12 hours, so that the electricity price prediction sequence acquired from the third party can be directly used in the subsequent processing.
In fact, the electricity rate prediction sequence issued by the third party is deviated from the actual electricity rate, and thus, in another embodiment, the step of acquiring the electricity rate prediction sequence in the electricity usage period may include: and predicting the electricity price prediction sequence in the electricity utilization period available for the power consumer based on the acquired historical electricity price prediction sequence and the corresponding deviation between the historical actual electricity prices, so that the accuracy of the electricity price prediction sequence based on which the energy sequence is generated is improved, and the accuracy of the generated energy sequence is further improved.
In one example, a third-party provided historical predicted sequence of electricity prices and corresponding historical actual electricity prices are first obtained. For example, a historical electricity price prediction sequence of a certain historical time period (such as the last year) may be obtained from a third party or other data platform, and a historical actual electricity price corresponding to at least one historical electricity price prediction value in the historical electricity price prediction sequence is obtained; then, counting the electricity price error between the historical electricity price predicted value and the corresponding historical actual electricity price to obtain an electricity price error range; and correcting the electricity price prediction sequence in the electricity utilization period obtained from the third party by taking the electricity price error range as a correction parameter to obtain an electricity price prediction sequence on which the energy sequence is generated. It should be noted that, when the electricity price errors between the historical electricity price prediction sequence and the historical actual electricity price are counted, a plurality of electricity price error ranges may be obtained according to the time length, and the electricity price prediction sequence of the corresponding time length in the electricity utilization cycle obtained from the third party may be corrected based on the plurality of electricity price error ranges.
In still other embodiments, the power supplier does not provide the electricity rate prediction sequence, and the step of obtaining the electricity rate prediction sequence in the electricity usage period may include: and predicting a power rate prediction sequence in the power utilization cycle based on the acquired power rate related information. Wherein the electricity price related information includes, but is not limited to, at least one of: historical actual electricity price sequences, electricity price rules for electricity markets, other factors that affect electricity price changes, and the like. The historical actual electricity price sequence refers to a set of a plurality of actual electricity prices in a time sequence within a certain historical time period. For example, the historical actual electricity price sequence may be obtained from a third party or other data platform. The electricity price rule of the electricity market refers to an electricity price rule set by a local government or an electricity supplier for a governed region, and includes but is not limited to: fine price of electricity set based on the electricity demand of the electricity consumers, and the like. Examples of the other factors that affect the change in electricity prices include weather, holidays, and the like. For example, a power rate prediction sequence in a power usage cycle is predicted based on the acquired weather forecast, the released holiday schedule, and the historical actual power rate sequence.
In one example, the prediction algorithm, such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN), etc., is used to calculate the power rate prediction sequence within the power cycle as an output, taking the above power rate related information into comprehensive consideration, and taking historical actual power rate sequence, weather forecast, holiday arrangement, etc. as the input of the prediction model.
It should be noted that the above embodiments of obtaining the electricity price prediction sequence are only examples, and are not limiting to the present application. One skilled in the art can construct a model for predicting the electricity price prediction sequence in conjunction with the various embodiments described above. For example, a power rate prediction sequence is calculated based on the input of the prediction model, the adopted prediction algorithm and the error range of the detected historical power rate data, so as to improve the accuracy of the subsequent prediction.
In further embodiments, where the consumer is configured with a self-powered system, i.e., the power available to the consumer comprises power provided by the self-powered system, the power prediction sequence further comprises a self-powered quantity prediction sequence. The self-supply amount prediction sequence refers to a set of a plurality of self-supply amounts predicted in time sequence in a power utilization cycle. The self-powered systems include, but are not limited to: photovoltaic power generation system, heat conversion system, trigeminy supplies system, wind power generation system etc..
Accordingly, the step of obtaining the power supply prediction sequence in the power utilization period in step S110 includes the step of obtaining a self-power supply prediction sequence in the power utilization period. The step S110 includes predicting a self-powered prediction sequence within the power utilization cycle based on the acquired power generation related information of the self-powered system according to the self-powered system used by the actual power consumer. Wherein the power generation related information includes, but is not limited to: historical power generation data, and factors influencing power generation based on the working principle of the self-powered system. For example, in the case of a self-powered system employing photovoltaic power generation, factors affecting power generation mainly include solar irradiance and the like. For another example, in the case that the self-powered system employs wind power generation, the factors influencing the power generation mainly include wind speed, wind direction, and the like. For another example, in the case of a self-powered system that employs thermal conversion to generate electricity, the factors that affect the generation of electricity include mainly the thermal conversion efficiency of the system, the detected temperature, and the like.
The self-power-supply prediction sequence in the power utilization cycle is obtained by taking the power generation related information as the input of the prediction model and adopting a prediction algorithm such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN) and the like to calculate, and the self-power-supply prediction sequence is taken as the output.
It should be noted that the above embodiments of obtaining the self-power prediction sequence are only examples, and are not intended to limit the present application. One skilled in the art can construct a model for predicting the self-supply power prediction sequence in conjunction with the various embodiments mentioned in the foregoing power rate prediction sequence. For example, a self-powered electricity supply amount prediction sequence is calculated based on the input of the prediction model, the adopted prediction algorithm and the error range obtained through detection so as to improve the accuracy of subsequent prediction.
It should also be noted that the above manner of self-power prediction by using the self-power supply system is only an example, and not a limitation of the present application. Those skilled in the art will understand that the power generation related information based on the self-powered electricity quantity prediction sequence differs according to the power supply mode of the actual self-powered system, and the details are not repeated here.
It should be further noted that, according to the actual situation, the power supply prediction sequence obtained by executing the step S110 may include only the power price prediction sequence or a self-power amount prediction sequence; or both the electricity price prediction sequence and the self-power prediction sequence. And may not be limiting herein.
Further, in step S110, the step of obtaining the power consumption prediction sequence of the power consumer includes: and acquiring power consumption related information according to the power consumption factors in the power consumption period, and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information. The electricity consumption prediction sequence refers to a set of a plurality of electricity consumptions predicted according to the time sequence in the electricity utilization period. The electricity consumption is obtained by the electricity consumers and is related to the electricity consumption factors of the daily production activities of the electricity consumers. Wherein the electricity utilization factors include but are not limited to: human programs such as scheduling programs, store activity programs, programs summarized according to weather or social activity rules (e.g., weekdays, holidays). For example, for the electricity utilization situation of the product a produced in the factory, the electricity utilization related information may include historical electricity utilization data of the product a produced, equipment usage information determined based on the scheduling plan of the product a, electricity utilization information of the equipment, and the like. For another example, the electricity consumption related information may include air conditioner usage information based on season setting, air conditioner usage information, use information of lighting lamps for working days and holidays, computers, and the like, for the electricity consumption situation of an office building. In some cases where air conditioner usage information is not set, the air conditioner usage information may also be determined based on weather forecast conditions. For example, the use of air conditioning is controlled in accordance with the forecasted air temperature.
The electricity consumption related information is used as the input of the prediction model, and a prediction algorithm such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN) and the like is adopted to calculate to obtain the electricity prediction sequence of the electricity consumption party in the electricity consumption period as the output.
The above-described method for predicting the amount of electricity used based on the electricity-related information is only an example, and is not a limitation of the present application. Those skilled in the art should understand that other electricity related information affecting the electricity consumption prediction sequence can also be used as an input of the prediction model to obtain the electricity consumption prediction sequence through a prediction algorithm, and details are not repeated here.
In step S120, an energy sequence of the energy storage device in the power utilization cycle is generated based on the energy storage parameters of the energy storage device acquired under the preset acquisition conditions, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization cycle, so that the energy storage device is managed based on the energy sequence.
Wherein the preset acquisition condition comprises at least one of the following: updating events of the power supply prediction sequence, events of the power consumption prediction sequence and an updating period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power usage prediction sequence.
Here, the event of updating the power supply prediction sequence includes, but is not limited to: and updating events of the third party electricity price prediction sequence, changes of factors influencing the electricity price and the like. Examples of the factors affecting the electricity price change include an event that the electricity consumption is increased due to a new activity day and then the electricity price changes, a factor affecting the power generation of the self-powered system changes, and the like. Examples of the change in the factor affecting the power generation from the power supply system include: and the solar photovoltaic power generation amount is reduced due to sudden weather change, and then the self-powered power supply amount is changed. Additionally, events that update the power usage prediction sequence also include, but are not limited to: events in which factors affecting power usage change. Such as events that increase or decrease the amount of power used due to a change in the scheduling plan.
Further, in some embodiments, the update period is determined based on an update period of a power supply prediction sequence. The update period of the power supply prediction sequence may be a preset update period, or may be an update period set according to a floating power rate change period. For example, in the case where the floating electricity prices are changed every 30 minutes, the update period is set to be updated every 30 minutes. In other embodiments, the update period is determined based on an update period of a power usage prediction sequence. The update cycle of the power consumption prediction sequence may be a preset update cycle, or may be set according to the adjustment of the power consumption plan. For example, when a scheduling plan is adjusted, an update period is set according to the corresponding adjustment event. In still other embodiments, the update period is determined based on an update period of a power supply forecast sequence and an update period of a power usage forecast sequence. For example, the energy storage parameters of the energy storage device are obtained every time the electricity price prediction sequence changes, and the energy storage parameters of the energy storage device are obtained every time the electricity utilization plan is adjusted. In addition, the update period also includes updates that are not performed in accordance with operations suggested by the energy storage management method. For example, when an operator is recommended to perform charging operation on the energy storage device at a certain time according to the energy storage management method, but the operator does not perform operation according to the recommendation, the operator needs to update the energy storage device when performing operation again, and then performs corresponding operation on the energy storage device based on the updated energy storage management recommendation.
The energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device. Wherein the capacity of the energy storage device comprises a maximum capacity and a minimum capacity of the energy storage device. The charge and discharge parameters of the energy storage device comprise the charge speed of the energy storage device, the discharge speed of the energy storage device, the upper limit and the lower limit of charge and discharge power and the like. The loss parameters of the energy storage device comprise the energy conversion rate of the energy storage process of the energy storage device, the energy conversion rate of the energy release process of the energy storage device and the energy loss rate of the idle process of the energy storage device. The energy storage parameter may also be a set of parameters determined based on a temperature dependent variable.
And when the server side reaches the preset acquisition condition in the working period, updating the power supply prediction sequence, the power consumption prediction sequence and the energy storage parameters of the energy storage device. In some examples, if either one of the power supply prediction sequence and the power consumption prediction sequence is updated, an energy sequence in a next power consumption cycle is generated based on the updated power supply prediction sequence, the power consumption prediction sequence, and the acquired energy storage parameter from the update time. Taking an update period of 30 minutes and a power consumption period of 24 hours as an example, the server generates an energy sequence of the next 24 hours every 30 minutes, wherein the energy sequence may include energy values stored in the energy storage device predicted and sorted according to a 30-minute time interval.
Here, the server side can perform energy storage management on the energy storage device according to the actual management requirement of the power consumer, and further generate an energy sequence meeting the management requirement. Wherein the management requirements include, but are not limited to: the total price of the electricity consumption is reduced as much as possible, the electricity consumption of the peak value of the electricity consumption is reduced as much as possible, and the like. And the server side performs energy management on the energy storage device based on the energy sequence generated by the acquisition condition.
In certain embodiments, step S120 comprises: and under at least one constraint condition, generating an energy sequence of the energy storage device in the power utilization period by taking the total price of the power utilization in the power utilization period as an optimization target. Wherein the constraints comprise constraints determined based on the energy storage parameters. Under the condition that the lowest total electricity price in the electricity utilization period is taken as an optimization target, the optimization target function is as follows:
wherein t represents the t-th time, EG2LRepresenting the amount of electricity purchased and used directly by consumers from the grid; eG2BRepresenting the amount of electricity purchased and stored by the consumer from the grid; eB2LThe electric quantity released and used by the energy storage device of the power consumer is represented; pGA real-time electricity rate representing a purchase of electricity from the power grid; pBRepresenting the price converted by the costs of charging and discharging, loss and the like of the energy storage device.
In addition, for an energy storage device, the mathematical description of the model may be:
Ebtty(t)＝Ebtty(t-Δt)+ΔE
wherein E isbtty(t) the amount of electricity stored in the energy storage device at time t, EbttyAnd (t-delta t) is the electric quantity stored in the energy storage device at the moment (t-delta t), and delta E is the electric quantity stored or released in delta t per unit time. Further, Δ E is expressed as:
ΔE＝EG2B×echarge；EB2L＝0
or, Δ E ═ EB2L×edischarge；EG2B＝0
Or, Δ E ═ Eloss；EB2L＝EG2B＝0
Wherein e ischargeAn energy conversion rate representing a charging process of the energy storage device; e.g. of the typedischargeAn energy conversion rate representing a discharge process of the energy storage device; elossRepresenting the amount of self-discharge of the energy storage device per unit time deltat.
In this case, at least one constraint condition is set according to the energy storage parameters of the energy storage device that can be actually obtained, which is intended to prevent an abnormality of the energy storage device when managing the energy storage device. For example, it is avoided that a certain energy value in the generated energy sequence exceeds the maximum capacity of the energy storage means, etc. Based on the optimization objective function and the model of the energy storage device, wherein the charging amount E of the energy storage deviceG2BAnd energy storage device discharge capacity EB2LControlled by a model of the energy storage device, constraints of the model including at least one of: constraints set for the energy storage device, and constraints set based on the relationship between the consumption of electric energy and the supply of electric energy.
Wherein the constraint condition set for the energy storage device includes at least one of:
1) capacity of energy storage device: ebtty_MIN≤Ebtty≤Ebtty_MAX
2) Charging and discharging speed of the energy storage device: delta E/delta t is more than or equal to 0 and less than or equal to CRchargeOr CRdischargeDelta E/delta t is less than or equal to 0; wherein E isbtty_MINRepresents a minimum capacity of the energy storage device; ebtty_MAXRepresents a maximum capacity of the energy storage device; CRchargeRepresenting a charging speed of the energy storage device; CRdischargeIndicating the discharge rate of the energy storage device. Meanwhile, the related variables of the energy storage device are all temperature related variables.
The constraint condition set based on the relationship between the power consumption and the power supply refers to a sum of at least one or more of power purchased from the power grid, power provided by discharging the energy storage device, and power corresponding to a self-power supply amount generated by the self-power supply system, that is, (E)G2L+EB2L+EP2L) Wherein E isP2LRepresenting the real-time self-powered amount of the power consumers. In the case where the self-power is used for the operation of the consumer apparatus, the difference between the total power demand of the consumer and the self-power prediction result is used as the sum of the power purchased from the grid and directly used and the power discharged and used from the energy storage device (E)G2L+EB2L) The constraint of (2). That is, during a certain period of time, the upper limit of the discharge capacity of the energy storage device is equal to the difference between the total demand capacity and the self-powered capacity, and if the discharge capacity of the energy storage device is insufficient, the energy storage device is complemented with the capacity purchased from the power grid.
It should be noted that the self-power supply of the self-power supply system may also sell redundant parts to the power supplier according to the actual situation, which does not affect the energy storage management scheme described in the present application and is not described in detail herein.
In one embodiment, the step S120 of generating an energy sequence of the energy storage device in the power utilization cycle with the total power utilization level in the power utilization cycle as an optimization target under at least one constraint condition includes: generating one or more candidate energy sequences within the power usage cycle under at least one constraint; and under at least one constraint condition and with the total price of electricity consumption in the electricity consumption period as an optimization target, optimizing the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage device in the electricity consumption period.
In certain embodiments, one or more candidate energy sequences over a power cycle are generated based on the predicted or detected energy stored by the energy storage device, and all of the constraints discussed above. Here, for the initialization candidate energy sequence (also referred to as an initialization candidate solution), one or more preset candidate energy sequences, that is, candidate solutions, may be generated in a random manner.
Wherein, in some examples, the generated candidate solution is one, and the candidate solution is optimized under at least one constraint condition and with the total price of electricity in the electricity utilization period as an optimization target. For example, under the constraints described above, a candidate solution to the power usage period is generated. And optimizing the generated candidate solution by using the change trend of the total electricity consumption price corresponding to the candidate solution in a delta t time length so as to obtain an energy sequence which takes the total electricity consumption price in the electricity consumption period as an optimization target under at least one constraint condition.
In other examples, the generated candidate solution is a plurality of candidate solutions, and the candidate solution is filtered and/or adjusted from the plurality of candidate solutions to obtain the energy sequence under at least one constraint condition and with the total price of electricity in the electricity utilization period as an optimization goal. For example, the total electricity consumption price corresponding to each of the plurality of candidate solutions generated under the constraint condition is calculated, and the candidate solution with the lowest total electricity consumption price is selected as the generated energy sequence. For another example, calculating the total electricity consumption price corresponding to each of the multiple candidate solutions generated under the constraint condition, and selecting the candidate solution with the lowest total electricity consumption price; and optimizing the generated candidate solution by using the change trend of the total electricity consumption price corresponding to the candidate solution in a delta t time length so as to obtain an energy sequence which takes the total electricity consumption price in the electricity consumption period as an optimization target under at least one constraint condition.
In some embodiments, the step of performing optimization processing on the generated one or more candidate energy sequences includes: determining a candidate energy sequence from one or more candidate energy sequences according to a cutoff condition set with the total price of electricity in the electricity utilization period as an optimization target, and taking the candidate energy sequence as the energy sequence; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
The updating strategy comprises but is not limited to Lagrange Multiplier (L) method, sequence linear programming (S L P), Sequence Quadratic Programming (SQP), Interior Point method (Interior Point), Exterior Point method (Exterior Point), Active Set method (Active Set), Trust domain reflection algorithm (Trust Region reflection), Heuristic algorithm (Hearistic Algorithms), Meta-Heuristic algorithm (Meta-Gaussian), Evolutionary algorithm (evolution Algorithms), Swarm Intelligent algorithm (Swarm Intigrithms), Neural network algorithm (Neural network strategy), taboo search algorithm, simulated annealing algorithm, ant colony optimization algorithm, greedy optimization algorithm, self-adaptive search algorithm, random adaptive search algorithm, and other artificial immune system optimization or similar artificial selection strategies.
Taking an update period of 30 minutes and a power consumption period of 24 hours as an example, based on the model constraint condition of the energy storage device and certain prior calculation, carrying out constraint limitation on a high-dimensional solution space, limiting the solution space in a local space range meeting the constraint condition, and obtaining a plurality of candidate solutions, wherein each candidate solution is 48-dimensional; all the solution is substituted into the optimization objective function to obtain the optimization objective value corresponding to each candidate solution (the evaluation step for short); then, sorting according to the optimized target values corresponding to the candidate solutions, screening and reserving a certain number of excellent solutions and eliminating the rest solutions (screening step for short); sorting the optimization target values in the order from small to large (namely the order from low to high of the total price of the electricity), screening out candidate solutions corresponding to the optimization target values of n (n is more than or equal to 1) before ranking, and eliminating the rest solutions. Next, a corresponding number of clones are performed on the n candidate solutions retained by the screening, and random variation with a certain probability (variation rate) is introduced in the cloning process, so as to generate new candidate solutions based on each retained candidate solution (referred to as a variant cloning step). Wherein the mutation rate is limited by the model constraint condition to ensure that the obtained new candidate solution is obtained based on the slight change of the candidate solution before the mutated clone. In this case, the variation rate may be introduced to all the clonal solutions of the retained candidate solutions, or only to the partial solutions. Repeating the steps of evaluating, screening and mutating cloning until the actual iteration times reach the cut-off condition of the preset iteration times; and all the finally obtained candidate solutions are substituted into the optimization objective function to obtain the optimization target value corresponding to each candidate solution, and the candidate solution corresponding to the minimum optimization target value is selected as the energy sequence of the energy storage device.
Taking the model constraint conditions and the optimization target of the energy storage device as examples, and obtaining a specific example of the energy sequence by using the SQP algorithm, the specific example is as follows: under the model constraint condition of the energy storage device and the constraint of certain prior calculation, converting an objective function and a constraint function by using Taylor expansion, and calculating by using the converted objective function and the constraint function to obtain a candidate solution and an error gradient; adjusting the candidate solution based on the obtained error gradient, and repeating the steps of calculating the candidate solution and adjusting until a cutoff condition that the error gradient is smaller than a preset gradient threshold value is met; and taking the finally obtained candidate solution as an energy sequence of the energy storage device.
It should be noted that the cutoff conditions described in any of the above examples are not strictly in one-to-one correspondence with the algorithms used, and may also be set according to actual design requirements, for example, the change of the optimal target result of the latest iterations is smaller than a preset threshold, and the like, which is not described herein again. In addition, the above numerical values are only examples and are not limiting to the present application, and those skilled in the art can arbitrarily select the numerical values to calculate based on the idea of the present application.
It should be further noted that, during the evaluation, screening and iteration process of the candidate solution, besides the variant clone processing manner, the above steps may be adapted and selected based on the aforementioned other algorithms, for which the aforementioned other algorithms and other manners applicable to the technical idea described in the present application to determine the energy sequence of the energy storage device are used, and should not be considered as a specific example based on the technical idea described in the present application, and detailed description thereof is omitted here.
In addition, the energy storage management method further comprises the step of displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence. Please refer to fig. 4a to 4d, which are schematic diagrams illustrating an electricity price prediction sequence, a self-powered electricity prediction sequence, a power consumption prediction sequence, and an energy sequence of an energy storage device in a power utilization period according to the energy storage management method of the present application. As shown in the drawings, in the present application, taking the power utilization period set as 24 hours as an example, fig. 4a shows an electricity price prediction sequence obtained based on the energy storage management method of the present application; fig. 4b shows a self-power prediction sequence obtained based on the energy storage management method of the present application, wherein a curve 4b-1 is a self-power upper-limit prediction sequence, a curve 4b-2 is a self-power prediction sequence, and a curve 4b-3 is a self-power lower-limit prediction sequence. Fig. 4c shows a power consumption prediction sequence of the power consumer obtained based on the energy storage management method of the present application, where a curve 4c-1 is a power consumption upper limit prediction sequence, a curve 4c-2 is a power consumption prediction sequence, and a curve 4c-3 is a power consumption lower limit prediction sequence. Fig. 4d shows an energy sequence of the energy storage device obtained based on the energy storage management method of the present application, where a curve 1 is a total electricity consumption price when the energy storage device is not used by the electricity consumer, and a curve 2 is a total electricity consumption price obtained by the electricity consumer based on the energy storage management method of the present application.
In addition, referring to fig. 5, fig. 5 is a schematic diagram showing a curve of the total electricity consumption rate obtained by the electricity consumer based on the energy storage management method of the present application and the total electricity consumption rate when the energy storage device is not used, as shown in the figure, a curve 1 represents the total electricity consumption rate obtained by the electricity consumer based on the energy storage management method of the present application, and a curve 2 represents the total electricity consumption rate when the energy storage device is not used by the electricity consumer, and as can be seen from the figure, compared with the case of not using the energy storage device, the total electricity cost is saved by about 5% to 20% due to the influences of the capacity of the energy storage system, the electricity consumption of the electricity consumer, and the like.
In summary, the energy storage management method generates the energy sequence of the energy storage device in a power utilization period based on the acquired power supply prediction sequence, the power consumption prediction sequence and the energy storage parameters of the energy storage device, so that the energy storage device can be managed based on the energy sequence, and the purpose of lowest total power consumption price is further achieved.
For the power consumers, under the floating electricity price mechanism, the users can purchase and store certain electric power through the own energy storage devices when the electricity price is low, and release the stored electric power for the users to use when the electricity price is high, so as to achieve the purpose of reducing the electricity charges to a certain degree. However, in practical applications, there is uncertainty as to when the consumer controls the energy storage device, whether the energy storage device is charging or discharging, and what amount of power needs to be charged or discharged. Therefore, the application also provides an energy storage control method for controlling an energy storage device for providing stored electric energy for a power consumer. The energy storage control method is mainly executed by an energy storage control system. The energy storage control system may be a software system configured on the computer device, and controls the energy storage device by using the electric party based on the acquired energy sequence of the energy storage device, so as to achieve the purpose of minimizing the total price of electricity used in the electricity utilization period.
Here, the computer device may be a device located in a power utilization control room of an enterprise, or a service end in the internet. The server includes, but is not limited to, a single server, a server cluster, a distributed server cluster, a cloud server, and the like. The Cloud Service end comprises a Public Cloud (Public Cloud) Service end and a Private Cloud (Private Cloud) Service end, wherein the Public or Private Cloud Service end comprises Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure as a Service (IaaS), and Infrastructure as a Service (IaaS). The private cloud service end is used for example for an Aliskian cloud computing service platform, an Amazon cloud computing service platform, a Baidu cloud computing platform, a Tencent cloud computing platform and the like.
The computer equipment is in communication connection with an electricity price publishing system of an electric power supplier, an energy storage control system of an energy storage device, an electricity utilization control system of an electricity consumer, a production activity management system, a self-powered system and the like, and even can be in data connection with a third party system, and acquires internet data and the like related to electricity utilization of the electricity consumer in the internet by utilizing a crawler technology. Wherein the electricity price distribution system is a system in which an electricity supplier (or an electricity market manager such as a government department) distributes electricity prices. The energy storage control system includes, but is not limited to: the energy storage device comprises a detection device for detecting the energy stored by the energy storage device, a charge and discharge control system of the energy storage device and the like. The electricity utilization control system includes but is not limited to: metering devices (e.g., electrical meters), electrical equipment control systems, etc. installed within an enterprise. The management system of the production activity includes but is not limited to: a Manufacturing Execution System (MES), an Enterprise Resource Planning System (ERP), and the like. The self-powered systems include, but are not limited to: a detection device for detecting the amount of power generation from the power supply system, a power generation control system of the self-powered system, and the like. Examples of the third-party system include a self-owned server for storing historical electricity consumption data, a self-owned server for storing historical electricity price data, a self-owned WEB server for acquiring an enterprise electricity consumption plan and the like. Examples of the internet data include weather forecast data, which may be predicted based on historical contemporaneous weather data obtained from the internet, or weather forecast data obtained directly from a weather website or other websites.
Referring to fig. 6, which is a schematic structural diagram of a computer device according to an embodiment of the present application, as shown in the figure, the computer device includes an interface unit 61, a storage unit 62, and a processing unit 63. The storage unit 62 includes a nonvolatile memory, a storage server, and the like. The nonvolatile memory is, for example, a solid state disk or a usb disk. The storage server is used for storing the acquired various electricity utilization related information and power supply related information. The interface unit 61 includes a network interface, a data line interface, and the like. Wherein the network interfaces include, but are not limited to: network interface devices based on ethernet, network interface devices based on mobile networks (3G, 4G, 5G, etc.), network interface devices based on near field communication (WiFi, bluetooth, etc.), and the like. The data line interface includes, but is not limited to: USB interface, RS232, etc. The interface unit is connected with data such as each system of a power supply party, each system of a power utilization party, a third party system, the Internet and the like. The processing unit 63 connects the interface unit 61 and the storage unit 62, and includes: a CPU or a chip integrated with a CPU, a programmable logic device (FPGA), and a multi-core processor. The processing unit 63 also includes memories, registers, etc. for temporarily storing data.
Please refer to fig. 7, which is a flowchart illustrating the energy storage control method. The processing unit 63 reads at least one program, power consumption-related information, and power supply-related information stored in the storage unit to perform an energy storage control method as described below. The power utilization related information and the power supply related information are acquired by the processing unit from the interface unit in advance and stored in the storage unit.
In step S710, an energy sequence of the energy storage device in a power utilization cycle generated by the energy storage management method is obtained. The specific implementation manner of step S710 is as described in fig. 2 to fig. 3 and the corresponding description thereof, and is not described herein again.
In step S720, control information used by the energy storage device to control the operation of the energy storage device in the operation time interval is determined based on the energy value corresponding to the operation time interval in the acquired energy sequence. The operation time interval may be defined by the power consumption, or may be set according to the time interval between adjacent energy values in the energy sequence of the energy storage device obtained in step S710. For example, in the case that the operation time interval is customized by the power consumer, the starting time customized by the power consumer may be used as the update period in step S710 to obtain the latest energy sequence of the energy storage device in the power consumption period, and then determine the control information for controlling the operation of the energy storage device in the customized operation time interval based on the corresponding energy value in the energy sequence. In the case where the operation time interval is set based on the energy sequence of the energy storage device acquired in step S710, for example, based on an energy sequence chart as shown in fig. 4d, the operation time intervals may be set to respectively correspond to the time intervals of the charging and discharging phases of the energy storage device in the energy sequence chart.
In addition, the control information includes charging and discharging control information of the energy storage device and/or a target energy storage value of the energy storage device in an operation time interval. Wherein the charge and discharge control information includes but is not limited to: charge and discharge speed, charge and discharge time and charge and discharge duration. The target storage energy value of the energy storage device in the operation time interval refers to the electric quantity charged or discharged by the energy storage device in a certain time period, and the charging and discharging speed of the energy storage device can be obtained based on the target storage energy value and the operation time interval.
In view of this, the energy storage control method further includes a step of controlling the operation of the energy storage device in the corresponding operation time interval based on the control information. For example, the energy storage device is controlled to perform charging and discharging operations at a certain charging and discharging speed for a certain charging and discharging duration from a certain charging and discharging time based on the charging and discharging control information. For another example, the energy storage device is controlled to select different charging and discharging speeds based on the target energy storage value so as to reach the target energy storage value within a certain operation time interval.
In addition, the energy storage control method further comprises the step of acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence, so that a user can visually observe the energy sequence and each prediction sequence of the energy storage device.
In addition, since the power supply prediction sequence, the power consumption prediction sequence, and the energy storage parameter are updated based on the preset acquisition condition in step S710, so as to obtain a new energy sequence, the energy storage control method of the present application correspondingly further includes a step of updating the control information based on the newly generated energy sequence. For example, taking a power utilization period as 24 hours and an update period as 30 minutes as an example, firstly, the energy sequence of the energy storage device within 24 hours is obtained according to step S710, control information for controlling the operation of the energy storage device in the operation time interval of the energy storage device is determined according to step S720, and then, the user operates the energy storage device based on the control information. When the update period of 30 minutes is reached, a new energy sequence of the energy storage device is updated for 24 hours from this point on, and control information based on the new energy sequence is generated again, and then the user operates the energy storage device based on the new control information. It follows that although the energy sequence of the energy storage device is shown as an overall change for 24 hours (power cycle) in the future, in practice, the user only needs to be concerned with the operational information within 30 minutes (update cycle), and the energy storage device is controlled accordingly based on the new energy sequence every 30 minutes.
In summary, the energy storage control method controls the energy storage device to operate based on the obtained energy sequence of the energy storage device, so as to achieve the purpose of lowest total electricity consumption price.
The application also provides an energy storage management system. The energy storage management system is a software system configured at a server side. Please refer to fig. 8, which is a schematic structural diagram of the energy storage management system according to an embodiment. The energy storage management system 2 includes program modules such as an obtaining module 21 and a generating module 22.
The obtaining module 21 is configured to obtain a power supply prediction sequence available to the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer. The power utilization cycle is the power utilization cycle to be predicted, and may be a pre-agreed power utilization cycle or a power utilization cycle set according to an available floating power price change cycle. Wherein, the floating electricity price change period refers to the time interval of electricity price change. For example, the floating electricity price change period is a time period during which a single electricity price is maintained. As another example, the floating electricity price change period is an update duration of a floating electricity price sequence. The power supply prediction sequence includes a set of a plurality of power supply amounts that a power supplier, an own power supply system, or a third party predicts in time order within a power consumption cycle. The third party comprises a set of a plurality of power supply amounts predicted in time sequence in a power utilization period, wherein the set is obtained by simulating power supply related parameter data, historical power supply data and the like acquired from power utilization parties.
In some embodiments, where the power available to the power consumer comprises a purchase from a power supplier, the power supply prediction sequence includes a power rate prediction sequence. Accordingly, the obtaining module 21 includes at least one of: a first obtaining unit for obtaining the electricity price prediction sequence in the electricity utilization period; a second obtaining unit configured to predict an electricity price prediction sequence within the electricity usage cycle available to the electricity consumer based on a deviation between the obtained historical electricity price prediction sequence and a corresponding historical actual electricity price; a third acquisition unit configured to predict an electricity price prediction sequence within the electricity usage cycle based on the acquired electricity price related information. In some embodiments, in the case where a third party (e.g., a separate electricity price prediction system, an electricity provider, or a separate electricity price pricing system) provides the electricity price prediction sequence, the first obtaining unit may be used to directly obtain the electricity price prediction sequence within the electricity usage period. However, actually, the electricity rate prediction sequence issued by the third party is deviated from the actual electricity rate, and thus, in this case, the second obtaining unit may be used to predict the electricity rate prediction sequence within the electricity usage cycle available for the electricity consumer based on the deviation between the obtained historical electricity rate prediction sequence and the corresponding historical actual electricity rate.
In still other embodiments, the power supplier does not provide the electricity rate prediction sequence, and the step of obtaining the electricity rate prediction sequence in the electricity usage period may include: and predicting a power rate prediction sequence in the power utilization cycle based on the acquired power rate related information. Wherein the electricity price related information includes, but is not limited to, at least one of: historical actual electricity price sequences, electricity price rules for electricity markets, other factors that affect electricity price changes, and the like. The historical actual electricity price sequence refers to a set of a plurality of actual electricity prices in a time sequence within a certain historical time period. For example, the historical actual electricity price sequence may be obtained from a third party or other data platform. The electricity price rule of the electricity market refers to an electricity price rule set by a local government or an electricity supplier for a governed region, and includes but is not limited to: fine price of electricity set based on the electricity demand of the electricity consumers, and the like. Examples of the other factors that affect the change in electricity prices include weather, holidays, and the like. For example, a power rate prediction sequence in a power usage cycle is predicted based on the acquired weather forecast, the released holiday schedule, and the historical actual power rate sequence.
In one example, the prediction algorithm, such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN), etc., is used to calculate the power rate prediction sequence within the power cycle as an output, taking the above power rate related information into comprehensive consideration, and taking historical actual power rate sequence, weather forecast, holiday arrangement, etc. as the input of the prediction model.
It should be noted that the above embodiments of obtaining the electricity price prediction sequence are only examples, and are not limiting to the present application. One skilled in the art can construct a model for predicting the electricity price prediction sequence in conjunction with the various embodiments described above. For example, a power rate prediction sequence is calculated based on the input of the prediction model, the adopted prediction algorithm and the error range of the detected historical power rate data, so as to improve the accuracy of the subsequent prediction.
In further embodiments, where the consumer is configured with a self-powered system, i.e., the power available to the consumer comprises power provided by the self-powered system, the power prediction sequence further comprises a self-powered quantity prediction sequence. The self-supply amount prediction sequence refers to a set of a plurality of self-supply amounts predicted in time sequence in a power utilization cycle. The self-powered systems include, but are not limited to: photovoltaic power generation system, heat conversion system, trigeminy supplies system, wind power generation system etc.. Accordingly, the retrieving module 21 comprises a fourth retrieving unit for predicting a self-powered prediction sequence within the power usage cycle based on the retrieved power generation related information of the self-powered system. Wherein the power generation related information includes, but is not limited to: historical power generation data, and factors influencing power generation based on the working principle of the self-powered system. For example, in the case of a self-powered system employing photovoltaic power generation, factors affecting power generation mainly include solar irradiance and the like. For another example, in the case that the self-powered system employs wind power generation, the factors influencing the power generation mainly include wind speed, wind direction, and the like. For another example, in the case of a self-powered system that employs thermal conversion to generate electricity, the factors that affect the generation of electricity include mainly the thermal conversion efficiency of the system, the detected temperature, and the like.
The self-power supply prediction sequence in the power utilization cycle is obtained as output by taking the power generation related information as input of the prediction model and adopting a prediction algorithm such as Random Forest (Random Forest), long-short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN) and the like for calculation.
It should be noted that the above embodiments of obtaining the self-power prediction sequence are only examples, and are not intended to limit the present application. One skilled in the art can construct a model for predicting the self-supply power prediction sequence in conjunction with the various embodiments mentioned in the foregoing power rate prediction sequence. For example, a self-powered electricity supply amount prediction sequence is calculated based on the input of the prediction model, the adopted prediction algorithm and the error range obtained through detection so as to improve the accuracy of subsequent prediction.
It should also be noted that the above manner of self-power prediction by using the self-power supply system is only an example, and not a limitation of the present application. Those skilled in the art will understand that the power generation related information based on the self-powered electricity quantity prediction sequence differs according to the power supply mode of the actual self-powered system, and the details are not repeated here.
It should be further noted that, according to the actual situation, the power supply prediction sequence available to the obtaining module 21 may only include the power price prediction sequence, or a self-power prediction sequence; or both the electricity price prediction sequence and the self-power prediction sequence. And may not be limiting herein.
In addition, the obtaining module 21 is further configured to obtain the electricity consumption related information according to the electricity consumption factor in the electricity consumption period; and a fifth acquiring unit for predicting the electricity consumption prediction sequence in the electricity utilization period according to the electricity utilization related information. The electricity consumption prediction sequence refers to a set of a plurality of electricity consumptions predicted according to the time sequence in the electricity utilization period. The electricity consumption is obtained by the electricity consumers and is related to the electricity consumption factors of the daily production activities of the electricity consumers. Wherein the electricity utilization factors include but are not limited to: human programs such as scheduling programs, store activity programs, programs summarized according to weather or social activity rules (e.g., weekdays, holidays). For example, for the electricity utilization situation of the product a produced in the factory, the electricity utilization related information may include historical electricity utilization data of the product a produced, equipment usage information determined based on the scheduling plan of the product a, electricity utilization information of the equipment, and the like. For another example, the electricity consumption related information may include air conditioner usage information based on season setting, air conditioner usage information, use information of lighting lamps for working days and holidays, computers, and the like, for the electricity consumption situation of an office building. In some cases where air conditioner usage information is not set, the air conditioner usage information may also be determined based on weather forecast conditions. For example, the use of air conditioning is controlled in accordance with the forecasted air temperature.
The electricity consumption related information is used as the input of the prediction model, and a prediction algorithm such as Random Forest (Random Forest), long and short term memory network (L STM), iterative decision tree (GBRT), Convolutional Neural Network (CNN) and the like is adopted for calculation, so that the electricity quantity prediction sequence of the electricity consumption party in the electricity consumption period is obtained as the output.
The above-described method for predicting the amount of electricity used based on the electricity-related information is only an example, and is not a limitation of the present application. Those skilled in the art should understand that other electricity related information affecting the electricity consumption prediction sequence can also be used as an input of the prediction model to obtain the electricity consumption prediction sequence through a prediction algorithm, and details are not repeated here.
The generating module 22 is configured to generate an energy sequence of the energy storage device in the power utilization cycle based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization cycle, so that the energy storage device is managed based on the energy sequence.
Wherein the preset acquisition condition comprises at least one of the following: updating events of the power supply prediction sequence, events of the power consumption prediction sequence and an updating period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power usage prediction sequence.
Here, the event of updating the power supply prediction sequence includes, but is not limited to: and updating events of the third party electricity price prediction sequence, changes of factors influencing the electricity price and the like. Examples of the factors affecting the electricity price change include an event that the electricity consumption is increased due to a new activity day and then the electricity price changes, a factor affecting the power generation of the self-powered system changes, and the like. Examples of the change in the factor affecting the power generation from the power supply system include: and the solar photovoltaic power generation amount is reduced due to sudden weather change, and then the self-powered power supply amount is changed. Additionally, events that update the power usage prediction sequence also include, but are not limited to: events in which factors affecting power usage change. Such as events that increase or decrease the amount of power used due to a change in the scheduling plan.
Further, in some embodiments, the update period is determined based on an update period of a power supply prediction sequence. The update period of the power supply prediction sequence may be a preset update period, or may be an update period set according to a floating power rate change period. For example, in the case where the floating electricity prices are changed every 30 minutes, the update period is set to be updated every 30 minutes. In other embodiments, the update period is determined based on an update period of a power usage prediction sequence. The update cycle of the power consumption prediction sequence may be a preset update cycle, or may be set according to the adjustment of the power consumption plan. For example, when a scheduling plan is adjusted, an update period is set according to the corresponding adjustment event. In still other embodiments, the update period is determined based on an update period of a power supply forecast sequence and an update period of a power usage forecast sequence. For example, the energy storage parameters of the energy storage device are obtained every time the electricity price prediction sequence changes, and the energy storage parameters of the energy storage device are obtained every time the electricity utilization plan is adjusted. In addition, the update period also includes updates that are not performed in accordance with operations suggested by the energy storage management method. For example, when an operator is recommended to perform charging operation on the energy storage device at a certain time according to the energy storage management method, but the operator does not perform operation according to the recommendation, the operator needs to update the energy storage device when performing operation again, and then performs corresponding operation on the energy storage device based on the updated energy storage management recommendation.
The energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device. Wherein the capacity of the energy storage device comprises a maximum capacity and a minimum capacity of the energy storage device. The charge and discharge parameters of the energy storage device comprise the charge speed of the energy storage device, the discharge speed of the energy storage device, the upper limit and the lower limit of charge and discharge power and the like. The loss parameters of the energy storage device comprise the energy conversion rate of the energy storage process of the energy storage device, the energy conversion rate of the energy release process of the energy storage device and the energy loss rate of the idle process of the energy storage device. The energy storage parameter may also be a set of parameters determined based on a temperature dependent variable.
In some embodiments, the generating module 22 includes a generating unit, configured to generate an energy sequence of the energy storage device in the power utilization cycle with an optimization goal of total price of power utilization in the power utilization cycle being low under at least one constraint condition; wherein the constraints comprise constraints determined based on the energy storage parameters. Under the condition that the lowest total electricity price in the electricity utilization period is taken as an optimization target, the optimization target function is as follows:
wherein, t tableShowing the time t, EG2LRepresenting the amount of electricity purchased and used directly by consumers from the grid; eG2BRepresenting the amount of electricity purchased and stored by the consumer from the grid; eB2LThe electric quantity released and used by the energy storage device of the power consumer is represented; pGA real-time electricity rate representing a purchase of electricity from the power grid; pBRepresenting the price converted by the costs of charging and discharging, loss and the like of the energy storage device.
In addition, for an energy storage device, the mathematical description of the model may be:
Ebtty(t)＝Ebtty(t-Δt)+ΔE
wherein E isbtty(t) the amount of electricity stored in the energy storage device at time t, EbttyAnd (t-delta t) is the electric quantity stored in the energy storage device at the moment (t-delta t), and delta E is the electric quantity stored or released in delta t per unit time. Further, Δ E is expressed as:
ΔE＝EG2B×echarge；EB2L＝0
or, Δ E ═ EB2L×edischarge；EG2B＝0
Or, Δ E ═ Eloss；EB2L＝EG2B＝0
Wherein e ischargeAn energy conversion rate representing a charging process of the energy storage device; e.g. of the typedischargeAn energy conversion rate representing a discharge process of the energy storage device; elossRepresenting the amount of self-discharge of the energy storage device per unit time deltat.
In this case, at least one constraint condition is set according to the energy storage parameters of the energy storage device that can be actually obtained, which is intended to prevent an abnormality of the energy storage device when managing the energy storage device. For example, it is avoided that a certain energy value in the generated energy sequence exceeds the maximum capacity of the energy storage means, etc. Based on the optimization objective function and the model of the energy storage device, wherein the charging amount E of the energy storage deviceG2BAnd energy storage device discharge capacity EB2LControlled by a model of the energy storage device, constraints of the model including at least one of: constraints set for the energy storage device, and constraints set based on the relationship between the consumption of electric energy and the supply of electric energy.
Wherein the constraint condition set for the energy storage device includes at least one of:
1) capacity of energy storage device: ebtty_MIN≤Ebtty≤Ebtty_MAX
2) Charging and discharging speed of the energy storage device: delta E/delta t is more than or equal to 0 and less than or equal to CRchargeOr CRdischargeDelta E/delta t is less than or equal to 0; wherein E isbtty_MINRepresents a minimum capacity of the energy storage device; ebtty_MAXRepresents a maximum capacity of the energy storage device; CRchargeRepresenting a charging speed of the energy storage device; CRdischargeIndicating the discharge rate of the energy storage device. Meanwhile, the related variables of the energy storage device are all temperature related variables.
The constraint condition set based on the relationship between the power consumption and the power supply refers to a sum of at least one or more of power purchased from the power grid, power provided by discharging the energy storage device, and power corresponding to a self-power supply amount generated by the self-power supply system, that is, (E)G2L+EB2L+EP2L) Wherein E isP2LRepresenting the real-time self-powered amount of the power consumers. In the case where the self-power is used for the operation of the consumer apparatus, the difference between the total power demand of the consumer and the self-power prediction result is used as the sum of the power purchased from the grid and directly used and the power discharged and used from the energy storage device (E)G2L+EB2L) The constraint of (2). That is, during a certain period of time, the upper limit of the discharge capacity of the energy storage device is equal to the difference between the total demand capacity and the self-powered capacity, and if the discharge capacity of the energy storage device is insufficient, the energy storage device is complemented with the capacity purchased from the power grid.
It should be noted that the self-power supply of the self-power supply system may also sell redundant parts to the power supplier according to the actual situation, which does not affect the energy storage management scheme described in the present application and is not described in detail herein.
In one embodiment, the generating unit is configured to generate one or more candidate energy sequences within the power utilization cycle under at least one constraint condition; and under at least one constraint condition and with the total price of electricity consumption in the electricity consumption period as an optimization target, optimizing the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage device in the electricity consumption period.
In certain embodiments, one or more candidate energy sequences over a power cycle are generated based on the predicted or detected energy stored by the energy storage device, and all of the constraints discussed above. Here, for the initialization candidate energy sequence (also referred to as an initialization candidate solution), one or more preset candidate energy sequences, that is, candidate solutions, may be generated in a random manner.
Wherein, in some examples, the generated candidate solution is one, and the candidate solution is optimized under at least one constraint condition and with the total price of the electricity in the electricity utilization period as an optimization target. For example, under the constraints described above, a candidate solution to the power usage period is generated. And optimizing the generated candidate solution by using the change trend of the total electricity consumption price corresponding to the candidate solution in a delta t time length to obtain an energy sequence which takes the total electricity consumption price in the electricity consumption period as an optimization target under at least one constraint condition.
In other examples, the generated candidate solution is a plurality of candidate solutions, and the candidate solution is filtered and/or adjusted from the plurality of candidate solutions to obtain the energy sequence under at least one constraint condition and with the total price of electricity in the electricity utilization period as an optimization goal. For example, the total electricity consumption price corresponding to each of the plurality of candidate solutions generated under the constraint condition is calculated, and the candidate solution with the lowest total electricity consumption price is selected as the generated energy sequence. For another example, calculating the total electricity consumption price corresponding to each of the multiple candidate solutions generated under the constraint condition, and selecting the candidate solution with the lowest total electricity consumption price; and optimizing the generated candidate solution by using the change trend of the total electricity consumption price corresponding to the candidate solution in a delta t time length so as to obtain an energy sequence which takes the total electricity consumption price in the electricity consumption period as an optimization target under at least one constraint condition.
In some embodiments, the generating unit is configured to determine one candidate energy sequence from the one or more candidate energy sequences as the energy sequence according to a cutoff condition set with total price of electricity in the electricity utilization period as an optimization goal; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
The updating strategy comprises but is not limited to Lagrange Multiplier (L) method, sequence linear programming (S L P), Sequence Quadratic Programming (SQP), Interior Point method (Interior Point), Exterior Point method (Exterior Point), Active Set method (Active Set), Trust domain reflection algorithm (Trust Region reflection), Heuristic algorithm (Hearistic Algorithms), Meta-Heuristic algorithm (Meta-Gaussian), Evolutionary algorithm (evolution Algorithms), Swarm Intelligent algorithm (Swarm Intigrithms), Neural network algorithm (Neural network strategy), taboo search algorithm, simulated annealing algorithm, ant colony optimization algorithm, greedy optimization algorithm, self-adaptive search algorithm, random adaptive search algorithm, and other artificial immune system optimization or similar artificial selection strategies.
In addition, the energy storage management system of the application further comprises an output module, and the output module is used for outputting at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence to be displayed.
Here, the working modes of the modules in the energy storage management system of the present application are the same as or similar to the corresponding steps in the energy storage management method, and are not described herein again.
The application also provides an energy storage control system. The energy storage control system is a software system configured on computer equipment. Please refer to fig. 9, which is a schematic structural diagram of the energy storage control system according to an embodiment. The energy storage control system 3 comprises program modules such as an acquisition module 31 and a determination module 32.
The obtaining module 31 is configured to obtain an energy sequence of the energy storage device in a power utilization cycle, which is generated by the energy storage management system. The determining module 32 is configured to determine, based on an energy value corresponding to an operation time interval in the acquired energy sequence, control information used by the energy storage device to control an operation of the energy storage device in the operation time interval. The operation time interval may be customized by the user, or may be set according to the time interval between adjacent energy values in the acquired energy sequence of the energy storage device.
The control information comprises charging and discharging control information of the energy storage device and/or a target energy storage value of the energy storage device in an operation time interval. Wherein the charge and discharge control information includes but is not limited to: charge and discharge speed, charge and discharge time and charge and discharge duration. The target storage energy value of the energy storage device in the operation time interval refers to the electric quantity charged or discharged by the energy storage device in a certain time period, and the charging and discharging speed of the energy storage device can be obtained based on the target storage energy value and the operation time interval.
In addition, the energy storage control system of the present application further includes a control module configured to control operation of the energy storage device within a corresponding operation time interval based on the control information.
In addition, the energy storage control system of the application also comprises a display module, and the display module is used for acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
In addition, because the obtaining module can update the power supply prediction sequence, the power consumption prediction sequence and the energy storage parameter based on the preset obtaining condition, and then obtain a new energy sequence, the energy storage control system further comprises an updating module, and the updating module is used for updating the control information based on the latest generated energy sequence.
Here, the working modes of the modules in the energy storage control system of the present application are the same as or similar to the corresponding steps in the energy storage control method, and are not described herein again.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that part or all of the present application can be implemented by software and combined with necessary general hardware platform. Based on this understanding, the present application also provides a computer-readable storage medium storing at least one program which, when invoked, performs any of the energy storage management methods described above. In addition, the present application also provides a computer-readable storage medium, where the storage medium stores at least one program, and the at least one program executes any one of the foregoing energy storage control methods when being called.
With this understanding in mind, the technical solutions of the present application and/or portions thereof that contribute to the prior art may be embodied in the form of a software product that may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may cause the one or more machines to perform operations in accordance with embodiments of the present application. For example, each step in the positioning method of the robot is performed. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. Wherein the storage medium may be located in the robot or in a third party server, such as a server providing an application mall. The specific application mall is not limited, such as the millet application mall, the Huawei application mall, and the apple application mall.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The application also provides an energy storage control system. The energy storage control system comprises the server and the computer device provided in any one of the preceding examples. Please refer to fig. 10, which is a schematic diagram illustrating a network architecture of the energy storage control system controlling an energy storage device according to an embodiment. The server 41 and the computer device 42 may be both located at the electric user side, or both located at any geographic location where data communication may be performed through a data transmission network such as the internet, a mobile network, or the like, or either located at the electric user side, and the other located at another geographic location where data communication may be performed. The computer device 42 may send a control command to the energy storage device 43 in a data communication manner, and collect energy storage parameters of the energy storage device 43. In some examples, the server 41 is further in data communication with the metering device 44 on the electricity consumer side to obtain the electricity consumption of the electricity consumer detected by the metering device 44, so that the server 41 can predict the electricity consumption prediction sequence in the electricity consumption cycle according to the electricity consumption related information containing the obtained electricity consumption. In still other examples, the power consumer further includes a self-powered system 45, and the server 41 obtains information related to power generation of the self-powered system 45 through data communication according to the actual type of the self-powered system 45. For example, the self-powered system 45 generates power by heat conversion, and the corresponding server 41 obtains temperature information of the self-powered system 45 as one of the power generation related information.
Taking fig. 10 as an example, the execution process of the energy storage control system is as follows: the server 41 predicts a self-power prediction sequence in a power utilization period by acquiring power generation related information of the self-power system 45; predicting a power consumption prediction sequence in the same power consumption period by acquiring power consumption related information of a power consumer, such as power consumption, production scheduling and the like; acquiring, by the computer device 42, energy storage parameters of the energy storage device 43 at the starting time of the power utilization cycle; and acquiring a power rate prediction sequence of the third party. The constraint conditions determined by the server 41 based on the energy storage parameters include: 1) capacity of the energy storage device 43: ebtty_MIN≤Ebtty≤Ebtty_MAXAnd 2) the charge and discharge rate of the energy storage device 43: delta E/delta t is more than or equal to 0 and less than or equal to CRchargeOr CRdischargeDelta E/delta t is less than or equal to 0; generating a plurality of candidate energy storage sequences in a random mode by taking the total price of the electricity consumption in the electricity utilization period as an optimization target; selecting n candidate energy storage sequences with the lowest total electricity consumption price by calculating the total electricity consumption price corresponding to each candidate energy storage sequence; the n reserved candidate energy storage sequences are cloned in a corresponding quantity, random variation with a certain probability (variation rate) is introduced in the cloning process, and new candidate energy storage sequences are obtained. Wherein the variation rate is limited by the above model constraint condition to ensure that the obtained new candidate energy storage sequence is obtained based on the slight change of the candidate solution before the variant clone. Wherein, the variation rate is introduced into all the solutions of the retained candidate energy storage sequence clone, or only partial solution. Under the constraint of the constraint condition, screening and mutational cloning are carried out on the generated candidate energy sequence until the actual iteration times reach a cut-off condition of preset iteration times; and finally, selecting the candidate energy storage sequence corresponding to the lowest total electricity price as the energy sequence of the energy storage device 43, and sending the obtained energy sequence to the computer device 42.
The computer device 42 generates control information for controlling the energy storage device 43 to adjust from the currently stored energy value E0 to E1 according to the energy value E1 at the latest moment in the acquired energy sequence, and controls the energy storage device 43 to perform energy storage adjustment according to the control information.
Here, when any one of the electricity price prediction sequence, the electricity consumption prediction sequence, the self-power prediction sequence, or the energy storage parameter is updated, the server 41 generates the energy sequence according to the latest data, so that the computer 42 controls the energy storage device 43 to perform energy storage adjustment in time. Therefore, the purpose of reducing the electricity utilization cost by utilizing stored energy under the floating electricity price mechanism is achieved.
It should be noted that the above working process is only an example and not a limitation of the present application, and in fact, any way provided by the aforementioned service end to generate the energy sequence of the energy storage device may be used to replace the generation way in this example. And will not be described in detail herein.
To sum up, this application generates energy sequence at an electric cycle energy memory based on the power supply prediction sequence that obtains, power consumption prediction sequence and energy memory's energy storage parameter to make can be based on energy sequence is right energy memory manages, and then realizes the purpose that the total price of electricity is minimum.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.
Claims (35)
- An energy storage management method for managing an energy storage device for providing reserve electric energy to a consumer, comprising the steps of:acquiring a power supply prediction sequence available for the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer; andand generating an energy sequence of the energy storage device in the power utilization period based on the energy storage parameters of the energy storage device acquired under preset acquisition conditions, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization period, so that the energy storage device is managed based on the energy sequence.
- The energy storage management method according to claim 1, wherein the power supply prediction sequence includes a power rate prediction sequence, and the step of obtaining the power rate prediction sequence in a power utilization cycle includes any one of:acquiring a power price prediction sequence in the power utilization period;predicting an electricity price prediction sequence in the electricity utilization period available for the electricity utilization party based on the obtained historical electricity price prediction sequence and the deviation between the corresponding historical actual electricity prices;and predicting a power rate prediction sequence in the power utilization cycle based on the acquired power rate related information.
- The energy storage management method according to claim 1, wherein the power supply prediction sequence comprises a self-power supply prediction sequence from a power supply system, and the step of obtaining the self-power supply prediction sequence in a power utilization cycle comprises:predicting a self-powered prediction sequence within the power utilization cycle based on the obtained power generation related information of the self-powered system.
- The energy storage management method according to claim 1, wherein the step of obtaining the predicted sequence of power consumption of the power consumers comprises:acquiring power utilization related information according to the power utilization factors in the power utilization period; andand predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
- The energy storage management method according to claim 1, wherein the step of generating the energy sequence of the energy storage device in the power utilization cycle based on the energy storage parameters of the energy storage device obtained under preset obtaining conditions, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization cycle comprises:under at least one constraint condition, generating an energy sequence of the energy storage device in the power utilization period by taking the total price of the power utilization in the power utilization period as an optimization target; wherein the constraints comprise constraints determined based on the energy storage parameters.
- The energy storage management method according to claim 5, wherein the step of generating a sequence of energy of the energy storage device in the power utilization cycle with the total price of power utilization in the power utilization cycle as an optimization goal under at least one constraint condition comprises:generating one or more candidate energy sequences within the power usage cycle under at least one constraint; andand under at least one constraint condition and with the total price of the electricity in the electricity utilization period as an optimization target, optimizing the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage device in the electricity utilization period.
- The energy storage management method according to claim 6, wherein the step of performing optimization processing on the generated one or more candidate energy sequences comprises:determining a candidate energy sequence from the one or more candidate energy sequences according to a cutoff condition set for an optimization goal of total price reduction of electricity in the electricity utilization period, and taking the candidate energy sequence as the energy sequence; andand when the cutoff condition is not met, updating at least one generated candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
- The energy storage management method according to claim 1 or 5, wherein the energy storage parameters include at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.
- The energy storage management method according to claim 1, wherein the obtaining condition comprises at least one of: updating events of the power supply prediction sequence, events of the power consumption prediction sequence and an updating period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power usage prediction sequence.
- The energy storage management method according to claim 1, further comprising the step of displaying at least one of the energy sequence, the power supply forecast sequence, and the power usage forecast sequence.
- An energy storage control method for controlling an energy storage device for supplying reserve electric energy to a consumer, comprising the steps of:acquiring an energy sequence of the energy storage device in a power utilization cycle generated by the energy storage management method according to any one of claims 1-9;and determining control information used by the energy storage device for controlling the operation of the energy storage device in the operation time interval based on the energy value corresponding to the operation time interval in the acquired energy sequence.
- The energy storage control method according to claim 11, further comprising the step of controlling the operation of the energy storage device during respective operation time intervals based on the control information.
- The energy storage control method according to claim 11, characterized by further comprising: and acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
- The energy storage control method according to any one of claims 11 to 13, further comprising the step of updating the control information based on a newly generated energy sequence.
- The energy storage control method according to claim 11, wherein the control information includes at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the operation time interval.
- An energy storage management system for managing energy storage devices that provide reserve electrical energy to consumers, comprising:the acquiring module is used for acquiring a power supply prediction sequence which can be used by the power consumer in a power utilization period and a power consumption prediction sequence of the power consumer; andthe generating module is used for generating an energy sequence of the energy storage device in the power utilization period based on the energy storage parameters of the energy storage device, which are acquired under preset acquisition conditions, and the power supply prediction sequence and the power consumption prediction sequence in the power utilization period, so that the energy storage device is managed based on the energy sequence.
- The energy storage management system according to claim 16, wherein the power supply prediction sequence comprises a power rate prediction sequence, and the obtaining module comprises at least one of:the first acquisition unit is used for acquiring the electricity price prediction sequence in the electricity utilization period;a second obtaining unit configured to predict an electricity price prediction sequence within the electricity usage cycle available to the electricity consumer based on a deviation between the obtained historical electricity price prediction sequence and a corresponding historical actual electricity price;a third obtaining unit, configured to predict an electricity price prediction sequence in the electricity usage cycle based on the obtained electricity price related information.
- The energy storage management system according to claim 16, wherein the power supply prediction sequence comprises a self-power supply prediction sequence from a power supply system, and the obtaining module comprises a fourth obtaining unit configured to predict the self-power supply prediction sequence in the power utilization cycle based on the obtained power generation related information of the self-power supply system.
- The energy storage management system according to claim 16, wherein the acquiring module includes a fifth acquiring unit, configured to acquire the electricity consumption related information according to the electricity consumption factor in the electricity consumption cycle; and predicting a power consumption prediction sequence in the power consumption period according to the power consumption related information.
- The energy storage management system of claim 16, wherein the generating module comprises:the generating unit is used for generating an energy sequence of the energy storage device in the power utilization period by taking the total price of the power utilization in the power utilization period as an optimization target under at least one constraint condition; wherein the constraints comprise constraints determined based on the energy storage parameters.
- The energy storage management system according to claim 20, wherein the generating unit is configured to generate one or more candidate energy sequences within the power utilization cycle under at least one constraint condition; and under at least one constraint condition and with the total price of electricity consumption in the electricity consumption period as an optimization target, optimizing the generated one or more candidate energy sequences to obtain the energy sequences of the energy storage device in the electricity consumption period.
- The energy storage management system according to claim 21, wherein the generating unit is configured to determine one candidate energy sequence from the one or more candidate energy sequences as the energy sequence according to a cutoff condition set with a total price of electricity in the electricity utilization period as an optimization goal; and when the cutoff condition is not met, updating the generated at least one candidate energy sequence according to an updating strategy under at least one constraint condition, and repeating the steps according to the updated candidate energy sequence until one candidate energy sequence meets the cutoff condition.
- The energy storage management system according to claim 16 or 20, wherein the energy storage parameters comprise at least two of: the detected or predicted energy stored by the energy storage device, a capacity of the energy storage device, a charge-discharge parameter of the energy storage device, a loss parameter of the energy storage device.
- The energy storage management system of claim 16, wherein the acquisition condition comprises at least one of: updating events of the power supply prediction sequence, events of the power consumption prediction sequence and an updating period; wherein the update period is determined based on an update period of the power supply prediction sequence and/or an update period of the power usage prediction sequence.
- The energy storage management system according to claim 16, further comprising an output module configured to output at least one of the energy sequence, the power supply forecast sequence, and the power usage forecast sequence for display.
- An energy storage control system for controlling an energy storage device that provides reserve electrical energy to a consumer, comprising:an acquisition module for acquiring a sequence of energy of the energy storage device during a power usage cycle generated by an energy storage management system according to any of claims 16-24;and the determining module is used for determining control information used for controlling the operation of the energy storage device in the operation time interval of the energy storage device based on the energy value corresponding to the operation time interval in the acquired energy sequence.
- The energy storage control system of claim 26, further comprising a control module configured to control operation of the energy storage device during respective operating time intervals based on the control information.
- The energy storage control system of claim 26, further comprising: and the display module is used for acquiring and displaying at least one of the energy sequence, the power supply prediction sequence and the power consumption prediction sequence.
- The energy storage control system of claims 26-28, further comprising: and the updating module is used for updating the control information based on the latest generated energy sequence.
- The energy storage control system of claim 26, wherein the control information comprises at least one of: the charging and discharging control information of the energy storage device and the target energy storage value of the energy storage device in the prediction time interval.
- A server, comprising:the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer;a storage unit for storing at least one program; anda processing unit for invoking the at least one program to coordinate the interface unit and the storage unit to perform the energy storage management method according to any one of claims 1-10.
- A computer device, comprising:the interface unit is used for acquiring power supply related information which can be used by a power consumer in a power utilization period and power utilization related information of the power consumer;a storage unit for storing at least one program; anda processing unit for invoking the at least one program to coordinate the interface unit and the storage unit to perform the energy storage control method according to any one of claims 11-15.
- A computer-readable storage medium, characterized by storing at least one program which, when invoked, performs the energy storage management method according to any one of claims 1-10.
- A computer-readable storage medium characterized by storing at least one program which, when invoked, performs the energy storage control method according to any one of claims 11-15.
- An energy storage control system, comprising: the server according to claim 31 and the computer device according to claim 32.
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| PCT/CN2018/116767 WO2020103048A1 (en) | 2018-11-21 | 2018-11-21 | Energy storage management and control method, system, computer device, and storage medium |
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