CN116316722A - Energy storage system capacity configuration method and system based on peak-valley electricity price operation - Google Patents
Energy storage system capacity configuration method and system based on peak-valley electricity price operation Download PDFInfo
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Abstract
The present disclosure provides a capacity configuration method and a system of an energy storage system based on peak-valley electricity price operation, the method comprises the steps of obtaining historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, wherein the energy storage configuration set comprises a plurality of capacity power combinations; calculating the total power actual value of the fan; constructing an expense-saving calculation model, determining a power price period based on historical power price data by the expense-saving calculation model, calculating charge and discharge capacity corresponding to the power price period based on rated capacity and rated power of an energy storage system, and calculating and outputting expense-saving total amount based on the charge and discharge capacity, total power actual generation value and the historical power price data; and selecting each capacity power combination one by one as the total sum of rated capacity and rated power calculation cost saving, and selecting the capacity power combination corresponding to the maximum total sum of cost saving to configure the rated capacity and rated power of the energy storage system. The method can expand the application range, give consideration to economic benefits and provide more excellent energy storage configuration.
Description
Technical Field
The disclosure belongs to the technical field of capacity configuration of wind power energy storage systems, and particularly relates to a method and a system for capacity configuration of an energy storage system based on peak-valley electricity price operation.
Background
In recent years, renewable energy sources such as photovoltaic power generation, wind power generation and the like are rapidly developed. Due to rapid increase of the installed quantity of renewable energy sources and uncertainty and instability of power generation, the wind rejection rate and the light rejection rate are obviously improved, so that the configuration of energy storage for a new energy system has become a current development trend of the power industry, and policies have clearly required the configuration of energy storage for a new energy power station.
Meanwhile, in order to relieve the power supply gap in the peak period, the optimal configuration of power resources is promoted, the peak power utilization is transferred to the valley period, the comprehensive economic benefit of the social power utilization can be improved, the energy conservation is facilitated, and the national economy development is promoted.
Therefore, the prior art presents a capacity configuration technology of an energy storage system considering electricity price. For example, in the chinese patent of the invention with the publication number CN108599146B, a method for configuring the capacity of a home photovoltaic and battery energy storage system is disclosed, which takes the step electricity price into consideration. However, the application range of the method is narrow, the model and the calculation can only carry out capacity configuration aiming at the energy storage scale of the household photovoltaic, and the annual illumination data of the household area are difficult to obtain and difficult to implement due to urban changes and larger related areas. Other energy storage system capacity allocation technologies in the prior art also have the problems that economic benefits are not compatible and energy storage allocation is excellent.
Disclosure of Invention
The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art. Therefore, the present disclosure provides a method and a system for configuring capacity of an energy storage system based on peak-to-valley electricity price operation, and aims to expand the application range, give consideration to economic benefits and provide more optimal energy storage configuration.
According to an embodiment of the first aspect of the present disclosure, there is provided a method for configuring capacity of an energy storage system based on peak-to-valley electricity price operation, including:
acquiring historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, wherein the energy storage configuration set comprises a plurality of capacity power combinations;
calculating total power actual sending values of all fans based on the historical fan operation parameters and the historical fan environment parameters;
constructing an expense-saving calculation model, wherein the expense-saving calculation model determines a power price time period based on historical power price data, further calculates charge and discharge capacity corresponding to the power price time period based on rated capacity and rated power of an energy storage system, and calculates and outputs an expense-saving total amount based on the charge and discharge capacity, the total power actual generation value and the historical power price data;
and selecting each capacity power combination one by one as the rated capacity and the rated power, calculating the total cost saving amount corresponding to each capacity power combination by using a cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as the optimal capacity and the optimal power to configure the rated capacity and the rated power of the energy storage system.
In one embodiment of the present disclosure, the electricity rate period includes a low electricity rate period, a peak electricity rate period, and an average period, the charge-discharge amount of the average period being zero.
In one embodiment of the disclosure, the charging and discharging amount of the off-peak electricity price time period is calculated based on the total power actual value, the rated capacity, the rated power and the current stored electric quantity of the energy storage system; and calculating the charge and discharge capacity of the peak electricity price time period based on the current stored electric quantity of the energy storage system and the rated power.
In one embodiment of the disclosure, the historical electricity price data includes electricity price data of a plurality of moments in a set time period, the electricity price data of each moment is obtained by sampling from a corresponding set time period interval of an electricity price curve according to a set time step, and the minimum moment of the set time period is an initial moment and the maximum moment is a termination moment.
In one embodiment of the present disclosure, the total cost saving amount is obtained by accumulating cost saving at each time, the determining the electricity price period based on the historical electricity price data, further calculating a charge and discharge amount corresponding to the electricity price period based on the rated capacity and the rated power of the energy storage system, and calculating the total cost saving amount based on the charge and discharge amount, the total power real power generation value and the historical electricity price data, including: and judging the electricity price time period where the electricity price data at the current moment are located, further calculating the charge and discharge capacity corresponding to the electricity price time period where the current moment is located, and calculating the cost saving at the current moment based on the charge and discharge capacity, the total power actual value at the current moment and the electricity price data at the current moment, and accumulating the cost saving at the initial moment and calculating the cost saving at the final moment to obtain the total cost saving amount.
In one embodiment of the present disclosure, the historical fan operating parameters include a total number of fans, a power conversion coefficient of the fans, a blade radius of the fans; the historical fan environment parameters comprise the atmospheric pressure and the temperature of the area where the fan is located and the wind speed of the height of the area where the fan is located.
According to a second aspect of the present disclosure, there is also provided an energy storage system capacity configuration system operating based on peak-to-valley electricity prices, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, and the energy storage configuration set comprises a plurality of capacity power combinations;
the calculation module is used for calculating total power actual values of all fans based on the historical fan operation parameters and the historical fan environment parameters;
the modeling module is used for constructing an expense-saving calculation model, the expense-saving calculation model determines the electricity price time period based on the historical electricity price data, further calculates the charge and discharge quantity corresponding to the electricity price time period based on the rated capacity and the rated power of the energy storage system, and calculates and outputs the expense-saving total based on the charge and discharge quantity, the total power actual power generation value and the historical electricity price data;
And the control module is used for selecting each capacity power combination one by one as the rated capacity and the rated power, calculating the total cost saving amount corresponding to each capacity power combination by using the cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as the optimal capacity and the optimal power to configure the rated capacity and the rated power of the energy storage system.
In one embodiment of the present disclosure, the electricity rate period includes a low electricity rate period, a peak electricity rate period, and an average period, and the modeling module is specifically configured to: calculating the charge and discharge capacity of the off-peak electricity price time period based on the total power actual generation value, the rated capacity, the rated power and the current storage capacity of an energy storage system; calculating the charge and discharge capacity of the peak electricity price time period based on the current stored electric quantity of the energy storage system and the rated power; the charge and discharge amount of the average period is zero.
In one embodiment of the present disclosure, the historical electricity price data includes electricity price data of a plurality of moments within a set period of time, a minimum moment of the set period of time is an initial moment, and a maximum moment is a termination moment, and the modeling module is specifically configured to: and judging the electricity price time period where the electricity price data at the current moment are located, further calculating the charge and discharge capacity corresponding to the electricity price time period where the current moment is located, and calculating the cost saving at the current moment based on the charge and discharge capacity, the total power actual value at the current moment and the electricity price data at the current moment, and accumulating the cost saving at the initial moment and calculating the cost saving at the final moment to obtain the total cost saving amount.
According to an embodiment of the third aspect of the present disclosure, there is also provided an energy storage system capacity configuration device operating based on peak-to-valley electricity prices, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the energy storage system capacity configuration method based on peak-to-valley electricity price operation set forth in the first aspect embodiment of the present disclosure.
In one or more embodiments of the present disclosure, historical fan operating parameters, historical fan environmental parameters, historical electricity price data, and an energy storage configuration set comprising a plurality of capacity power combinations are obtained; calculating total power actual sending values of all fans based on the historical fan operation parameters and the historical fan environment parameters; constructing an expense-saving calculation model, determining a power price period based on historical power price data by the expense-saving calculation model, further calculating charge and discharge capacity corresponding to the power price period based on rated capacity and rated power of an energy storage system, and calculating and outputting expense-saving total amount based on the charge and discharge capacity, total power actual generation value and the historical power price data; and selecting each capacity power combination one by one as rated capacity and rated power, calculating the total cost saving amount corresponding to each capacity power combination by using a cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as optimal capacity and optimal power to configure the rated capacity and rated power of the energy storage system. Under the condition, the energy storage system which considers different capacity power combinations under different electricity price time periods by combining the historical electricity price data can save the expense, and the capacity power combination corresponding to the maximum expense saving sum is selected as the optimal capacity and the optimal power of the energy storage system, so that the optimal energy storage configuration which gives consideration to economic benefits is obtained. In addition, the method is suitable for all power utilization areas containing wind power generation, and has a wide application range. Therefore, the application range is enlarged, economic benefits are simultaneously considered, and better energy storage configuration is provided.
Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
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The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 shows a flow chart of a method for configuring capacity of an energy storage system based on peak-to-valley electricity price operation according to an embodiment of the present disclosure;
fig. 2 is a flow chart illustrating a method for obtaining a total amount of cost savings according to an embodiment of the present disclosure;
FIG. 3 illustrates a block diagram of an energy storage system capacity configuration system operating based on peak to valley electricity prices provided by an embodiment of the present disclosure;
fig. 4 is a block diagram of an energy storage system capacity configuration device operated based on peak to valley electricity prices to implement an energy storage system capacity configuration method operated based on peak to valley electricity prices according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of embodiments of the present disclosure as detailed in the accompanying claims.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise. It should also be understood that the term "and/or" as used in this disclosure refers to and encompasses any or all possible combinations of one or more of the associated listed items.
Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.
The present disclosure provides a method and a system for configuring capacity of an energy storage system based on peak-valley electricity price operation, and aims at expanding application range, giving consideration to economic benefit and providing more optimal energy storage configuration. The energy storage system capacity configuration method and system based on peak-valley electricity price operation are applicable to new energy industry parks, wind energy building integrated areas, intelligent power plants, electricity utilization areas containing wind power generation and the like.
In a first embodiment, fig. 1 is a schematic flow chart of a method for configuring capacity of an energy storage system based on peak-to-valley electricity price operation according to an embodiment of the disclosure. As shown in fig. 1, the energy storage system capacity configuration method based on peak-to-valley electricity price operation includes:
step S11, acquiring historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, wherein the energy storage configuration set comprises a plurality of capacity power combinations.
In step S11, historical fan operation parameters, historical fan environment parameters, and historical electricity price data of a plurality of wind driven generators (simply referred to as fans) in a wind farm in recent years are obtained.
In step S11, the historical fan operating parameters include the total number of fans, the power conversion coefficient of the fans, the blade radius of the fans. Wherein the total number of fans may be denoted by the symbol n. The power conversion coefficient of the blower may be represented by the symbol η. The blade radius of the fan may be denoted by the symbol r. The power conversion factor and the blade radius may be obtained from stored specifications or test reports of the wind turbine. In addition, the historical fan operation parameters also comprise rated wind speed when each fan can generate rated power, minimum operation wind speed threshold value (cut-in wind speed) and maximum operation wind speed threshold value (cut-out wind speed) of each fan. The cut-in wind speed and the cut-out wind speed are generally 3m/s and 25m/s respectively.
In step S11, the historical fan environmental parameters include the atmospheric pressure, the temperature of the area where the fan is located, and the wind speed of the height of the area where the fan is located. Wherein the atmospheric pressure of the area where the fan is located can be denoted by the symbol p. The temperature of the area where the fan is located may be denoted by the symbol T. The wind speed of the area height where the fan is located can be represented by the symbol v.
In step S11, the embodiment of the present disclosure acquires the electricity price curve of the application electricity region from the database in a set time step, considering that sufficient accuracy will not be obtained using the data such as average and the process of stepped electricity price is too ambiguous. Specifically, the historical electricity price data includes electricity price data of a plurality of moments in a set time period, the electricity price data of each moment is obtained by sampling from a corresponding set time period section of an electricity price curve according to a set time step, the minimum moment of the set time period is an initial moment, and the maximum moment is a termination moment. The initial time may be marked with the symbol t 1 The termination time may be represented by the symbol t 2 And (3) representing. The set time period is denoted as [ t ] 1 ,t 2 ]. The set time step may be, for example, one hour.
In step S11, the historical fan operating parameters and the historical fan environmental parameters are also obtained for the set time period in accordance with the set time step, considering that sufficient accuracy will not be obtained using the data such as annual average power generation.
In step S11, the obtained set of energy storage configurations may be a set of randomly set capacity powers. Specifically, the set of stored energy configurations includes a plurality of volumetric power combinations. Each capacity power combination includes one capacity and one power. If there are k capacities and j powers, then optionally one capacity and optionally one power may form k x j combinations. Define x=k×j, so the energy storage configuration set X kj Including x capacity power combinations.
And step S12, calculating total power actual values of all fans based on the historical fan operation parameters and the historical fan environment parameters.
In step S12, a set period of time [ t ] is calculated based on the historical fan operating parameters and the historical fan environmental parameters 1 ,t 2 ]The total power actual value of all fans at each moment in the inner range. The total power actual value of all fans at each moment is the sum of the power actual values of all fans at the moment. Namely, the real total power value at the time t satisfies the following conditions:
wherein P is s,t Representing the total power real value at time t, P si,t And the actual power value of the ith fan at the time t is shown.
The actual power value of each fan is calculated based on the historical fan operation parameters and the historical fan environment parameters according to the fan characteristics. Real power transmitting value P of ith fan at t moment si,t The method meets the following conditions:
wherein v is i,t Wind speed v representing height of region where ith fan at time t is located min,i Represents the cut-in wind speed of the No. i fan, v r,i Indicating rated wind speed, v, of the ith fan when rated power can be generated max,i Represents the cut-out wind speed eta of the No. i fan i,t Power conversion coefficient ρ of ith fan at t time i,t The air density of the region where the ith fan at the moment t is positioned is represented by r i,t And the blade radius of the ith fan at the moment t is shown. Wherein the air density of the area where the fan is located is calculated based on the historical fan environmental parameters, namely
Wherein p is i,t The average atmospheric pressure at the ith fan at time t is indicated. R represents a gas constant, and is generally 287J/(kg.K). T (T) i,t No. i fan for indicating t momentAverage air kelvin temperature at that point. Therefore, the power actual value of each fan can be obtained more accurately based on different wind speeds of the areas where the fans are located, so that the accurate total power actual value is obtained, and the accuracy of the subsequent calculation process is improved.
And S13, constructing an expense-saving calculation model, wherein the expense-saving calculation model determines the electricity price time period based on the historical electricity price data, further calculates the charge and discharge quantity corresponding to the electricity price time period based on the rated capacity and the rated power of the energy storage system, and calculates and outputs the expense-saving total based on the charge and discharge quantity, the total power actual generation value and the historical electricity price data.
In step S13, the input data of the cost saving calculation model is the historical electricity price data and the total power actual value, and the output data is the cost saving total. Specifically, since the historical electricity rate data includes the electricity rate data at a plurality of times within the set period, the historical electricity rate data of the cost saving calculation model is input as the set period [ t ] 1 ,t 2 ]And collecting electricity price data at each time. Set time period t 1 ,t 2 ]The electricity rate data (also referred to as peak Gu Dianjia) at time t can be represented by Ct. The total power actual value of all fans is calculated in step S12. Since step S12 calculates the set time period [ t ] 1 ,t 2 ]The total power actual value of each moment in time is input into the cost-saving calculation model to be the set time period [ t ] 1 ,t 2 ]A set of total power real-time values for each time instant.
In step S13, the cost-saving calculation model is started from the initial time t when the electricity price period is determined based on the historical electricity price data 1 And judging the electricity price data of each moment one by one, so as to determine the electricity price period of each moment. Wherein the electricity rate period includes a low electricity rate period, a peak electricity rate period, and an average period. The low electricity price time period can be marked with the symbol t low The representation and peak electricity price time period can be represented by a symbol t high And (3) representing.
In step S13, if the current time belongs to an average period (also referred to as a normal electricity price period), no energy storage operation is performed. I.e. the charge-discharge amount of the average period is zero. I.e. charge-discharge amount Δe=0.
In step S13, if the current time belongs to the low electricity price segment (abbreviated as low segment), the system electricity consumption is obtained from the power grid as soon as possible at the current time, and the generated energy of the fan is stored in the energy storage device as much as possible, so the charge and discharge amount Δe stored in the energy storage system at the current time is the charge amount. At this time, the charge and discharge amount Δe is the minimum value of the wind-driven generator power generation amount in the time step, the maximum electric quantity storage capacity of the energy storage system in the time step, and the remaining storage capacity of the energy storage system. The method comprises the steps that the wind driven generator can generate electricity in the time step based on a total power actual generation value and the time step, the maximum electric quantity storage capacity of the energy storage system in the time step is obtained based on rated power of the energy storage system and the time step, and the residual storage capacity of the energy storage system is obtained based on rated capacity of the energy storage system and the current storage electric quantity of the energy storage system. Namely, Δe of the off-peak electricity price segment satisfies:
ΔE＝min(P s,t *T step ,P e *T step ,E max -E soc )
Wherein T is step Representing a time step, P e Indicating the rated power of the energy storage system E max Representing the rated capacity of the energy storage system, E soc Indicating the current stored power of the energy storage system. And calculating the charge and discharge capacity of the low electricity price time period based on the total power actual generation value, the rated capacity, the rated power and the current storage capacity of the energy storage system.
In step S13, if the current time belongs to a peak electricity price segment (abbreviated as a peak segment), the system electricity consumption is obtained from the energy storage system as much as possible, so the charge-discharge amount Δe stored in the energy storage system at the current time is the discharge electricity (i.e. the electricity consumed from the energy storage system). At this time, the charge-discharge amount Δe is a smaller value between the current stored electric quantity of the energy storage system and the maximum discharge capacity of the energy storage system in the current time step. I.e. the Δe of the peak segment satisfies Δe= -min (E soc ,P e *T step ). Wherein the negative sign represents the power consumption. Therefore, the charging and discharging capacity of the peak electricity price time period is calculated based on the current stored electric quantity and rated power of the energy storage system.
In step S13, the current time is saved by the expenditure S t The method meets the following conditions:
S t ＝C t *(P s,t *T step -ΔE)。
in step S13, the total cost savings at the current time is the total cost savings at the previous time plus the cost savings at the current time.
In step S13, the total amount of the saved expense is obtained by accumulating the saved expense at each moment, the electricity price time period is determined based on the historical electricity price data, the charge and discharge amount corresponding to the electricity price time period is further calculated based on the rated capacity and the rated power of the energy storage system, and the total amount of the saved expense is calculated based on the charge and discharge amount, the total power actual generation value and the historical electricity price data, including: and judging the electricity price time period of the current time of electricity price data, further calculating the charge and discharge capacity corresponding to the electricity price time period of the current time of electricity, and calculating the cost saving at the current time of electricity based on the charge and discharge capacity, the total power actual value at the current time of electricity and the current time of electricity price data, and accumulating the cost saving at the initial time of electricity and the cost saving at the final time of electricity price to obtain the total cost saving amount.
Specifically, fig. 2 shows a flow chart of a method for obtaining a total cost saving amount according to an embodiment of the present disclosure. As shown in FIG. 2, a set time period [ t ] is input first 1 ,t 2 ]Total power real value P of (2) s,t And peak-to-valley electricity valence C t And initializing and defining a cost-saving calculation model, wherein the total cost-saving sum S of an initial set time period is defined to be 0, and the current stored electric quantity of the energy storage system is defined to be 0. Peak-to-valley electricity price C based on input t Judging the electricity price time period of the current moment, if the electricity price time period is judged to be a valley period, calculating to obtain the charge and discharge quantity of the valley electricity price time period based on the total power actual generation value, the rated capacity, the rated power and the current storage electric quantity of the energy storage system, if the electricity price time period is not the valley period, judging whether the electricity price time period is a peak period, if the electricity price time period is the peak period, calculating to obtain the charge and discharge quantity of the peak electricity price time period based on the current storage electric quantity of the energy storage system and the rated power, if the electricity price time period is not the peak period, the charge and discharge quantity of the average time period is 0 at the moment, and obtaining the current energy storage system of the current moment by utilizing the current storage electric quantity of the energy storage system and the charge and discharge quantity of the energy storage system of the last momentThe electricity quantity is stored, the cost saving at the current moment is calculated based on the charge and discharge quantity, the total power practical value at the current moment and the electricity price data at the current moment, the cost saving total at the current moment is obtained by adding the cost saving total at the previous moment to the cost saving total at the current moment, the current moment is added with 1 to obtain a new current moment, and the current moment is judged to exceed a set time period, namely t & gtt 2 If not, continuously judging the electricity price period of the new current moment, and obtaining the total cost saving amount of the new current moment until t is more than t 2 The total cost saving amount at the last moment is the total cost saving amount S of the set time period.
And S14, selecting each capacity power combination one by one as rated capacity and rated power, calculating the total cost saving amount corresponding to each capacity power combination by using a cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as optimal capacity and optimal power to configure the rated capacity and rated power of the energy storage system.
In step S14, the stored energy configuration set X is utilized kj And selecting the capacity power combinations one by one as rated capacity and rated power in the cost-saving calculation model, obtaining the total cost-saving sum corresponding to the capacity power combinations by using the processing procedure shown in fig. 2, selecting the capacity power combination corresponding to the maximum value of all the total cost-saving sums as the optimal solution of the cost-saving calculation model, and configuring the rated capacity and rated power of the energy storage system by using the capacity power combination corresponding to the maximum total cost-saving sum as the optimal capacity and the optimal power.
In the energy storage system capacity configuration method based on peak-valley electricity price operation of the embodiment of the disclosure, historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set are obtained, wherein the energy storage configuration set comprises a plurality of capacity power combinations; calculating total power actual sending values of all fans based on the historical fan operation parameters and the historical fan environment parameters; constructing an expense-saving calculation model, determining a power price period based on historical power price data by the expense-saving calculation model, further calculating charge and discharge capacity corresponding to the power price period based on rated capacity and rated power of an energy storage system, and calculating and outputting expense-saving total amount based on the charge and discharge capacity, total power actual generation value and the historical power price data; and selecting each capacity power combination one by one as rated capacity and rated power, calculating the total cost saving amount corresponding to each capacity power combination by using a cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as optimal capacity and optimal power to configure the rated capacity and rated power of the energy storage system. Under the condition, the energy storage system which considers different capacity power combinations under different electricity price time periods by combining the historical electricity price data can save the expense, and the capacity power combination corresponding to the maximum expense saving sum is selected as the optimal capacity and the optimal power of the energy storage system, so that the optimal energy storage configuration which gives consideration to economic benefits is obtained. In addition, the method is suitable for all power utilization areas containing wind power generation, and has a wide application range. Therefore, the application range is enlarged, economic benefits are simultaneously considered, and better energy storage configuration is provided. The method is based on the characteristic of high peak-valley electricity price and renewable energy source wind-discarding rate, and the economic benefit and the energy source utilization rate are improved by storing the electric quantity for the energy storage system in the period of low-valley electricity price and using the electric quantity from the energy storage system in the period of high-peak electricity price. The energy storage system with different rated powers and capacities can save the expenditure, find the energy storage capacity configuration of the system with the most economic benefits, calculate the energy storage model with the most economic benefits according to the policy by calculating the systems such as a new energy industrial park, a wind energy building integrated area and the like, promote the optimal configuration of power resources, save the expenditure, cut peaks and fill valleys, relieve the supply and demand gap of peak power by reasonably utilizing the peak valley electricity price policy, improve the electricity consumption in the electricity consumption low valley time period to reduce the wind abandoning rate, and configure the power and the capacity with the most economic benefits on the premise that the policy requires energy storage.
The following are system embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the disclosed system, please refer to the embodiments of the disclosed method.
Referring to fig. 3, fig. 3 shows a block diagram of an energy storage system capacity configuration system operating based on peak-to-valley electricity prices according to an embodiment of the present disclosure. The energy storage system capacity configuration system based on peak-to-valley electricity price operation can be realized into all or part of the system through software, hardware or a combination of the two. The energy storage system capacity configuration system 10 based on peak-to-valley electricity price operation comprises an acquisition module 11, a calculation module 12, a modeling module 13 and a control module 14, wherein:
the acquisition module 11 is configured to acquire historical fan operation parameters, historical fan environmental parameters, historical electricity price data and an energy storage configuration set, where the energy storage configuration set includes a plurality of capacity power combinations;
a calculation module 12, configured to calculate total power actual values of all fans based on the historical fan operation parameters and the historical fan environmental parameters;
the modeling module 13 is configured to construct an expense-saving calculation model, and the expense-saving calculation model determines a power price period based on historical power price data, further calculates a charge and discharge amount corresponding to the power price period based on rated capacity and rated power of the energy storage system, and calculates and outputs an expense-saving total amount based on the charge and discharge amount, the total power actual generation value and the historical power price data;
The control module 14 is configured to select each capacity power combination one by one as a rated capacity and rated power, calculate a total cost saving amount corresponding to each capacity power combination by using a cost saving calculation model, and select a capacity power combination corresponding to a maximum total cost saving amount as an optimal capacity and an optimal power to configure the rated capacity and rated power of the energy storage system.
Optionally, the electricity rate period includes a low electricity rate period, a peak electricity rate period, and an average period.
Optionally, the modeling module 13 is specifically configured to: calculating to obtain the charge and discharge capacity of the low electricity price time period based on the total power actual generation value, the rated capacity, the rated power and the current storage capacity of the energy storage system; calculating to obtain the charge and discharge capacity of the peak electricity price time period based on the current stored electric quantity and rated power of the energy storage system; the charge-discharge amount in the average period is zero.
Optionally, the historical electricity price data includes electricity price data of a plurality of moments in a set time period, wherein a minimum moment in the set time period is an initial moment, and a maximum moment is a termination moment.
Optionally, the modeling module 13 is specifically configured to: and judging the electricity price time period of the current time of electricity price data, further calculating the charge and discharge capacity corresponding to the electricity price time period of the current time of electricity, and calculating the cost saving at the current time of electricity based on the charge and discharge capacity, the total power actual value at the current time of electricity and the current time of electricity price data, and accumulating the cost saving at the initial time of electricity and the cost saving at the final time of electricity price to obtain the total cost saving amount.
It should be noted that, when executing the energy storage system capacity configuration method based on the peak-to-valley electricity price operation, the energy storage system capacity configuration system based on the peak-to-valley electricity price operation provided by the embodiment is only exemplified by the division of the above functional modules, in practical application, the above functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the energy storage system capacity configuration device based on the peak-to-valley electricity price operation is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the energy storage system capacity configuration system based on peak-to-valley electricity price operation and the energy storage system capacity configuration method based on peak-to-valley electricity price operation provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the energy storage system capacity configuration system based on peak-to-valley electricity price operation are shown in the method embodiments, and are not described herein again.
The foregoing embodiment numbers of the present disclosure are merely for description and do not represent advantages or disadvantages of the embodiments.
In the energy storage system capacity configuration system based on peak-valley electricity price operation in the embodiment of the disclosure, the acquisition module is used for acquiring historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, wherein the energy storage configuration set comprises a plurality of capacity power combinations; the calculation module is used for calculating total power actual sending values of all fans based on the historical fan operation parameters and the historical fan environment parameters; the modeling module is used for constructing an expense-saving calculation model, the expense-saving calculation model determines the electricity price time period based on the historical electricity price data, further calculates the charge and discharge capacity corresponding to the electricity price time period based on the rated capacity and the rated power of the energy storage system, and calculates and outputs the expense-saving total amount based on the charge and discharge capacity, the total power actual generation value and the historical electricity price data; the control module is used for selecting each capacity power combination one by one as rated capacity and rated power, calculating the total cost saving amount corresponding to each capacity power combination by using the cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as the optimal capacity and the optimal power to configure the rated capacity and the rated power of the energy storage system. Under the condition, the energy storage system which considers different capacity power combinations under different electricity price time periods by combining the historical electricity price data can save the expense, and the capacity power combination corresponding to the maximum expense saving sum is selected as the optimal capacity and the optimal power of the energy storage system, so that the optimal energy storage configuration which gives consideration to economic benefits is obtained. In addition, the method is suitable for all power utilization areas containing wind power generation, and has a wide application range. Therefore, the application range is enlarged, economic benefits are simultaneously considered, and better energy storage configuration is provided. The system disclosed by the invention is based on the characteristics of high peak-valley electricity price and high renewable energy waste rate, and the economic benefit and the energy utilization rate are improved by storing the electric quantity for the energy storage system in the period of low-valley electricity price and using the electric quantity from the energy storage system in the period of high-peak electricity price. The energy storage system with different rated powers and capacities can save the expenditure, find the energy storage capacity configuration of the system with the most economic benefits, calculate the energy storage model with the most economic benefits according to the policy by calculating the systems such as a new energy industrial park, a wind energy building integrated area and the like, promote the optimal configuration of power resources, save the expenditure, cut peaks and fill valleys, relieve the supply and demand gap of peak power by reasonably utilizing the peak valley electricity price policy, improve the electricity consumption in the electricity consumption low valley time period to reduce the wind abandoning rate, and configure the power and the capacity with the most economic benefits on the premise that the policy requires energy storage.
According to embodiments of the present disclosure, the present disclosure also provides an energy storage system capacity configuration device, a readable storage medium, and a computer program product that operate based on peak-to-valley electricity prices.
Fig. 4 is a block diagram of an energy storage system capacity configuration device operated based on peak to valley electricity prices to implement an energy storage system capacity configuration method operated based on peak to valley electricity prices according to an embodiment of the present disclosure. The peak to valley electricity price based operating energy storage system capacity configuration device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The energy storage system capacity configuration device operating based on peak to valley electricity prices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable electronics, and other similar computing devices. The components, connections and relationships of components, and functions of components shown in this disclosure are exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed in this disclosure.
As shown in fig. 4, the energy storage system capacity configuration device 20 operating based on peak-to-valley electricity prices includes a computing unit 21 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 22 or a computer program loaded from a storage unit 28 into a Random Access Memory (RAM) 23. In the RAM 23, various programs and data required for the operation of the energy storage system capacity configuration device 20, which operates based on the peak-to-valley electricity prices, may also be stored. The computing unit 21, the ROM 22 and the RAM 23 are connected to each other via a bus 24. An input/output (I/O) interface 25 is also connected to bus 24.
Various components in the energy storage system capacity configuration device 20 that operate based on peak to valley electricity prices are connected to the I/O interface 25, including: an input unit 26 such as a keyboard, a mouse, etc.; an output unit 27 such as various types of displays, speakers, and the like; a storage unit 28, such as a magnetic disk, an optical disk, or the like, the storage unit 28 being communicatively connected to the computing unit 21; and a communication unit 29 such as a network card, modem, wireless communication transceiver, etc. The communication unit 29 allows the energy storage system capacity configuration device 20 operating on a peak to valley price to exchange information/data with other energy storage system capacity configuration devices operating on a peak to valley price through a computer network such as the internet and/or various telecommunication networks.
The computing unit 21 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 21 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 21 performs the respective methods and processes described above, for example, performs an energy storage system capacity configuration method based on peak-to-valley electricity price operation. For example, in some embodiments, the energy storage system capacity configuration method operating based on peak to valley electricity prices may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 28. In some embodiments, part or all of the computer program may be loaded and/or installed via the ROM 22 and/or the communication unit 29 onto the energy storage system capacity configuration device 20 operating on a peak to valley electricity price basis. When the computer program is loaded into the RAM 23 and executed by the computing unit 21, one or more steps of the energy storage system capacity configuration method of peak-to-valley electricity price based operation described above may be performed. Alternatively, in other embodiments, the computing unit 21 may be configured by any other suitable means (e.g., by means of firmware) to perform the energy storage system capacity configuration method based on peak to valley electricity prices operation.
Various implementations of the systems and techniques described above in this disclosure may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or energy storage system capacity configuration device that operates on a peak to valley electricity price. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or electronic device, or any suitable combination of the preceding. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical electronic storage device, a magnetic electronic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS") are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present disclosure may be performed in parallel, sequentially, or in a different order, so long as the desired result of the technical solution of the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. The energy storage system capacity configuration method based on peak-valley electricity price operation is characterized by comprising the following steps of:
acquiring historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, wherein the energy storage configuration set comprises a plurality of capacity power combinations;
calculating total power actual sending values of all fans based on the historical fan operation parameters and the historical fan environment parameters;
Constructing an expense-saving calculation model, wherein the expense-saving calculation model determines a power price time period based on historical power price data, further calculates charge and discharge capacity corresponding to the power price time period based on rated capacity and rated power of an energy storage system, and calculates and outputs an expense-saving total amount based on the charge and discharge capacity, the total power actual generation value and the historical power price data;
and selecting each capacity power combination one by one as the rated capacity and the rated power, calculating the total cost saving amount corresponding to each capacity power combination by using a cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as the optimal capacity and the optimal power to configure the rated capacity and the rated power of the energy storage system.
2. The energy storage system capacity allocation method based on peak-to-valley power rate operation according to claim 1, wherein the power rate period includes a low-to-valley power rate period, a peak power rate period, and an average period, and the charge-discharge amount of the average period is zero.
3. The energy storage system capacity allocation method based on peak-valley electricity price operation according to claim 2, wherein the charging and discharging amounts of the period of the valley electricity price are calculated based on the total power actual generation value, the rated capacity, the rated power and the current stored electric quantity of the energy storage system; and calculating the charge and discharge capacity of the peak electricity price time period based on the current stored electric quantity of the energy storage system and the rated power.
4. The energy storage system capacity allocation method based on peak-valley power rate operation according to claim 3, wherein the historical power rate data comprises power rate data of a plurality of moments in a set time period, the power rate data of each moment is obtained by sampling a corresponding set time period interval of a power rate curve according to a set time step, the minimum moment of the set time period is an initial moment, and the maximum moment of the set time period is a termination moment.
5. The method for configuring capacity of an energy storage system based on peak-valley power rate operation according to claim 4, wherein the total amount of cost saving is obtained by accumulating cost saving at each moment, the power rate period is determined based on historical power rate data, further charging and discharging amounts corresponding to the power rate period are calculated based on rated capacity and rated power of the energy storage system, and the total amount of cost saving is calculated based on the charging and discharging amounts, the total power actual generation value and the historical power rate data, comprising:
and judging the electricity price time period where the electricity price data at the current moment are located, further calculating the charge and discharge capacity corresponding to the electricity price time period where the current moment is located, and calculating the cost saving at the current moment based on the charge and discharge capacity, the total power actual value at the current moment and the electricity price data at the current moment, and accumulating the cost saving at the initial moment and calculating the cost saving at the final moment to obtain the total cost saving amount.
6. The energy storage system capacity allocation method based on peak-to-valley electricity price operation according to claim 1, wherein the historical fan operation parameters comprise total number of fans, power conversion coefficient of fans and blade radius of fans; the historical fan environment parameters comprise the atmospheric pressure and the temperature of the area where the fan is located and the wind speed of the height of the area where the fan is located.
7. An energy storage system capacity configuration system based on peak-to-valley electricity price operation, which is characterized by comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical fan operation parameters, historical fan environment parameters, historical electricity price data and an energy storage configuration set, and the energy storage configuration set comprises a plurality of capacity power combinations;
the calculation module is used for calculating total power actual values of all fans based on the historical fan operation parameters and the historical fan environment parameters;
the modeling module is used for constructing an expense-saving calculation model, the expense-saving calculation model determines the electricity price time period based on the historical electricity price data, further calculates the charge and discharge quantity corresponding to the electricity price time period based on the rated capacity and the rated power of the energy storage system, and calculates and outputs the expense-saving total based on the charge and discharge quantity, the total power actual power generation value and the historical electricity price data;
And the control module is used for selecting each capacity power combination one by one as the rated capacity and the rated power, calculating the total cost saving amount corresponding to each capacity power combination by using the cost saving calculation model, and selecting the capacity power combination corresponding to the maximum total cost saving amount as the optimal capacity and the optimal power to configure the rated capacity and the rated power of the energy storage system.
8. The energy storage system capacity allocation system operating based on peak to valley electricity prices of claim 7, wherein the electricity price period includes a low valley electricity price period, a peak electricity price period, and an average period, the modeling module being specifically configured to: calculating the charge and discharge capacity of the off-peak electricity price time period based on the total power actual generation value, the rated capacity, the rated power and the current storage capacity of an energy storage system; calculating the charge and discharge capacity of the peak electricity price time period based on the current stored electric quantity of the energy storage system and the rated power; the charge and discharge amount of the average period is zero.
9. The energy storage system capacity allocation system based on peak-valley power rate operation according to claim 8, wherein the historical power rate data comprises power rate data of a plurality of moments in a set time period, a minimum moment in the set time period is an initial moment, a maximum moment is a termination moment, and the modeling module is specifically configured to: and judging the electricity price time period where the electricity price data at the current moment are located, further calculating the charge and discharge capacity corresponding to the electricity price time period where the current moment is located, and calculating the cost saving at the current moment based on the charge and discharge capacity, the total power actual value at the current moment and the electricity price data at the current moment, and accumulating the cost saving at the initial moment and calculating the cost saving at the final moment to obtain the total cost saving amount.
10. An energy storage system capacity configuration device operating based on peak-to-valley electricity prices, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the peak to valley electricity price based operating energy storage system capacity configuration method of any one of claims 1-6.
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