CN116187702A - Source network and charge storage collaborative interaction optimization scheduling system - Google Patents
Source network and charge storage collaborative interaction optimization scheduling system Download PDFInfo
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Abstract
The invention discloses a collaborative interaction optimization scheduling system for source network charge storage, which comprises an edge calculation module, a state evaluation module and a collaborative scheduling module; the edge calculation module is used for carrying out data calculation on a new energy power station and an electric vehicle charging station in the power grid and transmitting a calculation result to the state evaluation module; the state evaluation module evaluates the state of the power grid and sends an evaluation result to the cooperative scheduling module; the collaborative scheduling module performs collaborative scheduling on each unit of the source network load storage in the power grid according to the evaluation result; the invention can implement different source network charge storage collaborative interaction optimization scheduling aiming at different power grid states.
Description
Technical Field
The invention relates to the technical field of source network charge storage scheduling, in particular to a source network charge storage collaborative interaction optimization scheduling system.
Background
The new energy power generation has the characteristics of randomness and intermittence, along with the continuous increase of the installed scale of the new energy, the serious challenges are brought to the dispatching planning and the safe economic dispatching operation of the power grid, the double-side randomness problem of the supply and demand of the power system is increasingly prominent, the active supporting capability of the distributed photovoltaic power supply to the power grid is not considered in the traditional power distribution network operation control, and the distributed photovoltaic power supply does not have the control performances such as voltage ride-through control, power grid adaptability and the like in the fault state at present. Therefore, the traditional scheduling control mode of 'source follow-up' can not meet the development needs of the power system, and the traditional scheduling control mode is gradually changed to the source network load storage collaborative interaction scheduling mode.
The scheduling mode of 'source follow-up' is converted into the source network load storage cooperative control mode, and is a theoretical basis for constructing a novel power system. Along with the access of a high-proportion distributed power supply, a flexible load and an energy storage system, a source network and load storage collaborative interaction optimization scheduling system is required to be developed, and friendly interaction of source network and load storage in a wide area of a power grid is supported, so that large-scale consumption of new energy is realized.
Disclosure of Invention
1. The technical problems to be solved are as follows:
aiming at the technical problems, the invention provides the source network load storage collaborative interaction optimization scheduling system which can effectively improve the source network load storage collaborative adjustment capability of the power system, thereby ensuring the stable, safe and economic operation of the power system.
2. The technical scheme is as follows:
a collaborative interaction optimization scheduling system for source network and load storage is characterized in that: the system comprises an edge calculation module, a state evaluation module and a cooperative scheduling module; the edge calculation module is used for carrying out data calculation on a new energy power station and an electric vehicle charging station in the power grid and transmitting a calculation result to the state evaluation module; the state evaluation module evaluates the state of the power grid and sends an evaluation result to the cooperative scheduling module; the collaborative scheduling module performs collaborative scheduling on each unit of the source network load storage in the power grid according to the evaluation result; each source network load storage unit comprises a new energy power station, an electric automobile charging station, a thermal power station and an energy storage unit in a power grid;
the new energy power station comprises a photovoltaic power generation power station and a wind power generation power station;
the data calculation of the photovoltaic power generation station specifically comprises the following steps: the power generation at the future time T of the photovoltaic power generation is calculated and predicted through a built-in photovoltaic power generation prediction algorithm, then a photovoltaic power generation fluctuation value is calculated, and the photovoltaic power generation fluctuation value is calculated in the following manner:
ΔP G ＝P G -P GT (1)
(1) Wherein DeltaP G Representing the fluctuation value of photovoltaic power generation, P G Representing the current photovoltaic power generation power, P GT Representing the power generated at the future time T of photovoltaic power generation;
the data calculation of the wind power generation station is specifically as follows: acquiring wind power real-time meteorological data, calculating and predicting the power generation of the wind power at the future time T through a built-in wind power prediction algorithm, and then calculating the fluctuation value of the power generation power of the wind power, wherein the calculation mode of the fluctuation value of the power generation power of the wind power is as follows:
ΔP F ＝P F -P FT (2)
(2) Wherein DeltaP F Representing the fluctuation value of wind power generation, P F Representing the current wind power generation power, P FT Representing the power generated at the future time T of wind power generation;
the data calculation of the electric vehicle charging station is specifically as follows: acquiring the residual capacity information SOC of each electric vehicle battery in the area, and then calculating the adjustable electric energy margin of the current electric vehicle charging station, wherein the calculation formula is as follows:
ΔP C ＝P filling material -P’ Filling material -P Put and put
Wherein DeltaP C Representing an adjustable electric energy margin of a current electric vehicle charging station, P Filling material Indicating the power, P ', of the current electric vehicle charging station that needs to be charged' Filling material Indicating the charge power, P, at which the current electric vehicle charging station can stop Put and put Indicating the power available for discharge at the current electric vehicle charging station;
the state evaluation module receives the data calculated by the edge calculation module and evaluates the state of the power grid; specifically evaluated as follows; when P By T ＝P Hair brush +ΔP G +ΔP F Marking the state of the power grid as a state 1; when P By T >P Hair brush +ΔP G +ΔP F When the state is marked as state 2; when P By T <P Hair brush +ΔP G +ΔP F When the state is marked as a state 3; in the above formula: p (P) By T The power consumption predicted value of the power dispatching system at the future time T based on a preset prediction algorithm is represented; p (P) Hair brush The method comprises the steps of representing the sum of electricity generation, thermal power generation, discharge of an electric vehicle charging station and energy storage and power supply of a photovoltaic power generation station, a wind power generation station in a power grid at the current moment;
the coordination scheduling of the coordination scheduling module comprises the following steps:
s1: when the evaluation result of the state evaluation module is state 1, the cooperative scheduling module is not started;
s2: when the state evaluation module evaluates that the state is state 2, the delta P is the same time By using >Theta, theta is a pre-determined valueThe cooperative scheduling module increases the thermal power generation amount and the energy storage and power supply amount at the same time; otherwise, preferentially increasing the thermal power generation amount, and then increasing the energy storage and supply amount until the whole power grid enters a state 1; wherein DeltaP By using The fluctuation value of the electricity consumption at the future time T is represented by the following calculation formula:
ΔP by using ＝P By T -P By using ；
S3: if the power grid cannot enter the state 1 through the step S2, stopping the charge load which can be stopped by the current electric vehicle charging station, increasing the load which can be used for discharging by the current electric vehicle charging station, and reducing the adjustable load in the power grid until the whole power grid enters the state 1;
s4: and if the state evaluation module evaluates that the state is 3, the redundant power generation amount is used for the energy storage equipment, and meanwhile, the power generation amount of the thermal power is reduced until the whole power grid enters the state 1.
Further, the system also comprises a data acquisition module; the data acquisition module is used for acquiring real-time electricity utilization data, real-time power supply data and data returned by the edge calculation module from the power grid system.
Further, the photovoltaic power generation edge module can also automatically adjust whether the calculation of the photovoltaic power generation power station data is in a working state or a silent state according to the photovoltaic power generation real-time meteorological data acquired in real time and the time period; when entering the silent state, no calculation work is performed on the data of the photovoltaic power generation station.
3. The beneficial effects are that:
(1) According to the source network load storage collaborative interaction optimization scheduling system, partial applications, services and data are deployed in the distributed Internet of things edge nodes, a large number of calculation tasks are downloaded to the servers of the edge nodes, so that the requirement of the service on high real-time performance is met, communication transmission blockage caused by data interaction between a large number of times and multiple times of scheduling end servers is avoided, and therefore the calculation performance of the scheduling end servers can be released.
(2) According to the invention, through evaluating the overall state of the power grid, different source network charge storage collaborative interaction optimization scheduling methods are implemented for different power grid states, the friendly interaction of source network charge storage in the wide area of the power grid is supported, the safe operation of the system is ensured, and meanwhile, the light and wind discarding rate is reduced.
Drawings
FIG. 1 is a diagram of the relationship between each system module of a collaborative interaction optimization scheduling system for source network and load storage;
FIG. 2 is a schematic diagram illustrating the functional components of an edge computing module according to the present invention;
fig. 3 is a flowchart for implementing collaborative interaction optimization scheduling of source network load storage in the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, a source network load storage collaborative interaction optimization scheduling system is characterized in that: the system comprises an edge calculation module, a state evaluation module and a co-scheduling module. As shown in fig. 2, the edge calculation module is configured to perform data calculation on a new energy power station and an electric vehicle charging station in the power grid, and transmit a calculation result to the state evaluation module; the state evaluation module evaluates the state of the power grid and sends an evaluation result to the cooperative scheduling module; the collaborative scheduling module performs collaborative scheduling on each unit of the source network load storage in the power grid according to the evaluation result; each source network load storage unit comprises a new energy power station, an electric automobile charging station, a thermal power station and an energy storage unit in a power grid;
the new energy power station comprises a photovoltaic power generation power station and a wind power generation power station;
the data calculation of the photovoltaic power generation station specifically comprises the following steps: the power generation at the future time T of the photovoltaic power generation is calculated and predicted through a built-in photovoltaic power generation prediction algorithm, then a photovoltaic power generation fluctuation value is calculated, and the photovoltaic power generation fluctuation value is calculated in the following manner:
ΔP G ＝P G -P GT (1)
(1) Wherein DeltaP G Representing the fluctuation value of photovoltaic power generation, P G Representing the current photovoltaic power generation power, P GT Representing the power generated at the future time T of photovoltaic power generation;
the data calculation of the wind power generation station is specifically as follows: acquiring wind power real-time meteorological data, calculating and predicting the power generation of the wind power at the future time T through a built-in wind power prediction algorithm, and then calculating the fluctuation value of the power generation power of the wind power, wherein the calculation mode of the fluctuation value of the power generation power of the wind power is as follows:
ΔP F ＝P F -P FT (2)
(2) Wherein DeltaP F Representing the fluctuation value of wind power generation, P F Representing the current wind power generation power, P FT Representing the power generated at the future time T of wind power generation;
the data calculation of the electric vehicle charging station is specifically as follows: acquiring the residual capacity information SOC of each electric vehicle battery in the area, and then calculating the adjustable electric energy margin of the current electric vehicle charging station, wherein the calculation formula is as follows:
ΔP C ＝P filling material -P’ Filling material -P Put and put
Wherein DeltaP C Representing an adjustable electric energy margin of a current electric vehicle charging station, P Filling material Indicating the power, P ', of the current electric vehicle charging station that needs to be charged' Filling material Indicating the charge power, P, at which the current electric vehicle charging station can stop Put and put Indicating the power available for discharge at the current electric vehicle charging station;
the state evaluation module receives the data calculated by the edge calculation module and evaluates the state of the power grid; specifically evaluated as follows; when P By T ＝P Hair brush +ΔP G +ΔP F Marking the state of the power grid as a state 1; when P By T >P Hair brush +ΔP G +ΔP F When the state is marked as state 2; when P By T <P Hair brush +ΔP G +ΔP F When the state is marked as a state 3; p in the above By T The power consumption predicted value of the power dispatching system at the future time T based on a preset prediction algorithm is represented; p (P) Hair brush The method comprises the steps of representing the sum of electricity generation, thermal power generation, discharge of an electric vehicle charging station and energy storage and power supply of a photovoltaic power generation station, a wind power generation station in a power grid at the current moment;
as shown in fig. 3, the coordinated scheduling of the coordinated scheduling module includes the following steps:
s1: when the evaluation result of the state evaluation module is state 1, the cooperative scheduling module is not started;
s2: when the state evaluation module evaluates that the state is state 2, the delta P is the same time By using >θ, θ is a preset threshold, and the cooperative scheduling module increases the thermal power generation amount and the energy storage and supply amount at the same time; otherwise, preferentially increasing the thermal power generation amount, and then increasing the energy storage and supply amount until the whole power grid enters a state 1; wherein DeltaP By using The fluctuation value of the electricity consumption at the future time T is represented by the following calculation formula:
ΔP by using ＝P By T -P By using ；
S3: if the power grid cannot enter the state 1 through the step S2, stopping the charge load which can be stopped by the current electric vehicle charging station, increasing the load which can be used for discharging by the current electric vehicle charging station, and reducing the adjustable load in the power grid until the whole power grid enters the state 1;
s4: and if the state evaluation module evaluates that the state is 3, the redundant power generation amount is used for the energy storage equipment, and meanwhile, the power generation amount of the thermal power is reduced until the whole power grid enters the state 1.
Further, the system also comprises a data acquisition module; the data acquisition module is used for acquiring real-time electricity utilization data, real-time power supply data and data returned by the edge calculation module from the power grid system.
Further, the photovoltaic power generation edge module can also automatically adjust whether the calculation of the photovoltaic power generation power station data is in a working state or a silent state according to the photovoltaic power generation real-time meteorological data acquired in real time and the time period; when entering the silent state, no calculation work is performed on the data of the photovoltaic power generation station. Generally, the silence state is performed at night, because photovoltaic power generation cannot be performed at night, the silence state of the photovoltaic power generation edge calculation function can save electric power; and whether the illumination of the day can meet the photovoltaic power generation requirement can be judged through meteorological data, and if the illumination of the day does not meet the photovoltaic power generation requirement, the solar energy generation device enters a silence state.
The built-in prediction algorithm used in the calculation of the data of the photovoltaic power generation station and the wind power generation station is the existing algorithm. Different edge computing functions can be processed in a diversified mode aiming at different access terminals, meanwhile, algorithms built in the different edge computing functions are different, photovoltaic power generation prediction algorithms are mainly built in the photovoltaic power generation edge computing functions, wind power prediction algorithms are mainly built in the wind power edge computing functions, and the prediction algorithms are currently more mainstream algorithms.
While the invention has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention, and it is intended that the scope of the invention shall be limited only by the claims appended hereto.
Claims (3)
1. A collaborative interaction optimization scheduling system for source network and load storage is characterized in that: the system comprises an edge calculation module, a state evaluation module and a cooperative scheduling module; the edge calculation module is used for carrying out data calculation on a new energy power station and an electric vehicle charging station in the power grid and transmitting a calculation result to the state evaluation module; the state evaluation module evaluates the state of the power grid and sends an evaluation result to the cooperative scheduling module; the collaborative scheduling module performs collaborative scheduling on each unit of the source network load storage in the power grid according to the evaluation result; each source network load storage unit comprises a new energy power station, an electric automobile charging station, a thermal power station and an energy storage unit in a power grid;
the new energy power station comprises a photovoltaic power generation power station and a wind power generation power station;
the data calculation of the photovoltaic power generation station specifically comprises the following steps: the power generation at the future time T of the photovoltaic power generation is calculated and predicted through a built-in photovoltaic power generation prediction algorithm, then a photovoltaic power generation fluctuation value is calculated, and the photovoltaic power generation fluctuation value is calculated in the following manner:
ΔP G ＝P G -P GT (1)
(1) Wherein DeltaP G Representation ofPhotovoltaic power generation fluctuation value, P G Representing the current photovoltaic power generation power, P GT Representing the power generated at the future time T of photovoltaic power generation;
the data calculation of the wind power generation station is specifically as follows: acquiring wind power real-time meteorological data, calculating and predicting the power generation of the wind power at the future time T through a built-in wind power prediction algorithm, and then calculating the fluctuation value of the power generation power of the wind power, wherein the calculation mode of the fluctuation value of the power generation power of the wind power is as follows:
ΔP F ＝P F -P FT (2)
(2) Wherein DeltaP F Representing the fluctuation value of wind power generation, P F Representing the current wind power generation power, P FT Representing the power generated at the future time T of wind power generation;
the data calculation of the electric vehicle charging station is specifically as follows: acquiring the residual capacity information SOC of each electric vehicle battery in the area, and then calculating the adjustable electric energy margin of the current electric vehicle charging station, wherein the calculation formula is as follows:
ΔP C ＝P filling material -P’ Filling material -P Put and put
Wherein DeltaP C Representing an adjustable electric energy margin of a current electric vehicle charging station, P Filling material Indicating the power, P ', of the current electric vehicle charging station that needs to be charged' Filling material Indicating the charge power, P, at which the current electric vehicle charging station can stop Put and put Indicating the power available for discharge at the current electric vehicle charging station;
the state evaluation module receives the data calculated by the edge calculation module and evaluates the state of the power grid; specifically evaluated as follows; when P By T ＝P Hair brush +ΔP G +ΔＰ Ｆ Marking the state of the power grid as a state 1; when P By T >Ｐ Hair brush +ΔP G +ΔP F When the state is marked as state 2; when P By T <P Hair brush +ΔP G +ΔP F When the state is marked as a state 3; in the above formula: p (P) By T The power consumption predicted value of the power dispatching system at the future time T based on a preset prediction algorithm is represented; p (P) Hair brush Representing a photovoltaic power generation station in a power grid at the current moment,The sum of power generation of a wind power generation station, thermal power generation, discharge of an electric vehicle charging station and energy storage and power supply; the coordination scheduling of the coordination scheduling module comprises the following steps:
s1: when the evaluation result of the state evaluation module is state 1, the cooperative scheduling module is not started;
s2: when the state evaluation module evaluates that the state is state 2, the delta P is the same time By using >θ, θ is a preset threshold, and the cooperative scheduling module increases the thermal power generation amount and the energy storage and supply amount at the same time; otherwise, preferentially increasing the thermal power generation amount, and then increasing the energy storage and supply amount until the whole power grid enters a state 1; wherein DeltaP By using The fluctuation value of the electricity consumption at the future time T is represented by the following calculation formula: ΔP By using ＝P By T -P By using ；
S3: if the power grid cannot enter the state 1 through the step S2, stopping the charge load which can be stopped by the current electric vehicle charging station, increasing the load which can be used for discharging by the current electric vehicle charging station, and reducing the adjustable load in the power grid until the whole power grid enters the state 1;
s4: and if the state evaluation module evaluates that the state is 3, the redundant power generation amount is used for the energy storage equipment, and meanwhile, the power generation amount of the thermal power is reduced until the whole power grid enters the state 1.
2. The source network load storage collaborative interaction optimization scheduling system according to claim 1, wherein the system is characterized in that: the system also comprises a data acquisition module; the data acquisition module is used for acquiring real-time electricity utilization data, real-time power supply data and data returned by the edge calculation module from the power grid system.
3. The source network load storage collaborative interaction optimization scheduling system according to claim 1, wherein the system is characterized in that: the photovoltaic power generation edge module can also automatically adjust whether the calculation of the photovoltaic power generation power station data is in a working state or a silent state according to the photovoltaic power generation real-time meteorological data acquired in real time and the time period; when entering the silent state, no calculation work is performed on the data of the photovoltaic power generation station.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116629565A (en) * | 2023-05-31 | 2023-08-22 | 湖北华中电力科技开发有限责任公司 | Power supply service capability improving method and system based on platformization |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116629565A (en) * | 2023-05-31 | 2023-08-22 | 湖北华中电力科技开发有限责任公司 | Power supply service capability improving method and system based on platformization |
| CN116629565B (en) * | 2023-05-31 | 2024-03-29 | 湖北华中电力科技开发有限责任公司 | Power supply service capability improving method and system based on platformization |
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