Light storage and charging intelligent coordinated regulation method and device considering complementary regulation interval

Abstract

The invention discloses a light storage and charging intelligent coordinated regulation method and device considering a complementary regulation interval, relates to the field of light storage and charging coordinated regulation, can comprehensively utilize unique advantages of photovoltaic power generation, an energy storage system and an electric vehicle charging facility to realize real-time refined regulation of a power grid, and adopts a generative adversarial network technology to realize real-time regulation of the power grid. According to the method, the effects of different regulation and control strategies can be simulated and predicted, the strategies can be adjusted in real time so as to adapt to dynamic changes of power grid operation, the regulation and control flexibility and response efficiency of the power grid can be greatly improved, and powerful technical support is provided for power grid management in the new energy era.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic storage and charging coordinated regulation, and in particular to a photovoltaic storage and charging intelligent coordinated regulation method considering a complementary regulation interval. Background Art

[0002] In the context of the current global energy transformation, the widespread application of new energy has become a key driving force for sustainable development. With the continuous growth of renewable energy such as wind power and solar energy in the global energy structure, the operation of the power grid faces unprecedented challenges. The volatility and intermittent characteristics of these new energy sources have put forward higher requirements on the stability and dispatching strategies of the power grid. Therefore, developing effective control strategies to ensure the stable operation of the power grid and the efficient use of energy has become an important topic in the research of power systems.

[0003] At present, grid control mainly relies on traditional cogeneration, large hydropower stations and regulating power stations. However, these methods are difficult to meet the needs of large-scale grid connection of new energy in terms of response speed and regulation range. Especially in the coordinated control of photovoltaic power generation, energy storage systems and charging facilities, existing technologies have not been able to fully utilize the potential complementarity of these facilities, resulting in the failure to optimize the configuration and use of energy resources. Summary of the invention

[0004] In order to overcome the deficiencies in the prior art, the present invention proposes a method and device for intelligent coordinated regulation of photovoltaic storage and charging taking into account a complementary regulation range, which can ensure the stable operation of the power grid and the efficient use of energy.

[0005] In order to achieve the above object, the present invention proposes a photovoltaic storage and charging intelligent coordinated regulation method considering the complementary regulation interval, which is applied to a photovoltaic storage and charging system. The photovoltaic storage and charging system includes an energy storage station, a photovoltaic station and a charging station. The method includes the following steps:

[0006] S1. Construct a coordinated control strategy model for the photovoltaic storage and charging system, and output the control amount of the photovoltaic storage and charging system in 1, 2...n cycles;

[0007] S2, based on the control quantity output by S1, uses the generative adversarial network generator to establish a simulation scenario of photovoltaic storage and charging coordinated control, and outputs the grid operation status under the current control quantity time series of the photovoltaic storage and charging system;

[0008] S3, based on the grid operation status output by S2, uses the generative adversarial network to establish a collaborative regulation effect evaluation model for the photovoltaic storage and charging system, and calculates the completion degree of the grid regulation demand of the current collaborative regulation strategy model;

[0009] S4, feeds back the completion degree of the grid regulation demand output by S3 to the photovoltaic storage and charging coordinated regulation simulation scenario in S2, and optimizes the timing sequence of the regulation quantity.

[0010] Furthermore, the collaborative regulation strategy model construction method includes:

[0011] The control cycle of the solar storage and charging system is divided into K control cycles.

[0012] The collaborative control strategy model includes: control state, control behavior, and control target. The control amount of the solar-storage-charging system in the current cycle is obtained by solving the model.

[0013] Furthermore, the solar storage and charging system i is in the regulation period t k The regulatory status It can be expressed as

[0014]

[0015] In the formula, is the control period t k The overall regulation amount obtained by the solar-storage-charging system cluster; The solar storage charging system i in the regulation period t k The operating status of the photovoltaic station at that time; The solar storage charging system i in the regulation period t k The operating status of the energy storage station at that time; The solar storage charging system i in the regulation period t k The operating status of the charging station at that time.

[0016] Furthermore, the regulation behavior of the solar storage and charging system Including energy storage station regulation behavior PV station regulation behavior and charging station regulation behavior It can be expressed as

[0017] Furthermore, the control target model of the solar-storage-charging system can be expressed as:

[0018]

[0019] in, is the control target of the solar-storage-charging system i; is the objective function of the photovoltaic storage and charging system i in the regulation cycle k, specifically the regulation task response of the photovoltaic storage and charging system i under the current regulation, which can be expressed as:

[0020]

[0021] In the formula, The regulation demand of photovoltaic stations in the solar storage and charging system; It is the regulation response of the photovoltaic station in the solar storage and charging system; The regulation demand of charging stations in the solar-storage-charging system; It is the regulation response of the charging station in the solar energy storage and charging system; The regulation demand of energy storage stations in the solar-storage-charging system; It is the regulation response of the energy storage station in the solar-storage-charging system.

[0022] Furthermore, the complementary regulation range of the photovoltaic storage and charging system It can be expressed as:

[0023]

[0024] in, They respectively represent the upper and lower limits of the complementary control range of the photovoltaic station, energy storage station or charging station in the solar-storage-charging system.

[0025] Furthermore, the simulation scenario of photovoltaic energy storage and charging coordinated control includes the control quantity time series of photovoltaic stations, energy storage stations and charging stations.

[0026] Among them, the time series of the control quantity of the photovoltaic station can be expressed as:

[0027]

[0028] Where Y PV is the time series of the control quantity of the photovoltaic station; They are the control quantities of the photovoltaic stations in the 1st, 2nd…nth cycles respectively;

[0029] Among them, the time series of the regulation quantity of the energy storage station can be expressed as:

[0030]

[0031] Where Y W is the time series of the regulation quantity of the energy storage station; They are the control quantities of the energy storage station in the 1st, 2nd…nth cycles respectively;

[0032] Among them, the timing sequence of the control quantity of the charging station can be expressed as:

[0033]

[0034] Where Y LOAD is the timing sequence of the control quantity of the charging station; They are the control quantities of the charging station in the 1st, 2nd…nth cycles respectively.

[0035] Furthermore, it is characterized in that the simulation scenario of photovoltaic storage and charging coordinated control includes the control quantity time series of photovoltaic stations, energy storage stations and charging stations, and the optimization target of the photovoltaic storage and charging system control sequence based on the generative adversarial network algorithm is:

[0036]

[0037] Among them, V is the optimization target of the control sequence of the photovoltaic storage and charging system, G represents the control simulation scenario generated in step 2; D represents the evaluation model of the coordinated control effect of the photovoltaic storage and charging system; x is the coordinated control sequence of the photovoltaic storage and charging system; z is the random fluctuation of the power of the photovoltaic storage and charging system; p z () is the probability distribution function of random fluctuation of power in the photovoltaic storage and charging system; E() is the mathematical expectation function.

[0038] The present invention also proposes a photovoltaic storage and charging intelligent collaborative adjustment device taking into account the complementary regulation interval, including a processor and a memory, wherein a program is stored in the memory, and when the program is executed by the processor, the photovoltaic storage and charging intelligent collaborative adjustment method taking into account the complementary regulation interval is implemented.

[0039] The present invention has the following beneficial effects:

[0040] The present invention can comprehensively utilize the unique advantages of photovoltaic power generation, energy storage systems and electric vehicle charging facilities to achieve real-time and refined control of the power grid. By adopting generative adversarial network technology, it can not only simulate and predict the effects of different control strategies, but also adjust the strategies in real time to adapt to the dynamic changes in power grid operation. This method will greatly improve the control flexibility and response efficiency of the power grid, and provide strong technical support for power grid management in the new energy era. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described and explained below in conjunction with the accompanying drawings.

[0042] Figure 1 It is a parameter diagram of a system using a photovoltaic storage and charging intelligent collaborative regulation method that considers the complementary regulation range.

[0043] Figure 2 This is a diagram showing the regulation effect of the intelligent coordinated regulation method of photovoltaic storage and charging based on different algorithms.

[0044] Figure 3 It is the iterative convergence diagram of the adaptive artificial bee colony algorithm. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be more clearly and completely explained below through description of preferred embodiments of the present invention in combination with the accompanying drawings.

[0046] Example

[0047] An intelligent coordinated regulation method for photovoltaic storage and charging considering a complementary regulation interval is applied to a photovoltaic storage and charging system. The photovoltaic storage and charging system includes an energy storage station, a photovoltaic station and a charging station. The regulation method includes the following steps:

[0048] S1. Construct a coordinated control strategy model for the photovoltaic storage and charging system, and output the control amount of the photovoltaic storage and charging system in 1, 2...K cycles;

[0049] The collaborative regulation strategy model construction method includes:

[0050] The control cycle of the solar storage and charging system is divided into 1, 2...n control cycles, where any control cycle is set to n.

[0051] The collaborative control strategy model includes: control state, control behavior, and control target. The control amount of the solar-storage-charging system in the current cycle is obtained by solving the model.

[0052] The solar storage and charging system i in the regulation period t k The regulatory status It can be expressed as

[0053]

[0054] In the formula, is the control period t k The overall regulation amount obtained by the solar-storage-charging system cluster; The solar storage charging system i in the regulation period t k The operating status of the photovoltaic station at that time; The solar storage charging system i in the regulation period t k The operating status of the energy storage station at that time; The solar storage charging system i in the regulation period t k The operating status of the charging station at that time.

[0055] Regulation behavior of solar-storage-charging system Including energy storage station regulation behavior PV station regulation behavior and charging station regulation behavior It can be expressed as

[0056] The control target model of the solar storage and charging system can be expressed as:

[0057]

[0058] in, is the control target of the solar-storage-charging system i; is the objective function of the photovoltaic storage and charging system i in the regulation cycle k, specifically the regulation task response of the photovoltaic storage and charging system i under the current regulation, which can be expressed as:

[0059]

[0060] In the formula, The regulation demand of photovoltaic stations in the solar storage and charging system; It is the regulation response of the photovoltaic station in the solar storage and charging system; The regulation demand of charging stations in the solar-storage-charging system; It is the regulation response of the charging station in the solar energy storage and charging system; The regulation demand of energy storage stations in the solar-storage-charging system; It is the regulation response of the energy storage station in the solar-storage-charging system.

[0061] Complementary control range of photovoltaic storage and charging system It can be expressed as:

[0062]

[0063] in, They respectively represent the upper and lower limits of the complementary control range of the photovoltaic station, energy storage station or charging station in the solar-storage-charging system.

[0064] The Markov decision process can be used to continuously adjust the control behavior based on the current control state and control target, so as to obtain the control amount of the solar-storage-charging system in the current cycle.

[0065] S2, based on the control quantity output by S1, uses the generative adversarial network generator to establish a simulation scenario of photovoltaic storage and charging coordinated control, and outputs the grid operation status under the current control quantity time series of the photovoltaic storage and charging system;

[0066] The operating status of the power grid is affected by the following parameters: the topological structure and parameters of the power grid, as well as the operating status of photovoltaic stations, energy storage stations and charging stations at each node of the power grid.

[0067] The simulation scenario of coordinated control of photovoltaic, energy storage and charging includes the timing sequence of control quantities of photovoltaic stations, energy storage stations and charging stations. The present invention considers changing the timing sequence of control quantities of photovoltaic stations, energy storage stations and charging stations in the simulation scenario to change the operating state of the power grid.

[0068] Among them, the time series of the control quantity of the photovoltaic station can be expressed as:

[0069]

[0070] Where Y PV is the time series of the control quantity of the photovoltaic station; They are the control quantities of the photovoltaic stations in the 1st, 2nd…nth cycles respectively;

[0071] Among them, the time series of the regulation quantity of the energy storage station can be expressed as:

[0072]

[0073] Where Y W is the time series of the regulation quantity of the energy storage station; They are the control quantities of the energy storage station in the 1st, 2nd…nth cycles respectively;

[0074] Among them, the timing sequence of the control quantity of the charging station can be expressed as:

[0075]

[0076] Where Y LOAD is the timing sequence of the control quantity of the charging station; They are the control quantities of the charging station in the 1st, 2nd…nth cycles respectively.

[0077] S3, based on the grid operation status under the time series of the control quantity output by S2, a generative adversarial network is used to establish a collaborative control effect evaluation model for the photovoltaic storage and charging system, and the grid regulation demand completion degree of the current collaborative control strategy model is calculated. When the grid regulation demand completion degree does not reach the set threshold, the next step is executed; the grid regulation demand completion degree is calculated as follows:

[0078]

[0079] Among them, ψ d The degree of completion of grid regulation demand; P ad ,P nd They are the actual regulation amount and the grid regulation demand respectively.

[0080] S4, feeds back the completion degree of the grid regulation demand output by S3 to the photovoltaic storage and charging coordinated regulation simulation scenario in S2, and optimizes the timing sequence of the regulation quantity.

[0081] The optimization objective of the control sequence of the photovoltaic storage and charging system based on the generative adversarial network algorithm is:

[0082]

[0083] Among them, V is the optimization target of the control sequence of the photovoltaic storage and charging system, G represents the control simulation scenario generated in step S2; D represents the evaluation model of the coordinated control effect of the photovoltaic storage and charging system; x is the coordinated control sequence of the photovoltaic storage and charging system; z is the random fluctuation of the power of the photovoltaic storage and charging system; p z () is the probability distribution function of random fluctuation of power in the photovoltaic storage and charging system; E() is the mathematical expectation function.

[0084] A photovoltaic storage and charging intelligent coordinated regulation device considering complementary regulation intervals comprises a processor and a memory, wherein a program is stored in the memory, and when the program is executed by the processor, a photovoltaic storage and charging intelligent coordinated regulation method considering complementary regulation intervals is implemented.

[0085] In order to verify the performance of the present invention, the IEEE standard simulation system is selected as the application example system. The main parameters of the system are as follows Figure 1As shown in the figure, the system consists of 14 nodes (bus), 5 generators, 11 transmission lines, 11 loads (load) and 3 transformers. The system also connects to a 200kW photovoltaic station, an 80kW energy storage device and a 120kW charging station.

[0086] Figure 2 The M1 curve is the regulation effect of the photovoltaic, storage and charging intelligent collaborative regulation method based on different algorithms. The M2 curve is the regulation effect of the photovoltaic, storage and charging intelligent collaborative regulation method based on the method proposed in the present invention; the M3 curve is the regulation effect of the photovoltaic, storage and charging intelligent collaborative regulation method based on the traditional optimization algorithm considering the complementary collaborative space; the M3 curve is the regulation effect of the photovoltaic, storage and charging intelligent collaborative regulation method based on reinforcement learning without considering the complementary collaborative space. In the figure, the horizontal axis is the photovoltaic, storage and charging regulation time, and the vertical axis is the photovoltaic, storage and charging regulation effect.

[0087] It can be seen that the regulation effect of the photovoltaic storage and charging intelligent coordinated regulation method based on the method proposed by the present invention is better than the regulation effect of other comparison algorithms as a whole. Comparing the M1 and M3 curves, it can be seen that although the M3 curve is slightly better than the method proposed by the present invention at some times, the regulation effect is inferior to the method proposed by the present invention at most times, especially in the time period of 18 to 30, because the complementary coordinated regulation curve is not considered, resulting in insufficient coordination between resources, making it difficult to achieve the expected regulation effect.

[0088] Figure 3 It is the reinforcement learning iterative convergence diagram. It can be seen that the algorithm can converge quickly at the beginning of the iteration, and gradually remain stable after a certain number of iterations, thereby obtaining the optimal value of the parameters of the photovoltaic storage and charging intelligent collaborative regulation method considering the complementary regulation range.

[0089] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A photovoltaic storage and charging intelligent coordinated regulation method considering complementary regulation intervals, applied to a photovoltaic storage and charging system, wherein the photovoltaic storage and charging system includes an energy storage station, a photovoltaic station and a charging station, characterized in that: The method comprises the following steps: S1. Construct a coordinated control strategy model for the photovoltaic storage and charging system, and output the control amount of the photovoltaic storage and charging system in 1, 2...n cycles; S2, based on the control quantity output by S1, uses the generative adversarial network generator to establish a simulation scenario of photovoltaic storage and charging coordinated control, and outputs the grid operation status under the current control quantity time series of the photovoltaic storage and charging system; S3, based on the grid operation status under the control quantity time series output by S2, a generative adversarial network is used to establish a photovoltaic storage and charging system collaborative control effect evaluation model, and the grid regulation demand completion degree of the current collaborative control strategy model is calculated. When the grid regulation demand completion degree does not reach the set threshold, the next step is executed; S4, feeds back the completion degree of the grid regulation demand output by S3 to the photovoltaic storage and charging coordinated regulation simulation scenario in S2, and optimizes the timing sequence of the regulation quantity.

2. According to claim 1, a method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals is characterized in that: The collaborative control strategy model includes: control state, control behavior, and control target. The control amount of the solar-storage-charging system in the current cycle is obtained by solving the model.

3. According to claim 2, a method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals is characterized in that: The solar storage system i has a regulation period t n The regulatory status It can be expressed as: In the formula, is the control period t n The overall regulation amount obtained by the solar-storage-charging system cluster; The solar storage charging system i in the regulation period t n The operating status of the photovoltaic station at that time; The solar storage charging system i in the regulation period t n The operating status of the energy storage station at that time; The solar storage charging system i in the regulation period t n The operating status of the charging station at that time.

4. According to claim 2, a method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals is characterized in that: The regulation behavior of the solar storage and charging system Including energy storage station regulation behavior PV station regulation behavior and charging station regulation behavior It can be expressed as 5. According to claim 2, a method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals is characterized in that: The control target model of the solar storage and charging system can be expressed as: in, is the control target of the solar-storage-charging system i; is the objective function of the photovoltaic storage and charging system i in the regulation cycle n, specifically the regulation task response of the photovoltaic storage and charging system i under the current regulation, which can be expressed as: In the formula, The regulation demand of photovoltaic stations in the solar storage and charging system; It is the regulation response of the photovoltaic station in the solar storage and charging system; The regulation demand of charging stations in the solar-storage-charging system; It is the regulation response of the charging station in the solar energy storage and charging system; The regulation demand of energy storage stations in the solar-storage-charging system; It is the regulation response of the energy storage station in the solar-storage-charging system.

6. According to claim 2, a method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals is characterized in that: The complementary control range of the light storage and charging system It can be expressed as: in, They respectively represent the upper and lower limits of the complementary control range of the photovoltaic station, energy storage station or charging station in the solar-storage-charging system.

7. The method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals according to claim 1 is characterized in that: The photovoltaic storage and charging coordinated control simulation scenario includes the control quantity time series of photovoltaic stations, energy storage stations and charging stations. Among them, the time series of the control quantity of the photovoltaic station can be expressed as: Where Y PV is the time series of the control quantity of the photovoltaic station; They are the control quantities of the photovoltaic stations in the 1st, 2nd…nth cycles respectively; Among them, the time series of the regulation quantity of the energy storage station can be expressed as: Where Y W is the time series of the regulation quantity of the energy storage station; They are the control quantities of the energy storage station in the 1st, 2nd…nth cycles respectively; Among them, the timing sequence of the control quantity of the charging station can be expressed as: Where Y LOAD is the timing sequence of the control quantity of the charging station; They are the control quantities of the charging station in the 1st, 2nd…nth cycles respectively.

8. The method for intelligent coordinated regulation of photovoltaic storage and charging considering complementary regulation intervals according to claim 1 is characterized in that: The optimization objective of the control sequence of the photovoltaic storage and charging system based on the generative adversarial network algorithm is: Among them, V is the optimization target of the control sequence of the photovoltaic storage and charging system, G represents the control simulation scenario generated in step S2; D represents the evaluation model of the coordinated control effect of the photovoltaic storage and charging system; x is the coordinated control sequence of the photovoltaic storage and charging system; z is the random fluctuation of the power of the photovoltaic storage and charging system; p z () is the probability distribution function of random fluctuation of power in the photovoltaic storage and charging system; E() is the mathematical expectation function.

9. An intelligent coordinated regulation device for photovoltaic storage and charging considering complementary regulation intervals, comprising a processor and a memory, characterized in that: A program is stored in the memory, and when the program is executed by the processor, the method for intelligent coordinated regulation of light storage and charging considering complementary regulation intervals as described in any one of claims 1 to 8 is implemented.

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