Scheduling strategy determination method for participation of wind storage combined system in electric power spot market

Abstract

The invention relates to a scheduling strategy determination method and device for participation of a wind storage combined system in an electric power spot market, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a day-ahead market predicted electricity price, an intra-day market predicted electricity price and wind power output predicted power in a preset time period, determining an uncertainty set of the day-ahead market electricity price based on the day-ahead market predicted electricity price, obtaining a first sample set of wind power output according to the wind power output predicted power, and obtaining a second sample set of wind power output according to the intra-day market predicted electricity price. Obtaining a second sample set of the daily market predicted electricity price; according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set and a pre-constructed robust random model, determining a power dispatching strategy of the wind storage combined system; and based on the power dispatching strategy, dispatching the wind-storage combined system. By adopting the method, the reliability of a power dispatching strategy can be improved.

Description

Technical Field

[0001] The present application relates to the field of renewable energy technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for determining a dispatching strategy for a wind-storage combined system participating in a spot electricity market. Background Art

[0002] As carbon emissions from traditional energy continue to rise, traditional energy shortages and environmental pollution issues become increasingly prominent, the development of clean energy has become an urgent need. Among them, wind-storage combined system technology can effectively alleviate the above problems. Specifically, by combining wind energy and traditional energy storage systems, the flexibility and reliability of the power system can be improved. However, wind power generation has obvious uncertainty, which affects the power dispatch decision-making process of the power system and, to a certain extent, hinders the development of wind-storage combined system technology.

[0003] Traditional solutions usually rely on historical data and statistical models to simply predict wind power output. For example, based on historical weather, historical temperature and historical wind power output, statistical methods or simple time series analysis are used to determine the factors affecting wind power output, thereby predicting future wind power output, and further optimizing the power dispatch strategy based on the predicted wind power output.

[0004] However, wind power generation is often affected by multiple factors such as meteorological conditions, topography, and seasonal changes, resulting in large fluctuations in wind power output. In addition, the uncertainties affecting power dispatch strategies are relatively complex, and it is difficult to accurately describe the uncertainty of wind power output through traditional solutions, which in turn makes the reliability of the power dispatch strategy of the wind-storage combined system low. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining a dispatching strategy for a wind-storage combined system participating in the electricity spot market, which can improve the reliability of the power dispatching strategy in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for determining a dispatching strategy for a wind-storage combined system to participate in a spot electricity market, including:

[0007] Obtain the day-ahead market forecast electricity price, intraday market forecast electricity price and wind power output forecast power within a preset time period;

[0008] Based on the day-ahead market forecast electricity price, determining an uncertainty set of the day-ahead market electricity price, wherein the uncertainty set is used to characterize the uncertainty of the day-ahead market forecast electricity price;

[0009] Determine a first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and determine a second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price;

[0010] Sampling the first cumulative probability distribution function to obtain a first sample set of wind power output, and sampling the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices, wherein the first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity prices;

[0011] Determine a power dispatch strategy for the wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model;

[0012] Based on the power dispatching strategy, the wind power storage combined system is dispatched.

[0013] In one of the embodiments, determining the first cumulative probability distribution function of the wind power output prediction error according to the wind power output prediction power includes:

[0014] Determining first normal distribution data of wind power output prediction error according to the wind power output prediction power and the preset wind farm installed capacity;

[0015] A first cumulative probability distribution function of the wind power output prediction error is determined according to the first normal distribution data.

[0016] In one embodiment, determining a second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price includes:

[0017] Determining second normal distribution data of intraday market electricity price forecast error according to the intraday market forecast electricity price;

[0018] A second cumulative probability distribution function of the intraday market electricity price forecast error is determined based on the second normal distribution data.

[0019] In one embodiment, determining the uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price includes:

[0020] Determine a box uncertainty set of the day-ahead market electricity price according to the day-ahead market forecast electricity price, a preset robust control coefficient and a preset day-ahead market forecast electricity price deviation;

[0021] The uncertainty set includes the boxed uncertainty set.

[0022] In one embodiment, determining the power dispatch strategy of the wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model includes:

[0023] Determine a wind-storage combined system power dispatch model according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model;

[0024] Based on the constraints in the wind-storage combined system power dispatch model, determine the optimal solution of the objective function in the wind-storage combined system power dispatch model, wherein the objective function is used to maximize the total return resources of the wind-storage combined system in the day-ahead market, the intraday market, and the real-time market;

[0025] According to the optimal solution, a power dispatching strategy is determined.

[0026] In one of the embodiments, the constraints include: supplementary combustion compressed air energy storage output constraints, wind power output constraints, and wind farm combined energy storage system output constraints.

[0027] In one embodiment, it is characterized in that the determining the optimal solution of the objective function in the wind-storage combined system power dispatch model based on the constraint conditions in the wind-storage combined system power dispatch model comprises:

[0028] According to the wind-storage combined system power dispatch model, determining the power dispatch main problem and power dispatch sub-problems;

[0029] Solving the main problem of power dispatch, determining the decision variables of the power dispatch model of the wind-storage combined system, and updating the upper bound of the power dispatch model of the wind-storage combined system;

[0030] Solving the power dispatch sub-problem according to the decision variables, determining the uncertain variables of the wind-storage combined system power dispatch model, and updating the lower bound of the wind-storage combined system power dispatch model;

[0031] According to the upper bound, the lower bound and the preset convergence accuracy of the wind-storage combined system power dispatching model, judging whether the wind-storage combined system power dispatching model has converged;

[0032] When the wind-storage combined system power dispatch model converges, determining the optimal solution of the objective function according to the decision variables and the uncertain variables;

[0033] When the wind-storage combined system power dispatch model does not converge, updating the constraint conditions to obtain an updated power dispatch main problem;

[0034] Return to the step of solving the main problem of power dispatching, determining the decision variables of the wind-storage combined system power dispatching model, and updating the upper bound of the wind-storage combined system power dispatching model until the wind-storage combined system power dispatching model converges.

[0035] In a second aspect, the present application also provides a device for determining a dispatching strategy for a wind-storage combined system to participate in a spot electricity market, including:

[0036] A data acquisition module is used to obtain the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within a preset time period;

[0037] A first uncertainty module is used to determine an uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, wherein the uncertainty set is used to characterize the uncertainty of the day-ahead market electricity price;

[0038] A second uncertainty module is used to determine a first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and to determine a second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price;

[0039] a sampling module, configured to sample the first cumulative probability distribution function to obtain a first sample set of wind power output, and to sample the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices, wherein the first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity prices;

[0040] A strategy determination module, configured to determine a power dispatch strategy of a wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model;

[0041] The power dispatching module is used to dispatch the wind-storage combined system based on the power dispatching strategy.

[0042] In a third aspect, the present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of each embodiment of the method for determining the dispatching strategy for the wind-storage combined system participating in the electricity spot market.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of each embodiment of the method for determining a dispatching strategy for a wind-storage combined system participating in the electricity spot market.

[0044] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of each embodiment of the method for determining a dispatching strategy for a wind-storage combined system participating in the electricity spot market.

[0045] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for determining the dispatching strategy of the wind-storage combined system participating in the electricity spot market obtain the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within a preset time period, and determine the uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, the uncertainty set is used to characterize the uncertainty of the day-ahead market forecast electricity price, determine the first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and determine the second cumulative probability distribution function of the intraday market price forecast error according to the intraday market forecast electricity price. Further, the first cumulative probability distribution function is sampled to obtain a first sample set of wind power output, and the second cumulative probability distribution function is sampled to obtain a second sample set of the intraday market forecast electricity price, the first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity price. Finally, the power dispatching strategy of the wind-storage combined system is determined according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-constructed robust stochastic model, and the wind-storage combined system is dispatched based on the power dispatching strategy. Different from the traditional scheme, the above scheme adopts different modeling methods for uncertainty factors of different natures. Specifically, the uncertainty of the day-ahead market electricity price is characterized by an uncertainty set, and the uncertainty of the intraday market electricity price and wind power output is described by a sample set. It not only takes into account the multi-dimensional uncertainty factors that affect power dispatch, but also can accurately describe the uncertainty of each uncertainty factor, providing more comprehensive and accurate information for power dispatch decisions. Therefore, when making power dispatch decisions for the wind-storage combined system, the impact of various uncertainty factors can be fully considered, so that the final power dispatch strategy has higher reliability; further, compared with the random optimization model in the traditional scheme, which is greatly affected by the prediction accuracy of various uncertainty factors, the above scheme introduces a robust random model to deal with uncertainty factors such as electricity price and wind power output, which can reduce the dependence on prediction accuracy in the decision-making process and improve the robustness and reliability of the power dispatch strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 An application environment diagram of a method for determining a dispatching strategy for a wind-storage combined system participating in a power spot market in an embodiment;

[0048] Figure 2 A schematic flow chart of a method for determining a dispatching strategy for a wind-storage combined system participating in a spot electricity market in one embodiment;

[0049] Figure 3 A schematic flow chart of a method for determining a dispatching strategy for a wind-storage combined system to participate in a spot electricity market in another embodiment;

[0050] Figure 4 A schematic diagram of a process for determining a power dispatching strategy in an embodiment;

[0051] Figure 5 It is a flowchart of a method for determining a dispatching strategy for a wind-storage combined system to participate in a spot electricity market in a detailed embodiment;

[0052] Figure 6 (1) is a wind power output scenario generation diagram;

[0053] Figure 6 (2) is a wind power output reduction scenario diagram;

[0054] Figure 6 (3) is a probability diagram of wind power output scenarios;

[0055] Figure 7(1) shows the generation diagram of the intraday market electricity price scenario;

[0056] Figure 7(2) shows the reduction diagram of the intraday market electricity price scenario;

[0057] Figure 7(3) is a probability diagram of intraday market electricity price scenarios;

[0058] Figure 8 Predicted electricity price curve for the day-ahead market;

[0059] Fig. 9 It is a bidding strategy diagram for the day-ahead market;

[0060] Fig.10 It is a bidding strategy chart for the intraday market;

[0061] Fig.11 This is a diagram showing the changes in CVaR and WF&ESS returns;

[0062] Figure 12(1) is a diagram of the day-ahead market bidding strategy when B = 0;

[0063] Figure 12(2) is a diagram of the day-ahead market bidding strategy when B=9;

[0064] Figure 12(3) is a diagram of the day-ahead market bidding strategy when B=18;

[0065] Fig.13 It is a structural block diagram of a device for determining a dispatching strategy for a wind-storage combined system to participate in a spot electricity market in one embodiment;

[0066] Fig.14 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0068] The method for determining the dispatching strategy for the wind-storage combined system participating in the electricity spot market provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0069] Specifically, the operator can upload the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within the preset time period to the server 104 through the terminal 102, or the server 104 can directly retrieve the above data from the data storage system. Then, the server 104 determines the uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, determines the first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and determines the second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price. Further, the server 104 samples the first cumulative probability distribution function to obtain the first sample set of wind power output, and samples the second cumulative probability distribution function to obtain the second sample set of the intraday market forecast electricity price. The first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity price. According to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-constructed robust stochastic model, the power dispatch strategy of the wind-storage joint system is determined, and finally the wind-storage joint system is dispatched based on the power dispatch strategy.

[0070] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices, and the portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0071] In an exemplary embodiment, Figure 2 As shown in the figure, a method for determining the dispatching strategy of a wind-storage combined system participating in the electricity spot market is provided. Figure 1 The server 104 in the example is used as an example to illustrate, and the following steps are included:

[0072] S100, obtaining a day-ahead market forecast electricity price, an intraday market forecast electricity price, and a wind power output forecast power within a preset time period.

[0073] Among them, the day-ahead market electricity price refers to the electricity market price of the day before the transaction, and the day-ahead market forecast electricity price refers to the forecast value of the electricity market electricity price one day ago. The intraday market electricity price refers to the electricity market price on the day of the transaction, and the intraday market forecast electricity price refers to the forecast value of the electricity market electricity price on that day. For example, it is 8 o'clock in the morning, so the intraday market forecast electricity price is a forecast of the electricity price for the next few hours, and it can also be updated every hour. This kind of forecast is more real-time and the forecast results are more accurate. The predicted wind power output is the predicted value of the electric power that a wind turbine can generate within a preset time period. The above-mentioned forecast data can be predicted based on historical data, weather data, consumer demand and other factors, or it can be directly obtained from the meteorological forecast system and the power market analysis system.

[0074] S200, determining an uncertainty set of the day-ahead market electricity price based on the day-ahead market electricity price forecast.

[0075] Among them, since the day-ahead market electricity price has a certain volatility, it is usually impossible to fully determine the exact value of the day-ahead market electricity price in the process of power dispatching decision-making for the wind-storage combined system. Therefore, the uncertainty of the day-ahead market electricity price is described by the uncertainty set method, so as to fully consider the prediction error and volatility of the day-ahead market electricity price.

[0076] S300, determining a first cumulative probability distribution function of a wind power output prediction error based on the wind power output prediction power, and determining a second cumulative probability distribution function of an intraday market electricity price prediction error based on the intraday market electricity price prediction.

[0077] Among them, the cumulative probability distribution function can describe the cumulative probability distribution of random variables under different values, indicating the probability that the random variable is less than or equal to a certain specific value. In this embodiment, the first cumulative probability distribution function of the wind power output prediction error can be constructed based on the wind power output prediction power, for example, the wind power output prediction error is fitted using normal distribution, lognormal distribution, etc.; similarly, the second cumulative probability distribution function of the intraday market electricity price prediction error can also be constructed based on the intraday market prediction electricity price.

[0078] S400, sampling the first cumulative probability distribution function to obtain a first sample set of wind power output, and sampling the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices.

[0079] The sampling method may be Latin hypercube sampling, the first sample set may include multiple typical scenarios of wind power output, and the second sample set may include multiple typical scenarios of intraday market electricity prices. For example, if there are K random input variables, any one of which is represented by X k (k=1,2...,K), then its cumulative distribution probability function can be expressed as Y k =F k (X k ), for the cumulative distribution probability function, the vertical axis of the function curve can be divided into N equally spaced and non-overlapping intervals, each interval length is 1 / N, and the end point of each interval is selected as Y k Then use the inverse function method to calculate X k The sampling value of X k The sampling value of can be expressed by formula (1):

[0080]

[0081] After sampling at equal intervals, a sample matrix with K rows and N columns can be obtained. In order to reduce the correlation between samples, the Cholesky decomposition method can be used to sort them, and then the K-means clustering method can be used to cluster the sorted samples to generate typical scenarios, that is, sample sets, such as reducing the intraday market electricity price to 4 scenarios per period and reducing the wind power output to 16 scenarios per period. In this way, the first sample set of wind power output and the second sample set of intraday market forecast electricity prices can be obtained respectively.

[0082] S500, determining a power dispatch strategy for the wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-built robust stochastic model.

[0083] Among them, the robust stochastic model is a mathematical model used to deal with uncertainty and randomness. It can help consider various possible situations in the decision-making process and formulate more robust strategies. The robust stochastic optimization model can also avoid risks well. By artificially setting different risk preference coefficients, more reasonable decisions can be made at the cost of reducing a very small part of the benefits.

[0084] In this embodiment, the robust stochastic model can be pre-constructed, and the relevant parameters in the model are initialized. In the process of applying the robust stochastic model, based on the uncertainty set of the day-ahead market electricity price, the first sample set, and the second sample set, a model that conforms to the power system can be constructed, so as to solve the model and obtain the power dispatch strategy of the wind-storage combined system. It should be noted that the power dispatch system in this embodiment is a wind-storage combined system, in which energy storage refers to compressed air energy storage with supplementary combustion, including a compressor, a heat exchanger, an air storage chamber, a burner, an expander, and a heat storage tank, and the wind-storage combined system in this embodiment satisfies the following assumptions: (a) air is an ideal gas; (b) the air pressure loss at the connection between the pipeline and the element is ignored; (c) the entire system is in a steady-state process; (d) all effects of kinetic energy and potential energy are ignored; (e) the heat storage tank is adiabatic; (f) the compressor and the turbine expander are both operated as isentropic processes.

[0085] S600, dispatches the wind and energy storage combined system based on the power dispatch strategy.

[0086] Among them, the power dispatching strategy can be used to guide decision makers to conduct power dispatch of the wind-storage combined system. For example, the power dispatching strategy can be used to guide the bidding decision of the wind-storage combined system with the goal of maximizing the total revenue of the wind-storage combined system in the day-ahead, intra-day and real-time markets.

[0087] The above-mentioned method for determining the dispatching strategy of the wind-storage combined system participating in the electricity spot market obtains the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within a preset time period, and determines the uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, the uncertainty set is used to characterize the uncertainty of the day-ahead market forecast electricity price, and determines the first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and determines the second cumulative probability distribution function of the intraday market price forecast error according to the intraday market forecast electricity price. Further, the first cumulative probability distribution function is sampled to obtain a first sample set of wind power output, and the second cumulative probability distribution function is sampled to obtain a second sample set of the intraday market forecast electricity price. The first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity price. Finally, the power dispatching strategy of the wind-storage combined system is determined according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-constructed robust stochastic model, and the wind-storage combined system is dispatched based on the power dispatching strategy. Different from the traditional scheme, the above scheme adopts different modeling methods for uncertainty factors of different natures. Specifically, the uncertainty of the day-ahead market electricity price is characterized by an uncertainty set, and the uncertainty of the intraday market electricity price and wind power output is described by a sample set. It not only takes into account the multi-dimensional uncertainty factors that affect power dispatch, but also can accurately describe the uncertainty of each uncertainty factor, providing more comprehensive and accurate information for power dispatch decisions. Therefore, when making power dispatch decisions for the wind-storage combined system, the impact of various uncertainty factors can be fully considered, so that the final power dispatch strategy has higher reliability; further, compared with the random optimization model in the traditional scheme, which is greatly affected by the prediction accuracy of various uncertainty factors, the above scheme introduces a robust random model to deal with uncertainty factors such as electricity price and wind power output, which can reduce the dependence on prediction accuracy in the decision-making process and improve the robustness and reliability of the power dispatch strategy.

[0088] In one embodiment, Figure 3 As shown, S300 includes:

[0089] S310, determining first normal distribution data of wind power output prediction error according to the wind power output prediction power and the preset wind farm installed capacity, and determining a first cumulative probability distribution function of the wind power output prediction error according to the first normal distribution data.

[0090] S320, determining second normal distribution data of the intraday market electricity price forecast error according to the intraday market forecast electricity price, and determining a second cumulative probability distribution function of the intraday market electricity price forecast error according to the second normal distribution data.

[0091] The installed capacity of a wind farm refers to the total rated power of all wind turbines in the wind farm. Since wind power output cannot be accurately predicted, there is an error between the predicted wind power output and the actual wind power output. To simplify the process, it can be assumed that the wind power output prediction error is normally distributed. Based on this assumption, the wind power output prediction power and normal distribution can be used to generate multiple typical wind power output scenarios.

[0092] Specifically, assuming that the wind power output forecast error in period t follows a normal distribution, the first normal distribution data can be expressed by formula (2):

[0093]

[0094] Where P t The actual power of wind power output; Forecast power for wind power output; is the absolute error of wind power output prediction. Among them, the standard deviation of the absolute error of wind power output prediction σ t The wind power output forecast and wind farm installed capacity P in this period C It can be expressed by formula (3):

[0095]

[0096] Similarly, assuming that the intraday market electricity price forecast error in period t follows a normal distribution, the second normal distribution data can be expressed by formula (4):

[0097]

[0098] In the formula, is the actual electricity price in the market during the day; is the intraday market forecast electricity price. The standard deviation of the intraday market electricity price forecast error is 4% of the forecast value. The standard deviation of the intraday market electricity price forecast error is σ′ t It can be expressed by formula (5):

[0099]

[0100] Further, after determining the first normal distribution data and the second normal distribution data, the first cumulative probability distribution function and the second cumulative probability distribution function can be determined respectively in the following manner: if there are K random input variables, any one of which is represented by X k (k=1,2...,K), then its cumulative distribution probability function can be expressed as Y k =F k (X k ).

[0101] In this embodiment, based on the predicted wind power output and the intraday market predicted electricity price, the corresponding prediction error normal distribution data are determined respectively, and the corresponding cumulative probability distribution function is further determined, so that the probability distribution of the prediction error can be intuitively quantified, so that when making power dispatching decisions, uncertainty can be better considered and handled, thereby improving the robustness and accuracy of the power dispatching strategy.

[0102] In one embodiment, Figure 3 As shown, the uncertainty set includes a box-type uncertainty set, S200 includes:

[0103] S210, determining a box uncertainty set of the day-ahead market electricity price according to the day-ahead market forecast electricity price, a preset robust control coefficient and a preset day-ahead market forecast electricity price deviation.

[0104] Among them, the box uncertainty set refers to a method used to describe all possible value ranges of uncertain parameters. Specifically, based on the day-ahead electricity price forecast results, the box uncertainty set can fully describe the uncertainty of the day-ahead electricity price. The box uncertainty set can be expressed by equations (6) to (8):

[0105]

[0106] In the formula, is the day-ahead market forecast electricity price for period t; represents the actual market electricity price in the day-ahead period t; and is a 0-1 integer variable; δ is the forecast deviation of the day-ahead electricity price; B is the robust control coefficient. The higher the robust control coefficient, the more times the worst day-ahead market electricity price is considered, which means that decision makers will pay more attention to the risk of day-ahead electricity price fluctuations and make more conservative decisions.

[0107] In this embodiment, a box uncertainty set method is used to accurately describe the uncertainty of the day-ahead market electricity price forecast, and a more stable and robust power dispatching strategy is formulated. This power dispatching strategy can better cope with the volatility of the day-ahead market electricity price, so that the wind-storage combined system can achieve better performance and economic benefits under the dispatch of the power dispatching strategy.

[0108] In one embodiment, Figure 4 As shown, S500 includes:

[0109] S510, determining a wind-storage combined system power dispatch model according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model.

[0110] S520, based on the constraints in the wind-storage combined system power dispatch model, determine the optimal solution of the objective function in the wind-storage combined system power dispatch model.

[0111] S530, determining a power dispatching strategy according to the optimal solution.

[0112] Among them, the pre-built robust stochastic model can be an initialized robust stochastic model, which contains constraints and objective functions. The objective function can be minimizing the cost of the wind-storage combined system or maximizing the total benefit of the wind-storage combined system. The constraints can include wind power output constraints, energy storage system capacity constraints, market electricity price constraints and system balance constraints, etc.

[0113] For example, the uncertainty set of the day-ahead market electricity price, the typical scenarios of multiple wind power outputs in the first sample set, the typical scenarios of multiple intraday market electricity prices in the second sample set, and other related parameters can be substituted into the pre-built robust stochastic model to obtain a wind-storage combined system power dispatch model suitable for the actual power system. In this embodiment, the decision-making goal of the wind-storage combined system power dispatch model is to maximize the total return resources of the wind-storage combined system in the day-ahead market, the intraday market, and the real-time market. The total return resources can be the total revenue, and the objective function can be expressed by formula (9):

[0114]

[0115] In formula (9), the objective function of the first stage represents the profit obtained in the intraday market + the profit obtained in the real-time market + the conditional risk value – the cost of energy storage; the objective function of the second stage represents the profit obtained in the day-ahead market.

[0116] In this model, the operating costs associated with wind turbines are ignored. represents the electricity price in period t under intraday market scenario p; represents the active power output of WF&ESS (wind farm with energy storage systems) in period t under intraday market scenario p; It indicates the predicted market electricity price on the day before; and They represent the positive and negative deviations of the real-time market price ratio in time period t under scenario ω in scenario p, respectively; and They represent the positive and negative deviations between the actual active power output and the planned active power output of WF&ESS in the real-time market in period t under scenario ω in scenario p; β represents the risk coefficient, which ranges from [0,1]; θ and α represent the risk value and confidence level, respectively; is an auxiliary variable, which represents the portion of the total return of the intraday market and the real-time market that is lower than the risk value; represents the electricity price in the day-ahead market during period t; N represents the active power output of WF&ESS in the day-ahead market during period t; t is 24, that is, 24 hours; N p and N ω They are the typical scenario numbers of intraday market electricity price and wind power output respectively.

[0117] C t,p The operating cost of energy storage can be expressed by formula (10):

[0118]

[0119] In the formula, H DIS represents the heat consumption rate of energy storage in discharge mode, G represents the natural gas price, represents the operation and maintenance cost of energy storage in pumping mode, Represents the operation and maintenance costs of energy storage in compression mode.

[0120] Furthermore, based on the constraints in the wind-storage combined system power dispatching model, the above objective function is solved to obtain the optimal solution of the objective function in the wind-storage combined system power dispatching model, which can guide the formulation of power dispatching strategies.

[0121] In this embodiment, while taking uncertainty and randomness factors into consideration, the power dispatch strategy of the wind-storage combined system is accurately formulated with the goal of maximizing the total revenue of the wind-storage combined system in the day-ahead market, intraday market and real-time market, thereby optimizing the coordinated work of wind power processing and energy storage systems, maximizing the use of renewable energy power generation capacity, and effectively improving the economic benefits of the wind-storage combined system.

[0122] In one embodiment, the constraints include: a supplementary-fired compressed air energy storage output constraint, a wind power output constraint, and a wind farm combined energy storage system output constraint.

[0123] Among them, compressed air energy storage (CAES) refers to the use of excess electricity when the power system is under load. An electric motor drives an air compressor to compress air into an air storage chamber. When the power system generates insufficient electricity, the compressed air is mixed with oil or natural gas through a heat exchanger and burned, and then introduced into the turbine to generate electricity.

[0124] Specifically, the output constraints of supplementary-fired compressed air energy storage include the day-ahead market CAES output constraints, the intraday market CAES output constraints, the CAES planned output constraints, the active output constraints of CAES in three operating modes, the CAES operating mode state constraints, the CAES state transition equation, and the CAES planned energy constraints, which are expressed by the following formulas:

[0125] Day-ahead market CAES output constraints:

[0126]

[0127] In the formula, It represents the active output of energy storage in the day-ahead market during period t; and Indicates the maximum power of energy storage in compression and pumping mode; U t A binary variable representing the operation mode of the energy storage during period t.

[0128] Intraday market CAES output constraints:

[0129]

[0130] In the formula, represents the active output of wind farm and energy storage in the intraday market in time period p; U t,p A 0-1 integer variable indicating the operation mode of energy storage in the intraday market under scenario p during period t; δ represents the proportional coefficient of the intraday market bidding volume.

[0131] CAES planned output constraint: The planned output of energy storage is equal to the sum of its active output in the day-ahead and intraday market transactions, that is:

[0132]

[0133] In the formula, represents the planned active output of energy storage under scenario p in period t; energy storage has three modes: charging, discharging and simple cycle operation. The active output can be defined by (14); and They represent the active output of the energy storage during simple cycle, charging and discharging in scenario p during period t respectively.

[0134] Active power output constraints of CAES in three operating modes:

[0135]

[0136] CAES operation mode status constraints:

[0137]

[0138] The state transition equation of CAES:

[0139]

[0140] The planned energy constraints of CAES are:

[0141]

[0142] Wind power output constraints include day-ahead market wind power output constraints, intraday market wind power output constraints, and wind power planned output constraints, which are expressed by the following formulas:

[0143] Current market wind power output constraints:

[0144]

[0145] In the formula, It represents the active power output of wind power in the day-ahead market during period t; Indicates the maximum active output of wind power.

[0146] Intraday market wind power output constraints:

[0147]

[0148] In the formula, It represents the active power output of wind power in the intraday market in the scenario p during period t.

[0149] Wind power planned output constraints:

[0150]

[0151] In the formula, It represents the planned active power output of wind power in scenario p during period t.

[0152] The output constraints of the wind farm combined with the energy storage system include the WF&ESS output constraints of the day-ahead market, the WF&ESS output constraints of the intraday market, the WF&ESS planned output constraints, and the WF&ESS output constraints of the real-time market, which are respectively expressed by the following formulas:

[0153] WF&ESS output constraints in the day-ahead market:

[0154]

[0155] Intraday market WF&ESS output constraints:

[0156]

[0157] WF&ESS planned output constraint: The planned output of WF&ESS is equal to the sum of its output in the day-ahead and intraday market transactions, and is also equal to the sum of the planned outputs of wind power and energy storage, that is:

[0158]

[0159] Real-time market WF&ESS output constraints:

[0160]

[0161] In order to balance the fluctuations in power provided by wind power and energy storage, the real-time market can be used as a compensator between the day-ahead market and the intraday market. represents the actual output of wind power; equations (34) and (35) limit the positive and negative imbalances in the real-time market. In this embodiment, it is assumed that the actual energy storage output is equal to its planned output.

[0162] In addition, the constraints also include conditional risk value constraints. The uncertainty of wind power output and intraday market electricity prices is described by typical scenarios, which may bring certain risks to the income of the wind-storage combined system. Therefore, it is necessary to measure the risk. The risk measurement methods can be loss expectation method, risk value method and CVaR (Condition Value at Risk, conditional risk value model). Taking CvaR as an example, CVaR is a convex risk measurement, so the bidding model based on CVaR must have a solution with minimum risk, and it can better reflect the potential risk of income. The specific conditional risk value constraints are shown in equations (36) to (37):

[0163]

[0164] Among them, α is the confidence level; Y is the return set under different scenarios of WF&ESS; θ is the risk value; ρ ω is the probability of scene ω; η ω is the difference between the return less than the risk value and the risk value under the scenario ω, It represents the auxiliary variable of CVaR, and its actual meaning is: the part of the total return of the intraday market and the real-time market that is lower than the risk value.

[0165] In this embodiment, by setting the above-mentioned constraints, the final power dispatch strategy can be made practical and feasible, and can meet the actual goals as much as possible while maintaining the stability and safety of the wind-storage combined system. For example, setting the CAES output constraint condition can make each device in the wind-storage combined system operate within the load range. Through some charging and discharging constraints, the wind-storage combined system can store electricity when the electricity price is low and discharge it when the electricity price is high, thereby optimizing the economic benefits of the wind-storage combined system.

[0166] In one embodiment, S520 includes: determining the power dispatching main problem and the power dispatching sub-problem according to the wind-storage combined system power dispatching model, solving the power dispatching main problem, determining the decision variables of the wind-storage combined system power dispatching model, and updating the upper bound of the wind-storage combined system power dispatching model; solving the power dispatching sub-problem according to the decision variables, determining the uncertain variables of the wind-storage combined system power dispatching model, and updating the lower bound of the wind-storage combined system power dispatching model; judging whether the wind-storage combined system power dispatching model converges according to the upper bound, lower bound and preset convergence accuracy of the wind-storage combined system power dispatching model; when the wind-storage combined system power dispatching model converges, determining the optimal solution of the objective function according to the decision variables and the uncertain variables; when the wind-storage combined system power dispatching model does not converge, updating the constraints to obtain the updated power dispatching main problem; returning to the steps of solving the power dispatching main problem, determining the decision variables of the wind-storage combined system power dispatching model, and updating the upper bound of the wind-storage combined system power dispatching model, until the wind-storage combined system power dispatching model converges.

[0167] Following the above embodiment, the process of determining the optimal solution of the objective function in the wind-storage combined system power dispatch model can be to first decompose the wind-storage combined system power dispatch model into a power dispatch main problem and a power dispatch sub-problem, wherein the power dispatch main problem is shown in formula (38), and the power dispatch sub-problem is shown in formula (39):

[0168]

[0169] Among them, the main problem MP1 will be solved according to the bad scenario set u of the day-ahead market electricity price generated by the sub-problem SP1, and the sub-problem SP1 will be solved according to the main problem optimization decision x * Therefore, when making power dispatch decisions, the wind-storage joint system can fully consider the impact of various uncertain factors. The main and sub-problems are easy-to-solve mixed integer linear programming problems. The specific solution steps are as follows:

[0170] First, set the initial harsh scenario u l , initialization problem upper and lower bounds U 0 =+∞,L 0 = -∞, initial number of iterations k = 1, maximum number of allowed iterations n max Furthermore, the main problem of power dispatch is solved, the decision variables of the wind-storage combined system power dispatch model are determined, and the upper bound of the wind-storage combined system power dispatch model is updated. Specifically, the severe scenario set u is brought into the main problem MP1 for solution to obtain the optimal solution x of the decision variables. k , where u={u 1 ,u 2 ,...,uk}, and update the upper bound U of the model k Then, according to the decision variables, solve the power dispatch sub-problem, determine the uncertain variables of the wind-storage combined system power dispatch model, and update the lower bound of the wind-storage combined system power dispatch model. Specifically, change x k Substitute the known variables into subproblem SP1 to obtain the optimal solution u of the uncertain variables k+l And the sub-problem objective function value, update the model lower bound to L k .

[0171] Then, according to the upper bound, lower bound and preset convergence accuracy of the wind-storage combined system power dispatch model, it is judged whether the wind-storage combined system power dispatch model has converged, or it can be judged whether the model has reached the maximum number of iterations according to the number of iterations. If it is judged that the wind-storage combined system power dispatch model has converged, or the number of iterations of the wind-storage combined system power dispatch model has reached the maximum number, the optimal solution of the objective function is determined according to the decision variables and uncertain variables; if it is judged that the wind-storage combined system power dispatch model has converged, the severe scenario set u is updated and k=k+1 is set, and the corresponding control variable y is increased. k+l and constraints, obtain the updated main problem of power dispatch, and return to solve the main problem of power dispatch, determine the decision variables of the wind-storage combined system power dispatch model, and update the upper bound of the wind-storage combined system power dispatch model until the wind-storage combined system power dispatch model converges. The solution process of the above wind-storage combined system power dispatch model can be implemented based on the MATLAB platform using YALMIP to call the GUROBI solver.

[0172] In this embodiment, the column and constraints generation (C&CG) algorithm is used to divide the wind-storage combined system power dispatch model into the C&CG main problem and the C&CG sub-problem. The C&CG sub-problem searches for the worst day-ahead market electricity price scenario based on the optimization results of the C&CG main problem and feeds it back to the C&CG main problem. The C&CG main problem adds corresponding variables and constraints based on the optimization results of the C&CG sub-problem. The C&CG main problem and the C&CG sub-problem are continuously iterated alternately in this way until the wind-storage combined system power dispatch model converges and obtains the optimal solution of the objective function, thereby efficiently determining a robust power dispatch strategy.

[0173] In order to make a clearer description of the method for determining the dispatching strategy of the wind power storage combined system participating in the power spot market provided by the present application, the following is a specific embodiment and the attached Figure 5 To illustrate, this specific embodiment includes the following steps:

[0174] S501, obtaining the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within a preset time period, and determining the box uncertainty set of the day-ahead market electricity price according to the day-ahead market forecast electricity price, the preset robust control coefficient and the preset day-ahead market forecast electricity price deviation.

[0175] S502, determining first normal distribution data of wind power output prediction error according to the wind power output prediction power and the preset wind farm installed capacity, and determining a first cumulative probability distribution function of the wind power output prediction error according to the first normal distribution data.

[0176] S503, determining second normal distribution data of the intraday market electricity price forecast error according to the intraday market forecast electricity price, and determining a second cumulative probability distribution function of the intraday market electricity price forecast error according to the second normal distribution data.

[0177] S504, sampling the first cumulative probability distribution function to obtain a first sample set of wind power output, and sampling the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices.

[0178] S505, determining a wind-storage combined system power dispatch model based on the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-built robust stochastic model. Based on the constraints in the wind-storage combined system power dispatch model, determining the optimal solution of the objective function in the wind-storage combined system power dispatch model.

[0179] S506, determining a power dispatching strategy according to the optimal solution, and dispatching the wind and energy storage combined system based on the power dispatching strategy.

[0180] The above-mentioned method for determining the dispatching strategy of the wind-storage combined system participating in the electricity spot market is applied to an actual wind-storage combined system as an example. The installed capacity of the wind farm is 250MW (megawatts), and a supplementary-fired CAES with a capacity of 15MW / 30MWh (milliwatt-hours) is configured. The heat rate of CAES is 0.837 in the simple cycle operation mode, and the heat rate of CAES in the pumping power mode is 0.4185. The operating costs of the charging and discharging modes are both 34.8 yuan / MWh, the operating cost of the cycle mode is 69.6 yuan / MWh, and the natural gas price is 27 yuan / GJ (gigajoules). The air storage level of the CAES cave is limited to between 3 and 27MWh, the initial gas storage level is 3MWh, and the energy ratio is 0.95. Assuming that the wind power output and electricity price forecast errors are both normally distributed, the standard deviation of the intraday electricity price forecast error is 4% of the predicted value. The Latin hypercube sampling method was used to generate 1,000 wind power output scenarios and intraday electricity price scenarios, and then reduced to 16 wind power scenarios, 4 intraday market electricity price scenarios, and 16 real-time market electricity price curves corresponding to wind power scenarios. The wind power output scenario diagram, scenario reduction diagram, and scenario probability diagram generated by sampling are shown in Figure 2. Figure 6(1) to Figure 6(3) As shown in the figure, the intraday market electricity price scenario diagram, scenario reduction diagram and scenario probability diagram are as follows Figure 7(1) to Figure 7(3) As shown in the figure, the day-ahead market forecast electricity price curve is as follows Figure 8 As shown, the proportional coefficient of the real-time market electricity price and Based on experience, we take 1.1 and 1.15 respectively.

[0181] When the risk preference coefficient is 0, WF&ESS makes day-ahead bidding decisions in a risk-neutral manner according to the maximum expected return. When wind power bids alone on the day-ahead, the bidding decision is mainly based on the predicted value of wind power output, which leads to a conservative bidding strategy and a large risk. It is impossible to strategically use energy storage to track market electricity prices, which reduces the competitiveness of wind power in the market. However, when wind power is combined with energy storage, the flexible adjustment capability of energy storage can effectively reduce the uncertainty of wind power output, and the price tracking characteristics of energy storage can be used to shift wind power output in time, so that the bidding volume is higher than the bidding volume of wind power participating in the day-ahead market alone, thereby increasing the benefits in the market. Fig. 9 is the bidding strategy diagram of the day-ahead market, Fig. 9 It can be seen that when the electricity price in the day-ahead market is low, the energy storage is charged, and WF&ESS reduces the bid volume in the day-ahead market during this period. When the electricity price in the day-ahead market is high, the energy storage is discharged, and WF&ESS increases the bid volume in the day-ahead market during this period. The energy storage charging and discharging operation helps WF&ESS obtain greater benefits.

[0182] When the risk preference coefficient is 0, the intraday market also stores energy at a lower electricity price and sells it at a higher electricity price. Since energy storage can only perform one of the operations of charging or discharging in a period of time, the action status of energy storage in the day-ahead market and the intraday market needs to be considered comprehensively to make the overall bidding decision more reasonable. For example, the following is an analysis of the impact of energy storage on the overall revenue of WF&ESS when the risk preference coefficient is 0: As shown in Table 1, due to the role of energy storage, in the day-ahead market, energy storage is charged when the electricity price is low and discharged when it is high. After deducting the energy storage operating costs, the profit revenue is 4,994 yuan; in the intraday market, energy storage is charged when the electricity price is low and discharged when it is high. After deducting the energy storage operating costs, the profit revenue is 1,977 yuan. Fig.10 This is the intraday market bidding strategy diagram. Since the decision-making assumes that energy storage only participates in the day-ahead market and the intraday market, the real-time market electricity sales revenue comes from the actual output of the wind farm being greater than the sum of the day-ahead and intraday wind farm bids. Therefore, in this model, whether or not to install energy storage has no effect on the real-time market revenue. In summary, when the risk preference coefficient is 0 and the uncertainty of the day-ahead market electricity price is not considered, the total revenue of installing energy storage is 5,601 yuan more than that of not installing energy storage.

[0183] Table 1 Energy storage market revenue

[0184]

[0185] Fig.11 is the change of CVaR and WF&ESS income, refer to Fig.11 It can be seen that CVaR is positively correlated with the risk preference coefficient. This means that as the risk preference coefficient increases, CVaR will also increase. This is because in this case, WF&ESS is more risk-averse, so it adopts a more conservative bidding strategy in the spot electricity market, resulting in a decrease in expected returns. When the risk preference coefficient is greater than or equal to 0.4, WF&ESS's bidding decisions are more sensitive to risk, which leads to an increase in CVaR and a decrease in bidding returns. When the risk preference coefficient is less than 0.4, WF&ESS's bidding decisions are very sensitive to risk, so the decrease in bidding returns is relatively rapid. Considering that the overall expected return of the bid is less affected by the risk preference coefficient, because the uncertainty factors of wind power and electricity prices as well as energy storage are taken into account in the modeling process, WF&ESS has a certain risk resistance and can achieve stable returns under a certain degree of risk preference.

[0186] The uncertainty of the day-ahead market electricity price is described by an uncertainty set, and the number of day-ahead market electricity price deviation periods is controlled by the robust control coefficient B. The upper and lower limits of the day-ahead market price deviation are set to 0.2 times the predicted value. By adjusting the robust control coefficient B, the degree of uncertainty in the day-ahead market electricity price can be controlled. The robust control coefficient B takes the value of [0,24], indicating that among the 24 time periods of a day, there are several time periods in which the day-ahead market electricity price is offset. By setting the robust control coefficient B, the overall bidding strategy can be made more robust.

[0187] Figure 12(1) to Figure 12(3) The risk preference coefficient is 0.1, and the robust control coefficient B is 0, 9 and 18 respectively, and the corresponding day-ahead market bidding strategy diagrams are shown. The day-ahead market bidding power diagrams drawn by three different robust control parameters show that: (1) When B = 0, CAES takes into account the electricity prices of the day-ahead market and the intraday market in the t period. It can be seen from the figure that CAES performs energy storage at lower electricity prices and discharges energy at higher electricity prices to maximize the benefits. (2) When B = 9, the lowest electricity prices in the 9 periods of the day-ahead market are considered. For example, at 9 o'clock and 11 o'clock, after considering the price deviation, the electricity price is low at this time. CAES will no longer discharge in this period, and consider discharging electricity to the other periods of the day-ahead market or the higher period of the intraday market to obtain the overall maximum benefits of WF&ESS. (3) When B = 18, the lowest electricity prices in the 18 periods of the day-ahead market are considered. CAES only performs discharge operations at 10 o'clock and 20 o'clock, and does not operate or perform energy storage operations at other times. Therefore, by controlling the robust control parameter B, it is possible to obtain as much profit as possible when there is a large fluctuation in the market electricity price on the day before, and the operation of WF&ESS is more stable.

[0188] From the analysis of Table 2, it can be seen that when the robust control parameters are 0, 9, and 18, the installed energy storage will generate more profits of 6768 yuan, 7382 yuan, and 7351 yuan respectively compared with not installing energy storage. It can be seen from the table that due to the fluctuation of the market electricity price the day before, the electricity price in the B period is the lowest. The energy storage charges during these periods and stores the electricity, and puts more of this electricity into the intraday market, so that the intraday market income compensates for the income from selling electricity at a low price the day before, and also obtains additional income. In summary, the installation of energy storage can effectively prevent the loss of income caused by the fluctuation of the market electricity price the day before.

[0189] Table 2 Energy storage market revenue

[0190]

[0191] By considering the risk preference coefficients of 0.1, 0.4 and 1, the robust control coefficients are 3, 5, 9, 14 and 18 respectively. The analysis shows that: (1) Under the same risk preference coefficient β, as the robust control parameter B increases, the overall profit obtained by WF&ESS becomes smaller. The reason is that there are B periods with the lowest electricity prices in the market before the day. Under the premise of maximizing the overall profit, wind power and energy storage strive to reduce the bidding volume in this period and increase bidding in other periods before the day or in the intraday market to obtain greater profits. However, since the electricity price in this period may be at a high point, the overall profit will still decrease, but compared with the case without consideration, the system faces smaller risks and smaller profit losses. (2) Under the same robust control parameter B, as the risk preference coefficient increases, the overall profit of the system decreases slightly. The reason is that as the risk preference coefficient increases, the bidding strategy of WF&ESS in the intraday and real-time markets becomes more conservative, seeking smaller risks, so the profit decreases slightly.

[0192] In summary, the above scheme verifies that the WF&ESS using supplementary compressed air as energy storage can obtain benefits directly in the spot electricity market without relying on the CFD and green certificate markets in the medium and long-term markets. In addition, the above scheme, taking into account the traditional scheme of using stochastic optimization methods to deal with the uncertainty of electricity prices and wind power output, characterizes the uncertainty of the day-ahead market electricity price in the form of probability distribution. Since the day-ahead market transaction volume is large and the stochastic optimization method is only applicable to scenarios with small uncertainties or that can be accurately predicted by historical data, it is used to deal with the uncertainty of the day-ahead market electricity price. There is a greater risk. Therefore, the risk-averse robust stochastic optimization method proposed in this scheme uses an uncertain set to characterize the uncertainty of the day-ahead market electricity price, and uses typical scenarios to describe the uncertainty of intraday market electricity prices and wind power output. It considers the randomness of uncertainty factors and tries to ensure the benefits of WF&ESS under the worst conditions, providing a method to balance risks and benefits. Furthermore, the risk-averse wind-storage combined system power dispatching model constructed by the present invention takes into account the tail returns of the intraday market and the real-time market where the total returns are lower than the risk value. By artificially setting different risk preference coefficients, it is possible to enter the market with a variety of attitudes towards risks. Compared with traditional models that do not measure risks, in the face of a market with greater risks, the wind-storage combined system power dispatching model in this scheme can make more reasonable decisions at the cost of reducing a very small part of the returns.

[0193] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0194] Based on the same inventive concept, the embodiment of the present application also provides a device for determining a dispatching strategy for a wind-storage combined system to participate in the electricity spot market, which is used to implement the method for determining a dispatching strategy for a wind-storage combined system to participate in the electricity spot market. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more wind-storage combined systems to participate in the electricity spot market provided below can refer to the limitations of the method for determining a dispatching strategy for a wind-storage combined system to participate in the electricity spot market, and will not be repeated here.

[0195] In an exemplary embodiment, Fig.13 As shown, a device 600 for determining a dispatching strategy for a wind-storage joint system to participate in a power spot market is provided, comprising: a data acquisition module 610, a first uncertainty module 620, a second uncertainty module 630, a sampling module 640, a strategy determination module 650 and a power dispatching module 660, wherein:

[0196] The data acquisition module 610 is used to obtain the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within a preset time period.

[0197] The first uncertainty module 620 is used to determine an uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, where the uncertainty set is used to characterize the uncertainty of the day-ahead market electricity price.

[0198] The second uncertainty module 630 is used to determine a first cumulative probability distribution function of a wind power output prediction error based on the wind power output prediction power, and to determine a second cumulative probability distribution function of an intraday market electricity price prediction error based on the intraday market electricity price prediction.

[0199] The sampling module 640 is used to sample the first cumulative probability distribution function to obtain a first sample set of wind power output, and to sample the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices. The first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of intraday market forecast electricity prices.

[0200] The strategy determination module 650 is used to determine the power dispatch strategy of the wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-built robust stochastic model.

[0201] The power dispatching module 660 is used to dispatch the wind power storage combined system based on the power dispatching strategy.

[0202] In one embodiment, the second uncertainty module 630 is also used to determine the first normal distribution data of the wind power output prediction error based on the predicted wind power output power and the preset installed capacity of the wind farm, determine the first cumulative probability distribution function of the wind power output prediction error based on the first normal distribution data, determine the second normal distribution data of the intraday market electricity price prediction error based on the intraday market predicted electricity price, and determine the second cumulative probability distribution function of the intraday market electricity price prediction error based on the second normal distribution data.

[0203] In one embodiment, the uncertainty set includes a box uncertainty set, and the first uncertainty module 620 is further used to determine the box uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, a preset robust control coefficient and a preset day-ahead market forecast electricity price deviation.

[0204] In one embodiment, the strategy determination module 650 is also used to determine the wind-storage combined system power dispatching model based on the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model, and to determine the optimal solution of the objective function in the wind-storage combined system power dispatching model based on the constraints in the wind-storage combined system power dispatching model, and to determine the power dispatching strategy based on the optimal solution.

[0205] In one embodiment, the constraints include: a supplementary-fired compressed air energy storage output constraint, a wind power output constraint, and a wind farm combined energy storage system output constraint.

[0206] In one embodiment, the strategy determination module 650 is also used to determine the power dispatching main problem and the power dispatching sub-problem according to the wind-storage combined system power dispatching model, solve the power dispatching main problem, determine the decision variables of the wind-storage combined system power dispatching model, and update the upper bound of the wind-storage combined system power dispatching model; solve the power dispatching sub-problem according to the decision variables, determine the uncertain variables of the wind-storage combined system power dispatching model, and update the lower bound of the wind-storage combined system power dispatching model; judge whether the wind-storage combined system power dispatching model converges according to the upper bound, lower bound and preset convergence accuracy of the wind-storage combined system power dispatching model; when the wind-storage combined system power dispatching model converges, determine the optimal solution of the objective function according to the decision variables and uncertain variables; when the wind-storage combined system power dispatching model does not converge, update the constraints to obtain the updated power dispatching main problem; return to the steps of solving the power dispatching main problem, determining the decision variables of the wind-storage combined system power dispatching model, and updating the upper bound of the wind-storage combined system power dispatching model, until the wind-storage combined system power dispatching model converges.

[0207] Each module in the above-mentioned wind-storage joint system participating in the power spot market dispatch strategy determination device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0208] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.14 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the day-ahead market forecast electricity price. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining a dispatching strategy for a wind-storage joint system to participate in the electricity spot market is implemented.

[0209] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of each embodiment of the method for determining a dispatching strategy for a wind-storage combined system participating in a spot electricity market are implemented.

[0210] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of each embodiment of the method for determining a dispatching strategy for a wind-storage combined system participating in a spot electricity market are implemented.

[0211] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of each embodiment of the method for determining a dispatch strategy for a wind-storage combined system participating in a spot electricity market.

[0212] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0213] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0214] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0215] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining a dispatching strategy for a wind-storage combined system participating in a spot electricity market, characterized in that: The method comprises: Obtain the day-ahead market forecast electricity price, intraday market forecast electricity price and wind power output forecast power within a preset time period; Based on the day-ahead market forecast electricity price, determining an uncertainty set of the day-ahead market electricity price, wherein the uncertainty set is used to characterize the uncertainty of the day-ahead market forecast electricity price; Determine a first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and determine a second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price; Sampling the first cumulative probability distribution function to obtain a first sample set of wind power output, and sampling the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices, wherein the first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity prices; Determine a power dispatch strategy for the wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model; Based on the power dispatching strategy, the wind power storage combined system is dispatched.

2. The method according to claim 1, characterized in that Determining the first cumulative probability distribution function of the wind power output prediction error according to the wind power output prediction power includes: Determining first normal distribution data of wind power output prediction error according to the wind power output prediction power and the preset wind farm installed capacity; A first cumulative probability distribution function of the wind power output prediction error is determined according to the first normal distribution data.

3. The method according to claim 1, characterized in that Determining a second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price includes: Determining second normal distribution data of intraday market electricity price forecast error according to the intraday market forecast electricity price; A second cumulative probability distribution function of the intraday market electricity price forecast error is determined based on the second normal distribution data.

4. The method according to claim 1, characterized in that: The step of determining an uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price comprises: Determine a box uncertainty set of the day-ahead market electricity price according to the day-ahead market forecast electricity price, a preset robust control coefficient and a preset day-ahead market forecast electricity price deviation; The uncertainty set includes the boxed uncertainty set.

5. The method according to any one of claims 1 to 3, characterized in that: The step of determining the power dispatch strategy of the wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and the pre-built robust stochastic model includes: Determine a wind-storage combined system power dispatch model according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model; Based on the constraints in the wind-storage combined system power dispatch model, determine the optimal solution of the objective function in the wind-storage combined system power dispatch model, wherein the objective function is used to maximize the total return resources of the wind-storage combined system in the day-ahead market, the intraday market, and the real-time market; According to the optimal solution, a power dispatching strategy is determined.

6. The method according to claim 5, characterized in that The constraints include: supplementary combustion compressed air energy storage output constraints, wind power output constraints and wind farm combined energy storage system output constraints.

7. The method according to claim 6, characterized in that The determining the optimal solution of the objective function in the wind-storage combined system power dispatch model based on the constraint conditions in the wind-storage combined system power dispatch model comprises: According to the wind-storage combined system power dispatch model, determining the power dispatch main problem and power dispatch sub-problems; Solving the main problem of power dispatching, determining the decision variables of the power dispatching model of the wind-storage combined system, and updating the upper bound of the power dispatching model of the wind-storage combined system; Solving the power dispatch sub-problem according to the decision variables, determining the uncertain variables of the wind-storage combined system power dispatch model, and updating the lower bound of the wind-storage combined system power dispatch model; According to the upper bound, the lower bound and the preset convergence accuracy of the wind-storage combined system power dispatching model, judging whether the wind-storage combined system power dispatching model has converged; When the wind-storage combined system power dispatch model converges, determining the optimal solution of the objective function according to the decision variables and the uncertain variables; When the wind-storage combined system power dispatch model does not converge, updating the constraint conditions to obtain an updated power dispatch main problem; Return to the step of solving the main problem of power dispatching, determining the decision variables of the wind-storage combined system power dispatching model, and updating the upper bound of the wind-storage combined system power dispatching model until the wind-storage combined system power dispatching model converges.

8. A device for determining a dispatching strategy for a wind-storage combined system participating in a spot electricity market, characterized in that: The device comprises: A data acquisition module is used to obtain the day-ahead market forecast electricity price, the intraday market forecast electricity price and the wind power output forecast power within a preset time period; A first uncertainty module is used to determine an uncertainty set of the day-ahead market electricity price based on the day-ahead market forecast electricity price, wherein the uncertainty set is used to characterize the uncertainty of the day-ahead market electricity price; A second uncertainty module is used to determine a first cumulative probability distribution function of the wind power output forecast error according to the wind power output forecast power, and to determine a second cumulative probability distribution function of the intraday market electricity price forecast error according to the intraday market forecast electricity price; a sampling module, configured to sample the first cumulative probability distribution function to obtain a first sample set of wind power output, and to sample the second cumulative probability distribution function to obtain a second sample set of intraday market forecast electricity prices, wherein the first sample set is used to characterize the uncertainty of wind power output, and the second sample set is used to characterize the uncertainty of the intraday market forecast electricity prices; A strategy determination module, configured to determine a power dispatch strategy of a wind-storage combined system according to the uncertainty set of the day-ahead market electricity price, the first sample set, the second sample set, and a pre-built robust stochastic model; The power dispatching module is used to dispatch the wind-storage combined system based on the power dispatching strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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