A bidding method for energy storage in the electricity market considering the uncertainty of wind and solar power

Abstract

The present invention discloses a method for energy storage to participate in the bidding in the electricity market taking into account the uncertainty of wind and solar power. The method takes into account the uncertainty of renewable energy output, describes wind power output based on fuzzy opportunity constraint programming, and analyzes photovoltaic output based on uncertainty sets. A two-layer optimization model for bidding game in which power supply-side energy storage participates in multi-scenario applications in the electric energy and standby markets is constructed. The upper power supply side acts as a leader to make bidding decisions with the goal of maximizing wind and solar power output, and the lower power trading center acts as a follower to complete market clearance with the minimum social power purchase cost and balancing adjustment cost. Optimize the power supply-side energy storage to participate in bidding for multi-scenario applications in the electricity market, and improve the rationality and effectiveness of the comprehensive operating benefits of energy storage.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and specifically to a method for energy storage to participate in power market bidding taking into account the uncertainty of wind and solar power. Background Art

[0002] With the increase of intermittent renewable energy generation such as solar and wind power, the stability of the power system will be subjected to increasingly severe tests in the future. Power operators urgently need a means to stably and effectively reduce the risk of system fluctuations and avoid the hazards of power generation uncertainty. Due to the technological progress of chemistry, materials science and chemical engineering, energy storage technology has made great progress in recent years. Energy storage equipment of various sizes and types has been built and put into use in various places. Energy storage can make important contributions to the stability of the power system due to its safe and adjustable characteristics. However, there are currently two difficulties for energy storage equipment entities to participate in power system regulation through market means: first, the main body of distributed energy storage equipment is complex, the single unit capacity is insufficient, and the geographical distribution is wide, making it difficult to effectively control and form market competitiveness; second, the fluctuation of electricity prices and load demand has brought great uncertainty to the market bidding process. At present, there is a lack of effective bidding strategies suitable for energy storage equipment, which makes it difficult to expand settlement income and stimulate market participation enthusiasm. Summary of the invention

[0003] The present invention proposes a method, device and storage medium for energy storage to participate in electricity market bidding taking into account the uncertainty of wind and solar power, which can at least solve one of the technical problems in the background technology.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for energy storage to participate in power market bidding considering the uncertainty of wind and solar power, comprising the following steps:

[0006] S100. Under the premise of new energy grid connection, considering the uncertainty of renewable energy output, the fuzzy opportunity constraint programming method is used to describe wind power output, and the photovoltaic output is analyzed based on the uncertainty set;

[0007] S200, based on S100, proposed a two-layer optimization model for bidding game of power supply side energy storage participating in multiple scenarios of electric energy and backup market;

[0008] S300, based on S100 and S200, the upper power supply side, as the leader, makes bidding decisions with the goal of maximizing photovoltaic and wind power output. The lower power trading center, as the follower, completes market clearing with the minimum social power purchase cost and balancing regulation cost, and gives the optimal solution.

[0009] Furthermore, in S100 of the present invention, a fuzzy chance-constrained programming method is used to describe wind power output, and a specific method for analyzing photovoltaic output based on uncertainty set is as follows:

[0010] S101. Using fuzzy chance-constrained programming method to describe wind power output;

[0011] M[g(x,ξ)≤0]≥α(1)

[0012] Where: α is the confidence level; g is the set of constraints; ξ is the fuzzy variable; x is the decision variable; M is the measurement function; the credibility measure of fuzzy event A is expressed as:

[0013] M(A)=C r (A)(2)

[0014]

[0015] Where μ is the membership function of the fuzzy variable ξ, sup is the upper bound;

[0016] The uncertainty of wind turbine output is transformed into the uncertainty of output prediction error. That is, assuming that the predicted value of wind turbine output is certain data, the prediction error is modeled using fuzzy theory from a new perspective. The uncertainty of wind turbine output is indirectly described by the uncertainty of wind turbine output prediction error. Assuming that the percentage of wind turbine output prediction error is ε wind , the function expression is as follows:

[0017]

[0018] Where p' wind 、p wind Respectively represent the predicted output and actual output of wind power;

[0019] There are two forms of prediction error: if the actual output is higher than the predicted output, it is a positive error; if the actual output is lower than the predicted output, it is a negative error, and its membership μ can be expressed by Cauchy distribution. wind , the specific formula is as follows:

[0020]

[0021] Where: Represent the statistical average of positive error and negative error respectively; σ is the weight. According to formula (3), for εwind∈R, the credibility measure of the prediction error ξ is:

[0022]

[0023] S102, analyzing photovoltaic output based on uncertainty set, performing statistical analysis on historical data of photovoltaic output, selecting a certain time period, selecting a continuous range covering all photovoltaic output scenarios within the time period, thereby generating an uncertainty set under a certain radius constraint;

[0024]

[0025] Where: is the uncertain set of photovoltaic power output; is the actual output of the photovoltaic unit in the T period; and They represent the minimum and maximum output of the photovoltaic unit in the T period respectively; T is the number of periods in a day. This paper takes 1 hour as the unit and decomposes each day into 24 periods; Λ is the robustness factor with a value range of [0, 24], which is used to control the interval range of the uncertainty set. The larger the value, the larger the range covered by the uncertainty set.

[0026] Furthermore, the bidding game two-layer optimization model in S200 described in the present invention includes an upper power supply side energy storage bidding decision layer and a lower power market clearing layer. The upper model plans its own quantity-price bidding plan and reports it to the lower power trading center. The lower layer returns the winning quantity of the power supply side energy storage in each market and the marginal electricity price of the LMP node to the power supply side energy storage decision layer.

[0027] Furthermore, the specific structural mechanism of the bidding game two-layer optimization model of the present invention is as follows:

[0028] The upper structure mechanism of the two-layer optimization model of bidding game:

[0029] The decision-makers of power supply and energy storage bidding plan their own quantity-price bidding plan to maximize the benefits of wind and solar power output according to the constraints of photovoltaic power output and wind power output, and report it to the lower-level power trading center;

[0030] The underlying structure mechanism of the two-layer optimization model of bidding game:

[0031] The power trading center coordinates the bidding data reported by various trading entities such as power-side energy storage and traditional units, comprehensively considers the net load deviation caused by the predicted output deviation of new energy, and the electricity-carbon coupling quotation under the carbon trading of traditional units, and clears the day-ahead electricity energy market and the standby auxiliary market with the goal of minimizing the market purchase cost of electricity, and clears the real-time balancing market with the goal of minimizing the market balancing cost. Finally, the winning bids of power-side energy storage in each market and the marginal electricity price of the LMP node are returned to the power-side energy storage decision-making layer, so that the energy storage can optimize the initial bidding strategy and obtain a better bidding plan.

[0032] Furthermore, the optimal solution method of the bidding game double-layer optimization model in S300 of the present invention is as follows:

[0033] Firstly, the KKT condition is used to transform the double-layer model into a single-layer model, that is, a mathematical programming model with equilibrium constraints. Then the MPEC model is linearly processed by the big M method and binary expansion method and transformed into a mixed integer linear programming model. Finally, the model is solved on the Matlab platform with the help of the Yamlip+Gurobi professional solver.

[0034] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0035] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0036] It can be seen from the above technical solution that a two-layer optimization model for bidding game of power supply side energy storage participating in multi-scenario applications of electric energy and reserve market is disclosed. The wind power output is described based on fuzzy opportunity constraint programming method, and the photovoltaic output is analyzed based on uncertainty set. The upper power supply side, as the leader, makes bidding decisions with the goal of maximizing wind and solar output, and the lower power trading center, as the follower, completes market clearance with the minimum social power purchase cost and balancing regulation cost. Optimize the power supply side energy storage to participate in bidding for multi-scenario applications in the power market, and improve the rationality and effectiveness of the comprehensive operation benefits of energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the present invention;

[0038] Figure 2 It is a diagram of the principle of master-slave game;

[0039] Figure 3 It is a schematic diagram of the structure of the double-layer optimization model of the bidding game of the present invention;

[0040] Figure 4 This is the overall process of the bidding game double-layer optimization model of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0042] like Figure 1As shown, the energy storage participating in the power market bidding strategy taking into account the uncertainty of wind and solar power described in this embodiment includes the following steps:

[0043] S100. Under the premise of new energy grid connection, considering the uncertainty of renewable energy output, the fuzzy opportunity constraint programming method is used to describe wind power output, and the photovoltaic output is analyzed based on the uncertainty set;

[0044] S200, based on S100, proposed a two-layer optimization model for bidding game of power supply side energy storage participating in multiple scenarios of electric energy and backup market;

[0045] S300, based on S100 and S200, the upper power supply side, as the leader, makes bidding decisions with the goal of maximizing photovoltaic and wind power output. The lower power trading center, as the follower, completes market clearing with the minimum social power purchase cost and balancing regulation cost, and gives the optimal solution.

[0046] The following are specific instructions:

[0047] S100. Under the premise of new energy grid connection, considering the uncertainty of renewable energy output, the fuzzy opportunity constraint programming method is used to describe wind power output, and the photovoltaic output is analyzed based on the uncertainty set;

[0048] The specific method of describing wind power output based on fuzzy chance constraint programming and analyzing photovoltaic output based on uncertainty set is as follows:

[0049] S101. Using fuzzy chance-constrained programming method to describe wind power output;

[0050] M[g(x,ξ)≤0]≥α(1)

[0051] Where: α is the confidence level; g is the set of constraints; ξ is the fuzzy variable; x is the decision variable; M is the measurement function.

[0052] The credibility measure of fuzzy event A is expressed as:

[0053] M(A)=C r (A)(2)

[0054]

[0055] Where μ is the membership function of the fuzzy variable ξ, and sup is the upper bound.

[0056] The uncertainty of wind turbine output is transformed into the uncertainty of output prediction error. That is, assuming that the predicted value of wind turbine output is certain data, the prediction error is modeled using fuzzy theory from a new perspective. The uncertainty of wind turbine output is indirectly described by the uncertainty of wind turbine output prediction error. Assuming that the percentage of wind turbine output prediction error is ε wind , the function expression is as follows:

[0057]

[0058] Where p' wind 、p wind They represent the predicted wind power output and actual wind power output respectively.

[0059] There are two forms of prediction error: if the actual output is higher than the predicted output, it is a positive error; if the actual output is lower than the predicted output, it is a negative error. The membership degree μ can be expressed by the Cauchy distribution. wind , the specific formula is as follows:

[0060]

[0061] Where: Represent the statistical average of positive error and negative error respectively; σ is the weight. According to formula (3), for εwind∈R, the credibility measure of the prediction error ξ is:

[0062]

[0063] S102, analyzing photovoltaic output based on uncertainty set, performing statistical analysis on historical data of photovoltaic output, selecting a certain time period, selecting a continuous range covering all photovoltaic output scenarios within the time period, thereby generating an uncertainty set under a certain radius constraint;

[0064]

[0065] Where: is the uncertain set of photovoltaic power output; is the actual output of the photovoltaic unit in the T period; and They represent the minimum and maximum output of the photovoltaic unit in the T period respectively; T is the number of periods in a day. This paper takes 1 hour as the unit and decomposes each day into 24 periods; Λ is the robustness factor with a value range of [0, 24], which is used to control the interval range of the uncertainty set. The larger the value, the larger the range covered by the uncertainty set.

[0066] S200, based on S100, proposed a two-layer optimization model for bidding game of power supply side energy storage participating in multiple scenarios of electric energy and backup market;

[0067] The master-slave game is a dynamic game based on game theory. The parties involved in the game have a sequence of decision-making, that is, a master-slave relationship of leadership and following. The subject who makes the decision first is called the leader, and the subject who makes the decision based on the leader's decision is called the follower. In the power market, the power trading center needs to rely on the bidding data of various bidders such as energy storage to organize the orderly operation of the power market and reduce the cost of power trading. The winning bid and clearing electricity price related to energy storage revenue are affected by the decision of the power trading center. Therefore, considering that there is a balance of interests between energy storage and power trading centers during the operation of the power market, and the characteristics of each pursuing maximum benefits are similar to the characteristics of the research object of game theory, the power supply side energy storage participating in the day-ahead and intraday power market bidding and market clearing process can be modeled as a master-slave game model.

[0068] There is a mutually influencing coupling relationship between the bidding quotation of energy storage on the power supply side and the market clearing electricity price. The bidding process of energy storage on the power supply side in the electricity market can be specifically modeled as a two-layer model in which decision variables can be transmitted between the upper and lower layers to highlight the master-slave game process.

[0069] like Figure 3 As shown in the figure, the two-layer optimization model of bidding game is divided into an upper power supply side energy storage bidding decision-making layer and a lower power market clearing layer. The upper model plans its own quantity-price bidding plan and reports it to the lower power trading center. The lower layer returns the winning quantity and LMP (node ​​marginal price) of the power supply side energy storage in each market to the power supply side energy storage decision-making layer.

[0070] The specific structural mechanism of the bidding game two-layer optimization model is as follows:

[0071] The upper structure mechanism of the two-layer optimization model of bidding game:

[0072] The decision-makers of power supply and energy storage bidding plan their own quantity-price bidding plan to maximize the benefits of wind and solar power output according to the constraints of photovoltaic power output and wind power output, and report it to the lower-level power trading center;

[0073] The underlying structure mechanism of the two-layer optimization model of bidding game:

[0074] The power trading center coordinates the bidding data reported by various trading entities such as power-side energy storage and traditional units, comprehensively considers the net load deviation caused by the predicted output deviation of new energy, and the electricity-carbon coupling quotation under the carbon trading of traditional units, and clears the day-ahead electricity energy market and the standby auxiliary market with the goal of minimizing the market purchase cost of electricity, and clears the real-time balancing market with the goal of minimizing the market balancing cost. Finally, the winning bids and LMP (node ​​marginal price) of power-side energy storage in each market are returned to the power-side energy storage decision-making layer, so that energy storage can optimize the initial bidding strategy and obtain a better bidding plan.

[0075] The upper power supply side, as the leader, makes bidding decisions with the goal of maximizing photovoltaic and wind power output. The lower power trading center, as the follower, completes market clearing with the minimum social power purchase cost and balancing regulation cost, and provides the optimal solution.

[0076] S300, based on S100 and S200, the upper power supply side, as the leader, makes bidding decisions with the goal of maximizing photovoltaic and wind power output. The lower power trading center, as the follower, completes market clearing with the minimum social power purchase cost and balancing regulation cost, and gives the optimal solution.

[0077] The optimal solution method of the bidding game double-layer optimization model in S300 is as follows:

[0078] A two-layer optimization model for bidding game in which power-side energy storage participates in multiple scenarios of electric energy and backup market applications is a mixed integer nonlinear problem, which is difficult to solve directly. Therefore, the KKT condition is used to first convert the two-layer model into a single-layer model, that is, a mathematical programming model with equilibrium constraints. However, since the model contains a two-variable product term, it is a nonlinear problem. Therefore, the MPEC model is further processed linearly using the big M method and the binary expansion method, and finally transformed into a mixed integer linear programming model. At this time, the model can be solved on the Matlab platform with the help of the Yamlip+Gurobi professional solver. The overall process of the bidding game two-layer optimization model is as follows: Figure 4 shown.

[0079] In summary, the present invention adopts a fuzzy opportunity constrained programming method to describe wind power output and analyzes photovoltaic output based on uncertainty sets. The upper power supply side, as a leader, makes bidding decisions with the goal of maximizing wind and solar output, and the lower power trading center, as a follower, completes market clearance with the minimum social power purchase cost and balancing adjustment cost. Optimize the power supply side energy storage to participate in the bidding for multiple scenarios in the power market, and improve the rationality and effectiveness of the comprehensive operation benefits of energy storage.

[0080] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0081] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0082] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer is run on a computer, the computer executes any of the energy storage methods for participating in electricity market bidding that considers the uncertainty of wind and solar power in the above embodiments.

[0083] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0084] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0086] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy storage to participate in power market bidding considering the uncertainty of wind and solar power, characterized in that: The following steps are involved: S100. Under the premise of new energy grid connection, considering the uncertainty of renewable energy output, the fuzzy opportunity constraint programming method is used to describe wind power output, and the photovoltaic output is analyzed based on the uncertainty set; S200, based on S100, proposed a two-layer optimization model for bidding game of power supply side energy storage participating in multiple scenarios of electric energy and backup market; S300, based on S100 and S200, the upper power supply side, as the leader, makes bidding decisions with the goal of maximizing photovoltaic and wind power output. The lower power trading center, as the follower, completes market clearing with the minimum social power purchase cost and balancing regulation cost, and gives the optimal solution.

2. The method for energy storage participating in power market bidding considering the uncertainty of wind and solar power according to claim 1 is characterized in that: In S100, a fuzzy chance-constrained programming method is used to describe wind power output, and a specific method for analyzing photovoltaic output based on uncertainty set is as follows: S101. Using fuzzy chance-constrained programming method to describe wind power output; M[g(x,ξ)≤0]≥α(1) Where: α is the confidence level; g is the set of constraints; ξ is the fuzzy variable; x is the decision variable; M is the measurement function; The credibility measure of fuzzy event A is expressed as: M(A)=C r (A)(2) Where μ is the membership function of the fuzzy variable ξ, sup is the upper bound; The uncertainty of wind turbine output is transformed into the uncertainty of output prediction error. That is, assuming that the predicted value of wind turbine output is certain data, the prediction error is modeled using fuzzy theory from a new perspective. The uncertainty of wind turbine output is indirectly described by the uncertainty of wind turbine output prediction error. The percentage of wind turbine output prediction error is ε wind , the function expression is as follows: Where p' wind 、p wind Respectively represent the predicted output and actual output of wind power; There are two forms of prediction error: if the actual output is higher than the predicted output, it is a positive error; if the actual output is lower than the predicted output, it is a negative error, and its membership μ can be expressed by Cauchy distribution. wind , the specific formula is as follows: Where: Represent the statistical average of positive error and negative error respectively; σ is the weight; According to formula (3), for εwind∈R, the credibility measure of the prediction error ξ is: S102, analyzing photovoltaic output based on uncertainty set, performing statistical analysis on historical data of photovoltaic output, selecting a certain time period, selecting a continuous range covering all photovoltaic output scenarios within the time period, thereby generating an uncertainty set under a certain radius constraint; Where: is the uncertain set of photovoltaic power output; is the actual output of the photovoltaic unit in the T period; and They represent the minimum and maximum output of the photovoltaic unit in the T period respectively; T is the number of periods in a day, with 1 hour as the unit, and each day is divided into 24 periods; Λ is the robustness factor, with a value range of [0, 24], which is used to control the interval range of the uncertainty set. The larger the value, the larger the range covered by the uncertainty set.

3. The method for energy storage participating in power market bidding considering the uncertainty of wind and solar power according to claim 1 is characterized in that: The bidding game two-layer optimization model in S200 includes an upper power supply side energy storage bidding decision layer and a lower power market clearing layer. The upper model plans its own quantity-price bidding plan and reports it to the lower power trading center. The lower layer returns the winning quantity of the power supply side energy storage in each market and the marginal electricity price of the LMP node to the power supply side energy storage decision layer.

4. The method for energy storage participating in power market bidding considering the uncertainty of wind and solar power according to claim 3 is characterized in that: The specific structural mechanism of the bidding game two-layer optimization model is as follows: The upper structure mechanism of the two-layer optimization model of bidding game: The decision-makers of power supply and energy storage bidding plan their own quantity-price bidding plan to maximize the benefits of wind and solar power output according to the constraints of photovoltaic power output and wind power output, and report it to the lower-level power trading center; The underlying structure mechanism of the two-layer optimization model of bidding game: The power trading center coordinates the bidding data reported by various trading entities such as power-side energy storage and traditional units, integrates the net load deviation caused by the output deviation of new energy forecasts, and the electricity-carbon coupling quotation under the carbon trading of traditional units, and clears the day-ahead electricity energy market and the standby auxiliary market with the goal of minimizing the market purchase cost of electricity. It clears the real-time balancing market with the goal of minimizing the market balancing cost. Finally, it returns the winning bids of power-side energy storage in each market and the marginal electricity price of the LMP node to the power-side energy storage decision-making layer, so that the energy storage can optimize the initial bidding strategy and obtain a better bidding plan.

5. The method for energy storage participating in power market bidding considering the uncertainty of wind and solar power according to claim 1 is characterized in that: The optimal solution method of the bidding game double-layer optimization model in S300 is as follows: Firstly, the KKT condition is used to transform the double-layer model into a single-layer model, that is, a mathematical programming model with equilibrium constraints. Then the MPEC model is linearly processed by the big M method and binary expansion method and transformed into a mixed integer linear programming model. Finally, the model is solved on the Matlab platform with the help of the Yamlip+Gurobi professional solver.

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