Bidding strategy optimization method and device for participation of wind and light storage station in multiple markets

Abstract

The invention relates to the technical field of power markets, and discloses a bidding strategy optimization method and device for participation of a wind and light storage station in multiple markets, and the method comprises the steps: obtaining bidding data of participation of a single unit in a power market in the wind and light storage station, determining the income of the wind and light storage station participating in multiple markets based on the bidding data of the single unit participating in the power market in the wind and light storage station; establishing a total income model of the wind and light storage stations participating in the multiple markets based on the income of the wind and light storage stations participating in the multiple markets; obtaining a wind-solar power probability density prediction function, and constructing an expected profit model of the wind-solar storage station participating in multiple markets based on the wind-solar power probability density prediction function and the profit model of the wind-solar storage station participating in multiple markets; and solving the expected income model of the wind and light storage station participating in the multiple markets to obtain an optimal bidding strategy of the wind and light storage station participating in the multiple markets. According to the method, the benefits of wind and light participating in multiple markets are improved, and the bidding strategy of multi-source collaborative stations participating in multiple markets is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a bidding strategy optimization method and device for a wind-solar-storage station participating in multiple markets. Background Art

[0002] Wind, solar and storage combined power generation stations can not only participate in the electricity spot market, but also participate in the ancillary service market. In addition, due to the volatility of wind and solar power, the demand for grid frequency regulation has increased. Therefore, letting wind and solar power bear part of the grid frequency regulation costs has become a solution in the practice of the power market. Therefore, establishing appropriate power market trading rules, allowing wind and solar power to participate in the frequency regulation market alone or jointly, and formulating the best bidding plan for wind and solar power to participate in the power market will help alleviate the pressure on grid frequency regulation, reduce wind and solar power abandonment, increase the utilization rate of new energy, and improve the revenue of wind farms.

[0003] However, due to the lack of research on the multi-market trading mechanism and related revenue models of wind, solar and storage combined power generation stations, the bidding strategy of wind and solar participating in the electricity market affects the normal operation of wind, solar and storage combined power generation stations. Summary of the invention

[0004] In view of this, the present invention provides a bidding strategy optimization method and device for wind, solar and storage stations participating in multiple markets, so as to solve the problem of lack of research on the multi-market trading mechanism and related profit model of wind, solar and storage multi-source combined power generation stations participating in multiple markets.

[0005] In a first aspect, the present invention provides a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets, the method comprising:

[0006] Obtain bidding data of a single unit in a wind-solar-storage station participating in the power market, and determine the revenue of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market;

[0007] Based on the benefits of wind-solar-storage stations participating in multiple markets, a total benefit model for wind-solar-storage stations participating in multiple markets is established;

[0008] Obtain the wind-solar power probability density prediction function, and build the expected profit model of the wind-solar power storage station participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar power storage station participating in multiple markets profit model;

[0009] The expected profit model of wind, solar and storage stations participating in multiple markets is solved, and the optimal bidding strategy for wind, solar and storage stations participating in multiple markets is obtained.

[0010] The present embodiment provides a bidding strategy optimization method for wind-solar-storage stations participating in multiple markets. The total revenue model for wind-solar-storage stations participating in multiple markets is established, and an expected revenue model for wind-solar-storage stations participating in multiple markets is constructed based on a wind-solar power probability density prediction function and a wind-solar-storage stations revenue model. The expected revenue model for wind-solar-storage stations participating in multiple markets is then solved to obtain the optimal bidding strategy for wind-solar-storage stations participating in multiple markets. The wind-solar power probability density prediction function is introduced into the expected revenue model for wind-solar-storage stations participating in multiple markets, which takes into account the uncertainty of wind and solar power, can improve the revenue of wind and solar power participating in multiple markets, is beneficial to grid dispatching, realizes the optimization of the bidding strategy for multi-source collaborative stations participating in multiple markets, and ensures the normal operation of wind-solar-storage stations.

[0011] In an optional implementation, the revenue of the wind-solar-storage station participating in multiple markets is determined based on the bidding data of a single unit in the wind-solar-storage station participating in the power market, including:

[0012] Based on the bidding data of individual units participating in the electricity market, the profits of wind, solar and storage stations participating in the spot market are calculated respectively;

[0013] Obtain comprehensive frequency regulation performance indicators, based on the bidding data of individual units participating in the electricity market and comprehensive frequency regulation performance indicators, and calculate the benefits of wind, solar and storage stations participating in the frequency regulation market.

[0014] This embodiment provides a bidding strategy optimization method for wind-solar-storage stations participating in multiple markets. Since wind-solar-storage stations participate in both the spot market and the frequency regulation market and make the best bid, they can put part of their output power into the frequency regulation market to participate in grid frequency regulation, which can reduce wind and solar power abandonment and increase the income of wind-solar-storage stations. Therefore, calculating the income of wind-solar-storage stations participating in the spot market and the frequency regulation market lays a foundation for the subsequent optimization of bidding strategies for wind-solar-storage stations participating in multiple markets.

[0015] In an optional implementation, the benefits of wind, solar and storage stations participating in the spot market are calculated based on the bidding data of individual units participating in the electricity market, including:

[0016] Based on the bidding data of a single unit participating in the electricity market, determine the bidding power of a single unit participating in the spot market, the actual output of a single unit in the day-ahead spot market, and the price of a single unit in the day-ahead spot market;

[0017] The penalty electricity price for a single unit participating in the spot market is calculated based on the bid power of the single unit participating in the spot market, the actual output of the single unit in the day-ahead spot market, and the price of the single unit in the day-ahead spot market;

[0018] The benefits of wind, solar and storage stations participating in the spot market are calculated based on the bid power of a single unit participating in the spot market, the price of a single unit in the day-ahead spot market and the penalty electricity price of a single unit participating in the spot market.

[0019] This embodiment provides a bidding strategy optimization method for wind, solar and storage stations participating in multiple markets. By calculating the penalty electricity price of a single unit participating in the spot market, the revenue of the wind, solar and storage stations participating in the spot market is calculated based on the bid power of the single unit participating in the spot market, the price of the single unit in the day-ahead spot market and the penalty electricity price of the single unit participating in the spot market, which fully considers the difference between the actual output of the single unit and the bid data. The accurate calculation of the revenue of a single unit participating in the spot market is achieved, providing a data basis for the subsequent calculation of the total revenue of the wind, solar and storage stations participating in multiple markets.

[0020] In an optional implementation, based on the bidding data of a single unit participating in the power market and the comprehensive frequency regulation performance index and calculating the benefits of the wind, solar and storage station participating in the frequency regulation market, the calculation includes:

[0021] Based on the bidding data of a single unit participating in the electricity market, determine the bidding power of a single unit participating in the frequency regulation market, the actual output of a single unit in the day-ahead frequency regulation market, and the price of a single unit in the day-ahead frequency regulation market;

[0022] The penalty electricity price for a single unit participating in the frequency regulation market is calculated based on the bid power of the single unit participating in the frequency regulation market, the actual output of the single unit in the day-ahead frequency regulation market, and the price of the single unit in the day-ahead frequency regulation market;

[0023] The benefits of wind, solar and storage stations participating in the frequency regulation market are calculated based on the comprehensive frequency regulation performance indicators, the bid power of a single unit participating in the frequency regulation market, the price of a single unit in the day-ahead frequency regulation market and the penalty electricity price of a single unit participating in the frequency regulation market.

[0024] This embodiment provides a bidding strategy optimization method for wind, solar and storage stations participating in multiple markets, which calculates the penalty electricity price of a single unit participating in the frequency regulation market, and calculates the benefits of the wind, solar and storage stations participating in the frequency regulation market based on comprehensive frequency regulation performance indicators, the bid power of a single unit participating in the frequency regulation market, the price of a single unit in the day-ahead frequency regulation market and the penalty electricity price of a single unit participating in the frequency regulation market; introducing comprehensive frequency regulation performance indicators in the calculation of the benefits of wind, solar and storage stations participating in the frequency regulation market is conducive to alleviating the frequency regulation pressure of the power grid, reducing wind and solar power abandonment, and increasing the utilization rate of new energy, laying a foundation for the subsequent optimization of bidding strategies for wind, solar and storage stations participating in multiple markets.

[0025] In an optional implementation, an expected revenue model for wind-solar-storage stations participating in multiple markets is constructed based on a wind-solar-storage station probability density prediction function and a wind-solar-storage station participating in multiple markets revenue model, including:

[0026] Taking the maximum expected revenue of wind-solar-storage stations as the goal, an optimization objective function is constructed based on the wind-solar power probability density prediction function and the wind-solar-storage station participation in multi-market revenue model;

[0027] Obtain the bidding power constraints, use them as constraints for the optimization objective function, and build an expected profit model for wind, solar and storage stations participating in multiple markets.

[0028] This embodiment provides a bidding strategy optimization method for wind-solar-storage stations participating in multiple markets. Since wind power and photovoltaic power have a certain correlation in output under similar space and time, the joint probability distribution of wind power and photovoltaic power is described by a wind-solar power probability density prediction function, and then the correlation between wind power and photovoltaic power is established, so that the expected profit model of wind-solar-storage stations participating in multiple markets fully considers the uncertainty of wind and solar power, and ensures the optimization of the bidding strategy of wind-solar-storage stations participating in multiple markets while improving the profits of wind-solar-storage stations participating in multiple markets.

[0029] In an optional implementation, an optimization objective function is constructed based on the wind-solar power probability density prediction function and the wind-solar storage station participating in a multi-market revenue model; wherein the expression of the optimization objective function is as follows:

[0030]

[0031] in, represents the expected revenue of the wind-solar-storage station, represents the wind-solar-storage station participating in the multi-market revenue model, P v,t Indicates the real-time output of the v-node station. It represents the bidding power of the v-node station participating in the spot market. represents the bid power of the v-node station participating in the frequency regulation market, f(P v,t ) represents the probability density prediction function of wind and solar power at time t the next day, P b,t,max Indicates the predicted maximum wind and solar power at time t.

[0032] In a second aspect, the present invention provides a bidding strategy optimization device for a wind-solar-storage station participating in multiple markets, the device comprising:

[0033] A determination module is used to obtain the bidding data of a single unit in a wind-solar-storage station participating in the power market, and determine the benefits of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market;

[0034] Establish a module for establishing a total revenue model of wind-solar-storage stations participating in multiple markets based on the revenue of wind-solar-storage stations participating in multiple markets;

[0035] A construction module is used to obtain a wind-solar power probability density prediction function, and to construct an expected profit model for wind-solar storage stations participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar storage station participating in multiple markets profit model;

[0036] The solution module is used to solve the expected profit model of wind, solar and storage stations participating in multiple markets and obtain the optimal bidding strategy for wind, solar and storage stations participating in multiple markets.

[0037] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the bidding strategy optimization method for wind-solar-storage stations participating in multiple markets according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to the first aspect or any corresponding embodiment thereof.

[0039] In a fifth aspect, the present invention provides a computer program product, including computer instructions, which are used to enable a computer to execute the bidding strategy optimization method for wind-solar-storage stations participating in multiple markets according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a flow chart of a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to an embodiment of the present invention;

[0042] Figure 2 It is a flow chart of another method for optimizing bidding strategies for wind-solar-storage stations participating in multiple markets according to an embodiment of the present invention;

[0043] Figure 3 It is a flow chart of another method for optimizing bidding strategies for wind-solar-storage stations participating in multiple markets according to an embodiment of the present invention;

[0044] Figure 4It is a flow chart of a bidding strategy optimization method for a wind-solar-storage station to participate in both the spot market and the frequency regulation market according to an embodiment of the present invention;

[0045] Figure 5 It is a structural block diagram of a bidding strategy optimization device for a wind-solar-storage station participating in multiple markets according to an embodiment of the present invention;

[0046] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0048] Due to the late start and relative immaturity of the electricity market, the policies for wind and solar power to participate individually or jointly in the frequency regulation market are relatively imperfect. The trading mechanism for wind and solar power to participate in the electricity market to provide frequency regulation services is still imperfect, and there is little research on the multi-market trading mechanism and related profit models for wind, solar and storage multi-source combined power generation stations. Therefore, it is necessary to further construct a multi-market interaction model for wind, solar and storage stations.

[0049] The embodiment of the present invention provides a bidding strategy optimization method for a wind-solar-storage station to participate in multiple markets. It should be noted that the bidding strategy optimization method for a wind-solar-storage station to participate in multiple markets provided by the embodiment of the present invention may be executed by a device for optimizing the bidding strategy for a wind-solar-storage station to participate in multiple markets. The device for optimizing the bidding strategy for a wind-solar-storage station to participate in multiple markets may be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware, wherein the electronic device may be a server or a terminal, wherein the server in the embodiment of the present application may be a single server or a server cluster composed of multiple servers, and the terminal in the embodiment of the present application may be a smart phone, a personal computer, a tablet computer, an intelligent robot, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0050] According to an embodiment of the present invention, an embodiment of a method for optimizing bidding strategies for wind, solar and storage stations participating in multiple markets is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0051] In this embodiment, a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets is provided, which can be used for the above-mentioned electronic equipment. Figure 1 is a flow chart of a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0052] Step S101, obtaining bidding data of a single unit in a wind-solar-storage station participating in the power market, and determining the income of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market.

[0053] Specifically, the units participating in the electricity market submit bids for the spot market and the frequency regulation market at the same time on the day before. Based on the day-ahead market electricity price, the revenue of the units' day-ahead bids is settled. The difference between the actual output and the bid needs to be settled incrementally according to the penalty electricity price. The revenue of a single unit participating in the electricity market can be expressed as:

[0054] R=P·L1+ΔP·L2 (1)

[0055] Among them, R is the revenue of a single unit participating in the electricity market; P is the day-ahead bidding plan of a single unit participating in the electricity market, L1 is the day-ahead market electricity price, ΔP is the deviation between the bidding plan and the actual output of the unit; L2 is the penalty electricity price; among them, the penalty electricity price is the assessment revenue when there is a deviation between the actual output of the unit and the bidding plan. When the actual output is less than the bidding plan, the unit is under-generated and the penalty electricity price is negative; when the actual output is more than the bidding plan, the unit is over-generated and the penalty electricity price is positive.

[0056] Furthermore, the benefits of wind, solar and storage stations participating in multiple markets are calculated according to the above formula (1).

[0057] Step S102, establishing a total revenue model of the wind, solar and storage stations participating in multiple markets based on the revenue of the wind, solar and storage stations participating in multiple markets.

[0058] Specifically, when wind, solar and energy storage stations only participate in the spot market, the uncertainty of wind and solar energy will cause a deviation between the actual output and the bid power, thereby reducing the revenue of wind and solar energy; although energy storage will have a certain regulatory effect, there are still special circumstances that lead to lower revenue (for example, energy storage cannot be discharged for regulation during the wind season, and wind and solar energy are over-generated); when wind farms participate in both the spot market and the frequency regulation market and make the best bid, wind, solar and energy storage stations can invest part of their output power into the frequency regulation market to participate in grid frequency regulation. This method can both reduce wind and solar power abandonment and increase the revenue of wind, solar and energy storage stations. Therefore, the total revenue of wind, solar and energy storage stations participating in multiple markets is calculated by combining the revenue of wind, solar and energy storage stations participating in the spot market and the frequency regulation market respectively.

[0059] Step S103, obtaining a wind-solar power probability density prediction function, and constructing an expected profit model for wind-solar power storage stations participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar power storage station participating in multiple markets profit model.

[0060] Specifically, since the output of wind power and photovoltaics in similar space and time has a certain correlation, when considering the uncertainty of wind and solar power output, the joint probability distribution of wind power and photovoltaics can be further described, thereby establishing the correlation of wind and solar power output, and then constructing the expected profit model of wind and solar power storage stations participating in multiple markets based on the probability density prediction results of wind and solar power.

[0061] Step S104, solving the expected profit model of the wind, solar and storage stations participating in multiple markets, and obtaining the optimal bidding strategy for the wind, solar and storage stations participating in multiple markets.

[0062] The present embodiment provides a bidding strategy optimization method for wind-solar-storage stations participating in multiple markets. The total revenue model for wind-solar-storage stations participating in multiple markets is established, and an expected revenue model for wind-solar-storage stations participating in multiple markets is constructed based on a wind-solar power probability density prediction function and a wind-solar-storage stations revenue model. The expected revenue model for wind-solar-storage stations participating in multiple markets is then solved to obtain the optimal bidding strategy for wind-solar-storage stations participating in multiple markets. The wind-solar power probability density prediction function is introduced into the expected revenue model for wind-solar-storage stations participating in multiple markets, which takes into account the uncertainty of wind and solar power, can improve the revenue of wind and solar power participating in multiple markets, is beneficial to grid dispatching, realizes the optimization of the bidding strategy for multi-source collaborative stations participating in multiple markets, and ensures the normal operation of wind-solar-storage stations.

[0063] In this embodiment, a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets is provided, which can be used for the above-mentioned electronic equipment. Figure 2 is a flow chart of a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0064] Step S201, obtaining bidding data of a single unit in a wind-solar-storage station participating in the power market, and determining the income of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market.

[0065] Specifically, the above step S201 includes:

[0066] Step S2011, based on the bidding data of individual units participating in the electricity market, calculate the profits of wind, solar and storage stations participating in the spot market.

[0067] In some optional implementations, the above step S2011 includes:

[0068] Step a1, based on the bidding data of a single unit participating in the electricity market, determine the bidding power of a single unit participating in the spot market, the actual output of a single unit in the day-ahead spot market and the price of a single unit in the day-ahead spot market.

[0069] Step a2, calculating the penalty electricity price of the single unit participating in the spot market based on the bid power of the single unit participating in the spot market, the actual output of the single unit in the day-ahead spot market and the price of the single unit in the day-ahead spot market.

[0070] Specifically, taking a single unit in a wind and solar storage station as a node, the calculation formula for the penalty electricity price of a single unit participating in the spot market is as follows:

[0071]

[0072] in, It represents the penalty electricity price for the v-node station to participate in the spot market. It represents the actual output of the v-node station in the day-ahead spot market. represents the bidding power of the v-node station participating in the spot market, L v S,da represents the price of the day-ahead spot market at the v-node station, L v- S,da , L v+ S,da Respectively Less than / greater than Negative / positive penalty electricity price at the time of <L v+ S,da <L v S,da <L v- S,da .

[0073] Step a3, calculating the revenue of the wind-solar-storage station participating in the spot market based on the bid power of a single unit participating in the spot market, the price of a single unit in the day-ahead spot market and the penalty electricity price of a single unit participating in the spot market.

[0074] Specifically, the calculation formula for the revenue of wind and solar storage stations participating in the spot market is as follows:

[0075]

[0076] Among them, R v S,da It represents the income of v-node station participating in the spot market. Represents the node set of wind and solar storage stations.

[0077] Step S2012, obtaining comprehensive frequency regulation performance indicators, based on the bidding data of a single unit participating in the power market and the comprehensive frequency regulation performance indicators and calculating the benefits of the wind, solar and storage stations participating in the frequency regulation market.

[0078] In some optional implementations, the above step S2012 includes:

[0079] Step b1, based on the bidding data of a single unit participating in the electricity market, determine the bidding power of a single unit participating in the frequency regulation market, the actual output of a single unit in the day-ahead frequency regulation market, and the price of a single unit in the day-ahead frequency regulation market.

[0080] Step b2, calculating the penalty electricity price of a single unit participating in the frequency regulation market based on the bid power of the single unit participating in the frequency regulation market, the actual output of the single unit in the day-ahead frequency regulation market and the price of the single unit in the day-ahead frequency regulation market.

[0081] Specifically, taking a single unit in a wind-solar-storage station as a node, the calculation formula for the penalty electricity price of a single unit participating in the frequency regulation market is as follows:

[0082]

[0083] in, It represents the penalty electricity price for the v-node station to participate in the frequency regulation market. is the bid power of the v-node station participating in the frequency regulation market, represents the actual output of the v node station in the frequency modulation market, L v- F,da , L v+ F,da Respectively Less than / greater than Negative / positive penalty electricity price at the time of <L v+ F,da <L v F,da <L v- F,da .

[0084] Step b3, calculate the benefits of the wind-solar-storage station participating in the frequency regulation market based on the comprehensive frequency regulation performance index, the bid power of a single unit participating in the frequency regulation market, the price of a single unit in the day-ahead frequency regulation market, and the penalty electricity price of a single unit participating in the frequency regulation market.

[0085] Specifically, the comprehensive frequency modulation performance indicators include regulation rate, regulation accuracy and response time, and the calculation formula is as follows:

[0086] K=K1K2K3 (5)

[0087] Among them, K represents the comprehensive frequency modulation performance index, K1 represents the regulation rate, K2 represents the regulation accuracy, and K3 represents the response time.

[0088] Furthermore, the calculation formula for the benefits of wind and solar storage stations participating in the frequency regulation market is as follows:

[0089]

[0090] Among them, R v F,da represents the income of the v-node station participating in the frequency regulation market, L v F,da It is the electricity price of the station in the frequency regulation market.

[0091] Step S202, establishing a total revenue model of the wind, solar and storage stations participating in multiple markets based on the revenue of the wind, solar and storage stations participating in multiple markets.

[0092] Specifically, considering the benefits of wind-solar-storage stations participating in the spot market and frequency regulation market respectively, the calculation formula for the benefits of wind-solar-storage stations participating in multiple markets is as follows:

[0093]

[0094] Among them, R v da It represents the benefits of wind and solar storage stations participating in multiple markets.

[0095] Furthermore, the electricity price in the total revenue model of the above-mentioned wind, solar and storage stations participating in multiple markets meets the following expression:

[0096] 0 <L v+ F,da <L v+ S,da <L v F,da <L v- F,da <L v S,da <L v- S,da (8)

[0097] Furthermore, the total revenue model of wind-solar-storage stations participating in multiple markets can also be expressed as:

[0098]

[0099] Among them, P v,t is the real-time output of the v-node station, P v,t,max is the predicted maximum value of wind and solar power at time t.

[0100] Step S203, obtain the wind-solar power probability density prediction function, and build the expected profit model of the wind-solar power storage station participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar power storage station participating in multiple markets profit model. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0101] Step S204, solve the expected profit model of the wind, solar and storage station participating in multiple markets to obtain the optimal bidding strategy for the wind, solar and storage station participating in multiple markets. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0102] This embodiment provides a bidding strategy optimization method for wind-solar-storage stations participating in multiple markets. Since wind-solar-storage stations participate in both the spot market and the frequency regulation market and make the best bid, they can put part of their output power into the frequency regulation market to participate in grid frequency regulation, which can reduce wind and solar power abandonment and increase the income of wind-solar-storage stations. Therefore, calculating the income of wind-solar-storage stations participating in the spot market and the frequency regulation market lays a foundation for the subsequent optimization of bidding strategies for wind-solar-storage stations participating in multiple markets.

[0103] In this embodiment, a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets is provided, which can be used for the above-mentioned electronic equipment. Figure 3 is a flow chart of a bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0104] Step S301, obtaining the bidding data of a single unit in the wind-solar-storage station participating in the power market, and determining the income of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0105] Step S302: Based on the revenue of wind-solar-storage station participating in multiple markets, a total revenue model of wind-solar-storage station participating in multiple markets is established. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0106] Step S303, obtaining a wind-solar power probability density prediction function, and constructing an expected profit model for wind-solar power storage stations participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar power storage station participating in multiple markets profit model.

[0107] Specifically, the above step S303 includes:

[0108] Step S3031, taking the maximization of the expected revenue of the wind-solar-storage station as the goal, constructing an optimization objective function based on the wind-solar power probability density prediction function and the wind-solar-storage station participating in a multi-market revenue model.

[0109] Specifically, the expression of the optimization objective function is as follows:

[0110]

[0111] in, represents the expected revenue of the wind-solar-storage station, represents the wind-solar-storage station participating in the multi-market revenue model, P v,t Indicates the real-time output of the v-node station. It represents the bidding power of the v-node station participating in the spot market. represents the bid power of the v-node station participating in the frequency regulation market, f(P v,t ) represents the probability density prediction function of wind and solar power at time t the next day, P v,t,max represents the predicted maximum wind and solar power at time t; where,

[0112] Step S3032, obtaining the bidding power constraint, taking the bidding power constraint as the constraint condition of the optimization objective function, and constructing an expected profit model for wind, solar and storage stations participating in multiple markets.

[0113] Furthermore, the constraint expression is as follows:

[0114]

[0115] Step S304, solving the expected profit model of the wind, solar and storage stations participating in multiple markets, and obtaining the optimal bidding strategy for the wind, solar and storage stations participating in multiple markets.

[0116] Specifically, the particle swarm algorithm is used to solve the expected profit model of wind-solar-storage stations participating in multiple markets, with the goal of maximizing the profit of wind-solar-storage stations and optimizing and The two parameters are used to obtain the optimal bidding power of wind, solar and storage stations participating in both the spot market and the frequency regulation market, and then the optimal bidding strategy for wind, solar and storage stations participating in multiple markets is determined.

[0117] This embodiment provides a bidding strategy optimization method for wind-solar-storage stations participating in multiple markets. Since wind power and photovoltaic power have a certain correlation in output under similar space and time, the joint probability distribution of wind power and photovoltaic power is described by a wind-solar power probability density prediction function, and then the correlation between wind power and photovoltaic power is established, so that the expected profit model of wind-solar-storage stations participating in multiple markets fully considers the uncertainty of wind and solar power, and ensures the optimization of the bidding strategy of wind-solar-storage stations participating in multiple markets while improving the profits of wind-solar-storage stations participating in multiple markets.

[0118] The following is a specific example to illustrate the specific steps of the bidding strategy optimization method for wind, solar and storage stations participating in both the spot market and the frequency regulation market.

[0119] Embodiment 1:

[0120] like Figure 4 As shown in the figure, the specific steps of the bidding strategy optimization method for wind and solar storage stations participating in the spot market and frequency regulation market include:

[0121] Analyze the revenue mechanism of single units participating in the electricity market;

[0122] Determine the revenue mechanism for wind, solar and storage stations to participate in the spot market, and consider the revenue mechanism for stations to participate in the frequency regulation market under comprehensive frequency regulation performance indicators;

[0123] Establish a multi-market revenue model for wind, solar and storage stations;

[0124] Based on the wind-solar power probability density prediction function and taking the maximum station revenue as the objective function, the particle swarm algorithm is used to solve the optimal bidding plan for wind-solar-storage stations to participate in multiple markets (i.e. the optimal bidding strategy for wind-solar-storage stations to participate in multiple markets).

[0125] In this embodiment, a bidding strategy optimization device for a wind-solar-storage station participating in multiple markets is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0126] This embodiment provides a bidding strategy optimization device for wind, solar and storage stations participating in multiple markets, such as Figure 5 As shown, including:

[0127] Determination module 501, used to obtain bidding data of a single unit in a wind-solar-storage station participating in the power market, and determine the income of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market;

[0128] Establishing module 502, for establishing a total revenue model of wind-solar-storage station participating in multiple markets based on the revenue of wind-solar-storage station participating in multiple markets;

[0129] Construction module 503, used to obtain a wind-solar power probability density prediction function, and to construct an expected profit model for a wind-solar power storage station participating in multiple markets based on the wind-solar power probability density prediction function and a wind-solar power storage station participating in multiple markets profit model;

[0130] The solution module 504 is used to solve the expected profit model of the wind, solar and storage stations participating in multiple markets to obtain the optimal bidding strategy for the wind, solar and storage stations participating in multiple markets.

[0131] In some optional implementations, the determining module 501 includes:

[0132] The first calculation unit is used to calculate the income of the wind, solar and storage stations participating in the spot market based on the bidding data of the single unit participating in the power market;

[0133] The second calculation unit is used to obtain the comprehensive frequency regulation performance index based on the bidding data of a single unit participating in the power market and the comprehensive frequency regulation performance index and calculate the benefits of the wind, solar and storage station participating in the frequency regulation market.

[0134] In some optional implementations, the first computing unit includes:

[0135] The first determination subunit is used to determine the bid power of the single unit participating in the spot market, the actual output of the single unit in the day-ahead spot market and the price of the single unit in the day-ahead spot market based on the bid data of the single unit participating in the power market;

[0136] The first calculation subunit is used to calculate the penalty electricity price of the single unit participating in the spot market based on the bid power of the single unit participating in the spot market, the actual output of the single unit in the day-ahead spot market and the price of the single unit in the day-ahead spot market;

[0137] The second calculation subunit is used to calculate the income of the wind, solar and storage station participating in the spot market based on the bid power of a single unit participating in the spot market, the price of a single unit in the day-ahead spot market and the penalty electricity price of a single unit participating in the spot market.

[0138] In some optional implementations, the second computing unit includes:

[0139] The second determination subunit is used to determine the bid power of the single unit participating in the frequency regulation market, the actual output of the single unit in the day-ahead frequency regulation market and the price of the single unit in the day-ahead frequency regulation market based on the bid data of the single unit participating in the power market;

[0140] The third calculation subunit is used to calculate the penalty electricity price of the single unit participating in the frequency regulation market based on the bid power of the single unit participating in the frequency regulation market, the actual output of the single unit in the day-ahead frequency regulation market and the price of the single unit in the day-ahead frequency regulation market;

[0141] The fourth calculation subunit is used to calculate the benefits of wind, solar and storage stations participating in the frequency regulation market based on the comprehensive frequency regulation performance indicators, the bid power of a single unit participating in the frequency regulation market, the price of a single unit in the day-ahead frequency regulation market and the penalty electricity price of a single unit participating in the frequency regulation market.

[0142] In some optional implementations, the construction module 503 includes:

[0143] The first construction unit is used to maximize the expected revenue of the wind-solar-storage station and to construct an optimization objective function based on the wind-solar power probability density prediction function and the wind-solar-storage station participating in a multi-market revenue model;

[0144] The second construction unit is used to obtain the bidding power constraint, take the bidding power constraint as the constraint condition of the optimization objective function, and construct the expected profit model of wind, solar and storage stations participating in multiple markets.

[0145] In some optional implementations, the expression of the optimization objective function in the first building block is as follows:

[0146]

[0147] in, represents the expected revenue of the wind-solar-storage station, represents the wind-solar-storage station participating in the multi-market revenue model, P v,t Indicates the real-time output of the v-node station. It represents the bidding power of the v-node station participating in the spot market. represents the bid power of the v-node station participating in the frequency regulation market, f(P v,t ) represents the probability density prediction function of wind and solar power at time t the next day, P v,t,max Indicates the predicted maximum wind and solar power at time t.

[0148] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0149] In this embodiment, a bidding strategy optimization device for a wind, solar and storage station participating in multiple markets is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0150] The embodiment of the present invention also provides a computer device having the above Figure 5 A bidding strategy optimization device for a wind-solar-storage station participating in multiple markets is shown.

[0151] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0152] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0153] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0154] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0155] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0156] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0157] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0158] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0159] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A bidding strategy optimization method for wind-solar-storage stations participating in multiple markets, characterized in that: The method comprises: Obtain bidding data of a single unit in a wind-solar-storage station participating in the power market, and determine the revenue of the wind-solar-storage station participating in multiple markets based on the bidding data of the single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include a spot market and a frequency regulation market; Based on the income of the wind-solar-storage station participating in multiple markets, a total income model of the wind-solar-storage station participating in multiple markets is established; Obtaining a wind-solar power probability density prediction function, and constructing an expected profit model for a wind-solar power storage station participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar power storage station participating in multiple markets profit model; The expected profit model of the wind-solar-storage station participating in multiple markets is solved to obtain the optimal bidding strategy for the wind-solar-storage station participating in multiple markets.

2. The method according to claim 1, characterized in that The determining of the benefits of the wind-solar-storage station participating in multiple markets based on the bidding data of a single unit in the wind-solar-storage station participating in the power market includes: Based on the bidding data of the single unit participating in the electricity market, the profits of the wind, solar and storage stations participating in the spot market are calculated respectively; Obtain a comprehensive frequency regulation performance index based on the bidding data of the single unit participating in the electricity market and the comprehensive frequency regulation performance index and calculate the income of the wind, solar and storage station participating in the frequency regulation market.

3. The method according to claim 2, characterized in that The calculation of the revenue of the wind, solar and storage stations participating in the spot market based on the bidding data of the single unit participating in the electricity market includes: Determine the bid power of the single unit participating in the spot market, the actual output of the single unit in the day-ahead spot market, and the price of the single unit in the day-ahead spot market based on the bid data of the single unit participating in the power market; Calculate the penalty electricity price of the single unit participating in the spot market based on the bid power of the single unit participating in the spot market, the actual output of the single unit in the day-ahead spot market and the price of the single unit in the day-ahead spot market; The profit of the wind-solar-storage station participating in the spot market is calculated based on the bid power of the single unit participating in the spot market, the price of the single unit in the day-ahead spot market and the penalty electricity price of the single unit participating in the spot market.

4. The method according to claim 2, characterized in that: The bidding data of the single unit participating in the power market and the comprehensive frequency regulation performance index and the calculation of the income of the wind-solar-storage station participating in the frequency regulation market include: Determine the bid power of the single unit participating in the frequency regulation market, the actual output of the single unit in the day-ahead frequency regulation market, and the price of the single unit in the day-ahead frequency regulation market based on the bid data of the single unit participating in the power market; Calculate the penalty electricity price of the single unit participating in the frequency regulation market based on the bid power of the single unit participating in the frequency regulation market, the actual output of the single unit in the day-ahead frequency regulation market and the price of the single unit in the day-ahead frequency regulation market; The benefits of the wind-solar-storage station participating in the frequency regulation market are calculated based on the comprehensive frequency regulation performance index, the bid power of the single unit participating in the frequency regulation market, the price of the single unit in the day-ahead frequency regulation market and the penalty electricity price of the single unit participating in the frequency regulation market.

5. The method according to claim 1, characterized in that The method of constructing an expected profit model for a wind-solar storage station participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar storage station participating in multiple markets profit model includes: Taking the expected revenue of the wind-solar-storage station as the goal, an optimization objective function is constructed based on the wind-solar power probability density prediction function and the wind-solar-storage station participating in a multi-market revenue model; The bidding power constraint is obtained, and the bidding power constraint is used as a constraint condition of the optimization objective function to construct an expected profit model of the wind-solar-storage station participating in multiple markets.

6. The method according to claim 5, characterized in that The optimization objective function is constructed based on the wind-solar power probability density prediction function and the wind-solar storage station participating in the multi-market revenue model; wherein the expression of the optimization objective function is as follows: in, represents the expected revenue of the wind-solar-storage station, represents the wind-solar-storage station participating in the multi-market revenue model, P v,t Indicates the real-time output of the v-node station. It represents the bidding power of the v-node station participating in the spot market. represents the bid power of the v-node station participating in the frequency regulation market, f(P v,t ) represents the probability density prediction function of wind and solar power at time t the next day, P v,t,max Indicates the predicted maximum wind and solar power at time t.

7. A bidding strategy optimization device for wind-solar-storage stations participating in multiple markets, characterized in that: The device comprises: A determination module is used to obtain bidding data of a single unit in a wind-solar-storage station participating in the power market, and determine the income of the wind-solar-storage station participating in multiple markets based on the bidding data of the single unit in the wind-solar-storage station participating in the power market; wherein the multiple markets include the spot market and the frequency regulation market; Establishing a module for establishing a total revenue model of the wind-solar-storage station participating in multiple markets based on the revenue of the wind-solar-storage station participating in multiple markets; A construction module is used to obtain a wind-solar power probability density prediction function, and to construct an expected profit model for a wind-solar power storage station participating in multiple markets based on the wind-solar power probability density prediction function and the wind-solar power storage station participating in multiple markets profit model; The solution module is used to solve the expected profit model of the wind-solar-storage station participating in multiple markets to obtain the optimal bidding strategy for the wind-solar-storage station participating in multiple markets.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the bidding strategy optimization method for a wind-solar-storage station participating in multiple markets as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises computer instructions, and the computer instructions are used to enable a computer to execute the bidding strategy optimization method for a wind-solar-storage station participating in multiple markets according to any one of claims 1 to 6.

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