Power resource benefit allocation method, device and system and storage medium

Abstract

The invention discloses an electric power resource benefit allocation method, device and system and a storage medium, relates to the technical field of electric power systems, and aims to at least solve the problem that an allocation result is not matched with contribution degrees of all parties due to direct benefit allocation under the condition of equal risk. The method comprises the following steps: determining benefit participation ends in a current time period, wherein the benefit participation ends comprise one or more of three candidate ends, namely a wind power supply end, a thermal power supply end and an electricity selling end; solving the variable constraint relational expression by taking the lowest total operation cost of the three candidate ends in different operation periods as a target to obtain the electricity selling power generation power, the thermal power generation power and the wind power generation power of each operation period; determining each target benefit allocation weight of each benefit participation end; selecting each target benefit offensive degree of each benefit participation end; and according to each target benefit allocation weight and each target benefit offensive degree, determining a benefit allocation proportion of each benefit participation end.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, system and storage medium for allocating benefits of power resources. Background Art

[0002] Against the backdrop of global energy transition, wind and thermal power companies face new challenges and opportunities in their electricity sales strategies within the power market. Currently, wind-thermal bundled systems and large users utilize a bilateral negotiated direct power purchase model. This involves bundling wind farms and thermal power plants to form power generation groups, which then sign bilateral direct power purchase contracts with large industrial users to specify the price and volume of direct power purchased, and distribute profits within the power generation group. Most of these models assume equal risk for all parties. If the risks assumed by each party are unequal or significantly different, the contributions made by each party to the transaction cannot be accurately reflected, leading to a mismatch between profit distribution and contributions. Summary of the Invention

[0003] The present invention provides a method, device, system, and storage medium for allocating benefits from electric power resources, to at least address the problem of mismatching the distribution results and contributions of each party when directly distributing benefits under equal risk conditions. The technical solution of the present invention is as follows:

[0004] According to a first aspect of an embodiment of the present invention, a method for distributing benefits of electric power resources is provided, which is applied to an electric power system, wherein the electric power system includes a wind power supply end, a thermal power supply end and a power selling end, and the method includes: determining the benefit participating end in the current period, wherein the benefit participating end includes one or more of the three candidate ends of the wind power supply end, the thermal power supply end and the power selling end; taking the lowest total operating cost in different operating periods of the three candidate ends as the goal, solving the variable constraint relationship constructed by the parameter variables of the three candidate ends, and obtaining the power selling power generation power, thermal power generation power and wind power generation power of the power selling end, the thermal power supply end and the wind power supply end in each operating period respectively; dividing According to the electricity sales power generation power, thermal power generation power and wind power generation power in the current period, the first benefit distribution weight of the wind power supply end, the second benefit distribution weight of the thermal power supply end and the third benefit distribution weight of the electricity sales end are determined to obtain the target benefit distribution weights of each stakeholder end; from the first benefit contribution of the wind power supply end, the second benefit contribution of the thermal power supply end and the third benefit contribution of the electricity sales end, the target benefit contribution of each stakeholder end is selected; according to the target benefit distribution weights and the target benefit contribution of each stakeholder end, the benefit distribution proportion of each stakeholder end is determined.

[0005] In one implementation, with the goal of minimizing the total operating cost of the three candidate terminals in different operating periods, the variable constraint relationship constructed by the parameter variables of the three candidate terminals is solved to obtain the sales power generation power, thermal power generation power and wind power generation power of the power sales terminal, thermal power supply terminal and wind power supply terminal in each operating period respectively, including: constructing a dynamic constraint relationship based on the dynamic response threshold of the virtual power plant of the power sales terminal when the power sales terminal is in a state of reducing demand response, the dynamic response threshold of the power sales terminal when the power sales terminal is in a state of increasing demand response and the dynamic output threshold of the thermal power supply terminal, and constructing a static constraint relationship based on the static variables of the three candidate terminals; the variable constraint relationship includes a dynamic constraint relationship and a static constraint relationship; with the goal of minimizing the total operating cost of the wind power supply terminal, thermal power supply terminal and power sales terminal in different operating periods, the variable constraint relationship is solved to obtain the sales power generation power, thermal power generation power and wind power generation power that constitute the lowest total operating cost in each operating period.

[0006] The power supply capabilities of different power supply terminals are different in different operating periods, and the corresponding thresholds are constantly changing. The dynamic response threshold of the virtual power plant and the dynamic output threshold of the thermal power supply terminal are determined to ensure the accuracy of the generated power.

[0007] In another implementation method, the first profit distribution weight of the wind power supply end, the second profit distribution weight of the thermal power supply end, and the third profit distribution weight of the power sales end are determined according to the power sales power generation power, thermal power generation power, and wind power generation power in the current time period, so as to obtain the target profit distribution weights of each stakeholder end, including: determining the first power generation cost and the first risk cost of the wind power supply end, the second power generation cost and the second risk cost of the thermal power supply end, and the third power generation cost and the third risk cost of the power sales end according to the power sales power generation power, thermal power generation power, and wind power generation power in the current time period; determining the first profit distribution weight of the wind power supply end, the second profit distribution weight of the thermal power supply end, and the third profit distribution weight of the power sales end according to the cost ratio corresponding to the first power generation cost and the first risk cost, the second power generation cost and the second risk cost, and the third power generation cost and the third risk cost; and selecting the target profit distribution weights of each stakeholder end from the first profit distribution weight, the second profit distribution weight, and the third profit distribution weight.

[0008] Fully consider the impact of cost input and risk sharing contribution of each stakeholder on benefit distribution, so that the benefit distribution results are more in line with the expected returns of each stakeholder.

[0009] In another implementation method, the interest distribution proportion of each stakeholder end is determined based on the target interest distribution weights of each stakeholder end and the target interest dedication of each stakeholder end, including: weighting the target interest distribution weights and the target interest dedication of each stakeholder end respectively to obtain the weighted results of each stakeholder end; summing up the weighted results of each stakeholder end to obtain the total weighted result; and taking the ratio between the weighted results of each stakeholder end and the total weighted result as the interest distribution proportion of each stakeholder end.

[0010] In another implementation, the power resource benefit distribution method further includes: using a Shapley value model to determine a first benefit contribution, a second benefit contribution, and a third benefit contribution.

[0011] Consider the contribution of each stakeholder to the overall benefits in all possible cooperation combinations, and distribute the total benefits based on the contribution to ensure the rationality of benefit distribution.

[0012] In another implementation method, the variable constraint relationship includes: dynamic response threshold range constraint, dynamic output threshold range constraint, power balance constraint, wind curtailment constraint, new energy proportion constraint, thermal power output constraint, virtual power plant output constraint and power sales company demand response output constraint; among them, the power balance constraint represents the balance between power supply and power demand, and the wind curtailment constraint represents that the wind curtailment volume of the wind turbine generator set is within a preset range throughout the year.

[0013] In another implementation method, the variable constraint relationship constructed by the parameter variables of the three candidate terminals is solved to obtain the sales power generation power, thermal power generation power and wind power generation power of the power sales terminal, thermal power supply terminal and wind power supply terminal in each operating period, including: integrating the multi-channel Tent mapping MCTent into the quantum particle swarm optimization QPSO algorithm to obtain the MTQPSO algorithm; using the MTQPSO algorithm, the total operating cost is solved under the variable constraint relationship constructed by the parameter variables of the three candidate terminals to obtain the sales power generation power, thermal power generation power and wind power generation power with the lowest total operating cost.

[0014] The use of MTQPSO algorithm can improve computational efficiency and effectively solve the local optimal problem, thereby obtaining the optimal dynamic threshold and ensuring the accuracy of the generated power.

[0015] According to a second aspect of an embodiment of the present invention, there is provided an electric power resource benefit distribution device, which is applied to an electric power system, wherein the electric power system includes a wind power supply end, a thermal power supply end and a power selling end, and the device includes: a determination unit, which is configured to determine the benefit-participating end in the current period, and the benefit-participating end includes one or more of the three candidate ends of the wind power supply end, the thermal power supply end and the power selling end; a solution unit, which is configured to solve the variable constraint relationship constructed by the parameter variables of the three candidate ends with the goal of minimizing the total operating cost in different operating periods of the three candidate ends, and obtain the power selling power generation power, thermal power generation power and wind power generation power of the power selling end, the thermal power supply end and the wind power supply end in each operating period respectively; a weight distribution unit, which is configured to According to the electricity sales power generation power, thermal power generation power and wind power generation power in the current period, the first profit distribution weight of the wind power supply end, the second profit distribution weight of the thermal power supply end and the third profit distribution weight of the electricity sales end are determined respectively to obtain the target profit distribution weights of each stakeholder end; the profit contribution distribution unit is configured to select the target profit contribution of each stakeholder end from the first profit contribution of the wind power supply end, the second profit contribution of the thermal power supply end and the third profit contribution of the electricity sales end; the profit proportion distribution unit is configured to determine the profit distribution proportion of each stakeholder end according to the target profit distribution weights of each stakeholder end and the target profit contribution of each stakeholder end.

[0016] According to a third aspect of an embodiment of the present invention, there is provided a power system configured to execute the power resource benefit allocation method according to the first aspect and any possible implementation thereof.

[0017] According to a fourth aspect of an embodiment of the present invention, there is provided an electric power resource benefit allocation device, which is configured to execute the electric power resource benefit allocation method according to the first aspect and any possible implementation thereof.

[0018] According to the fifth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which instructions are stored. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the power resource benefit distribution method such as the first aspect and any possible implementation thereof.

[0019] The technical solution provided by the embodiment of the present invention brings at least the following beneficial effects: by utilizing the three candidate terminals of the electricity sales end (i.e., the load aggregator with normal energy demand, the virtual power plant with reduced energy demand, and the demand response unit with increased energy demand), it is possible to fully consider the impact of the power supply situation of each power supply end on the benefit distribution. Based on this, with the goal of minimizing the total operating cost of the three candidate terminals in different operating periods, each stakeholder terminal is constrained, and the fairness of the benefit distribution of each stakeholder terminal is guaranteed under the premise of minimizing the cost of all parties. At the same time, the benefit distribution ratio is determined by the target benefit distribution weight and target benefit contribution of each stakeholder terminal, so as to fully consider the risk ratio and actual contribution value borne by each stakeholder terminal, which can more comprehensively reflect the contribution of each stakeholder terminal, ensure the rationality of benefit distribution, and make the benefit distribution result more consistent with the expected benefits of each stakeholder terminal.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0022] Figure 1 is a schematic diagram of a power system according to an exemplary embodiment;

[0023] Figure 2 A method for allocating benefits of electric power resources is shown in accordance with an exemplary embodiment. Figure 1 ;

[0024] Figure 3 A method for allocating benefits of electric power resources is shown in accordance with an exemplary embodiment. Figure 2 ;

[0025] Figure 4 This is a flow chart of a quantum particle swarm optimization algorithm based on an improved chaotic map according to an exemplary embodiment;

[0026] Figure 5 This is a diagram illustrating a key parameter chart of each stakeholder according to an exemplary embodiment;

[0027] Figure 6 is a schematic diagram of an optimization threshold chart according to an exemplary embodiment;

[0028] Figure 7 is a supply and demand balance diagram according to an exemplary embodiment;

[0029] Figure 8 This is a schematic diagram of a wind power supply terminal benefit contribution chart according to an exemplary embodiment;

[0030] Figure 9 This is a schematic diagram of a benefit contribution chart of a thermal power supply terminal according to an exemplary embodiment;

[0031] Figure 10 This is a schematic diagram of a profit contribution chart of a power sales terminal according to an exemplary embodiment;

[0032] Figure 11 is a schematic diagram of a profit distribution result chart based on the Shapley value method according to an exemplary embodiment;

[0033] Figure 12 is a schematic diagram of a profit distribution result chart according to an exemplary embodiment;

[0034] Figure 13 is a block diagram of a device for allocating benefits of electric power resources according to an exemplary embodiment;

[0035] Figure 14 The figure is a schematic diagram showing a power resource benefit distribution device according to an exemplary embodiment. DETAILED DESCRIPTION

[0036] In order to enable ordinary people in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0037] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0038] Before giving a detailed introduction to the power resource benefit distribution method provided in the embodiment of the present application, a brief introduction to the application scenarios involved in the embodiment of the present application is first given.

[0039] Against the backdrop of the global energy transition, wind and thermal power companies face new challenges and opportunities in their electricity sales strategies within the power market. In particular, with the rapid development of renewable energy, wind power companies' electricity sales strategies need to adapt to market mechanisms, policy changes, and other factors. Simultaneously, thermal power companies need to adjust their strategies to cope with increasingly fierce market competition. Furthermore, power sales companies play a key intermediary role in the power market, and their electricity purchasing and sales strategies are directly related to market efficiency and company profits. Game theory, as an important tool for analyzing market behavior and strategies, has also been widely used in power marketing and market analysis. To address the instability caused by the large-scale integration of renewable energy, the country is vigorously promoting the participation of renewable energy in the power market and its independent consumption. Furthermore, due to continuous technological advancements, many users have shifted from being single energy consumers to being energy suppliers and demanders. Therefore, integrating diverse energy sources—such as wind, solar, thermal, hydro, demand response, and virtual power plants—to better meet user needs has become a key technology focus in the energy sector. The power-based allocation method is a straightforward and relatively simple way to distribute benefits among wind power companies, thermal power companies, and power sales companies. However, this technology fails to fully reflect contributions, potentially leading to unfair distribution and potentially triggering malicious competition, leading to unfair tactics to increase power generation or sales. Agreement-based allocation offers another highly flexible and stable approach. However, this technology also has the following drawbacks. First, negotiation costs are high. Second, agreements are difficult to enforce. Fluctuations in the market environment and individual companies' circumstances can lead to a mismatch between the actual and anticipated benefits of each party, making the agreement difficult to enforce.

[0040] Research has found that currently, wind-thermal bundled systems and large users utilize a bilaterally negotiated direct power purchase model. This involves bundling wind farms with thermal power plants to form a power generation group, which then signs bilateral direct power purchase contracts with large industrial users to specify the price and volume of direct power purchased. Benefits are then distributed within the power generation group. Based on the principle of maximizing wind power capacity while maintaining system peak load regulation, and considering a reasonable wind-thermal bundle ratio, the ideal ratio for direct power transmission from large wind power bases in my country is generally 1:1.5-1:2.2. However, within this model, the Shapley value method is often used to allocate benefits, assuming equal risk for all parties. If the risks assumed by each party are unequal or significantly different, this method fails to accurately reflect the contributions and efforts made by each party to achieve the transaction.

[0041] In response to the above problems, the present application provides a method for distributing benefits of electric power resources, which determines the benefit-participating end in the current period, including one or more of the three candidate ends: the wind power supply end, the thermal power supply end, and the power sales end; with the goal of minimizing the total operating cost of the three candidate ends in different operating periods, the variable constraint relationship is solved to obtain the power sales power, thermal power generation power, and wind power generation power of each operating period; thereby determining the target benefit distribution weights of each benefit-participating end; selecting the target benefit contribution of each benefit-participating end; and determining the benefit distribution ratio of each benefit-participating end based on the target benefit distribution weights and the target benefit contribution. This more comprehensively reflects the contribution of each benefit-participating end, ensures the rationality of benefit distribution, and makes the benefit distribution results more consistent with the expected benefits of each benefit-participating end.

[0042] Next, the implementation architecture involved in this application is briefly introduced below.

[0043] Figure 1 This is a schematic diagram of a power system provided by this application. Figure 1 As shown, the power system 10 includes a wind power supply terminal 11, a thermal power supply terminal 12, and a power sales terminal 13. The wind power supply terminal 11, the thermal power supply terminal 12, and the power sales terminal 13 are dynamically connected through a tripartite cooperation agreement and power grid transmission and distribution.

[0044] The electricity sales end 13 may be an electricity sales company. The wind power supply end 11 and the thermal power supply end 12 may reach a cooperation agreement with the electricity sales end 13 to jointly meet the electricity demand of the electricity sales end 13 .

[0045] The electricity sales end 13 is divided into three candidate electricity sales ends, including a virtual power plant 131 , a load aggregator 132 , and a demand response unit 133 .

[0046] Virtual power plants 131 are used by electricity sellers to reduce energy demand when power supply is insufficient. Load aggregators 132 are used by electricity sellers to reduce energy demand when power supply exceeds demand. Demand response units 133 are used by electricity sellers to increase energy demand when power supply exceeds demand.

[0047] In some embodiments, power system 10 is configured to perform the following five phases.

[0048] (1) Formulate a direct purchase electricity price strategy: Wind power supply terminal 11, thermal power supply terminal 12, and electricity sales terminal 13 jointly formulate a direct purchase electricity price that is lower than the grid electricity sales price, adopting a "price-for-volume" strategy. The formulation of the direct purchase electricity price must take into account the interests of all parties to ensure the conclusion of the cooperation agreement.

[0049] (2) Determine the amount of electricity purchased directly: The electricity sales terminal 13 provides the corresponding historical load demand and future electricity forecast results, and the three parties jointly analyze and discuss how to meet the cooperation needs.

[0050] (3) Signing of a cooperation agreement: The wind power supply terminal 11, the thermal power supply terminal 12 and the power sales terminal 13 reach a cooperation agreement to jointly fully meet the electricity demand of the power sales terminal, and purchase the shortfall from the power grid to ensure that the electricity demand of the power sales company is met.

[0051] (4) Implementing Demand Response 133 and Virtual Power Plant 131: The electricity seller 13 optimizes the balance between electricity supply and demand and obtains additional revenue through means such as demand response units 133 and virtual power plants 131. The wind power supply terminal 11 and the thermal power supply terminal 12 cooperate with the demand response strategy of the electricity seller 13 to adjust the power generation.

[0052] (5) Benefit distribution and risk sharing: The three parties need to clarify the benefit distribution mechanism to ensure fairness and sustainability in the cooperation process. A risk sharing mechanism should be established to deal with risks such as power market fluctuations and policy changes.

[0053] The power resource benefit distribution method provided in the embodiment of the present application can be applied to the aforementioned Figure 1 For ease of understanding, the power resource benefit allocation method provided by this application is specifically introduced below with reference to the accompanying drawings.

[0054] Figure 2 is a flow chart showing a method for allocating benefits of electric power resources according to an exemplary embodiment. Figure 2 As shown, the power resource benefit distribution method includes the following steps.

[0055] S21, determining the stakeholder terminals in the current period, including one or more of the three candidate terminals: the wind power supply terminal, the thermal power supply terminal, and the power sales terminal;

[0056] The interest participants in the current period represent that the power supply capabilities of the wind power supply end and the thermal power supply end are different in different periods during actual operation, and the candidate power sales ends that need to adjust the power supply demand in accordance with the power supply capacity of the current period are different. Therefore, the interest participants in the current period are different.

[0057] The three candidate ends for electricity sales include load aggregators, virtual power plants, and demand response units.

[0058] In some embodiments, in one case, the power supply values ​​of the wind power supply terminal and the thermal power supply terminal meet the demand value of the power sales terminal, and the load aggregator with normal energy demand among the candidate power sales terminals serves as the interest participating terminal in the current period.

[0059] In another case, the power supply value of the wind power supply end and the thermal power supply end is lower than the demand value of the power sales end. The power sales end needs to reduce the demand value to meet the supply and demand balance. To solve this situation, the power sales end acts as a virtual power plant to make up for the missing power supply value for the power supply end. At this time, the virtual power plant that reduces energy demand among the candidate ends of the power sales end serves as the beneficiary participating end in the current period.

[0060] In another case, the power supply value is higher than the demand value of the power sales end, and the excess power value needs to be abandoned to meet the supply and demand balance. To solve this situation, the power sales end increases the demand value as a demand response unit. At this time, the demand response unit that increases energy demand among the candidate ends of the power sales end serves as the interest participant end in the current period.

[0061] S22, with the goal of minimizing the total operating cost of the three candidate terminals in different operating periods, solve the variable constraint relationship constructed by the parameter variables of the three candidate terminals, and obtain the power sales power, thermal power generation power and wind power generation power of the power sales terminal, thermal power supply terminal and wind power supply terminal in each operating period respectively.

[0062] The total operating costs in different operating periods include the operating costs of wind power supply, thermal power supply, electricity sales, and power purchase costs of the power grid.

[0063] Variable constraint expressions include dynamic constraint expressions and static constraint expressions.

[0064] Further, as follows Figure 3 As shown, the above step S22 can be specifically implemented through the following steps S221 to S222.

[0065] S221: Determine the lowest total operating costs of the three candidate terminals in different operating time periods.

[0066] In one embodiment, the lowest total operating cost is determined according to the following formulas (1) to (5).

[0067] The goal is to minimize the total operating cost in different time periods. The objective function is expressed as the following formula (1).

[0068] minC T =C WF +C TPP +C ESC +C GPE (1).

[0069] Among them, C T is the total operating cost, C WF is the wind farm operating cost, C TPP is the operating cost of the thermal power plant, C ESC is the cost of the electricity sales company; C GPE The cost of purchasing electricity from the grid.

[0070] The operating costs of wind power supply mainly include the operation and maintenance costs of wind turbines, grid access fees, etc., and the objective function is expressed as the following formula (2).

[0071]

[0072] Among them, C WF P is the operating cost of wind power supply end; WF is the wind power output; k om,wf k is the wind power operation and maintenance cost coefficient; iac is the network cost coefficient.

[0073] The operating costs of thermal power supply mainly include the operation and maintenance costs of thermal power units, grid access fees, etc., and the objective function is expressed as the following formula (3).

[0074]

[0075] Among them, C TPP is the operating cost of thermal power supply end; P TPP is the thermal power output; k om,tpp k is the thermal power operation and maintenance cost coefficient; iac is the network cost coefficient.

[0076] The electricity sales cost mainly includes demand response cost, etc., and the objective function is expressed as the following formula (4).

[0077]

[0078] Among them, C ESC is the cost of electricity sales; P ESG k is the output power of the electricity sales end; esg It is the operation and maintenance cost coefficient of the electricity sales end.

[0079] The cost of purchasing electricity from the power grid is mainly the cost of purchasing electricity from the power grid to meet the supply and demand balance. The objective function is expressed as the following formula (5).

[0080]

[0081] Among them, C GPE P is the cost of purchasing electricity from the power grid; GPE Power purchased for the grid; p gpe The unit price of electricity purchased from the power grid.

[0082] S222, solving the variable constraint relationship constructed by the parameter variables of the three candidate terminals to obtain the sales power generation power, thermal power generation power and wind power generation power of the power sales terminal, thermal power supply terminal and wind power supply terminal in each operating period respectively.

[0083] Optionally, a dynamic constraint relationship is constructed based on the dynamic response threshold of the virtual power plant at the power sales end when the power sales end is in a state of reducing demand response, the dynamic response threshold of the power sales end when the power sales end is in a state of increasing demand response, and the dynamic output threshold of the thermal power supply end, and a static constraint relationship is constructed based on the static variables of the three candidate ends. The variable constraint relationship includes a dynamic constraint relationship and a static constraint relationship. With the goal of minimizing the total operating cost of the wind power supply end, the thermal power supply end, and the power sales end in different operating periods, the variable constraint relationship is solved to obtain the power sales power, thermal power power, and wind power power that have the lowest total operating cost in each operating period.

[0084] In one embodiment, the variable constraint relationships are solved by formulas (6) to (15) to determine the electricity sales power, thermal power generation power, and wind power generation power.

[0085] First, the specific process of determining the dynamic constraint relationship from formula (6) to formula (8) is as follows.

[0086] It is understandable that the cost of the thermal power supply end is different under different loads. Therefore, during actual operation, it is necessary to determine the response threshold of the virtual power plant that reduces energy demand, the response threshold of the demand response unit that increases energy demand, and the output threshold of the thermal power supply end. Since the capabilities of the virtual power plant and the demand response unit are different in different time periods, the threshold is constantly changing and needs to be less than the maximum threshold that the participants can participate in.

[0087] The dynamic response threshold of the virtual power plant at the electricity sales end when the electricity sales end is in the state of reducing demand response is expressed as the following formula (6).

[0088] 0≤TS VPP ≤T VPP (6).

[0089] Among them, TS VPP is the dynamic response threshold of the virtual power plant, T VPP is the upper threshold of the virtual power plant.

[0090] The dynamic response threshold of the electricity sales end when the electricity sales end is in the process of improving demand response is expressed as the following formula (7).

[0091] 0≤TS DR ≤T DR (7).

[0092] Among them, TS DR is the dynamic response threshold of the electricity sales demand response, T DR It is the upper limit of the demand response threshold at the electricity sales end.

[0093] The dynamic output threshold of the thermal power supply terminal is expressed as the following formula (8).

[0094] 0≤TS TP ≤T TP (8).

[0095] Among them, TS TP is the dynamic threshold of thermal power company, T TP It is the upper limit of the threshold for thermal power companies.

[0096] Secondly, the dynamic constraint relationship formula determined above is combined with the static constraint relationship formula to determine the variable constraint relationship formula.

[0097] Optionally, the variable constraint relationship includes dynamic response threshold range constraint, dynamic output threshold range constraint, power balance constraint, wind curtailment constraint, new energy proportion constraint, thermal power output constraint, virtual power plant output constraint and power sales company demand response output constraint.

[0098] The power balance constraint represents the balance between power supply and power demand. The wind curtailment constraint represents the wind curtailment amount of wind turbines that falls within a preset range throughout the year.

[0099] Specifically, the variable constraint relationship is determined by formula (9) to formula (14).

[0100] The power balance constraint of the electric system is established as shown in the following formula (9).

[0101]

[0102] P WF is the wind power output; P TPP is the thermal power output; P VPP is the virtual power plant power; P GPE Power purchased from the grid; P DR is the demand response power; E is the electric load; P AW The wind power is abandoned.

[0103] Due to national policy requirements, the wind curtailment rate of wind turbines should be within a certain range throughout the year. The curtailment constraint is expressed as the following formula (10).

[0104]

[0105] Among them, P AW is the wind power curtailment; TS AW It is the annual wind curtailment limit.

[0106] The constraint on the proportion of new energy is expressed as the following formula (11).

[0107]

[0108] Among them, ε is the proportion of new energy; E is the electricity load; P WFThe wind power output.

[0109] The thermal power output constraint is expressed as the following formula (12).

[0110] 0≤P TPP ≤TS TP (12).

[0111] Among them, TS TP is the dynamic threshold of thermal power company; P TPP It is the thermal power output.

[0112] The virtual power plant output constraint is expressed as the following formula (13).

[0113] 0≤P VPPP ≤TS VPP (13).

[0114] Among them, TS VPP is the dynamic threshold of virtual power plant; P VPP is the virtual power plant power.

[0115] The demand response output constraint of the power sales company is expressed as the following formula (14).

[0116] 0≤P DRP ≤TS DR (14).

[0117] Among them, TS DR P is the dynamic threshold for demand response of power supply companies; DR Demand response power.

[0118] The power supply capabilities of different power supply terminals are different in different operating periods, and the corresponding thresholds are constantly changing. The dynamic response threshold of the virtual power plant and the dynamic output threshold of the thermal power supply terminal are determined to ensure the accuracy of the generated power.

[0119] Third, with the goal of minimizing the total operating cost in different operating periods determined in the above step S221, the variable constraint relationship is solved to obtain the optimal dynamic thresholds of the thermal power company, demand response, and virtual power plant, thereby obtaining the electricity sales power, thermal power generation power, and wind power generation power that have the lowest total operating cost in each operating period.

[0120] Optionally, the multi-channel tent mapping (MCTent) is integrated into the quantum particle swarm optimization (QPSO) algorithm to obtain the MTQPSO algorithm. The MTQPSO algorithm is used to solve the total operating cost under the variable constraint relationship constructed by the parameter variables of the three candidate terminals to obtain the sales power generation, thermal power generation, and wind power generation at the lowest total operating cost.

[0121] MTQPSO algorithm characterizes the quantum particle swarm optimization algorithm based on improved chaotic mapping.

[0122] Specifically, the process of obtaining the global optimal solution through the MTQPSO algorithm is as follows: Figure 4 shown.

[0123] S41, start and set algorithm parameters.

[0124] S42, randomly initialize the example population.

[0125] S43, update the particle optimal point and the population optimal point.

[0126] S44, calculate the particle fitness.

[0127] S45, select elite particles to perform chaotic search and update the historical optimal point and population optimal point of the corresponding particles.

[0128] S46, select mediocre particles for chaotic search and update the historical optimal point and population optimal point of the corresponding particles.

[0129] S47, calculate the local attraction point of the particle and the average optimal position of the population.

[0130] S48, determine whether the maximum number of iterations has been reached, if so, proceed to step S49, otherwise return to step S43.

[0131] S49, output the optimal planning solution.

[0132] S50, end.

[0133] In each iteration of the MTQPSO algorithm, a certain proportion of excellent particles are selected as elite particles, and a certain proportion of particles are randomly selected from the remaining particles as mediocre particles. Then, chaotic searches of different ranges are performed on each elite particle and each mediocre particle. Mediocre particles directly perform chaotic searches in the entire solution space. The chaotic search range of the elite particles is centered on the individual historical optimal point, and the search range is constantly changing. The chaotic search radius of the ith elite particle in the d-dimensional direction in the t+1 iteration is expressed as the following formula (15):

[0134]

[0135] Where t is the number of iterations, is the average value of the optimal position of the particle population, x dmin 、x dmax are the lower and upper limits of the d-dimensional direction of the solution space respectively.

[0136] The use of MTQPSO algorithm can improve computational efficiency and effectively solve the local optimal problem, thereby obtaining the optimal dynamic threshold and ensuring the accuracy of the generated power.

[0137] S23, respectively, determines the first benefit distribution weight of the wind power supply end, the second benefit distribution weight of the thermal power supply end, and the third benefit distribution weight of the power sales end based on the power sales power generation, thermal power generation power, and wind power generation power in the current period, so as to obtain the target benefit distribution weights of each stakeholder end.

[0138] Taking into account the contribution of wind power supply, thermal power supply and electricity sales, benefits are distributed based on the concept of "risk sharing and benefit sharing" and taking into account the satisfaction of all participants.

[0139] It is understandable that the direct purchase benefits of wind power and thermal power supply terminals are greater than their own marginal costs of power generation, and the benefits of virtual power plants from load reduction are greater than their own marginal costs. This is the bottom line for ensuring cooperation between wind power supply terminals, thermal power supply terminals, and virtual power plants. At the same time, the volatility and uncertainty of wind power increase the risk that the bundled system will not be able to complete the direct purchase of electricity. In this case, thermal power units need to reserve spare capacity to make up for the direct purchase of electricity that cannot be completed by the wind power supply terminal. This to some extent damages the interests of thermal power units and cannot be represented solely by power generation costs. Therefore, the basic distribution weight of benefits is determined by power generation costs and risk loss costs.

[0140] Optionally, the first power generation cost and first risk cost of the wind power supply terminal, the second power generation cost and second risk cost of the thermal power supply terminal, and the third power generation cost and third risk cost of the power sales terminal are determined based on the power sales power, the thermal power generation power, and the wind power generation power in the current time period. A first profit distribution weight of the wind power supply terminal, a second profit distribution weight of the thermal power supply terminal, and a third profit distribution weight of the power sales terminal are determined based on the cost ratios corresponding to the first power generation cost and the first risk cost, the second power generation cost and the second risk cost, and the third power generation cost and the third risk cost. From the first profit distribution weight, the second profit distribution weight, and the third profit distribution weight, a target profit distribution weight is selected for each stakeholder terminal.

[0141] In one embodiment, first, the first power generation cost of the wind power supply end, the second power generation cost of the thermal power supply end, and the third power generation cost of the power sales end are determined by formulas (16) to (18).

[0142] The cost of power generation mainly includes the primary investment costs of all parties, operation and maintenance costs, and regulation costs. The greater the cost investment, the greater the expected benefits.

[0143]

[0144] Among them, E1 is the first power generation cost of wind power supply end; C W0 P is the unit power generation cost of wind power; W,t is the wind power generation power in period t.

[0145]

[0146] Among them, E2 is the second power generation cost of thermal power supply end, C G0 is the unit power generation cost of thermal power, P G,t is the thermal power generation power in period t.

[0147]

[0148] Among them, E3 is the third power generation cost at the electricity sales end, C V0 is the unit power generation cost at the electricity sales end, P V,t is the electricity generation power sold during period t.

[0149] The unit power generation costs at the three terminals are fixed costs, and the power generation power for electricity sales, thermal power generation power and wind power generation power are obtained in the above step S22 based on the optimal dynamic threshold.

[0150] Secondly, the first risk cost of the wind power supply end, the second risk cost of the thermal power supply end, and the third risk cost of the power sales end are determined by formulas (19) to (21).

[0151] Understandably, in order to account for risk loss costs, thermal power generators must reserve capacity for shortfalls, sacrificing some of their profit opportunities from participating in the electricity market and resulting in a loss of power generation profits. Wind farms can still participate in market transactions to generate corresponding profits, but this also impacts power generation profits. Virtual power plants and demand response may undermine customer satisfaction with electricity retailers, causing them to switch to other retailers or entities, resulting in a loss of customers, which also results in risk loss. The greater the risk loss costs, the greater the risk-sharing contribution and the higher the expected return.

[0152]

[0153] Among them, F1 is the first risk cost of wind power supply end; F2 is the second risk cost of thermal power supply end; F3 is the third risk cost of electricity sales end; P wt,m The amount of electricity that wind power supply side participates in the electricity market; P eload,m is the equivalent power of market electricity demand (converted according to the market load curve), p n is the market transaction price, k t,m is the market share of thermal power, k t,h is the average annual utilization hours of thermal power, IC tis the installed capacity of thermal power participating in the market, P VPP is the total adjustment amount of the virtual power plant, P DR is the total amount of demand response adjustment, P eload is the total amount of user load, P eload,n for n The user load at that time.

[0154] Finally, the first benefit distribution weight of the wind power supply end, the second benefit distribution weight of the thermal power supply end, and the third benefit distribution weight of the power sales end are determined, which are expressed as the following formula (22).

[0155]

[0156] x i The weight of the benefits allocated to the basic distribution of benefits; E i is the power generation cost and F i Risk cost, i=1 represents a wind farm, i=2 represents a thermal power plant, and i=3 represents a virtual power plant.

[0157] Fully consider the impact of cost input and risk sharing contribution of each stakeholder on benefit distribution, so that the benefit distribution results are more in line with the expected returns of each stakeholder.

[0158] S24, selecting target benefit contributions of each stakeholder end from the first benefit contribution of the wind power supply end, the second benefit contribution of the thermal power supply end, and the third benefit contribution of the power sales end.

[0159] Optionally, a Shapley value model is used to determine the first benefit dedication, the second benefit dedication, and the third benefit dedication.

[0160] In one embodiment, the benefit contribution is determined by formula (23) to formula (24).

[0161]

[0162] Where S is the coalition subset composed of different participants in the total set N; |S| is the number of participants in each subset; v(S)-v(S-{i}) is the contribution of the participation of subset participant i to the subset; W(|S|) is the distribution coefficient of subset S. φ(v) is the theoretical benefit that each stakeholder should receive.

[0163] S25, determining the interest distribution proportion of each stakeholder based on the target interest distribution weights of each stakeholder and the target interest contribution of each stakeholder.

[0164] Optionally, the target benefit distribution weights and the target benefit contributions of each stakeholder are weighted to obtain weighted results for each stakeholder. The weighted results for each stakeholder are summed to obtain a total weighted result. The ratio of each weighted result to the total weighted result is used as the benefit distribution proportion for each stakeholder.

[0165] Specifically, first, the profit distribution ratio of each stakeholder is determined by formula (25).

[0166]

[0167] Among them, φ(v) is the theoretical benefit that each stakeholder should obtain; x i Assign weights to the benefits of the basic distribution of benefits.

[0168] In one embodiment, the profit distribution result of the total profit of the profit participating terminals in the current period is obtained according to the determined profit distribution ratio, including one or more of the three candidate terminals: wind power supply terminal, thermal power supply terminal and power sales terminal.

[0169] Specifically, the results are distributed into the following three stages.

[0170] In the first stage of profit distribution, when the electricity sales end is a load aggregator, the supply of electricity is completed jointly by the wind power supply end and the thermal power supply end. Therefore, the profit distribution only involves the wind power supply end and the thermal power supply end. The specific profit distribution result is specifically expressed in the following formula (26).

[0171]

[0172] In the second stage of profit distribution, when the electricity sales end is a virtual power plant, the supply of electricity is completed jointly by the wind power supply end, the thermal power supply end, and the virtual power plant. Therefore, the profit distribution involves the wind power supply end, the thermal power supply end, and the virtual power plant. The specific profit distribution result is specifically expressed in the following formula (27).

[0173]

[0174] The third section of profit distribution is when the electricity sales end is a demand response unit. Since the wind power supply is greater than the demand, the electricity sales end needs to increase the electricity demand. Therefore, the electricity sales end needs to pay the electricity charges for the increased electricity demand and pay the demand response fees to the users who participate in the demand response. However, at the same time, the wind farm should pay its risk loss cost to the electricity sales company, which is the third risk cost of the electricity sales end expressed by the above formula (21).

[0175] Consider the contribution of each stakeholder to the overall benefits in all possible cooperation combinations, and distribute the total benefits based on the contribution to ensure the rationality of benefit distribution.

[0176] In one implementation, according to the above steps, the specific calculation process of the power resource benefit distribution method is as follows.

[0177] To validate the effectiveness of the proposed method, we selected daily load and wind power output data from a region in my country and constructed a regional network model encompassing wind power, thermal power, and electricity sales companies. Planning simulations were conducted on an Intel(R) Core(TM) i5-10400 computer running Windows 10 (64-bit), using MATLAB 2016b. The initial population size was set to 1000, and the number of iterations was set to 200.

[0178] The results of operation optimization with economy as the goal are greatly affected by energy prices. In order to better simulate and analyze the case, the wind power on-grid electricity price is selected as the purchase contract price of 0.365 yuan / kWh and the grid purchase price of 0.51 yuan / kWh.

[0179] First, based on regional characteristics, determine the key parameters of each stakeholder, such as Figure 5 As shown in the figure, the installed capacity of the wind turbine is 100 MW, the grid purchase ratio is 0.8, and the rated wind speed is 12 m / s. The installed capacity of the thermal power plant is 150 MW, the upper threshold is 20%, and the lower threshold is 0%. The upper threshold for the virtual power plant is 20%, the lower threshold is 0%, and the response speed is in minutes. The upper threshold for demand response is 20%, the lower threshold is 0%, and the response speed is in seconds.

[0180] Secondly, according to the above step S22, the corresponding optimal dynamic threshold and supply and demand balance result are obtained.

[0181] For example, the optimized threshold (i.e. the above-mentioned optimal dynamic threshold) is as follows Figure 6 As shown in Figure 2, the optimized threshold results are as follows: thermal power supply is 6.60%, virtual power plant is 19.49%, and demand response is 4.41%.

[0182] The supply and demand balance results are as follows Figure 7 Except for periods of low wind speed at 6:00 and 24:00, which resulted in a lack of wind in the system, the system has been performing a certain degree of demand response to increase wind energy absorption and improve the overall system revenue. At 6:00 and 24:00, the virtual power plant supplemented its output based on its own regulation capabilities, based on the cost comparison between thermal power and virtual power plants. At the same time, thermal power met the remaining load demand when the cost dropped to a certain level.

[0183] Third, according to the above step S24, the target interest contribution of each stakeholder is determined.

[0184] For example, first, the benefit contribution of wind power supply end is determined based on Shapley method, as follows: Figure 8 shown.

[0185] (1) The V(S) value of wind power is 284752.47, the V(S-{i}) value is 0.00, the |S| value is 1.00, and the W(|S|) value is 1 / 3.

[0186] (2) The V(S) value of wind power and thermal power is 298531.38, the V(S-{i}) value is 161918.81, the |S| value is 2.00, and the W(|S|) value is 1 / 6.

[0187] (3) The V(S) value of wind power and electricity sales is 285698.62, the V(S-{i}) value is 47495.43, the |S| value is 2.00, and the W(|S|) value is 1 / 6.

[0188] (4) The V(S) value of wind power, thermal power and electricity sales is 298542.00, the V(S-{i}) value is 201114.31, the |S| value is 3.00, and the W(|S|) value is 1 / 3.

[0189] Formula (23) is used to determine that the benefit that the wind power supply end should distribute according to the degree of contribution is 189,862.68 yuan.

[0190] Secondly, the benefit contribution of thermal power supply end is determined based on Shapley method, as follows: Figure 9 shown.

[0191] (1) The V(S) value of thermal power is 161918.81, the V(S-{i}) value is 0.00, the |S| value is 1.00, and the W(|S|) value is 1 / 3.

[0192] (2) The V(S) value of wind power and thermal power is 298531.38, the V(S-{i}) value is 284752.47, the |S| value is 2.00, and the W(|S|) value is 1 / 6.

[0193] (3) The V(S) value of thermal power and electricity sales is 201114.31, the V(S-{i}) value is 47495.43, the |S| value is 2.00, and the W(|S|) value is 1 / 6.

[0194] (4) The V(S) value of wind power, thermal power and electricity sales is 298542.00, the V(S-{i}) value is 285698.62, the |S| value is 3.00, and the W(|S|) value is 1 / 3.

[0195] Formula (23) is used to determine that the benefit that the thermal power supply end should distribute according to its contribution is RMB 86,153.69.

[0196] Finally, the profit contribution of the electricity sales end is determined based on the Shapley method, as follows: Figure 10 shown.

[0197] Among them, (1) the V(S) value at the power sales end is 47495.43, the V(S-{i}) value is 0.00, the |S| value is 1.00, and the W(|S|) value is 1 / 3.

[0198] (2) The V(S) value of wind power and electricity sales is 285698.62, the V(S-{i}) value is 284752.47, the |S| value is 2.00, and the W(|S|) value is 1 / 6.

[0199] (3) The V(S) value of thermal power and electricity sales is 201114.31, the V(S-{i}) value is 161918.81, the |S| value is 2.00, and the W(|S|) value is 1 / 6.

[0200] (4) The V(S) value of wind power, thermal power and electricity sales is 298542.00, the V(S-{i}) value is 298531.38, the |S| value is 3.00, and the W(|S|) value is 1 / 3.

[0201] Formula (23) is used to determine that the profit that should be distributed to the electricity sales end based on the degree of dedication is RMB 22,525.62.

[0202] Fourthly, the power generation cost and risk cost of each stakeholder are determined according to the above step S23. Figure 11 The profit distribution results determined based on the Shapley value method are shown.

[0203] The Shapley value allocation result of the wind power supply end is 189,862.68 yuan, accounting for 63.60%, and the risk cost is 156,528.01 yuan, accounting for 36.66%.

[0204] The Shapley value distribution result of the thermal power supply end is 86,153.69 yuan, accounting for 28.86%, and the risk cost is 89,163.09 yuan, accounting for 20.88%.

[0205] The Shapley value distribution result at the electricity sales end is 22,525.62 yuan, accounting for 7.55%, and the risk cost is 181,242.57 yuan, accounting for 42.45%.

[0206] The above results show that, based on the Shapley value method, the profit distribution of each system alliance is: power sales companies (22525.62) < thermal power (86153.69) < wind power (189862.68). This demonstrates that wind power makes the greatest contribution to the system, while power sales companies make the least. Furthermore, a comparison of risk costs reveals significant differences between the risk costs and risk cost proportions borne by each company, their resulting profit distribution, and their profit distribution proportions. Therefore, the distribution results need to be revised.

[0207] Fifth, according to the above step S25, the profit distribution ratio of each stakeholder is determined based on the target profit distribution weight of each stakeholder and the target profit contribution of each stakeholder, and the distribution result is corrected. Figure 12 Determine the profit distribution results.

[0208] The total revenue of the wind power supply end is 214,380.83 yuan, the cost is 9,207.53 yuan, and the net profit is 205,173.30 yuan.

[0209] The total revenue of the thermal power supply end was 55,870.04 yuan, the cost was 6,023.2 yuan, and the net profit was 49,846.84 yuan.

[0210] The total revenue from the virtual power plant on the electricity sales side was 5,412.24 yuan, with a cost of 594.44 yuan. The total revenue from demand response on the electricity sales side was 22,878.89 yuan, with a cost of 2,512.83 yuan. The net revenue on the electricity sales side was 25,183.86 yuan.

[0211] The above results show that the total revenue of the wind power supply side increased by 12.91% after the adjustment. This is mainly because wind power, in addition to making the greatest contribution during operation, also bears the highest risk costs compared to thermal power supply and electricity sales. Therefore, its allocated revenue increased in this power resource benefit allocation method. In addition, because the risk costs borne by the thermal power supply side are lower than those of the wind power supply side, its final net revenue is 55,870.04 yuan. Although this is lower than the allocation based on the original Shapley value method, in the current context, the revenue of the thermal power supply side is additional to the normal operation of thermal power. For thermal power supply sides, this revenue is sufficient to make them have sufficient enthusiasm to participate in the power system. Compared with wind power and thermal power, the power sales company obtains the lowest profit in this distribution model, which increases from 22,525.62 yuan to 28,291.13 yuan, an increase of 25.60%. This is mainly due to its high risk cost. After comprehensive calculation, its value has increased. Its overall net profit is 25,183.86 yuan. Therefore, the power sales end is also motivated enough to participate in this power system.

[0212] In order to realize the above functions, the electric power resource benefit allocation device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0213] The present application also provides a method Figure 13 The power resource benefit distribution device shown includes: a determination unit 501, a solution unit 502, a weight distribution unit 503, a benefit contribution distribution unit 504 and a benefit proportion distribution unit 505.

[0214] The determining unit 501 is configured to determine the stakeholder terminals in the current period, where the stakeholder terminals include one or more of the three candidate terminals: a wind power supply terminal, a thermal power supply terminal, and a power sales terminal;

[0215] Solving unit 502 is configured to solve the variable constraint relationship constructed by the parameter variables of the three candidate terminals with the goal of minimizing the total operating cost in different operating periods of the three candidate terminals, and obtain the sales power generation power, thermal power generation power, and wind power generation power of the power sales terminal, thermal power supply terminal, and wind power supply terminal in each operating period respectively;

[0216] The weight allocation unit 503 is configured to determine a first profit distribution weight for the power sales end, a second profit distribution weight for the thermal power supply end, and a third profit distribution weight for the wind power supply end based on the power sales power, thermal power generation power, and wind power generation power in the current period, so as to obtain target profit distribution weights for each stakeholder end;

[0217] The profit contribution allocation unit 504 is configured to select target profit contributions of each stakeholder terminal from the first profit contribution of the electricity sales terminal, the second profit contribution of the thermal power supply terminal, and the third profit contribution of the wind power supply terminal;

[0218] The profit proportion allocating unit 505 is configured to determine the profit distribution proportion of each stakeholder terminal according to each target profit distribution weight of each stakeholder terminal and each target profit contribution of each stakeholder terminal.

[0219] As an implementation mode, the solving unit 502 is specifically configured to solve the variable constraint relationship constructed by the parameter variables of the three candidate ends with the goal of minimizing the total operating cost in different operating periods of the three candidate ends, and obtain the sales power generation power, thermal power generation power and wind power generation power of the power sales end, thermal power supply end and wind power supply end in each operating period respectively, including: constructing a dynamic constraint relationship based on the dynamic response threshold of the virtual power plant of the power sales end when the power sales end is in a state of reducing demand response, the dynamic response threshold of the power sales end when the power sales end is in a state of increasing demand response and the dynamic output threshold of the thermal power supply end, and constructing a static constraint relationship based on the static variables of the three candidate ends; the variable constraint relationship includes a dynamic constraint relationship and a static constraint relationship; with the goal of minimizing the total operating cost in different operating periods of the wind power supply end, thermal power supply end and power sales end, the variable constraint relationship is solved to obtain the sales power generation power, thermal power generation power and wind power generation power that constitute the lowest total operating cost in each operating period.

[0220] As an implementation mode, the weight allocation unit 503 is specifically configured to determine the first benefit distribution weight of the wind power supply end, the second benefit distribution weight of the thermal power supply end, and the third benefit distribution weight of the power sales end according to the power sales power generation power, thermal power generation power, and wind power generation power in the current time period, so as to obtain the target benefit distribution weights of each stakeholder end, including: determining the first power generation cost and the first risk cost of the wind power supply end, the second power generation cost and the second risk cost of the thermal power supply end, and the third power generation cost and the third risk cost of the power sales end according to the power sales power generation power, thermal power generation power, and wind power generation power in the current time period; determining the first benefit distribution weight of the wind power supply end, the second benefit distribution weight of the thermal power supply end, and the third benefit distribution weight of the power sales end according to the cost proportions corresponding to the first power generation cost and the first risk cost, the second power generation cost and the second risk cost, and the third power generation cost and the third risk cost; and selecting the target benefit distribution weights of each stakeholder end from the first benefit distribution weight, the second benefit distribution weight, and the third benefit distribution weight.

[0221] As an implementation method, the interest ratio allocation unit 505 is specifically configured to determine the interest distribution ratio of each stakeholder end based on the target interest distribution weights of each stakeholder end and the target interest dedication of each stakeholder end, including: weighting the target interest distribution weights and the target interest dedication of each stakeholder end respectively to obtain the weighted results of each stakeholder end; summing the weighted results of each stakeholder end to obtain the total weighted result; and taking the ratio between the weighted results of each stakeholder end and the total weighted result as the interest distribution ratio of each stakeholder end.

[0222] As an implementation manner, the benefit contribution allocation unit 504 is specifically configured such that the power resource benefit allocation method further comprises: using a Shapley value model to determine the first benefit contribution, the second benefit contribution, and the third benefit contribution.

[0223] As an implementation method, the solving unit 502 is specifically configured as follows: the variable constraint relationship includes: dynamic response threshold range constraint, dynamic output threshold range constraint, power balance constraint, wind curtailment constraint, new energy proportion constraint, thermal power output constraint, virtual power plant output constraint and power sales company demand response output constraint; among them, the power balance constraint represents the balance between power supply and power demand, and the wind curtailment constraint represents that the wind curtailment amount of the wind turbine generator set is within a preset range throughout the year.

[0224] As an implementation method, the solving unit 502 is specifically configured to solve the variable constraint relationship constructed by the parameter variables of the three candidate terminals, and obtain the sales power generation power, thermal power generation power and wind power generation power of the power sales terminal, thermal power supply terminal and wind power supply terminal in each operating period, including: integrating the multi-channel Tent mapping MCTent into the quantum particle swarm optimization QPSO algorithm to obtain the MTQPSO algorithm; using the MTQPSO algorithm, the total operating cost is solved under the variable constraint relationship constructed by the parameter variables of the three candidate terminals, and the sales power generation power, thermal power generation power and wind power generation power with the lowest total operating cost are obtained.

[0225] Regarding the device in the above embodiment, the specific manner in which each unit module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0226] Figure 14 This application provides a power resource benefit distribution device. Figure 14 The power resource benefit allocation device 60 may include at least one processor 601 and a memory 603 for storing processor-executable instructions. The processor 601 is configured to execute instructions in the memory 603 to implement the power resource benefit allocation method in the following embodiments.

[0227] In addition, the prediction device 60 may further include a communication bus 602 , at least one communication interface 604 , an input device 606 , and an output device 605 .

[0228] The processor 601 may be a central processing unit (CPU), a microprocessor unit, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0229] The communication bus 602 may include a pathway for transmitting information between the aforementioned components.

[0230] The communication interface 604 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc.

[0231] The input device 606 is used to receive input signals and the output device 605 is used to output signals.

[0232] The memory 603 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be independent and connected to the processing unit via a bus. The memory may also be integrated with the processing unit.

[0233] The memory 603 is used to store instructions for executing the solution of the present application, and the execution is controlled by the processor 601. The processor 601 is used to execute the instructions stored in the memory 603, thereby realizing the functions of the method of the present application.

[0234] In a specific implementation, as an embodiment, the processor 601 may include one or more CPUs, such as Figure 14 CPU0 and CPU1 in.

[0235] In a specific implementation, as an embodiment, the prediction device 60 may include multiple processors, such as Figure 14 6 and 607. Each of these processors may be a single-CPU processor or a multi-CPU processor. A processor herein may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0236] The prediction device Figure 14 The system includes a processor 601 and a memory 603 for storing executable instructions for the processor 601. The processor 601 is configured to execute the executable instructions to implement a method for constructing a virtual power plant source-load prediction model according to any of the above-described possible implementations. The methods can achieve the same technical effects and are not described here in detail to avoid repetition.

[0237] The present application also provides a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by a processor of a control device or a control apparatus, the control device or the control apparatus can execute the power resource benefit allocation method according to any of the above-described possible implementations. The above-described methods achieve the same technical effects and are not further described here to avoid repetition.

[0238] The present application also provides a computer program product, including a computer program or instructions, which are executed by a processor to implement the power resource benefit allocation method according to any of the above-described possible implementations. The computer program or instructions can achieve the same technical effects, and to avoid repetition, they are not further described here.

[0239] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0240] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for allocating benefits of electric power resources, characterized in that: Applied to a power system comprising a wind power supply terminal, a thermal power supply terminal, and a power sales terminal, the method comprises: Determine a stakeholder terminal for the current period, wherein the stakeholder terminal includes one or more of the three candidate terminals: the wind power supply terminal, the thermal power supply terminal, and the power sales terminal; With the goal of minimizing the total operating cost of the three candidate terminals in different operating periods, the variable constraint relationship constructed by the parameter variables of the three candidate terminals is solved to obtain the sales power generation power, thermal power generation power, and wind power generation power of the power sales terminal, the thermal power supply terminal, and the wind power supply terminal in each operating period respectively; Determine, based on the electricity sales power generation power, the thermal power generation power, and the wind power generation power in the current period, a first profit distribution weight of the wind power supply end, a second profit distribution weight of the thermal power supply end, and a third profit distribution weight of the electricity sales end, so as to obtain target profit distribution weights of each stakeholder end; Selecting target benefit contributions of each stakeholder from the first benefit contribution of the wind power supply end, the second benefit contribution of the thermal power supply end, and the third benefit contribution of the power sales end; The profit distribution proportion of each stakeholder terminal is determined according to each target profit distribution weight of each stakeholder terminal and each target profit contribution of each stakeholder terminal.

2. The method for allocating benefits of electric power resources according to claim 1, characterized in that: The method aims to minimize the total operating cost of the three candidate terminals in different operating periods, solves the variable constraint relationship constructed by the parameter variables of the three candidate terminals, and obtains the power sales power, thermal power generation power, and wind power generation power of the power sales terminal, the thermal power supply terminal, and the wind power supply terminal in each operating period, including: A dynamic constraint relationship is constructed based on the dynamic response threshold of the virtual power plant of the power sales end when the power sales end is in a state of reducing demand response, the dynamic response threshold of the power sales end when the power sales end is in a state of increasing demand response, and the dynamic output threshold of the thermal power supply end, and a static constraint relationship is constructed based on the static variables of the three candidate ends; the variable constraint relationship includes the dynamic constraint relationship and the static constraint relationship; With the goal of minimizing the total operating cost in different operating periods of the wind power supply end, the thermal power supply end and the power sales end, the variable constraint relationship is solved to obtain the power sales power, the thermal power generation power and the wind power generation power that constitute the lowest total operating cost in each operating period.

3. The method for allocating benefits of electric power resources according to claim 1, characterized in that: The determining, based on the electricity sales power generation power, the thermal power generation power, and the wind power generation power in the current period, a first benefit distribution weight of the wind power supply end, a second benefit distribution weight of the thermal power supply end, and a third benefit distribution weight of the electricity sales end, to obtain target benefit distribution weights of the respective stakeholder ends, includes: Determining, respectively, based on the electricity sales power generation power, the thermal power generation power, and the wind power generation power in the current time period, a first power generation cost and a first risk cost of the wind power supply end, a second power generation cost and a second risk cost of the thermal power supply end, and a third power generation cost and a third risk cost of the electricity sales end; Determine, according to the cost proportions corresponding to the first power generation cost and the first risk cost, the second power generation cost and the second risk cost, and the third power generation cost and the third risk cost, a first profit distribution weight of the wind power supply end, a second profit distribution weight of the thermal power supply end, and a third profit distribution weight of the power sales end; From the first benefit distribution weight, the second benefit distribution weight and the third benefit distribution weight, each target benefit distribution weight of each stakeholder is selected.

4. The method for allocating benefits of electric power resources according to claim 1, characterized in that: Determining the profit distribution proportion of each stakeholder terminal according to each target profit distribution weight of each stakeholder terminal and each target profit contribution of each stakeholder terminal includes: Weighting the target benefit distribution weights and the target benefit contributions of the stakeholder terminals respectively to obtain weighted results of the stakeholder terminals; Summing up the weighted results of the stakeholder groups to obtain a total weighted result; The ratio between the weighted results of each stakeholder and the total weighted result is used as the profit distribution ratio of each stakeholder.

5. The method for allocating benefits of electric power resources according to claim 1, characterized in that: The method further comprises: The Shapley value model is used to determine the first benefit dedication, the second benefit dedication, and the third benefit dedication.

6. The method for allocating benefits of electric power resources according to any one of claims 2 to 5, characterized in that: The variable constraint relationship includes the dynamic response threshold range constraint, the dynamic output threshold range constraint, the power balance constraint, the wind curtailment constraint, the new energy proportion constraint, the thermal power output constraint, the virtual power plant output constraint and the power sales company demand response output constraint; wherein, the power balance constraint represents the balance between power supply and power demand, and the wind curtailment constraint represents that the wind curtailment amount of the wind turbine generator set is within a preset range throughout the year.

7. The method for allocating benefits of electric power resources according to any one of claims 1 to 5, characterized in that: Solving the variable constraint relationship constructed by the parameter variables of the three candidate terminals to obtain the electricity sales power, thermal power generation power, and wind power generation power of the electricity sales terminal, the thermal power supply terminal, and the wind power supply terminal in each of the operating periods, respectively, includes: The multi-channel Tent mapping MCTent is integrated into the quantum particle swarm optimization QPSO algorithm to obtain the MTQPSO algorithm; The MTQPSO algorithm is used to solve the total operating cost under the variable constraint relationship constructed by the parameter variables of the three candidate terminals, and the electricity sales power generation power, the thermal power generation power and the wind power generation power under the lowest total operating cost are obtained.

8. An electric power resource benefit distribution device, characterized in that: Applied to a power system comprising a wind power supply terminal, a thermal power supply terminal, and a power sales terminal, the device comprises: A determining unit is configured to determine a stakeholder terminal in a current period, wherein the stakeholder terminal includes one or more of the three candidate terminals: the wind power supply terminal, the thermal power supply terminal, and the power sales terminal; a solving unit configured to solve a variable constraint relationship constructed by parameter variables of the three candidate terminals with the goal of minimizing the total operating cost in different operating periods of the three candidate terminals, and obtain the power sales power, thermal power generation power, and wind power generation power of the power sales terminal, the thermal power supply terminal, and the wind power supply terminal in each operating period, respectively; a weight distribution unit configured to determine, based on the electricity sales power generation power, the thermal power generation power, and the wind power generation power in the current period, a first profit distribution weight for the wind power supply end, a second profit distribution weight for the thermal power supply end, and a third profit distribution weight for the electricity sales end, so as to obtain target profit distribution weights for each of the stakeholder ends; a benefit contribution allocating unit configured to select target benefit contributions of each of the stakeholder terminals from the first benefit contribution of the wind power supply terminal, the second benefit contribution of the thermal power supply terminal, and the third benefit contribution of the power sales terminal; The profit proportion distribution unit is configured to determine the profit distribution proportion of each stakeholder terminal according to each target profit distribution weight of each stakeholder terminal and each target profit contribution of each stakeholder terminal.

9. A power system, characterized in that: The power system includes a wind power supply end, a thermal power supply end and a power sales end, and the power system is configured to execute the power resource benefit distribution method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the power resource benefit allocation method according to any one of claims 1 to 7.

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