Hybrid Game Optimization Method and System for Multi-Virtual Power Plants Considering the Uncertainty of Wind and Photovoltaic

Abstract

The present invention relates to the technical field of multi-virtual power plant optimal scheduling, and discloses a hybrid game optimization method and system for multi-virtual power plants considering the uncertainty of wind and light. The method includes: based on the game relationship between the power grid operator and the virtual power plant alliance, constructing a joint model combining the master-slave game and the cooperative game; aiming at the uncertainty of the output of wind and light generating units, through a data-driven two-stage distributed robust optimization method, selecting typical output scenarios of wind and light generating units and calculating the discrete scenario distribution values, calculating the probability distribution of the output scenarios of wind and light generating units in combination with historical data and the confidence formula, and constructing a probability model of wind and light uncertainty; based on the joint model and the probability model of wind and light uncertainty, reducing the total cost and carbon emissions of the virtual power plant alliance through collaborative optimal scheduling. The present invention enhances the response ability to the carbon trading market and provides strong support for promoting low-carbon transformation and achieving the goals of energy conservation and emission reduction.

Description

Technical Field

[0001] The present invention relates to an optimization scheduling method for multiple virtual power plants under multi-variety market transactions, and belongs to the technical field of operation and control of power systems containing various energy forms. Background Art

[0002] As an independent entity that can aggregate distributed resources within a certain area, a virtual power plant (VPP) aggregates distributed new energy, energy storage, and various loads into the power system, and can fully exploit the flexibility on the source-load-storage side, thereby improving energy utilization efficiency.

[0003] In the traditional power market, transactions between power producers and grid operators are usually carried out through a simple sales contract relationship. However, with the popularization of VPP technology, the relationships among market participants have become more complex. Multiple VPPs can be jointly operated through a cooperative alliance to achieve resource sharing and complementarity, thereby reducing costs and improving overall benefits, and forming stronger competitiveness in the market. In this cooperation mode, energy interaction needs to be coordinated among the members of the virtual power plant alliance, and at the same time, they can also play games with grid operators to optimize the purchase and sale prices of electricity. Therefore, a master-slave game is formed between the VPP alliance and the grid operator, and a cooperative game is formed within the alliance. Moreover, incorporating a carbon trading mechanism and a green certificate trading mechanism into the VPP can reduce the carbon emissions of the VPP. However, due to the presence of new energy power generation units within the VPP, their output is volatile and random, which in turn leads to inaccurate calculation of green certificate costs and inability to obtain market-adapted cost results, making the scheduling decision of the VPP complex, that is, the prior art does not fully consider the impact of wind-solar uncertainty on VPP decision-making.

[0004] Currently, Xu Huihui et al. proposed a hybrid game optimization scheduling method for multiple virtual power plants considering green certificate-carbon trading. First, a hybrid game optimization framework is established to analyze the game relationship between the grid operator and the virtual power plant alliance. Then, a hybrid game optimization model with the grid operator as the leader and the virtual power plant as the follower is constructed, and based on the Nash bargaining theory, minimizing the cost of the virtual power plant is equivalent to two sub-problems: maximizing the alliance benefit and allocating the cooperative income. Finally, the bisection method and the alternating direction multiplier method are combined to solve the hybrid game model. However, this method has the following limitations:

[0005] 1. In terms of the uncertainty of wind and light. When analyzing the output of wind and light generating units, this method only considers the constraint conditions of the maximum and minimum outputs, without considering the inherent uncertainty factors of wind and light power generation. In fact, due to the influence of spatio-temporal differences, the output of wind and light generating units is volatile and unpredictable. This output imbalance will lead to deviations in calculating costs, and ultimately affect the calculation accuracy of the objective function, resulting in a difference between the optimization result and the actual working conditions.

[0006] 2. In terms of the green certificate trading market. This method adopts a fixed green certificate price, which fails to reflect the dynamic impact of market supply and demand changes on prices, and does not conform to the price fluctuation characteristics of the actual green certificate market. At the same time, it does not consider the time value difference of green certificate trading, lacks the distinction between current selling and future selling strategies, and leads to the inability to optimize intertemporal benefits. The association between the green certificate supply and the renewable energy output is too simplified, only established through the liability weight coefficient (β), and fails to fully reflect the direct impact of the volatility of wind and light power generation on the green certificate supply. These defects make the model have obvious deficiencies in aspects such as price formation mechanism, time dimension, and market interaction.

[0007] 3. In terms of the carbon trading model. The linear calculation method of this method for the carbon trading cost model is too simplified. It neither considers the market volatility of carbon prices, nor sets the carbon emission quota and coefficient as fixed values. At the same time, the carbon capture volume is disconnected from the trading mechanism. Summary of the Invention

[0008] To solve the above problems, the present invention proposes a multi-virtual power plant hybrid game optimization method and system considering wind and light uncertainty. Multiple VPPs form a cooperative alliance and form a master-slave game with the grid operator, optimize the purchase and sale electricity prices of the alliance from the operator and the electricity interaction price among the alliance members, and use the Nash bargaining theory to allocate the cooperative benefits of the alliance, which is equivalent to two sub-problems of maximizing the alliance benefits and allocating the cooperative benefits. In addition, considering that the VPP contains renewable energy with volatility, wind and light uncertainty is considered in the model, and a distributionally robust optimization (DRO) method under comprehensive norm constraints is proposed.

[0009] The technical solution adopted by the present invention is as follows:

[0010] A multi-virtual power plant hybrid game optimization method considering wind and light uncertainty, including:

[0011] Based on the game relationship between the grid operator and the virtual power plant alliance, construct a joint model combining the master-slave game and the cooperative game;

[0012] In view of the output uncertainty of wind-solar generating units, a data-driven two-stage distributed robust optimization method is adopted to select typical output scenarios of wind-solar generating units and calculate the discrete scenario distribution values. The probability distribution of the output scenarios of wind-solar generating units is calculated by combining historical data and confidence formulas, and a wind-solar uncertainty probability model is constructed.

[0013] Based on the joint model and the wind-solar uncertainty probability model, the total cost and carbon emissions of the virtual power plant alliance are reduced through collaborative optimal scheduling.

[0014] Furthermore, based on the game relationship between the power grid operator and the virtual power plant alliance, a joint model combining the master-slave game and the cooperative game is constructed, including:

[0015] Establish a leader - power grid operator model in the master-slave game, with the maximum profit of the power grid operator itself as the objective function;

[0016] Establish a follower - virtual power plant alliance model in the master-slave game, with the minimum cost of the members of the virtual power plant alliance itself as the objective function;

[0017] Establish a Nash bargaining model of the virtual power plant alliance based on cooperative game, and respond to the decision of the power grid operator through cooperation, with the maximum overall benefit as the objective function.

[0018] Furthermore, in the virtual power plant alliance model, the cost of the members of the virtual power plant alliance itself at least includes the carbon trading cost, and the calculation method of the carbon trading cost includes:

[0019]

[0020] Among them, is the carbon quota of the unit, T is the total number of time periods, n is the number of virtual power plants participating in the game, is the output electric power of the unit of the virtual power plant at time t, is the power supply reference value, is the heating correction coefficient; is the actual carbon emission of the unit, is the carbon emission factor of the unit; is the heating ratio; is the carbon trading cost, is the carbon trading price.

[0021] Furthermore, in the virtual power plant alliance model, the cost of the members of the virtual power plant alliance itself at least includes the green certificate trading cost, and the calculation method of the green certificate trading cost includes:

[0022]

[0023] Among them, is the cost of green certificate trading; is the current green certificate price, is the future green certificate price; is the number of green certificates that can be sold, is the number of green certificates sold currently, is the number of green certificates sold in the future; T is the total number of time periods; is the estimated price ratio of green certificates in the future market.

[0024] Furthermore, in the virtual power plant alliance model, the constraint conditions at least include green certificate constraints, and the green certificate constraints include:

[0025]

[0026] Among them, is the green certificate price in the market, is the initial green certificate price; and are two positive parameters of the inverse function of the Cournot model price; is the number of green certificates that can be sold on the current day; is the green certificate trading price ratio coefficient calculated based on historical data; is the new energy consumption ratio in this region; is the total number of green certificates in a day; is t the output of the wind turbine of the virtual power plant at time is t the output of the photovoltaic of the virtual power plant at time is the dispatching unit duration; is the number of green certificates sold currently, is the number of green certificates sold in the future.

[0027] Furthermore, for the uncertainty of the output of wind-solar generating units, through a data-driven two-stage distributed robust optimization method, typical output scenarios of wind-solar generating units are selected and the discrete scenario distribution values are calculated, including:

[0028] Through the K-means algorithm, among the actually obtained M output samples of wind-solar generating units, X typical output scenarios are selected to represent the output uncertainty of wind-solar generating units, and the discrete scenario probability distribution values of the output of each wind-solar generating unit are obtained ( k = 1, 2, …, X );

[0029] Construct according to the discrete scenario probability distribution value The feasible region of probability distribution values centered around the 1-norm and ∞-norm sets as constraints Ω :

[0030]

[0031] wherein, is the actual value of the i th probability distribution; is the probability error value under the 1-norm distribution, is the probability error value under the ∞-norm distribution.

[0032] Furthermore, calculating the probability distribution of the output scenarios of wind-solar generating units by combining historical data and confidence formulas includes:

[0033] The probability of the output scenarios of wind-solar generating units needs to satisfy the following inequality requirements:

[0034]

[0035] wherein, is the probability of the inequality in holding, is the initial probability value screened from historical data, M is the number of output samples of wind-solar generating units;

[0036] Let the right sides of the above two inequalities be equal to the confidence levels and that can make them hold, and we get:

[0037]

[0038] Thus, the probability distribution of the output scenarios of wind-solar generating units is obtained.

[0039] Furthermore, reducing the total cost and carbon emissions of the virtual power plant alliance through collaborative optimal scheduling based on the joint model and the wind-solar uncertainty probability model includes:

[0040] According to the Nash bargaining model of the virtual power plant alliance based on cooperative game in the joint model, combined with the wind-solar uncertainty probability model for collaborative optimal scheduling, a hybrid game model is obtained:

[0041]

[0042] wherein, x is the transaction decision variable in the Nash bargaining model of the virtual power plant alliance, y is the adjustable decision variable; is the purchase and sale cost of the virtual power plant alliance, and the superscript represents the transpose operation; is the operating cost corresponding to the worst-case scenario probability distribution; a, b, c, d, g, w, A, G, E, F, U, V is the coefficient matrix, is the mathematical representation of the uncertainty of wind and light.

[0043] Furthermore, for the hybrid game model, the master-slave game model is iteratively solved by the bisection method; the cooperative game model, i.e., the Nash bargaining model of the virtual power plant alliance, is solved by the CCG-ADMM algorithm (Constrained Generation - Alternating Direction Method of Multipliers).

[0044] A multi-virtual power plant hybrid game optimization system considering the uncertainty of wind and light includes:

[0045] A joint model construction module configured to construct a joint model combining the master-slave game and the cooperative game based on the game relationship between the grid operator and the virtual power plant alliance;

[0046] A wind and light uncertainty probability model construction module configured to, for the uncertainty of the output of wind and light generating units, select typical output scenarios of wind and light generating units and calculate the discrete scenario distribution values through a data-driven two-stage distributionally robust optimization method, calculate the probability distribution of the output scenarios of wind and light generating units in combination with historical data and the confidence formula, and construct a wind and light uncertainty probability model;

[0047] A collaborative optimal scheduling module configured to, based on the joint model and the wind and light uncertainty probability model, reduce the total cost and carbon emissions of the virtual power plant alliance through collaborative optimal scheduling.

[0048] The beneficial effects of the present invention are as follows:

[0049] The present invention combines the game relationship between the grid operator and the virtual power plant alliance to construct an optimization framework combining the master-slave game and the cooperative game; considering the uncertainty of new energy such as wind and light in the virtual power plant alliance, a data-driven DRO model is proposed. Through optimal scheduling, the total cost and carbon emissions of the virtual power plant alliance can be effectively reduced. The present invention uses the Nash bargaining theory to fairly distribute the cooperative benefits, promotes the efficient cooperation of the virtual power plant alliance, and realizes the reasonable distribution of interests. Finally, while reducing costs, the members of the virtual power plant alliance enhance their response ability to the carbon trading market, providing strong support for promoting the low-carbon transformation and achieving the energy conservation and emission reduction goals.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] 1. In terms of the uncertainty of wind and light: The present invention adopts a data-driven two-stage distributionally robust optimization (DRO) method for two typical distributed energy sources, namely wind turbines and photovoltaic units. First, the K-means clustering algorithm is used to select X typical output scenarios from a number of actual wind and light output samples to represent the uncertainty of wind and light output. Through these typical scenarios, the discrete scenario probability distribution values corresponding to each wind and light output can be obtained. Subsequently, based on the probability distribution values screened from historical data and combined with the confidence formula, the probability distribution of the wind and light output scenarios is further calculated. Based on the above results, the minimum operating cost of the alliance under the worst-case scenario can be calculated.

[0052] 2. In terms of the green certificate trading market: In the present invention, a virtual power plant (VPP) can choose to sell the green certificates obtained through new energy power generation on the same day or at a certain future time. The relationship between the price and output of green certificates satisfies the inverse demand function relationship in economics. In the process of pursuing the maximum benefit of the VPP, the Cournot model based on the quantity competition theory is adopted to solve the quantity of green certificates sold on the same day and in the future. Under this model, the decision-making of the quantity of green certificates sold can reflect the game between market supply and demand, thus realizing the optimization of the overall revenue.

[0053] 3. In terms of the carbon trading model: The present invention dynamically adjusts the carbon emissions of combined heat and power through the heating correction coefficient and heating ratio, accurately distinguishes the calculation of carbon quotas and actual emissions, and establishes a market-based trading framework based on real-time carbon prices. At the same time, the unit differential modeling is realized by using the power supply reference value and emission factor, comprehensively improving the accuracy and market applicability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flowchart of a multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light according to Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to have a clearer understanding of the technical features, objectives, and effects of the present invention, the specific embodiments of the present invention will now be described. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention.

[0056] Embodiment 1

[0057] As Figure 1 shown, this embodiment provides a multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light, including:

[0058] Based on the game relationship between the power grid operator and the virtual power plant alliance, a joint model combining the master-slave game and the cooperative game is constructed;

[0059] Aiming at the uncertainty of the output of wind and photovoltaic generating units, through a data-driven two-stage distributed robust optimization method, typical output scenarios of wind and photovoltaic generating units are selected and the discrete scenario distribution values are calculated. Combining historical data and the confidence formula, the probability distribution of the output scenarios of wind and photovoltaic generating units is calculated, and a probability model of wind and photovoltaic uncertainty is constructed;

[0060] Based on the joint model and the probability model of wind and photovoltaic uncertainty, the total cost and carbon emissions of the virtual power plant alliance are reduced through coordinated optimal scheduling.

[0061] Preferably, based on the game relationship between the power grid operator and the virtual power plant alliance, a joint model combining the master-slave game and the cooperative game is constructed, including:

[0062] Establish a leader - power grid operator model in the master-slave game, with the maximum profit of the power grid operator itself as the objective function;

[0063] Establish a follower - virtual power plant alliance model in the master-slave game, with the minimum cost of the members of the virtual power plant alliance itself as the objective function;

[0064] Establish a Nash bargaining model of the virtual power plant alliance based on cooperative game, and respond to the decision of the power grid operator through cooperation, with the maximum overall benefit as the objective function.

[0065] Specifically, the multi-virtual power plant hybrid game optimization method of this embodiment can be implemented by the following steps:

[0066] (1) Establish a leader - power grid operator model in the master-slave game.

[0067] (1-1) Objective function: Aiming at the maximum profit of the power grid operator itself, it specifically includes the costs and revenues of electricity trading with the superior power grid and the VPP alliance.

[0068] (1)

[0069] In the formula, , are respectively t the grid electricity price and the on-grid electricity price at time , are respectively t the electricity purchase and sale prices of the VPP alliance from / to the power grid operator at time , are respectively t the electricity purchase and sale volumes of the VPP alliance from / to the power grid operator at time T is the total number of time periods.

[0070] Among them, the electricity purchase and sale volume of the VPP alliance is:

[0071] (2)

[0072] (3)

[0073] In the formula, , are respectively the electricity purchase and sale volumes of the VPP i at t time to the grid operator; n is the number of VPPs participating in the game.

[0074] Constraints (1-2).

[0075] The purchase and sale electricity prices set by the grid operator should be within a certain range:

[0076] (4)

[0077] (5)

[0078] In the formula: , are respectively the upper and lower limits of the VPP alliance's electricity purchase price; , are respectively the upper and lower limits of the VPP alliance's electricity sale price.

[0079] In order to maximize its own benefits, the grid operator will set the highest price for the alliance's electricity purchase and the lowest price for the alliance's electricity sale. To avoid this problem, the average value constraint of the alliance's purchase and sale electricity prices is set as:

[0080] (6)

[0081] (7)

[0082] In the formula, , are respectively the average values of the VPP's electricity purchase and sale prices.

[0083] (2) Establish the follower - VPP alliance model in the master - slave game.

[0084] It should be noted that in this embodiment, a common VPP is taken as an example for illustration. This VPP mainly includes wind turbines (WT), photovoltaic units (PV), combined heat and power units (CHP), gas boilers (GB), electrical energy storage (EES), and thermal energy storage tanks (TES).

[0085] (2-1) Objective function: The members of the VPP alliance aim to minimize their own costs, specifically including the costs of buying and selling electricity, the interaction costs between VPPs, carbon trading costs, gas costs, demand response costs, and energy storage operation and maintenance costs:

[0086] (8)

[0087] In the formula, and represent the costs of buying and selling electricity, the interaction costs between VPPs, carbon trading costs, green certificate trading costs, gas purchase costs, demand response costs, and energy storage operation and maintenance costs respectively.

[0088] (2-1-1) Follower's cost of buying and selling electricity:

[0089] (9)

[0090] (2-1-2) Interaction costs between VPPs:

[0091] (10)

[0092] Among them, is t the electricity trading price between VPP i and VPP j at time ; t is i the electricity trading volume between VPP j and VPP

[0093] at time

[0094] In the carbon market, VPP can obtain a certain amount of carbon quota according to the type and installed capacity of power generation equipment, and then choose to buy or sell carbon emission rights from the carbon market in combination with the actual carbon emissions.

[0095] (11)

[0096] (12)

[0097] (13)

[0098] (14)

[0099] Among them, , are the carbon quota of the unit and the actual carbon emissions of the unit, respectively; is the CHP output electric power of VPPi at time t; is the power supply reference value; is the heat supply correction coefficient; is the heat supply ratio; is the carbon emission factor of the unit; is the carbon trading cost, is the carbon trading price.

[0100] (2 - 1 - 4) Green certificate trading cost.

[0101] In the green certificate market, the VPP can choose to sell the green certificates obtained through new - energy power generation on the same day or in the future on the same day.

[0102] (15)

[0103] (16)

[0104] (17)

[0105] Among them, is the green certificate trading cost; , are the numbers of green certificates sold currently and in the future, respectively; , are the prices of green certificates currently and in the future, respectively; is the estimated price ratio of green certificates in the future market; is the number of green certificates that can be sold.

[0106] (2 - 1 - 5) Gas purchase cost:

[0107] (18)

[0108] Among them, is the unit gas purchase cost, are the gas consumption of CHP and GB of VPPi at time t, respectively.

[0109] (2 - 1 - 6) Demand response cost:

[0110] (19)

[0111] Among them, is the cost of electric load transfer, are the costs of electric load curtailment and heat load curtailment respectively; are respectively t the VPP at time i curtailable and transferable electric loads, is t the VPP at time i curtailable heat load.

[0112] (2-1-7) Energy storage operation and maintenance cost:

[0113] (20)

[0114] Among them, are respectively the charge and discharge powers of the EES of VPPi at time t; are respectively the heat charge and discharge powers of the TES of VPPi at time t; are respectively the operation and maintenance costs of electric energy storage and heat energy storage.

[0115] (2-2) Constraint conditions.

[0116] (2-2-1) The power trading constraint of P2P is:

[0117] (21)

[0118] (22)

[0119] In the formula, is the maximum interactive electricity between VPPs; is the electricity sold by VPPi to VPPj, is the electricity sold by VPPj to VPPi.

[0120] (2-2-2) The demand response constraint is:

[0121]

[0122] In the formula, and are respectively the upper limits of curtailable and transferable electric loads; is the upper limit of curtailable heat load.

[0123] (2-2-3) The power balance constraint is:

[0124]

[0125] In the formula: , are respectively the VPP i att The electricity purchase and sale volumes from / to the grid operator at time t; For t the fan output power of the virtual power plant at time t, For t the PV output power of the virtual power plant at time t; For t the CHP output electric power of VPPi at time t, are respectively the charge and discharge powers of the EES of VPPi at time t, For t the electrical load of VPPi at time t; are respectively the numbers of electric vehicles for discharging and charging; are respectively the i discharge and charge amounts of the nth electric vehicle at time t of VPP are respectively t the heat output power of the electric boiler, the heat output power of CHP, of VPPi at time t, t the heat load of VPPi at time t, are respectively the charge and discharge powers of the TES of VPPi at time t.

[0126] (2 - 2 - 4) The green certificate constraint is:

[0127]

[0128] Wherein, is the green certificate price in the market; is the initial green certificate price; And are two positive parameters of the price inverse function of the Cournot model; is the number of green certificates that can be sold on the day; is the green certificate trading price ratio coefficient calculated based on historical data; is the new energy consumption ratio in this region; is the total number of green certificates in a day; is the dispatching unit duration, which can be set to 1 h.

[0129] (3) Establish the VPP alliance Nash bargaining model.

[0130] The VPP alliance responds to the decisions of the grid operator through cooperation, and the cooperation goal is to maximize the overall benefit. Preferably, the VPP alliance Nash bargaining model is:

[0131] (34)

[0132] In the formula: is the revenue obtained by VPP i participating in the negotiation, is VPPi Benefits obtained without participating in the negotiation.

[0133] The Nash negotiation model of the VPP alliance is a non-convex and non-linear problem with multi-variable coupling. Therefore, the model is converted into a VPP alliance cost minimization sub-problem (P1) and a revenue allocation sub-problem (P2), and solved sequentially.

[0134] (3-1) Sub-problem (P1) - VPP alliance cost minimization:

[0135] (35)

[0136] (3-2) Sub-problem (P2) - Cooperative revenue allocation:

[0137] (36)

[0138] In the formula: is the VPP benefit obtained in sub-problem P1 i Benefit; is t At time i VPP j and the electricity trading price of VPP is t At time i VPP j and the transaction electricity quantity of VPP , are respectively the purchase and sale electricity prices at time t obtained in sub-problem P1.

[0139] (4) Establish a data-driven DRO model.

[0140] In this embodiment, the K-means algorithm is used to select M from the actually obtained X wind and solar power output samples ( k = 1, 2,..., X ) typical output scenarios to represent the output uncertainty of wind and solar power, and obtain the discrete scenario probability distribution values of each wind and solar power output Since the obtained initial probability distribution is still uncertain, a feasible region of probability distribution values centered on the discrete probability distribution value Ω and constrained by the 1-norm and ∞-norm sets is constructed:

[0141] (37)

[0142] In the formula, is the actual value of the i th probability distribution; , The probability error values under the 1-norm and ∞-norm distributions, respectively.

[0143] The probabilities of the wind and light output scenarios satisfy the following inequality requirements:

[0144]

[0145] In the formula: is the probability that the inequality in holds; the initial probability value selected from historical data. Let the right sides of the above two inequalities be equal to the confidence levels and that can make them hold, respectively, and we can get:

[0146] (40)

[0147] Preferably, the Nash bargaining model is combined with the constructed wind and light uncertainty probability model for collaborative optimal scheduling to obtain the following hybrid game model in matrix form:

[0148] (41)

[0149] In the formula: x is the transaction decision variable in the Nash bargaining model, y is the adjustable decision variable including the output of renewable energy, the output of equipment within the VPP alliance, etc.; is the purchase and sale electricity cost of the VPP alliance; is the operating cost corresponding to the probability distribution of the worst scenario; a, b, c, A, G, E, F, U, V, g, d, w is the coefficient matrix, is the mathematical representation of wind and light uncertainty.

[0150] Preferably, after constructing the above hybrid game model, the master-slave game model can be solved by iterative solution using the bisection method, and the cooperative game model can be solved using the CCG-ADMM algorithm (constraint generation - alternating direction multiplier method).

[0151] In summary, the method of this embodiment combines the game relationship between grid operators and virtual power plant alliances to construct an optimization framework that combines master-slave games and cooperative games. For the uncertainty of the output of wind and photovoltaic power generation units, through a data-driven two-stage distributed robust optimization method, typical output scenarios of new energy units are selected and the discrete scenario distribution values are calculated. Combining historical data and confidence formulas, the uncertainty of wind and light is accurately characterized to ensure that the alliance can still minimize the operating cost under the worst scenario. On this basis, the total cost and carbon emissions of the virtual power plant alliance are effectively reduced through optimal scheduling. This method further uses the Nash bargaining theory to fairly distribute the cooperative benefits, which not only promotes the efficient cooperation of the virtual power plant alliance but also realizes the reasonable distribution of interests. Finally, while reducing costs, the alliance members significantly improve their response ability to the carbon trading market, providing strong support for promoting the low-carbon transformation and achieving the goals of energy conservation and emission reduction.

[0152] Embodiment 2

[0153] Based on Embodiment 1, this embodiment:

[0154] This embodiment provides a multi-virtual power plant hybrid game optimization system considering the uncertainty of wind and light, including:

[0155] A joint model construction module configured to construct a joint model that combines master-slave games and cooperative games based on the game relationship between grid operators and virtual power plant alliances;

[0156] A wind and light uncertainty probability model construction module configured to, for the uncertainty of the output of wind and photovoltaic power generation units, select typical output scenarios of wind and photovoltaic power generation units and calculate discrete scenario distribution values through a data-driven two-stage distributed robust optimization method, calculate the probability distribution of the output scenarios of wind and photovoltaic power generation units by combining historical data and confidence formulas, and construct a wind and light uncertainty probability model;

[0157] A collaborative optimal scheduling module configured to reduce the total cost and carbon emissions of the virtual power plant alliance through collaborative optimal scheduling based on the joint model and the wind and light uncertainty probability model.

[0158] Embodiment 3

[0159] Based on Embodiment 1, this embodiment:

[0160] This embodiment provides a computer device including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0161] Embodiment 4

[0162] On the basis of Embodiment 1, this embodiment:

[0163] This embodiment provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the multi-virtual power plant hybrid game optimization method considering wind and light uncertainties in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file or some intermediate form, etc. The storage medium includes: any entity or device capable of carrying the computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the storage medium does not include electrical carrier signals and telecommunication signals.

[0164] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light, characterized in that Including: Based on the game relationship between the power grid operator and the virtual power plant alliance, a combined model of master-slave game and cooperative game is constructed; Aiming at the output uncertainty of wind and photovoltaic generating units, through a data-driven two-stage distributed robust optimization method, typical output scenarios of wind and photovoltaic generating units are selected and discrete scenario distribution values are calculated. Combining historical data and confidence formulas, the probability distribution of the output scenarios of wind and photovoltaic generating units is calculated, and a probability model of wind and photovoltaic uncertainty is constructed; Based on the combined model and the probability model of wind and photovoltaic uncertainty, the total cost and carbon emissions of the virtual power plant alliance are reduced through collaborative optimal scheduling; The combined model of master-slave game and cooperative game constructed based on the game relationship between the power grid operator and the virtual power plant alliance includes: Establish a leader - power grid operator model in the master-slave game, with the maximum benefit of the power grid operator itself as the objective function; Establish a follower - virtual power plant alliance model in the master-slave game, with the minimum cost of the virtual power plant alliance members themselves as the objective function; Establish a Nash bargaining model of the virtual power plant alliance based on cooperative game, and respond to the decision of the power grid operator through cooperation, with the maximum overall benefit as the objective function; In the virtual power plant alliance model, the cost of the virtual power plant alliance members themselves at least includes the carbon trading cost, and the calculation method of the carbon trading cost includes: Among them, is the carbon quota of the unit, T is the total number of time periods, n is the number of virtual power plants participating in the game, is the output electric power of the unit of the virtual power plant at time t, is the power supply reference value, is the heating correction coefficient; is the actual carbon emission of the unit, is the carbon emission factor of the unit; is the heating ratio; is the carbon trading cost, is the carbon trading price; In the virtual power plant alliance model, the cost of the virtual power plant alliance members themselves at least includes the green certificate trading cost, and the calculation method of the green certificate trading cost includes: Among them, is the cost of green certificate trading; is the current green certificate price, is the future green certificate price; is the number of salable green certificates, is the number of currently sold green certificates, is the number of future sold green certificates; T is the total number of time periods; is the estimated price ratio of green certificates in the future market.

2. The multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light according to claim 1, characterized in that In the virtual power plant alliance model, the constraint conditions at least include the green certificate constraint, and the green certificate constraint includes: Among them, is the green certificate price in the market, is the initial green certificate price; and are two positive parameters of the inverse function of the Cournot model price; is the number of green certificates that can be sold on the day; is the green certificate trading price ratio coefficient calculated based on historical data; is the new energy consumption ratio in this region;[[ID= 15]] is the total number of green certificates in a day; is t the fan output of the virtual power plant at time is t the PV output of the virtual power plant at time is the dispatching unit duration; is the number of green certificates currently sold, is the number of green certificates to be sold in the future.

3. The multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light according to claim 1, characterized in that, For the output uncertainty of wind and photovoltaic generating units, through a data-driven two-stage distributed robust optimization method, typical output scenarios of wind and photovoltaic generating units are selected and discrete scenario distribution values are calculated, including: Select X typical output scenarios from the actually obtained M output samples of wind-solar generating units through the K-means algorithm to represent the output uncertainty of wind-solar generating units, and obtain the discrete scenario probability distribution values of the outputs of each wind-solar generating unit M X X (( k = 1, 2, …, X ) Construct a feasible region of probability distribution values centered on the discrete scenario probability distribution values and bounded by the sets of 1-norm and ∞-norm Ω : Among them, is the actual value of the i th probability distribution; is the probability error value under the 1-norm distribution, is the probability error value under the ∞-norm distribution.

4. The multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light according to claim 3, characterized in that Combining historical data and confidence formulas to calculate the probability distribution of the output scenarios of wind and photovoltaic generating units, including: The probability of the output scenarios of wind and photovoltaic generating units needs to meet the following inequality requirements: Among them, is the probability that the inequality in is the initial probability value screened from historical data, M is the number of output samples of wind-solar generating units; Let the right sides of the above two inequalities be equal to the confidence levels that can make them hold respectively and , we get: Thus, the probability distribution of the output scenarios of wind and photovoltaic generating units is obtained.

5. The multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light according to claim 4, characterized in that Based on the combined model and the probability model of wind and photovoltaic uncertainty, the total cost and carbon emissions of the virtual power plant alliance are reduced through collaborative optimal scheduling, including: According to the Nash bargaining model of the virtual power plant alliance based on cooperative game in the combined model, combined with the probability model of wind and photovoltaic uncertainty, collaborative optimal scheduling is carried out to obtain a hybrid game model: Among them, x is the transaction decision variable in the Nash negotiation model of the virtual power plant alliance, y is the adjustable decision variable; is the power purchase and sale cost of the virtual power plant alliance, and the superscript represents the transpose operation; is the operating cost corresponding to the probability distribution of the worst-case scenario; a, b, c, d, g, w, A, G, E, F, U, V is the coefficient matrix, is the mathematical representation of the uncertainty of wind and light.

6. The multi-virtual power plant hybrid game optimization method considering the uncertainty of wind and light according to claim 5, characterized in that For the hybrid game model, the master-slave game model is solved iteratively by the bisection method; the cooperative game model, that is, the Nash bargaining model of the virtual power plant alliance, is solved by the constraint generation - alternating direction multiplier method.

7. A multi-virtual power plant hybrid game optimization system considering the uncertainty of wind and light, characterized in that Including: A combined model construction module, configured to construct a combined model of master-slave game and cooperative game based on the game relationship between the power grid operator and the virtual power plant alliance; A wind-solar uncertainty probability model construction module, configured to select typical wind-solar generator output scenarios and calculate discrete scenario distribution values for the output uncertainty of wind-solar power generation units through a data-driven two-stage distributionally robust optimization method, calculate the probability distribution of wind-solar generator output scenarios in combination with historical data and a confidence formula, and construct a wind-solar uncertainty probability model; A collaborative optimal scheduling module, configured to reduce the total cost and carbon emissions of the virtual power plant alliance through collaborative optimal scheduling based on the joint model and the wind-solar uncertainty probability model; Based on the game relationship between the grid operator and the virtual power plant alliance, a joint model combining a master-slave game and a cooperative game is constructed, including: Establish a leader - grid operator model in the master-slave game, with the maximum profit of the grid operator itself as the objective function; Establish a follower - virtual power plant alliance model in the master-slave game, with the minimum cost of the virtual power plant alliance members themselves as the objective function; Establish a Nash bargaining model of the virtual power plant alliance based on cooperative game, and respond to the decision of the grid operator through cooperation, with the maximization of the overall benefit as the objective function; In the virtual power plant alliance model, the cost of the virtual power plant alliance members themselves at least includes carbon trading costs, and the calculation method of the carbon trading costs includes: Among them, is the carbon quota of the unit, T is the total number of time periods, n is the number of virtual power plants participating in the game, is the output electric power of the unit of the virtual power plant at time t, is the power supply reference value, is the heating correction coefficient; is the actual carbon emission of the unit, is the carbon emission factor of the unit; is the heating ratio; is the carbon trading cost, is the carbon trading price; In the virtual power plant alliance model, the cost of the virtual power plant alliance members themselves at least includes green certificate trading costs, and the calculation method of the green certificate trading costs includes: Among them, is the green certificate trading cost; is the current green certificate price, is the future green certificate price; is the number of green certificates that can be sold, is the number of currently sold green certificates, is the number of future sold green certificates; T is the total number of time periods; is the estimated price ratio of green certificates in the future market.

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