Multi-agent collaborative power sharing double-layer nested game virtual power plant optimization method based on risk assessment and user satisfaction

Abstract

The invention discloses a multi-agent collaborative power sharing double-layer nested game virtual power plant optimization method based on risk assessment and user satisfaction, which belongs to the field of virtual power plant optimization and comprises the step of constructing multi-agent collaborative multi-virtual power plant nested game double-layer optimization research of multi-scene risk assessment and user satisfaction. A power distribution network operator serves as an upper-layer leader and interacts with a lower-layer virtual power plant alliance through price guidance, and balanced management of risks and benefits is achieved. The members of the virtual power plant alliance optimize the internal power transaction and enhance the flexibility and response capability of the system by sharing idle resources and responding to price signals. A user satisfaction quantitative index is added in the model, a virtual power plant scheduling strategy is optimized, and the requirements of user comfort and service quality are met while economic benefits are improved.

Description

Technical Field

[0001] The present invention belongs to the field of virtual power plant optimization, and particularly relates to a multi-agent collaborative power sharing two-layer nested game virtual power plant optimization method based on risk assessment and user satisfaction. Background Art

[0002] Under the background of the global energy crisis and environmental pollution, the development of clean energy has become the key to alleviating environmental pressure and promoting the transformation of the energy structure. Virtual power plants effectively participate in market transactions and grid dispatching by aggregating distributed energy resources such as wind power, photovoltaic power, energy storage, and adjustable loads, promoting the rational consumption of energy. However, the randomness and volatility of distributed energy resources pose challenges to the stability of the power grid, and optimizing the energy management of virtual power plants is crucial for ensuring energy supply and the quality of user energy consumption.

[0003] Existing research focuses on the optimal internal energy scheduling of virtual power plants, the satisfaction of users after demand response, and the collaborative optimization of multiple virtual power plants. Literature analyzes users' electricity consumption demands and proposes energy consumption reduction strategies, but often ignores user comfort. Optimizing scheduling strategies requires coordinating energy efficiency and user satisfaction, enhancing comfort and reducing energy consumption. With the reform of the trading mechanism, virtual power plants participate in market competition and face the transformation from single virtual power plant scheduling optimization to multi-virtual power plant collaborative optimization. Strategies include the energy management of virtual power plants considering the optimal power flow of the distribution network, establishing an energy sharing platform to promote direct electricity trading between virtual power plants, etc. Game theory has become an effective tool for analyzing interest equilibrium, but there are limitations in dealing with the problem of unequal contributions. To solve this problem, incentive mechanisms, improved game methods, and profit distribution models are proposed to achieve a more fair distribution of interests. Existing research has not fully considered the risk problems caused by the randomness of distributed energy resources. Conditional value at risk is widely used in the risk assessment of the power market, but the two-layer operation risk problem of multi-agent and multi-virtual power plant nested games needs further research. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a multi-agent collaborative power sharing two-layer nested game virtual power plant optimization method based on risk assessment and user satisfaction, including:

[0005] Calculating the uncertainty of distributed energy resources within the virtual power plant based on the value-at-risk theory, and constructing a risk assessment model for multiple scenarios;

[0006] Constructing an optimized virtual power plant scheduling model based on user participation and satisfaction;

[0007] Constructing a nested game two-layer optimization model based on the interaction problems in multi-agent and multi-virtual power plant collaborative scheduling;

[0008] Optimize the virtual power plant based on the risk assessment model, the virtual power plant scheduling model, and the nested game two-layer optimization model.

[0009] Preferably, in the risk assessment model of multiple scenarios, the average loss of the portfolio risk loss exceeding the value at risk under the set confidence level is:

[0010] CVaR β (X) = E[f(X, ξ)|f(X, ξ) > VaR β ;

[0011] where f(X, ξ) is the loss function of the portfolio X, and ξ represents the continuous random factors that may affect the loss function;

[0012] The expression for calculating the conditional value at risk in the risk assessment model of multiple scenarios is:

[0013]

[0014] where Ω is the number of discrete scenarios; y m is the random variable value under the m-th discrete scenario, and η is the value at risk.

[0015] Preferably, the process of constructing an optimized virtual power plant scheduling model based on user participation and satisfaction includes:

[0016] Define user participation and satisfaction as the degree of change in the load curve before and after response;

[0017] Construct flexible economic compensation based on user participation in demand response;

[0018] Construct the virtual power plant scheduling model based on the degree of change in the load curve before and after response and the flexible economic compensation.

[0019] Preferably, the expression for the degree of change in the load curve before and after response is:

[0020]

[0021] where are the electrical load powers before and after response respectively.

[0022] Preferably, the calculation expression for the flexible economic compensation is:

[0023]

[0024] where Cost of flexible load participating in demand response; are the cost coefficients of transfer load and interrupted load participating in demand response respectively; They are the transferred and interrupted load powers participating in demand response respectively.

[0025] Preferably, the process of constructing the nested game two-layer optimization model includes:

[0026] Constructing an upper-layer dynamic pricing model for the distribution network operator based on the upper-layer objective function and upper-layer constraint conditions;

[0027] Constructing a lower-layer virtual power plant aggregation model based on the lower-layer objective function and lower-layer constraint conditions

[0028] Solving the upper-layer dynamic pricing model for the distribution network operator and the lower-layer virtual power plant aggregation model based on the KKT conditions to obtain the nested game two-layer optimization model.

[0029] Preferably, the upper-layer objective function is:

[0030]

[0031] where i, t, w are the virtual power plant number, time, and scenario respectively; correspondingly, Ω, T, N are their numerical values; ρ i,w is the scenario probability; are the on-grid electricity price and the grid electricity price respectively; are the purchase and sale electricity prices of the virtual power plant respectively; are the purchase and sale electricity quantities of the virtual power plant respectively; α is the risk aversion coefficient; is the value at risk of the virtual power plant's revenue; γ is the confidence level; η i,w is an auxiliary variable representing the proportion of the virtual power plant's operating revenue exceeding in each scenario;

[0032] The upper-layer constraint conditions include: electricity price constraint and conditional value at risk constraint.

[0033] Preferably, the lower-layer objective function is:

[0034]

[0035] where, are the costs of whether there is internal trading within the virtual power plant aggregation respectively; is the payment cost for internal trading within the virtual power plant aggregation; β i,w is the Nash bargaining coefficient; are the operating cost of the gas turbine, the cost of the adjustable load participating in demand response, and the cost of converting user satisfaction respectively; b i , c i is the gas turbine cost coefficient; are the cost coefficients of the transferred load and the interrupted load participating in demand response respectively; is the conversion cost coefficient of the user satisfaction index; is the charge and discharge cost coefficient of the energy storage; is the charge and discharge power of the energy storage; is the output power of the gas turbine; are the transferred and interrupted load powers participating in the demand response respectively; are the electrical load powers before and after the response respectively;

[0036] The lower-layer constraint conditions include: power balance constraint, gas turbine constraint, power purchase and sale quantity constraint, electric vehicle constraint, commercial load constraint, battery energy storage system constraint, air conditioner constraint, demand response constraint and revenue constraint.

[0037] Preferably, the process of solving the upper-layer model of the distribution network operator's dynamic pricing and the lower-layer virtual power plant aggregation model based on the KKT conditions includes: using the KKT conditions to transform the upper-layer model of the distribution network operator's dynamic pricing and the lower-layer virtual power plant aggregation model into a non-linear single-layer problem containing complementary slack conditions and bilinear products, then using the Big-M method to handle the complementary slack conditions between the Lagrange multipliers and the constraints, and linearizing the bilinear products through the strong duality theory, and finally forming the equivalent nested game two-layer optimization model.

[0038] Compared with the prior art, the present invention has the following advantages and technical effects:

[0039] 1. By introducing the conditional value-at-risk theory and constructing a multi-scenario risk assessment model, the risks in the operation of the virtual power plant are effectively managed and quantified, the ability of the distribution network operator to cope with uncertainties is improved, and thus the stability and reliability of the entire power system are enhanced.

[0040] 2. The two-layer nested game model enhances the response ability of the virtual power plant to market price signals, improves the flexibility and adaptability of the system, enables the virtual power plant to better adapt to market changes and user needs. The multi-virtual power plant cooperative scheduling model promotes the effective cooperation between different virtual power plants, improves the overall scheduling efficiency, and realizes the optimal allocation of resources. The sharing of idle power between virtual power plants optimizes the power resource allocation, improves the energy utilization efficiency, reduces costs, and increases economic benefits. The virtual power plant enhances its competitiveness in the power market through accurate pricing strategies and flexible power sharing mechanisms and can better participate in market competition.

[0041] 3. Optimize the consumption of distributed renewable energy, reduce the pressure on the environment, promote the utilization of clean energy, and contribute to the sustainable development of the environment. Description of the Drawings

[0042] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0043] Figure 1 Schematic diagram of the energy trading framework according to an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the nested game double - layer optimization model according to an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of typical scenarios of wind power and photovoltaic power output according to an embodiment of the present invention;

[0046] Figure 4 Schematic diagram of the effective frontier curve of the revenue with respect to conditional value - at - risk according to an embodiment of the present invention;

[0047] Figure 5 Schematic diagram of the optimization result of the virtual power plant aggregated resources according to an embodiment of the present invention;

[0048] Figure 6 Schematic diagram of the optimization result of the nested electricity price according to an embodiment of the present invention;

[0049] Figure 7 Schematic diagram of the optimization result of the interactive electricity quantity according to an embodiment of the present invention;

[0050] Figure 8 Schematic diagram of the optimization result of the interactive electricity price according to an embodiment of the present invention;

[0051] Figure 9 Schematic diagram of the optimization result of the demand response positivity according to an embodiment of the present invention. Detailed implementation manners

[0052] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will describe this application in detail with reference to the drawings and in combination with the embodiments.

[0053] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer - executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0054] Embodiment 1

[0055] In this embodiment, a multi - agent collaborative power sharing double - layer nested game virtual power plant optimization method based on risk assessment and user satisfaction is provided, including:

[0056] Calculate the uncertainty of distributed energy resources within a virtual power plant based on the value-at-risk theory, and construct a risk assessment model for multiple scenarios;

[0057] Construct an optimized virtual power plant scheduling model based on user participation and satisfaction;

[0058] Construct a nested game two-layer optimization model based on the interaction problems in multi-agent multi-virtual power plant collaborative scheduling;

[0059] Optimize the virtual power plant based on the risk assessment model, the virtual power plant scheduling model, and the nested game two-layer optimization model.

[0060] In this embodiment, aiming at the risk management problem brought by the uncertainty of distributed energy resources within the virtual power plant, the conditional value-at-risk theory is introduced to construct a risk assessment model for multiple scenarios. In this model, the distribution network operator acts as the upper-layer leader and interacts with the lower-layer virtual power plant consortium through price guidance to achieve the balanced management of risks and benefits.

[0061] Regarding the issues of user participation and satisfaction, this embodiment adds a user satisfaction quantification index including a demand response mechanism to the model. By considering the economy and satisfaction of user load adjustment, the scheduling strategy of the virtual power plant is optimized to ensure that while improving the economic benefits of the system, the comfort and service quality requirements of users can also be met.

[0062] Regarding the interaction problems in multi-agent multi-virtual power plant collaborative scheduling, this embodiment proposes a two-layer nested model of power sharing. Among them, the virtual power plant consortium acts as the lower-layer follower, and members optimize internal power transactions and enhance the flexibility and response ability of the system by sharing idle resources and responding to the upper-layer price signal.

[0063] The nested game two-layer optimization model constructed in this embodiment forms a closed-loop decision-making and feedback mechanism throughout the process, enabling each subject to obtain benefits in power sharing. In the two-layer problem, the solution of the inner-layer problem depends on the solution of the outer-layer problem, and the KKT condition is a method for solving the two-layer problem, which transforms the two-layer problem into a single-layer problem and simplifies the solution.

[0064] The specific architecture of the multi-agent collaborative power sharing nested game two-layer optimization model for risk assessment and user satisfaction is as follows:

[0065] Virtual power plants aggregate distributed renewable energy, gas turbines, interruptible loads, and shiftable loads. Distributed renewable energy includes wind power and photovoltaic power. Interruptible loads include household air conditioners. Shiftable loads include electric vehicles, commercial loads, and battery energy storage systems. In the future, different virtual power plants will belong to different stakeholders, forming a pattern of competition and game among multiple decision-making entities. The uncertainty of distributed energy resources, the interest conflicts among operating entities, and the complexity of coordinated planning limit the potential of virtual power plants in the electricity market.

[0066] To solve the above problems, the present invention establishes a two-layer nested game structure composed of a distribution network operator and an aggregate of virtual power plants that aggregate distributed energy resources, solves the risk management problem caused by the uncertainty of distributed energy resources within the virtual power plant, and realizes power sharing and optimization of coordinated dispatching of multiple virtual power plants. By introducing the conditional value at risk theory to construct a multi-scenario risk assessment model, the distribution network operator interacts with the lower-layer virtual power plant aggregate through a pricing strategy to achieve balanced management of risks and benefits. The Nash game model of power sharing among the members of the lower-layer aggregate allows virtual power plants to share idle resources and respond to price signals, optimize internal power transactions, and improve the flexibility and response ability of the system. Through the user satisfaction quantification index, while meeting the requirements of user comfort, it actively guides flexible loads to participate in demand response.

[0067] To consider the uncertain risks of wind and light output and market electricity prices, this paper adopts the conditional value at risk theory. The distribution network operator interacts with the lower-layer virtual power plant aggregate through a pricing strategy to achieve balanced management of risks and benefits.

[0068] Conditional value at risk is an improvement of value at risk, defined as: within a specific investment period, the average loss of the portfolio risk loss exceeding the value at risk under a set confidence level is:

[0069] CVaR β (X) = E[f(X, ξ)|f(X, ξ) > VaR β

[0070] Where: f(X, ξ) is the loss function of the portfolio X, and ξ represents the continuous random factors that may affect the loss function.

[0071] Calculating the conditional risk value is equivalent to solving a linear constraint problem with discrete variables, which simplifies the calculation process. The specific calculation method in discrete scenarios is as follows:

[0072]

[0073] Where: Ω is the number of discrete scenarios; y m is the value of the random variable in the m-th discrete scenario, and this formula assumes that the probabilities of all discrete scenarios are equal; η is the value at risk.​

[0074] Due to the volatility of new - energy power generation, there is a deviation between the actual power generation and the expectation, which has a significant impact on the power grid. It is necessary to pay attention to its average degree and occurrence probability. Conditional Value at Risk (CVaR) can reflect both the magnitude of the deviation and the likelihood of occurrence. The new - energy output deviation is defined as follows:

[0075] f(P Xjt ,P Rjt )=|P Xjt -P Rjt |

[0076] Where: P Xjt is the planned output of new energy, and P Rjt is the actual output of new energy.

[0077] The problem of user participation and satisfaction is constructed as follows:

[0078] Demand - response projects can guide users to use energy rationally, optimize the terminal energy - consumption curve, and improve the energy - using efficiency of virtual power plants. In a user - friendly dispatching environment, formulating the demand - response dispatching method only based on the optimal benefit of the main body will lead to a decrease in user satisfaction, generate resistance, and greatly reduce the enthusiasm. Therefore, it is extremely important to consider the dispatching strategy of user satisfaction. Users usually arrange their electricity - using plans according to the production and living patterns that best suit themselves, so that the satisfaction with the electricity - using method is the greatest. However, after users participate in demand response, they need to change their original electricity - using plans to respond to the load dispatching of the virtual power plant, forming a new electricity - load curve. Define the satisfaction ε of the electricity - using method as the degree of change in the load curve before and after the response.

[0079]

[0080] Where: are the electric - load powers before and after the response respectively.

[0081] Virtual power plants aggregate distributed renewable energy, gas turbines, interruptible loads, and shiftable loads. Interruptible loads include household air conditioners, and shiftable loads include electric vehicles, commercial loads, and battery - energy - storage systems. These flexible loads participate in demand response, and the virtual power plant will compensate according to the amount of demand - response participation. Therefore, the flexible - load compensation cost is taken into consideration.

[0082]

[0083] Where: Cost of flexible - load participation in demand response; are the cost coefficients of shiftable loads and interruptible loads participating in demand response respectively; are the shiftable and interruptible load powers participating in demand response respectively.

[0084] 1. The two - layer optimization model for the nested game between the distribution network operator and the virtual power plant aggregator is constructed as follows:

[0085] S1: The dynamic pricing model of the distribution network operator in the upper layer of the game

[0086] The distribution network operator aims to maximize the overall goal, which includes the costs and revenues of power transactions with the superior power grid and the virtual power plant aggregator. At the same time, the risk - aversion coefficient is controlled to account for uncertainties. The decision - making model of the distribution network operator is as follows.

[0087] (1) Objective function

[0088] The objective function consists of two parts: one is the revenue generated by the distribution network operator's power transaction with the power grid;

[0089] The other is the risk - return trade - off.

[0090]

[0091] In the formula: i, t, w are the serial numbers of virtual power plants, time, and scenarios respectively; correspondingly, Ω, T, N are their numerical values; ρ i,w is the scenario probability; are the on - grid electricity price and the power grid electricity price respectively; are the purchase and sale electricity prices of the virtual power plant respectively; are the purchase and sale electricity quantities of the virtual power plant respectively; α is the risk - aversion coefficient; is the value - at - risk of the virtual power plant's revenue; γ is the confidence level; η i,w is an auxiliary variable, representing the proportion of the virtual power plant's operating revenue exceeding in each scenario.

[0092] (2) Constraint conditions

[0093] 1) Electricity price constraints

[0094] To prevent the distribution network operator from setting high purchase electricity prices and low sale electricity prices for the virtual power plant aggregator out of self - interest. The purchase and sale electricity prices set by the distribution network operator should meet the following constraints.

[0095]

[0096] In the formula: are the minimum values of the purchase and sale electricity prices respectively; are the maximum values of the purchase and sale electricity prices respectively; are the average values of the purchase and sale electricity prices respectively.

[0097] 2) Conditional value - at - risk constraints

[0098]

[0099] S2: Game Lower-layer Virtual Power Plant Aggregation Model

[0100] Under the strategic guidance of the distribution network operator, virtual power plants achieve mutual benefit and win-win results through energy interaction with the distribution network operator. Nash game theory is used to analyze the energy interaction among virtual power plants, prompting them to share resources to optimize costs. This mechanism promotes energy trading among virtual power plants and fairly distributes benefits according to contributions. By evaluating the response of virtual power plants to electricity price signals, the distribution network operator can formulate effective market mechanisms, and virtual power plants adopt optimal operation strategies under electricity price incentives.

[0101] (1) Objective Function

[0102] Each member in the virtual power plant aggregation aims to maximize its comprehensive benefits, including electricity purchase and sale benefits, interaction benefits among members, demand response costs, energy storage costs, gas turbine operation costs, and user satisfaction conversion costs.

[0103]

[0104] In the formula: are the costs of whether to trade within the virtual power plant aggregation respectively; is the payment cost for internal trading within the virtual power plant aggregation; β i,w is the Nash game bargaining coefficient; are the gas turbine operation cost, adjustable load participation in demand response cost, and user satisfaction conversion cost respectively; b i , c i is the gas turbine cost coefficient; are the cost coefficients of transfer load and interrupted load participating in demand response respectively; is the cost coefficient of user satisfaction index conversion; is the energy storage charge and discharge cost coefficient; is the charge and discharge power of the energy storage; is the output power of the gas turbine; are the transfer and interrupted load powers participating in demand response respectively; are the electrical load powers before and after response respectively.

[0105] (2) Constraint Conditions

[0106] 1) Power Balance Constraint

[0107]

[0108] In the formula: is the internal trading volume within the virtual power plant aggregation; are the output powers of wind and light respectively.

[0109] 2) Gas turbine constraints

[0110]

[0111] Where: is the maximum value of the gas turbine output power; is the constraint on the maximum value of the gas turbine ramp power.

[0112] 3) Constraints on electricity purchase and sale

[0113]

[0114] Where: are the maximum values of electricity purchase and sale respectively.

[0115] 4) Electric vehicle constraints

[0116] In this paper, it is assumed that the daily driving mileage distributions of electric vehicles and fuel vehicles are similar. The probability density function of the vehicle access time to the power grid is similar to the normal distribution function and is expressed as:

[0117]

[0118] The probability density function of the vehicle disconnection time from the power grid can be expressed as:

[0119]

[0120] The probability density of the daily driving kilometers of electric vehicles is as follows:

[0121]

[0122] Where: S is the daily driving mileage of the electric vehicle.

[0123] Estimate the required electricity according to the daily driving distance of the electric vehicle as follows:

[0124]

[0125] Where: Q EV,km is the power consumption per kilometer of the electric vehicle; η EV,dis is the discharge efficiency of the electric vehicle.

[0126] Considering that users participate in demand response, the charging capacity of electric vehicles should be within a reasonable range to ensure the daily travel needs of users and meet the following constraints.

[0127]

[0128] Where: are the initial load of the electric vehicle, the transferred load participating in demand response, the maximum value of the transfer amount, and the load of the electric vehicle after transfer respectively; is the battery level when the electric vehicle leaves; η EV,ch is the charging efficiency of the electric vehicle; is the responsiveness of the electric vehicle.

[0129] 5) Commercial load constraint

[0130]

[0131] In the formula: are respectively the initial commercial load, the transferred load participating in demand response, the maximum transferred amount, and the commercial load after transfer; is the responsiveness of the commercial load.

[0132] 6) Battery energy storage system constraint

[0133]

[0134] In the formula: is the maximum charge-discharge power of the battery energy storage system; is the charge-discharge efficiency of the battery energy storage system; are respectively the battery level of the battery energy storage system and the maximum and minimum values of the battery level.

[0135] 7) Air conditioner constraint

[0136]

[0137] In the formula: are respectively the initial load of the air conditioner, the interrupted load participating in demand response, the maximum interrupted amount, and the air conditioner load after interruption; is the responsiveness of the air conditioner; is the indoor and outdoor temperature; R i , C i are respectively the equivalent thermal resistance and capacitance of the building.

[0138] 8) Demand response constraint

[0139]

[0140] 9) Revenue constraint

[0141]

[0142] The constraint ensures that the collective benefit of the power sharing model is greater than the balance of individual operation and trading funds and the balanced distribution of benefits.

[0143] 2. The solution process of the nested game two-layer optimization model is as follows:

[0144] In the bilevel problem, the solution of the inner-level problem depends on the solution of the outer-level problem. The KKT conditions are a method for solving two-level problems, which transform the two-level problem into a single-level problem and simplify the solution. To solve the nested game bilevel optimization model constructed in the present invention, the internal energy trading model of the lower-level virtual power plant is first decomposed into two independent sub-problems, namely P1 and P2.

[0145] P1: The problem of maximizing the aggregate interest

[0146]

[0147] P2: The problem of interest distribution

[0148]

[0149] To accelerate the solution speed and reduce the number of iterations, logarithmic processing is performed on the power function of the above equation.

[0150]

[0151] To handle the bilevel optimization problem containing bilinear terms, the KKT conditions are used to transform the lower-level problem into a nonlinear single-level problem containing complementary slackness conditions and bilinear products. Then, the Big-M method is used to handle the complementary slackness conditions between the Lagrange multipliers and the constraints, and the bilinear product is linearized through the strong duality theory, finally forming an equivalent single-level mixed-integer linear programming model. The model in this paper is solved using the YALMIP toolbox and the CPLEX solver under the MATLAB environment.

[0152] The equivalent single-level mixed-integer linear programming formula is as follows:

[0153]

[0154] The complementary slackness conditions corresponding to the inequalities are shown as follows.

[0155]

[0156] In the above formula, there are nonlinear constraints. 0-1 Boolean variables are introduced and the Big-M method is used to transform the nonlinear constraints into mixed-integer linear constraints, as shown in the following formula:

[0157]

[0158] In the formula: are the introduced dual variables; M is a sufficiently large number; are the Boolean variables introduced for the complementary slackness conditions.

[0159] The following analyzes the scenario generation in combination with an example, as detailed in the following description:

[0160] The example selects the electricity market of a certain province in China for simulation. Taking one operator and three regional virtual power plants as the objects, the double-layer nested game model in this paper is tested and simplified verification is carried out in a certain area of Anhui, China. Figure 1 Energy trading framework Figure 2 Double-layer optimization model of nested game Figure 3 Typical scenarios of wind power and photovoltaic power output. The time-of-use electricity price of the power grid is shown in Table 1, the operating parameters of gas turbines and battery energy storage are shown in Tables 2 and 3, and the occurrence probabilities of each scenario are shown in Table 4.

[0161] Table 1

[0162]

[0163] Table 2

[0164]

[0165] Table 3

[0166]

[0167] Table 4

[0168]

[0169] Through the CVaR theory, the uncertain risk of wind and light output is evaluated, and the distribution network operator weighs the risk and return. Set the confidence level α = 0.95, and the risk aversion coefficient ranges from 0.01 to 1. The analysis results of the expected revenue and CVaR value of the distribution network operator under different risk aversion coefficients are shown in Table 5.

[0170] Table 5

[0171]

[0172]

[0173] As can be seen from Table 5, as the risk aversion coefficient gradually increases, the attitude of the distribution network operator towards risk changes from radical to conservative, the expected revenue gradually decreases, the losses caused by risks also gradually decrease, and in the operation strategy, it will be more inclined to set a higher selling electricity price and a lower purchasing electricity price. According to the calculation results in Table 5, the effective frontier curve of the expected revenue of the distribution network operator with respect to CvaR is obtained, as Figure 4 shown.

[0174] In this paper, according to the degree of risk aversion, the attitude of the distribution network operator towards risk is divided into five situations: radical, partially radical, neutral, partially conservative, and conservative, as Figure 4 shown. From Figure 4It can be seen that when the risk attitude of the distribution network operator is aggressive, as the degree of risk acceptance increases, the expected revenue changes slightly; when the risk attitude of the distribution network operator is conservative, as the degree of risk tolerance increases, the expected revenue increases slowly; when the distribution network operator is relatively conservative, the expected revenue has a linear relationship with the degree of risk tolerance, and the higher the degree of risk tolerance, the greater the expected revenue.

[0175] To verify the economy of the game model proposed in this paper, combined with the analysis in the previous section, considering the scenario where the risk aversion coefficient α = 0.1, the following two game models are established for comparative simulation. Scenario 1: The distribution network operator and the virtual power plant form a master-slave game model. Scenario 2: Nested game double-layer optimization.

[0176] After optimization and solution calculation, the revenues of the distribution network operator participating in the energy market, the revenues of each entity of the virtual power plant, and the operating costs under each scenario are shown in Tables 6 and 7 respectively. The idle power is shared among the virtual power plants in the lower layer of the nested game, and the payment costs for the power transactions between the virtual power plants are shown in Table 8.

[0177] Table 6

[0178]

[0179] Table 7

[0180]

[0181] Table 8

[0182]

[0183] It can be seen from the comparison that the total revenue of the distribution network operator in the method proposed in this paper, i.e., Scenario 2, is increased by 17,434.28 yuan compared with Scenario 1. This is because after considering the nested game, the dependence of the virtual power plant on the distribution network operator for power purchase decreases while the dependence on power sales increases, and the power purchase price from the virtual power plant is low, thus increasing the revenue of the distribution network operator. The complementary utilization of energy is realized through the idle power transaction among the members of the virtual power plant aggregate, resulting in an increase in the total revenue of the aggregate; the benefit of Virtual Power Plant 1 is increased by 7,391.8 yuan, the benefit of Virtual Power Plant 2 is increased by 7,681.26 yuan, and the benefit of Virtual Power Plant 3 is increased by 6,470.84 yuan. This is because the introduction of the Nash game improves the benefits of each member of the aggregate while reducing the total cost of the aggregate, enabling the fair distribution of cooperation benefits. In short, the nested game proposed in this paper enables the cost of each entity to decrease and the revenue to increase through the lower-layer power sharing and the optimization of each entity. It proves the economy of the nested game model established in this paper.

[0184] Under the two game methods, the optimized output of the aggregated resources within each virtual power plant is as Figure 5 shown, and the game electricity price is asFigure 6 as shown Figure 5 The left side shows the output of the equipment in virtual power plants 1-3 under scenario 1, and the right side shows the output of the equipment in virtual power plants 1-3 under scenario 2.

[0185] It can be seen from Figure 5 It can be seen that the electricity generated by the main body inside the virtual power plant not only meets the internal demand at some times, but also sells the surplus electricity to the market to obtain benefits. Among the electricity load demands of the 3 virtual power plants, virtual power plant 3 has the largest demand, and virtual power plant 2 has the smallest demand. From the vertical analysis, when the electricity generated by the main body inside the virtual power plant is not enough to meet the internal demand, the cost of buying electricity from the market is greater than the cost of gas turbine power generation. Gas turbine power generation is preferred. When there is a shortage of electricity in a certain period, energy storage discharge is used to meet the demand. At some times, the surplus electricity is determined according to the selling electricity price and the buying electricity price in other periods. Since virtual power plant 1 has a large amount of electricity generated by the main body inside, which is enough to meet the demand, and the cost coefficient of setting up a gas turbine in virtual power plant 1 is large, gas turbine power generation is not used throughout the period. At noon, the amount of electricity generated is large, and the surplus electricity is traded at the selling electricity price set by the distribution network operator. From the horizontal analysis, under the two game modes, in the right figure, due to the power sharing Nash game model, when the electricity is insufficient or in excess, the transactions between virtual power plants are considered. At some times, there are some changes in the output of internal equipment and the sale of surplus electricity. This is caused by the transaction costs of each virtual power plant and the game electricity price between virtual power plants. This also promotes the nearby consumption of energy and reduces other costs. Secondly, from the electricity load curve after response, it can be seen that the load curve after nested game is smoother. This is the optimal value of adjustable load participating in demand response under the premise of considering user satisfaction, indicating that the nested game constructed in this paper has a good effect of peak shaving and valley filling for the scheduling of controllable loads.

[0186] It can be seen from Figure 6 It can be seen that regardless of the game framework, the upper layer hopes to maximize its own interests, that is, the electricity price law set by the distribution network operator remains unchanged, that is, a higher selling electricity price and a lower buying electricity price. Therefore, under the two game frameworks, there will be a situation where the buying electricity price of the distribution network operator from the virtual power plant is the same, ensuring the lowest buying electricity cost of the distribution network operator. The buying electricity price of the virtual power plant from the distribution network operator formulated by the nested game is larger than that of the one-master-multi-slave game, and is close to the price ceiling. This is because the electricity shared by each virtual power plant reduces the amount of electricity that can be sold to the distribution network operator. Therefore, the distribution network operator formulates a higher electricity price to reduce the loss caused by the decrease in transaction electricity.

[0187] The interactive power between virtual power plant aggregates is as Figure 7 shown, and the game electricity price is as Figure 8 shown.

[0188] Figure 7-8It can be seen that the Nash game electricity price setting is related to the interactive electricity quantity among virtual power plant individuals. At each moment, there is always electricity purchase and sale by the virtual power plant aggregator. From the perspective of electricity purchase, the party with a smaller electricity purchase quantity sets a lower transaction electricity price, while the party with a larger transaction volume sets a higher transaction price. After the Nash game members who ensure electricity sharing complete the transaction, the benefit distribution of participating in electricity sharing becomes more fair and reasonable. The greater the electricity quantity participating in electricity sharing, the higher the benefit distribution. At the same time, it ensures that the upper and lower layer entities of the nested game model optimize their output and both their revenues increase.

[0189] Figure 6 Compared with 8 It can be seen from the comparison that the Nash game electricity price is lower than the master-slave game electricity price. This is because the nested game model of electricity sharing encourages energy trading among virtual power plant aggregators. The excess energy can be consumed nearby, reducing the impact of grid connection on the stability of the power grid and lowering the cost of the virtual power plant aggregator. All these prove the economy of the game model in this paper.

[0190] In this paper, flexible load models such as air conditioners, electric vehicles, commercial loads, and energy storage are constructed to participate in demand response, and are connected to the system through the created user satisfaction evaluation index. Finally, the response activity levels of air conditioners, electric vehicles, and commercial loads in the three virtual power plants obtained through optimization are as Figure 9 shown.

[0191] It can be seen from the figure that during the hot noon and afternoon periods, due to the urgent cooling demand, the response activity level of air conditioners is relatively low. While in the mild morning and evening, the response activity level is relatively high because the demand for air conditioners decreases at this time and users are more willing to participate in demand response. The response activity level of electric vehicles continuously decreases during the vehicle usage time and rest periods, and steadily increases during other non-working hours. The response activity level of commercial loads reaches the lowest point during the period with the most frequent commercial activities, slowly decreases during the normal business hours, and is relatively high during the periods with less commercial activities, and commercial users have more flexibility to adjust their electricity consumption. Multiple factors such as load characteristics, user behavior, market mechanisms, and incentive measures interact with each other, resulting in fluctuations in the response activity levels of loads at different time periods.

[0192] The above is only a preferred specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by this application should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A virtual power plant optimization method based on risk assessment and user satisfaction for multi-agent collaborative power sharing and double-layer nested game, characterized in that: include: Based on the risk value theory, the uncertainty of distributed energy resources within the virtual power plant is calculated and a multi-scenario risk assessment model is constructed; Build an optimized virtual power plant dispatch model based on user participation and satisfaction; A nested game two-level optimization model is constructed based on the interactive problem in the coordinated dispatch of multiple agents and multiple virtual power plants; The virtual power plant is optimized based on the risk assessment model, the virtual power plant scheduling model and the nested game two-layer optimization model.

2. The method according to claim 1, characterized in that In the multi-scenario risk assessment model, the average loss of the portfolio risk loss exceeding the risk value under the set confidence level is: CVARs β (X)=E[f(X,ξ)|f(X,ξ)>VaR β ]; Where f(X,ξ) is the loss function of portfolio X, ξ represents the continuous random factors that may affect the loss function; The expression for calculating the conditional risk value of the multi-scenario risk assessment model is: Where Ω is the number of discrete scenarios; y m is the random variable value under the mth discrete scenario, and η is the risk value.

3. The method according to claim 1, characterized in that The process of building an optimized virtual power plant dispatch model based on user engagement and satisfaction includes: User engagement and satisfaction were defined as the degree of change in the pre- and post-response load curves; Building flexible economic compensation based on user participation in demand response; The virtual power plant dispatching model is constructed based on the degree of change of the load curve before and after the response and the flexible economic compensation.

4. The method according to claim 3, characterized in that: The expression for the degree of change of the load curve before and after the response is: in, are the electric load power before and after the response respectively.

5. The method according to claim 3, characterized in that: The calculation expression of the flexible economic compensation is: in, The cost of flexible loads participating in demand response; are the cost coefficients for shifting load and interrupting load to participate in demand response; are the transfer and interruption load powers participating in demand response, respectively.

6. The method according to claim 1, characterized in that The process of constructing the nested game two-layer optimization model includes: Construct the upper-level model of dynamic pricing for distribution network operators based on the upper-level objective function and upper-level constraints; Constructing the lower-level virtual power plant aggregate model based on the lower-level objective function and lower-level constraints Based on the KKT condition, the upper-level dynamic pricing model of the distribution network operator and the lower-level virtual power plant aggregate model are solved to obtain the nested game two-level optimization model.

7. The method according to claim 6, characterized in that The upper objective function is: Among them, i, t, and w are the virtual power plant serial number, time, and scenario respectively; correspondingly, Ω, T, and N are their quantity values; ρ i,w is the scenario probability; They are on-grid electricity price and grid electricity price respectively; are the purchase and sale prices of electricity of the virtual power plant respectively; are the electricity purchase and sales of the virtual power plant; α is the risk aversion coefficient; is the risk value of the virtual power plant's benefits; γ is the confidence level; η i,w is an auxiliary variable, indicating that the operating profit of the virtual power plant in each scenario exceeds proportion; The upper-level constraints include: electricity price constraints and conditional risk value constraints.

8. The method according to claim 6, characterized in that The lower layer objective function is: in, They are the costs of whether transactions are conducted within the virtual power plant aggregate; is the payment cost of internal transactions within the virtual power plant aggregate; β i,w is the Nash game bargaining coefficient; are respectively the gas turbine operation cost, the adjustable load participation demand response cost, and the user satisfaction conversion cost; b i ,c i is the gas turbine cost factor; are the cost coefficients for shifting load and interrupting load to participate in demand response; Convert the cost coefficient to the user satisfaction index; is the energy storage charging and discharging cost coefficient; is the charging and discharging power of energy storage; Output power for the gas turbine; are the transfer and interruption load powers participating in demand response, respectively; are the electric load power before and after the response respectively; The lower-level constraints include: power balance constraints, gas turbine constraints, power purchase and sales constraints, electric vehicle constraints, commercial load constraints, battery energy storage system constraints, air conditioning constraints, demand response constraints and revenue constraints.

9. The method according to claim 6, characterized in that The process of solving the upper-level dynamic pricing model of the distribution network operator and the lower-level virtual power plant aggregate model based on the KKT condition includes: using the KKT condition to transform the upper-level dynamic pricing model of the distribution network operator and the lower-level virtual power plant aggregate model into a nonlinear single-layer problem containing complementary relaxation conditions and bilinear products, and then using the Big-M method to process the complementary relaxation conditions of the Lagrange multipliers and constraints, and linearizing the bilinear product through the strong duality theory, finally forming an equivalent nested game two-level optimization model.

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