Hybrid game optimization operation method considering uncertainty of renewable energy sources

Abstract

The invention discloses a hybrid game optimization operation method considering renewable energy uncertainty. The method comprises the following steps: establishing a hybrid game optimization model comprising a shared energy storage operator, a comprehensive energy microgrid alliance and a user aggregator; in the hybrid game optimization model, a shared energy storage operator, a comprehensive energy microgrid alliance and a user aggregator perform a master-slave game; determining the transaction electricity price and electric quantity among the three parties and the electric energy transaction volume among the comprehensive energy microgrid alliance members through game playing; performing a cooperative game among the alliance members of the integrated energy microgrid, and solving an electric energy transaction price among the alliance members of the integrated energy microgrid and the integrated energy microgrid based on a Nash negotiation theory according to the electric energy transaction volume; and solving the mixed game optimization model by adopting a KKT condition, and solving a cooperative game problem by adopting an ADMM. According to the method, the uncertainty of renewable energy sources in a micro-grid and a user side is considered, and the universality of a user aggregator model is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and in particular to a hybrid game optimization operation method taking into account the uncertainty of renewable energy. Background Art

[0002] As more and more renewable energy sources participate in the dispatching of power grid systems, how to make them run safely and smoothly is an urgent problem that we need to solve. At the same time, the integrated energy microgrid (IEM) can not only improve energy efficiency, enhance energy security, and support sustainable development, but also play an important role in promoting digital transformation and intelligent management. Therefore, it plays a vital role in the current power system construction. With the deepening of research, the integrated energy microgrid system gradually forms multiple microgrid subsystems in the same distribution area. Point-to-point power trading between IEMs can achieve more flexible and efficient energy distribution and use. It promotes environmental protection and sustainable development of social economy. With the rise of the concept of sharing economy, a new energy storage model, shared energy storage operators (ESO), has been proposed, which provides a new idea for solving the problems that traditional energy storage systems are difficult to achieve power complementarity between various entities and the investment cost is relatively expensive.

[0003] In existing research, game theory is often used to solve complex conflicts of interest and interactive relationships in energy trading. This method can effectively analyze the strategic choices between different subjects and their impact on the overall benefits of the system. Some have established a master-slave game model for microgrid operators and users, while also considering shared energy storage operators, making a significant contribution to the combination of game and shared energy storage. Some have comprehensively considered the interests of operators and users of multiple microgrids in energy trading, and used Nash bargaining to reasonably distribute the benefits. Some have combined master-slave games with cooperative games to establish a hybrid game optimization model for distribution network operators and IEM alliances.

[0004] At present, there are the following deficiencies in shared energy storage, multi-microgrid, and user-side electricity trading: On the one hand, most of the hybrid game models studied above take the distribution network as the leader, shared energy storage as the follower, or shared energy storage as a factor outside the system, and the interaction between shared energy storage, microgrids, and user aggregators is insufficient. On the other hand, there is a lack of analysis of the uncertainty of renewable energy, and the segmentation research on the user side is not in place. Summary of the invention

[0005] Technical purpose: In view of the defect of unstable output of renewable energy access to distribution network in the prior art, the present invention discloses a hybrid game optimization operation method considering the uncertainty of renewable energy, taking into account the uncertainty of renewable energy in microgrid and user side. To ensure the universality of user aggregator model, the present invention simultaneously considers different types of user models of electric vehicles and demand response users, thereby improving the overall energy utilization efficiency of the system.

[0006] Technical solution: In order to achieve the above technical objectives, the present invention adopts the following technical solution.

[0007] A hybrid game optimization operation method considering the uncertainty of renewable energy sources comprises the following steps:

[0008] Step 1: Establish a hybrid game optimization model including shared energy storage operators, integrated energy microgrid alliances, and user aggregators; in the hybrid game optimization model, shared energy storage operators, integrated energy microgrid alliances, and user aggregators play a master-slave game, and all three aim to maximize their own benefits; through the game, the transaction electricity price and electricity volume among the three, as well as the electricity transaction volume among the members of the integrated energy microgrid alliance, are determined, and the three participate in scheduling through game cooperation;

[0009] Step 2: The members of the integrated energy microgrid alliance conduct cooperative games, and according to the electric energy transaction volume obtained in step 1, the electric energy transaction price between the integrated energy microgrids of the integrated energy microgrid alliance members is obtained based on the Nash negotiation theory;

[0010] Step 3: Use KKT conditions to solve the mixed game optimization model in step 1, and use the alternating direction multiplier method to solve the cooperative game problem in step 1 to obtain the results, including the transaction price and electricity between the shared energy storage operator, the integrated energy microgrid alliance and the user aggregator, as well as the transaction price and electricity between the members of the integrated energy microgrid alliance.

[0011] Beneficial effects:

[0012] 1) The dual mixed game model of master-slave game and cooperative game proposed in this invention can stimulate the response potential of producers and consumers, and the analysis results have a guiding role in improving the economic performance and safety of micro-energy grids with multiple producers and consumers;

[0013] 2) The effect of mutual transactions between IEMs can prompt IEM members to respond actively, thereby reducing the operating costs of IEM aggregates and reducing the dependence of IEM aggregates on the power grid;

[0014] 3) The user aggregator, one of the followers in the master-slave game, is refined to include electric vehicles and DR users, which is also helpful to improve the universality of the user aggregator model;

[0015] 4) The present invention handles uncertainty by solving the distributed robust boundary of renewable energy output power, which can effectively deal with the impact of its uncertainty problem on the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A system model framework diagram of a hybrid game optimization operation method considering the uncertainty of renewable energy according to an embodiment of the present invention;

[0017] Figure 2 A double-layer optimization framework diagram of a hybrid game optimization operation method considering the uncertainty of renewable energy according to an embodiment of the present invention;

[0018] Figure 3 It is a flow chart of a hybrid game optimization operation method considering the uncertainty of renewable energy according to an embodiment of the present invention;

[0019] Figure 4 The energy price of the upper-level power grid in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0020] Figure 5 It is the wind and solar output power distribution robust boundary of IEM1 in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0021] Figure 6 It is a method for sharing energy storage charging and discharging power and power state in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0022] Figure 7 An interactive electricity price curve between IEMs in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0023] Figure 8 It is an IEM1 electric and thermal load diagram in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0024] Fig. 9 The IEM2 electric and thermal load diagram in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0025] Fig.10 It is an IEM3 electric and thermal load diagram in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0026] Fig.11 A diagram showing the power changes of various types of EVs on the user side in a hybrid game optimization operation method considering the uncertainty of renewable energy in an embodiment of the present invention;

[0027] Fig.12 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In the present invention, for the problem of instability of renewable energy output access to the distribution network, a hybrid game optimization scheduling model of ESO, IEM alliance and multi-type user aggregators considering the uncertainty of renewable energy output is proposed. Based on the KKT condition and Big-M method, the double-layer problem is converted into a single-layer mixed integer linear programming problem to solve the master-slave game, and the lower-layer cooperative game is solved in combination with the ADMM algorithm. As the game leader, ESO guides the IEM alliance and user aggregators to respond by dynamically formulating electricity prices; IEM alliance and user aggregators, as game followers, perform their own optimal scheduling according to the electricity prices formulated by ESO, and return the power purchase and sale strategies of each member of the IEM alliance to ESO. At the same time, the members of the IEM alliance conduct cooperative games and use Nash negotiation theory to distribute the cooperative benefits. Secondly, for user aggregators, the present invention also considers different types of user sides, such as electric vehicles and demand response (DR) users. Then, the uncertainty of internal renewable energy on the user side in the integrated energy microgrid alliance and user aggregators is also considered, and distributed robust opportunity constraints are used for processing. Finally, in order to improve the solution efficiency of the model, the present invention adopts a method combining the KKT condition with the alternating direction multiplier method to efficiently solve the constructed multi-objective optimization problem.

[0030] As attached Figure 1 , Attachment Figure 2 and attached Fig.12 As shown, a hybrid game optimization operation method considering the uncertainty of renewable energy in this embodiment includes the following steps:

[0031] Step 1: Establish a hybrid game optimization model including shared energy storage operators (ESOs), integrated energy microgrid alliances (IEM alliances) and user aggregators; in the hybrid game optimization model, shared energy storage operators, integrated energy microgrid alliances and user aggregators play a master-slave game, and all three aim to maximize their own benefits; through the game, the transaction electricity price and electricity volume among the three, as well as the electricity transaction volume among the members of the integrated energy microgrid alliance are determined, and the three participate in scheduling through game cooperation;

[0032] The hybrid game optimization model is also an optimization model of the shared energy storage-multi-microgrid hybrid game containing multiple types of user aggregators, and the game leader is the shared energy storage operator model.

[0033] Among them, the comprehensive energy microgrid alliance integrates two forms of energy, electric energy and thermal energy, and can achieve optimal configuration and flexible scheduling of energy;

[0034] The integrated energy microgrid in the integrated energy microgrid alliance can also be referred to as a microgrid. The microgrid can trade electricity with shared energy storage operators. When there is an oversupply of renewable energy in a microgrid in the integrated energy microgrid alliance, the excess electricity can be sold to shared energy storage operators or other microgrids. The integrated energy microgrid alliance mainly includes electric energy storage, gas boilers (GB), combined heat and power (CHP), wind turbines and photovoltaic systems. The integrated energy microgrid alliance uses these devices to meet their respective electricity and heat load requirements. The integrated energy microgrid alliance helps to improve its own operating efficiency and resource utilization;

[0035] For user aggregators, the present invention considers two specific user-side types, namely electric vehicles and demand response users. The user-side load is mainly composed of electric load and thermal load. The electricity selling price set by the shared energy storage operator is set to be lower than the electricity selling price on the grid side. Therefore, the present invention believes that users only purchase and sell electricity from shared energy storage operators. Each integrated energy microgrid alliance member and user side adjusts its own energy price, transaction volume, and electricity price according to its own operating objectives and resource characteristics, and optimizes the energy output of its internal units to effectively meet the demand for electricity and thermal loads. Through this optimization method, the operating efficiency of the system can be improved and energy resources can be reasonably allocated.

[0036] The hybrid game optimization model includes a model corresponding to the game leader and a mathematical model corresponding to the game follower. In the present invention, the shared energy storage operator is used as the game leader, and the integrated energy microgrid alliance and user aggregator are used as game followers. Each model is designed with corresponding objective functions and constraints.

[0037] Step 1.1: The game leader, i.e. the model of the shared energy storage operator, has the following objective function design process:

[0038] Shared energy storage operators aim to maximize their own benefits, including the costs and benefits of purchasing and selling electricity with external power grids, user aggregators, and integrated energy microgrid alliances:

[0039] maxU ESO =I IEM +I UA -CESO -C TR (1)

[0040] Where U ESO To share the benefits of energy storage operators, I IEM To share the electricity interactive benefits between energy storage operators and integrated energy microgrid alliances; UA The electricity interactive benefits between the shared energy storage operator and the user aggregator; C ESO is the charging and discharging cost of the storage equipment in the shared energy storage operator; C TR is the interaction cost between the shared energy storage operator and the external power grid; its calculation formula is as follows:

[0041]

[0042] Where: 1≤t≤T, 1≤i≤N, T is the 24 moments of the day, N is the number of microgrids in the integrated energy microgrid alliance, that is, the number of IEMs; are the purchase and sale prices of electricity from the shared energy storage operator of microgrid i in the integrated energy microgrid alliance at time t; They are respectively the amount of electricity purchased and sold by microgrid i in the integrated energy microgrid alliance from the shared energy storage operator at time t; The electricity purchase and sale price of the user aggregator to the shared energy storage operator at time t; are the amount of electricity purchased and sold by the user aggregator to the shared energy storage operator at time t; λ is the charging and discharging cost coefficient of the energy storage equipment in the shared energy storage operator; P t l，c , P t l，d is the charge and discharge amount of the energy storage equipment in the shared energy storage operator at time t; P is the price of electricity purchased and sold by the shared energy storage operator to the external power grid at time t; t DB , P t DS It is the amount of electricity purchased and sold by the shared energy storage operator to the external power grid at time t.

[0043] The constraints of the shared energy storage operator’s model are as follows:

[0044] The purchase and sale prices set by shared energy storage operators should be within certain limits, namely:

[0045]

[0046] Where: They are the upper and lower limits of the electricity purchase price of the Integrated Energy Microgrid Alliance; They are the upper and lower limits of the electricity sales price of the Integrated Energy Microgrid Alliance; They are the upper and lower limits of the electricity purchase price of the user aggregator respectively; They are respectively the upper and lower limits of the electricity selling price of user aggregators.

[0047] In addition, in order to prevent shared energy storage operators from always setting the highest energy sales price for their own interests, it is necessary to constrain the average purchase and sales prices of the integrated energy microgrid alliance and user aggregators, that is,

[0048]

[0049] Where: are the average power purchase and power sales prices of microgrid i in the integrated energy microgrid alliance, are the average prices of electricity purchased and sold by user aggregators, respectively.

[0050] At the same time, the shared energy storage operator should meet the following constraints during operation, namely:

[0051]

[0052] SOC min ≤SOC(t)≤SOC max (15)

[0053]

[0054] Where: SOC(t) represents the state of charge (SOC) of the shared energy storage operator at time t, that is, the SOC at the current moment; SOC(t-1) represents the state of charge at the shared energy storage operator at time t-1, that is, the SOC at the previous moment; η ESO，cha , η ESO，dis They are the charging and discharging efficiency of shared energy storage operators respectively; are the charging and discharging power of the shared energy storage operator at time t; Δt is the time interval from time t to the next time t+1; SOC min SOC max are the minimum and maximum states of charge of the shared energy storage operator, respectively; They are the upper and lower limits of the amount of electricity that a shared energy storage operator can sell to the external power grid; They are the upper and lower limits of the amount of electricity that a shared energy storage operator can purchase from the external power grid; They are the upper and lower limits of the charging capacity of energy storage equipment in the shared energy storage operator respectively; They are respectively the upper and lower limits of the discharge capacity of energy storage equipment in shared energy storage operators.

[0055] The power balance constraints of the shared energy storage operator are:

[0056]

[0057] Step 1.2: One of the game followers, the model of the integrated energy microgrid alliance, the design process of its objective function includes:

[0058] Members of the integrated energy microgrid alliance aim to maximize their own benefits, including the revenue from electricity purchase and sales with shared energy storage operators, the gas consumption costs of CHP and GB, and the charging and discharging costs of the integrated energy microgrid's energy storage equipment.

[0059]

[0060] Where: U IEMi is the comprehensive income of IEMi; It is the sum of the electricity interaction benefits between IEMi and other IEMs; The revenue from electricity purchase and sale of IEMi represents the total revenue from electricity purchase and sale from other places, which may be positive or negative; is the gas consumption cost of CHP and GB; is the charging and discharging cost of IEMi's power storage equipment; its calculation formula is as follows:

[0061]

[0062] In the formula: 1≤j≤N, ω is the gas purchase cost per unit of gas. are the natural gas consumption of CHP and GB at time t of IEMi, respectively; μ is the operation and maintenance cost of all IEMi power storage equipment; are the charge and discharge amounts of the power storage device at IEMi time t respectively; is the sum of the electric energy interaction benefits of IEMi and other IEMs; u i-j，t is the electricity transaction price between IEMi and IEMj at time t; is the amount of electricity traded between IEMi and IEMj at time t. is the electricity price sold by IEMi to ESO at time t, is the electricity purchase price of IEMi from ESO at time t, is the electricity sales power of IEMi at time t, is the power purchased by IEMi at time t.

[0063] The constraints of the model of the Integrated Energy Microgrid Alliance are as follows:

[0064] 1) Interaction power constraints between microgrids and shared energy storage service providers:

[0065]

[0066] Where: They are the maximum power purchased and sold by IEMi respectively; is the equivalent interactive power of the microgrid to the shared energy storage electricity seller at time t.

[0067] 2) Operational constraints of CHP / GB:

[0068]

[0069] Where: is the power generation of CHP in IEMi at time t, δ 1 is the power conversion efficiency of CHP, γ is the heat generated per unit volume when the fuel is burned; and are the upper and lower limits of the power generation of CHP in IEMi at time t; is the heating power of CHP in IEMi at time t, δ 2 is the heat conversion efficiency of CHP; is the heating power of GB in IEMi at time t, δ 3 is the heat conversion efficiency of GB; are the upper and lower limits of the heating power of GB in IEMi at time t.

[0070] 3) Operation constraints of IEMi’s energy storage equipment:

[0071]

[0072] Where: is the electrical energy storage capacity of IEMi at time t, is the electric energy storage capacity of IEMi at time t-1; They are the charge and discharge efficiency of IEMi respectively; They are the upper and lower limits of IEMi storage capacity respectively; They are the maximum charge and discharge capacity constraints of IEMi, is the initial electrical energy storage capacity of IEMi, is the final electrical energy storage capacity of IEMi.

[0073] 4) Constraints on P2P electricity trading between integrated energy microgrids:

[0074] P2P electricity trading needs to ensure that the transaction volume is within the limit and the transaction volume between integrated energy microgrids is equal, that is,

[0075]

[0076] Where: The maximum interaction limit between integrated energy microgrids is is the amount of electricity traded between IEMi and IEMj at time t, is the amount of electricity traded between IEMj and IEMi at time t.

[0077] 5) Electric and thermal power balance constraints considering the uncertainty of renewable energy:

[0078] In practical applications, the uncertainty of renewable energy often has a certain impact on the system. Therefore, corresponding measures need to be taken to deal with and optimize this problem. In this paper, a fuzzy set based on Wasserstein distance drive is used to construct a distributionally robust chance constraint (DRCC) model.

[0079] (a) Constructing fuzzy sets driven by Wasserstein distance

[0080] The present invention uses Wasserstein distance to measure the difference between two different probability distributions and constructs a fuzzy set based on this distance parameter. Define fuzzy sets as close to the empirical distribution A collection of a series of distributions, where the historical data of renewable energy refers to the output of renewable energy in the historical period; the empirical distribution The Wasserstein distance from any other probability distribution is The definition is as follows:

[0081]

[0082] Where: Ξ is the support set of the uncertain variable ξ, is the empirical distribution of the uncertain variable ξ, is the lower bound under Q distribution, dξ is the derivative of ξ, For Derivative, Q is ξ and The joint distribution of renewable energy output fuzzy set D in the present invention can be expressed as:

[0083]

[0084] Where: ψ(Ξ) is the total probability distribution on the support set Ξ; ε is the radius of the Wasserstein ball. D represents the Wasserstein ball with the empirical distribution as the center and ε as the radius. The confidence level is 1-β, β is the violation probability, to ensure accurate distribution within the fuzzy set. The calculation formula of the radius ε is:

[0085]

[0086] Where: C is a constant, which can be obtained by solving the following optimization problem:

[0087]

[0088] Where: is M historical samples, for is the sample mean of , ρ is a parameter, which can be set according to the actual situation and can be any value; ||·|| is the 1-norm.

[0089] (b) Demand DRCC model conversion, constructing the distributed robust chance constraint (DRCC) model:

[0090] The present invention combines the distributional robust optimization method and chance constraints, constructs a DRCC model, and formulates the following chance constraints:

[0091] inf p∈D P(0≤P t WT +P t PV ≤P t REW )≥1-α (44)

[0092] Where: P(·) is the probability of the event in brackets, P t WT is the output of the fan at time t, P t PV is the photovoltaic output at time t, P t REW is the total output of renewable energy at time t; α is the violation probability, which means that the minimum probability of satisfying the constraint condition under the worst probability distribution in the Wasserstein sphere is 1-α, and the sum of the power generated by the wind turbine and photovoltaic must be less than the total power generation output. The present invention re-describes the chance constraint by constructing a distributed robust boundary of the output power of renewable energy, as follows:

[0093]

[0094] Where: is the lower bound of renewable energy output; to ensure that the sum of the output of renewable energy sources under the worst probability distribution in the Wasserstein sphere must be less than or equal to the robust lower bound of the total output power, the lower bound variable is required The following constraints are met:

[0095]

[0096] Combined with formula (45), the distributed robust chance constraint problem is constructed as follows:

[0097]

[0098] Then use the sample information Wasserstein sphere radius ε to solve equation (47) Output the lower bound value, formula (47) is restated as:

[0099]

[0100] Where: is the robust lower bound of renewable energy output power at time t; N REW is the number of renewable energy samples; is the mth sample of the renewable energy output power sample set at time t, 1≤m≤N REW ; M is a large constant; v t , Z t，m is an auxiliary variable, which is set randomly; q t，m It is a binary variable, which can be either 1 or 0.

[0101] The above formula can be used to find the solution of the robust lower bound of renewable energy output power, which can be substituted into the electric and thermal power balance constraint, namely:

[0102]

[0103] Where: is the robust lower bound of the IEMi renewable energy output power at time t, is the electrical load in IEMi at time t, is the heat load in IEMi at time t.

[0104] Step 1.3, one of the game followers, in the design process of the user aggregator model, since the user aggregator includes two specific user-side types, namely electric vehicles and demand response users, the design of the user aggregator model also includes two parts: the electric vehicle charging station model and the demand response user model;

[0105] For the electric vehicle charging station model, the design process is as follows:

[0106] Compared with a single electric vehicle model, a multi-type electric vehicle cluster model in an electric vehicle system can more effectively capture the actual operating status of a charging station. The present invention lists five different initial power levels, arrival times, departure times, etc. to represent different types of electric vehicles.

[0107] 1) Constraints

[0108]

[0109] Where: are respectively the charge and discharge amounts of the w-th type of electric vehicle at time t, where the subscript w represents the type of the electric vehicle, and there are five types in this embodiment; are the upper limits of the charge and discharge capacity of the wth type of electric vehicle; w，t They are 0-1 variables of the charging and discharging process. In the charging scenario, 1 represents charging, and in the discharging scenario, 1 represents discharging; S w，t is the remaining power of the w-th electric vehicle at time t; η EV，cha , η EV，dis They are the charging and discharging efficiency of EV (electric vehicle) respectively; is the battery capacity of the w-th type of electric vehicle, is the remaining power of the w-th electric vehicle when it leaves.

[0110] 2) Calculation of electric vehicle charging and discharging satisfaction:

[0111]

[0112] Where: SAT w The charging and discharging satisfaction of the w-th electric vehicle; UT w，t is the unit utility of the wth type electric vehicle.

[0113] For the demand response user model, the design process is as follows:

[0114] The second user side type considered by the present invention is the demand response user, in which the electric load is mainly divided into transferable load and curtailable load. At the same time, the two types of electric loads should meet the following requirements:

[0115]

[0116] Where: P t cut For the load that can be reduced, P cut，max is the maximum value of the load that can be reduced, is the transferable electrical load (transfer-in) at time t, It is the transferable electrical load (transfer out); are the maximum values ​​of the transferable load in and out respectively. Meanwhile, the total transfer load of the demand response user is 0.

[0117] The heat load of demand response users mainly appears in the form of smart buildings. The temperature in a certain period of time in the building depends on the temperature in the previous period, the current heat load and the temperature outside the building. The constraints are as follows:

[0118]

[0119] Where: TEM in (t) is the temperature inside the building at time t, TEM in (t-1) is the temperature inside the building at time t-1, TEM out(t) is the temperature outside the building at time t, is the heat load of the smart building at time t; λ1, λ2, and λ3 are the weight coefficients of the temperature in the previous period, the current heat load, and the temperature outside the building. At the same time, the indoor temperature should also meet the following requirements:

[0120]

[0121] Where: is the upper limit of the temperature in the building; The upper limit of the heat load of the smart building; are the values ​​above and below the initial temperature, respectively; For ideal indoor temperature.

[0122] According to the constraint adjustment design of the above two types of users, the overall model of user aggregator is as follows:

[0123] The power balance constraint of the user aggregator is:

[0124]

[0125] Where: W is the total number of all types of electric vehicles; RAT(w) is the proportion of the wth type of vehicles in the total number; is the total number of cars in category w; P t l is the electricity load of the demand response user, P t cut To reduce the load; The efficiency of converting electricity to heat; They are the electricity purchased and sold by the user aggregator to the shared energy storage operator at time t; It is a robust lower bound for the output power of renewable energy sources at the user side.

[0126] The objective function of the user aggregator model is:

[0127]

[0128] Where: U UA is the revenue of the user aggregator; ψ1, ψ2, ψ3 are the cost coefficients of the reducible load, transferable load and temperature deviation respectively. are the values ​​above and below the initial temperature, respectively.

[0129] Step 2: The members of the integrated energy microgrid alliance conduct cooperative games. According to the electricity trading volume obtained in step 1, the electricity trading price between the members of the integrated energy microgrid alliance is obtained based on the Nash negotiation theory. That is, using the Nash negotiation theory, the cooperative game problem of the integrated energy microgrid alliance is decomposed into two sub-problems: the problem of maximizing the aggregate benefit and the problem of allocating cooperative benefits, and reasonable benefits are allocated through this theory.

[0130] In order to effectively promote the reasonable interaction between the sub-microgrids in the integrated energy microgrid alliance, the present invention introduces the Nash negotiation theory as the core tool for the internal cooperative game of the integrated energy microgrid alliance, so as to achieve mutual benefit and win-win results. Based on this theory, the present invention constructs the Nash negotiation model of the integrated energy microgrid alliance. As shown in formula (60), the product solution of the Nash negotiation model of the integrated energy microgrid alliance is the Pareto equilibrium solution of cooperation.

[0131]

[0132] Where: U i The benefits gained by IEMi from participating in the negotiations; The benefit obtained by IEMi without participating in the negotiation, i.e. the negotiation breakdown point.

[0133] 1) Sub-problem 1: Maximizing the benefits of the aggregate;

[0134]

[0135] Where: U IEM For the benefit of alliance cooperation.

[0136] 2) Sub-problem 2: Distribution of cooperative benefits;

[0137]

[0138] Where: Sell ​​electricity price for microgrid, is the electricity price purchased by the microgrid; variables with superscript * represent the optimal solution obtained in sub-problem 1; Represents the benefit of IEMi obtained in sub-problem 1.

[0139] Step 3: Use KKT conditions to solve the hybrid game optimization model in step 1, and use the alternating direction multiplier method to solve the cooperative game problem in step 1 to obtain the results, namely the transaction price and quantity between ESO, IEM aggregator and user aggregator, as well as the transaction price and quantity between IEM aggregator members. Among them, IEM aggregator is also an integrated energy microgrid alliance.

[0140] KKT condition solves the master-slave game model. The solution steps are as follows:

[0141] The double-layer model constructed by the present invention has nonlinear terms and nonlinear constraints, and there is a coupling relationship between the upper and lower models, which makes it difficult to solve directly. By constructing the Lagrangian function of the lower model, based on the KKT complementary relaxation condition of the lower model, the lower model is converted into the constraint condition of the upper model, and then the nonlinear terms in the converted single-layer nonlinear model are linearized using the Big-M method to form a single-layer mixed integer linear programming problem, which is easy to solve.

[0142] The KKT condition is used to transform the lower model into the constraints of the upper model, which can be expressed in the following compact form:

[0143]

[0144] Using Lagrange multipliers and complementary relaxation conditions, the above problem can be written as:

[0145]

[0146] The alternating direction multiplier method, ADMM, solves the IEM alliance cooperation game:

[0147] Using ADMM to solve the optimal electricity exchange price between IEM alliances, it is necessary to ensure that the exchange prices between two IEMs are equal, and decouple the P2P transaction price of IEM, and we can get

[0148] u i-j，t =u j-i，t =z i-j，t (63)

[0149] In the formula, z i-j，t is the shared variable of the energy interaction price between IEMi and IEMj. ADMM decomposes the complex optimization problem into multiple simple small problems. The specific steps are as follows:

[0150] 1) The IEMi distributed optimization model obtained by decomposition is:

[0151]

[0152] Where: L i is the Lagrangian function, is the transaction price between microgrid i and microgrid j, ω i-j，t represents the dual variable between IEMi and IEMj; Λ represents the penalty factor of the problem, where the problem refers to the IEMi distributed optimization model.

[0153] 2) Each IEM transaction price optimization variable is updated according to the following formula:

[0154] u i-j，t (k+1)=argminL i (λi-j，t (k),u i-j，t (k),u j-i，t (k)) (65)

[0155] u j-i，t (k+1)=argminL j (λ j-i，t (k),u j-i，t (k),u i-j，t (k+1)) (66)

[0156] Where: u i-j，t (k+1),u j-i，t (k+1) are the interactive electricity prices of microgrid i and microgrid j at the K+1th iteration at time t, λ i-j，t (k) is the Kth multiplier variable.

[0157] 3)λ i-j，t The update formula is:

[0158] λ i-j，t (k+1)=λ i-j，t (k)+ρ(u i-j，t (k+1)-u j-i，t (k+1)) (67)

[0159] Where: i-j，t (k+1) is the K+1th multiplier variable.

[0160] 4) Determine the convergence conditions:

[0161]

[0162] Where: Φ is the convergence coefficient of ADMM solution.

[0163] In each iteration, whether the convergence is achieved is determined based on the convergence criterion. If the convergence criterion is met, the loop is exited. If the convergence criterion is not met, the iteration continues until the convergence criterion is met.

[0164] In this embodiment, IEM stands for integrated energy microgrid, ESO stands for shared energy storage operator, UA stands for user, and DR stands for demand response.

[0165] like Figure 3As shown, there are nonlinear terms and nonlinear constraints in the two-layer model constructed by the present invention, and there is a coupling relationship between the upper and lower models, which makes it difficult to solve directly. By constructing the Lagrangian function of the lower model, based on the KKT complementary relaxation condition of the lower model, the lower model is converted into the constraint condition of the upper model, and then the Big-M method is used to linearize the nonlinear terms in the converted single-layer nonlinear model to form a single-layer mixed integer linear programming problem, which is easy to solve. In order to effectively promote the reasonable interaction between each sub-microgrid in the IEM aggregate, the present invention introduces the Nash negotiation theory as the core tool for cooperative game within the microgrid aggregate, so as to achieve mutual benefit and win-win results. Based on this theory, the present invention constructs a Nash negotiation model of the IEM aggregate to complete the reasonable allocation of each IEM.

[0166] The following is a simulation verification of the content of this embodiment:

[0167] The rationality of the method proposed in the embodiment of the present invention is verified by simulation. The parameters used in each model are listed in Table 1. The ESO power purchase price and the on-grid power price are as follows: Figure 4 As shown in Figure 2, the electrical heating loads of IEM1 to IEM 3 are as follows: Figure 8-Figure 10 The simulation and verification software used is Matlab R2021b, and the commercial solvers Cplex and Gurobi are called for simulation and verification.

[0168] Table 1. Related parameter settings: ESO basic parameters

[0169]

[0170]

[0171] IEM basic parameters

[0172]

[0173]

[0174] User Aggregator Basic Parameters

[0175]

[0176] Figure 5 There are 5 wind and solar power curves corresponding to the samples, all of which are shown in gray lines. Figure 5 It can be seen that increasing the violation probability α will increase the lower bound of the renewable energy output power, and conversely, decreasing the violation probability α will reduce the lower bound of the renewable energy output power. Therefore, we conclude that the larger the violation probability α, the more radical the decision made by the system.

[0177] In order to verify the feasibility and effectiveness of the method proposed in the present invention, four scenarios were set up for comparison in the simulation verification process of the present invention.

[0178] Scenario 1: The present invention proposes a two-tier game relationship between ESO, IEM alliance and user aggregator, and considers the uncertain output of renewable energy of IEM alliance and user aggregator.

[0179] Scenario 2: Consider the master-slave game relationship between ESO, IEM alliance and user aggregator, do not consider the cooperative game relationship between IEM alliances, and consider the uncertain output of renewable energy of IEM alliance and user aggregator.

[0180] Scenario 3: Considering the two-tier game relationship between ESO, IEM Alliance and user aggregators, the wind and solar output is reduced by 10% based on Scenario 1.

[0181] Scenario 4: Considering the two-tier game relationship between ESO, IEM Alliance and user aggregators, the wind and solar output is increased by 10% based on Scenario 1.

[0182] The ESO benefits and the costs of IEM aggregators and user aggregators in each scenario are shown in Table 2 and Table 3 respectively.

[0183] Table 2 DSO benefits under various scenarios

[0184]

[0185] Table 3 Costs of IEM aggregators and user aggregators in various scenarios

[0186]

[0187] Comparing Scenario 1 and Scenario 2 in Table 2 and Table 3, since Scenario 2 does not consider the cooperation between IEM alliances, IEM alliance members will be too dependent on ESO, and the power purchased from ESO will increase. Therefore, the ESO revenue in Scenario 2 is higher than that in Scenario 1, and the operating cost of the IEM alliance is increased. This also shows that P2P power sharing among IEM alliance members can effectively reduce the operating cost of the IEM alliance and improve energy utilization.

[0188] Figure 6 In the data, the shared energy storage operator reached the maximum charging power at 08:00, 09:00, and 10:00, and reached the maximum energy storage capacity at 11:00; reached the maximum discharge power at 13:00, 19:00, and 22:00, and was at the minimum energy storage capacity at 22:00. In the remaining time periods, the shared energy storage operator maintained its power balance through power transactions with other entities.

[0189] Figure 7In the above figure, the electricity purchase price set by ESO in scenario 1 is always lower than that set by ESO in scenario 2 because scenario 1 takes into account the energy cooperation transactions of the IEM alliance more than scenario 2. Therefore, the dependence of IEM members on ESO is reduced. At this time, ESO promotes interaction between IEM and itself by setting a lower electricity purchase price, so as to improve its own benefits.

[0190] By comparing scenarios 1, 2, 3 and 4 in Tables 2 and 3, it can be seen that, based on the method of the present invention, increasing the output of renewable energy will reduce the comprehensive operating cost and the revenue value of ESO. This is because increasing the output of renewable energy will reduce the output power of components such as CHP and gas boilers in the system, thereby reducing the amount of electricity and gas purchased by the IEM alliance and user aggregators, resulting in a reduction in the revenue of ESO. At the same time, it is verified that increasing the output of renewable energy can make the system operation have better low-carbon economy.

[0191] The operating costs and benefits of the IEM Alliance before and after the cooperation are shown in Table 4.

[0192] Table 4 Costs and benefits before and after IEM cooperation

[0193]

[0194] As can be seen from Table 4, the revenue of each member of the IEM alliance increased by 152.60 yuan, 152.68 yuan, and 152.74 yuan respectively before and after the cooperation, with a total increase of 458.02 yuan. This shows that the cooperative game between IEM alliances can effectively reduce their respective operating costs.

[0195] Analysis of EV operation results within user aggregators,In order to further study the characteristics of EV clusters, the charging and discharging characteristics of EVs are analyzed. The specific results are as follows Fig.11 As shown. Combined with the time-of-use electricity price, the EV cluster chooses to charge during the low electricity price periods of 0:00-6:00 and 14:00-18:00, and sells electricity to the ESO during the high electricity price periods of 6:00-14:00 and 18:00-22:00 to obtain revenue. At the same time, the EV cluster charging and discharging power boundary formed by tapping the dispatchable potential of the EV cluster has strong scalability, providing a foundation for the subsequent upgrade and construction of the park.

[0196] The "first" and "second" in the names such as "first" and "second" (if any) mentioned in the embodiments of the present application are only used as name identifiers and do not represent the first or second in order.

[0197] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, and the memory can be various types of memory, which can be a random access memory, a read-only memory, a flash memory, etc., such as a read-only memory (English: read-only memory, ROM) / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0198] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A hybrid game optimization operation method considering the uncertainty of renewable energy, characterized in that: The method comprises the following steps: Step 1: Establish a hybrid game optimization model including shared energy storage operators, integrated energy microgrid alliances, and user aggregators; in the hybrid game optimization model, shared energy storage operators, integrated energy microgrid alliances, and user aggregators play a master-slave game, and all three aim to maximize their own benefits; through the game, the transaction electricity price and electricity volume among the three, as well as the electricity transaction volume among the members of the integrated energy microgrid alliance, are determined, and the three participate in scheduling through game cooperation; Step 2: The members of the integrated energy microgrid alliance conduct cooperative games, and according to the electric energy transaction volume obtained in step 1, the electric energy transaction price between the integrated energy microgrids of the integrated energy microgrid alliance members is obtained based on the Nash negotiation theory; Step 3: Use KKT conditions to solve the mixed game optimization model in step 1, and use the alternating direction multiplier method to solve the cooperative game problem in step 1 to obtain the results, including the transaction price and electricity between the shared energy storage operator, the integrated energy microgrid alliance and the user aggregator, as well as the transaction price and electricity between the members of the integrated energy microgrid alliance.

2. A hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 1, characterized in that: In the hybrid game optimization model, the game leader, i.e. the model of the shared energy storage operator, has the following objective functions: maxU ESO =I IEM +I UA -C ESO -C TR Among them, U ESO To share the benefits of energy storage operators, I IEM To share the electricity interactive benefits between energy storage operators and integrated energy microgrid alliances; UA The electricity interactive benefits between the shared energy storage operator and the user aggregator; C ESO is the charging and discharging cost of the storage equipment in the shared energy storage operator; C TR To share the interaction costs between energy storage operators and external power grids; The constraints include: The purchase and sale prices of electricity set by shared energy storage operators should be within certain limits; Constraints are imposed on the average purchase and sales prices of the integrated energy microgrid alliance and user aggregators; Operational constraints of shared energy storage operators; Electric power balance constraints for shared energy storage operators.

3. A hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 2, characterized in that: Among them, 1≤t≤T, 1≤i≤N, T is the 24 moments of the day, and N is the number of microgrids in the integrated energy microgrid alliance, that is, the number of IEMs; are the purchase and sale prices of electricity from the shared energy storage operator of microgrid i in the integrated energy microgrid alliance at time t; They are respectively the amount of electricity purchased and sold by microgrid i in the integrated energy microgrid alliance from the shared energy storage operator at time t; The price of electricity purchased and sold by the user aggregator to the shared energy storage operator at time t; are the electricity purchased and sold by the user aggregator to the shared energy storage operator at time t; λ is the charging and discharging cost coefficient of the energy storage equipment in the shared energy storage operator; is the charge and discharge amount of the energy storage equipment in the shared energy storage operator at time t; P is the price of electricity purchased and sold by the shared energy storage operator to the external power grid at time t; t DB , P t DS It is the amount of electricity purchased and sold by the shared energy storage operator to the external power grid at time t.

4. The hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 1 is characterized by: In the hybrid game optimization model, one of the game followers, in the model of the integrated energy microgrid alliance, the objective function includes: Among them, U IEMi is the comprehensive income of IEMi; It is the sum of the electricity interaction benefits between IEMi and other IEMs; The revenue from the purchase and sale of electricity for IEMi; The gas consumption cost of combined heat and power and gas boilers in the integrated energy microgrid alliance; The charging and discharging costs of IEMi’s energy storage equipment; The constraints include: Interactive power constraints between microgrids and shared energy storage service providers; Operational constraints of CHP / GB; IEMi’s operating constraints for electricity storage equipment; Constraints on P2P electricity trading between integrated energy microgrids; Electric-thermal power balance constraints considering uncertainties in renewable energy sources.

5. A hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 4, characterized in that: Among them, 1≤t≤T, 1≤i≤N, T is the 24 moments of the day, N is the number of microgrids in the integrated energy microgrid alliance, that is, the number of IEMs; 1≤j≤N, ω is the gas purchase cost per unit of gas; are the natural gas consumption of the combined heat and power generation and gas boiler of IEMi at time t, respectively; μ is the operation and maintenance cost of the power storage equipment of all IEMi; I and I are the charge and discharge amounts of the storage device at IEMi time t respectively; i TRADE is the sum of the electric energy interaction benefits of IEMi and other IEMs; u i-j，t is the electricity transaction price between IEMi and IEMj at time t; is the amount of electricity traded between IEMi and IEMj at time t; is the electricity price sold by IEMi to the shared energy storage operator at time t, is the electricity purchase price that IEMi pays to the shared energy storage operator at time t, is the electricity sales power of IEMi at time t, is the power purchased by IEMi at time t.

6. The hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 1, characterized in that: In the electric and thermal power balance constraint considering the uncertainty of renewable energy, a distributed robust chance constraint model, namely DRCC model, is constructed based on the fuzzy set driven by Wasserstein distance. Among them, 1≤i≤N, N is the number of microgrids in the integrated energy microgrid alliance, that is, the number of IEMs; 1≤j≤N; is the robust lower bound of IEMi renewable energy output power, is the amount of electricity traded between IEMi and IEMj at time t, is the power generation of CHP in IEMi at time t, is the electricity sales power of IEMi at time t, is the power purchased by IEMi at time t, are the charge and discharge amounts of the power storage device at IEMi time t respectively; is the heating power of CHP in IEMi at time t, is the heating power of GB in IEMi at time t.

7. The hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 1 is characterized by: In the hybrid game optimization model, one of the game followers, the user aggregator’s model includes an electric vehicle charging station model and a demand response user model; in the user aggregator’s model, the objective function includes: Among them, ψ1, ψ2, and ψ3 are the cost coefficients of curtailable load, transferable load, and temperature deviation, respectively; are the values ​​above and below the initial temperature, SAT w P is the charging and discharging satisfaction of the w-th type of electric vehicle; t cut To reduce the load, is the transferable electrical load (transfer-in) at time t, is the transferable electric load (transfer out); W is the total number of electric vehicles of all types; The price of electricity purchased and sold by the user aggregator to the shared energy storage operator at time t; are the electricity purchased and sold by the user aggregator to the shared energy storage operator at time t; In the user aggregator model, the constraints include the power balance constraints of the user aggregator, specifically: Where: RAT(w) is the proportion of the wth type of vehicles in the total number; is the total number of vehicles in category w; To respond to the user's electricity load, The efficiency of converting electricity to heat; It is the robust lower bound of the output power of renewable energy at the user side; is the heat load of the smart building at time t; are the charging and discharging amounts of the w-th electric vehicle at time t respectively.

8. The hybrid game optimization operation method considering the uncertainty of renewable energy according to claim 1 is characterized by: In step 2, the Nash negotiation theory is used to decompose the cooperative game problem of the integrated energy microgrid alliance into two sub-problems: the problem of maximizing the aggregate benefit and the problem of allocating cooperative benefits, and this theory is used to make reasonable benefit distribution.

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