Distributed energy sharing method and system based on hybrid game model

Abstract

The invention discloses a distributed energy sharing method and system based on a hybrid game model, and relates to the field of economic operation of a power distribution network, and the method comprises the steps: taking upper-layer power distribution network income maximization as an objective function, considering power distribution network price constraints and power distribution network operation constraints, and building a power distribution network-aggregator master-slave game model; and minimizing the operation cost of the lower-layer optical storage all-in-one machine aggregator alliance as a target function, considering the operation constraint of the aggregator, establishing an aggregator alliance cooperative game model, and forming a power distribution network-aggregator mixed game model by the cooperative game model and a master-slave game model. Solving the power distribution network-aggregator mixed game model to obtain a power distribution network-aggregator distributed energy sharing optimal result; the hybrid game model is considered in the distribution network-aggregator distributed energy sharing, so that the overall benefit of the aggregator cluster and the rationality of the transaction are effectively improved, and the benefit of the aggregator in the alliance can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of economic operation of distribution networks, and in particular to a distributed energy sharing method and system based on a hybrid game model. Background Art

[0002] With the development of the new energy industry, more and more distributed photovoltaics, energy storage and electric vehicles are entering rural areas. Due to the configuration of distributed energy storage and electric vehicles, many users have transformed from electricity consumers to electricity producers and consumers. In the same distribution area, by aggregating flexible resources such as distributed photovoltaics, energy storage, electric vehicles and loads, multiple village-level photovoltaic storage integrated machine aggregators have been formed. Through point-to-point transactions between aggregators, the economic operation and photovoltaic consumption of the aggregator alliance can be effectively realized. Regarding the operation of the distribution network and the aggregator cluster, on the one hand, the distribution network occupies a dominant position in the electricity price setting link. It sets electricity prices for aggregators by obtaining electric power transaction information. The rationality of electricity price setting will affect the transactions between the distribution network and the aggregators. On the other hand, the alliances formed by aggregators through point-to-point transactions have brought about the problem of profit distribution. Uneven profit distribution will also cause the rupture of the aggregator alliance.

[0003] Current technologies mainly focus on energy trading and sharing mechanisms between distribution networks and aggregators, but there are some shortcomings, especially in terms of efficiency, revenue distribution and coordinated operation; many existing systems allocate power resources through the pricing mechanism of the power market. Usually, market prices are determined by supply and demand, but this model lacks flexibility and adaptability in the distributed energy environment; some studies use different optimization strategies (such as linear programming and dynamic programming) to coordinate energy sharing between distribution networks and aggregators. These methods generally focus on a single goal (such as cost minimization or benefit maximization), but ignore the game relationship and revenue distribution between different participants; existing energy management systems sometimes integrate some basic prediction models to improve resource utilization, but rarely consider the interaction and strategic response of multiple participants; the existing distribution network electricity price mechanism is mostly fixed and cannot flexibly respond to market changes and the real-time situation of distributed resources. This leads to the inability to effectively motivate aggregators to participate in power trading in some cases, thus affecting the effect of energy sharing; traditional methods are often unfair in terms of revenue distribution, fail to fully consider the contributions of each aggregator, and easily cause dissatisfaction, leading to the breakdown of the aggregator alliance. However, the existing mechanisms usually adopt an equal sharing or fixed ratio allocation method, which cannot reflect the actual supply, demand and contribution situation; the current optimization methods usually only focus on a single goal, such as maximizing cost or benefit, and easily ignore other important factors, such as cooperation and reputation, which may cause aggregators with less profit to withdraw from the alliance and affect the stability of the overall system; the existing methods lack the ability to conduct systematic research on the two-way interaction between distribution networks and aggregators from a game theory perspective, and usually only consider one-way benefit distribution, resulting in poor collaborative operation among the various participants. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to achieve economic operation and reasonable profit distribution between distribution networks and aggregators in a distributed energy environment through a hybrid game model to improve overall benefits and transaction rationality.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a distributed energy sharing method based on a hybrid game model, comprising:

[0008] Obtaining distribution network and aggregator operating parameters;

[0009] Obtain node power load information, aggregator power load information, and photovoltaic output information;

[0010] Based on the distribution network operation parameters and node load information, the distribution network revenue maximization is taken as the first objective function, and the distribution network pricing constraints and distribution network operation constraints are taken as constraints to establish a distribution network-aggregator master-slave game model.

[0011] According to the aggregator's operating parameters, aggregator's electrical load and photovoltaic output information, the minimization of the operating cost of the lower-level photovoltaic storage aggregator alliance is taken as the second objective function, and the aggregator's operating constraints are taken as constraints. The aggregator alliance cooperation game model is established, and the cooperation game model and the master-slave game model are combined to form a distribution network-aggregator hybrid game model.

[0012] The distribution network-aggregator mixed game model is solved to obtain the optimal result of distributed energy sharing between distribution network and aggregator.

[0013] As a preferred solution of the distributed energy sharing method based on the hybrid game model, wherein:

[0014] The distribution network and aggregator operating parameters include micro coal-fired power units, power lines, energy storage equipment and electric vehicle information.

[0015] As a preferred solution of the distributed energy sharing method based on the hybrid game model, wherein:

[0016] The solution to the distribution network-aggregator hybrid game model includes:

[0017] Based on the dichotomy method and Nash bargaining theory, the mathematical programming solver is used to solve the distribution network-aggregator hybrid game model.

[0018] As a preferred solution of the distributed energy sharing method based on the hybrid game model, wherein:

[0019] The first objective function is expressed as:

[0020]

[0021] Where W represents the revenue of the distribution network, t represents the dispatch time, T represents the dispatch end time, i represents the aggregator number, and N represents the number of aggregators. They represent the electricity purchase and sales prices set by the distribution network for the aggregator at time t, They represent the electricity purchase and sales of aggregator i at time t, m represents the number of micro coal-fired units, M represents the number of micro coal-fired units, and λ m represents the unit power generation cost of micro coal-fired unit m, represents the active power generated by the micro coal-fired unit m at time t;

[0022] The distribution network pricing constraint is expressed as:

[0023]

[0024] in, They represent the lower and upper limits of the electricity purchase price set by the distribution network for the aggregator at time t, They represent the lower and upper limits of the electricity price set by the distribution network for the aggregator at time t, They represent the upper limit of the average purchase price and the lower limit of the average sales price set by the distribution network for the aggregator at time t respectively;

[0025] The distribution network operation constraints are expressed as:

[0026]

[0027]

[0028] Among them, r, l, n all represent power grid nodes, E m (l) represents the set of micro coal-fired units associated with node l, E i (l) represents the set of aggregators associated with node l, B n (l) represents the set of branches with node l as the first node, P rl,t and P ln,t represents the active power transmitted by branches rl and ln at time t, represents the amount of electricity purchased and sold by aggregator i to aggregator j, represents the active power demand of node l at time t, Q rl,t and Q ln,t represents the reactive power transmitted by branches rl and ln at time t, represents the reactive power generated by the micro coal-fired unit m at time t, represents the reactive power demand of node l at time t, V l,t and V r,t represents the voltage amplitude of node l and node r at time t, r rl and x rl Represents the resistance and reactance of branch rl, V 0 Indicates the rated voltage, and Indicates the upper limit of active power and reactive power transmitted by branch rl, represents the upper and lower limits of the voltage at node l, Indicates the upper and lower limits of the active power generated by the micro coal-fired unit m, and Indicates the upper and lower limits of the reactive power generated by the micro coal-fired unit m.

[0029] As a preferred solution of the distributed energy sharing method based on the hybrid game model, wherein:

[0030] The second objective function is expressed as:

[0031]

[0032] Among them, C i represents the operating cost of aggregator i, represents the transaction cost between aggregator i and distribution network, represents the transmission cost of peer-to-peer transactions between aggregator i and other aggregators, represents the energy storage equipment operating cost of aggregator i, λ tran represents the transmission cost per unit electric power, λ EES It represents the cost of charging and discharging unit electric power of energy storage equipment. It represents the electric power released and stored by the energy storage device of aggregator i at time t.

[0033] As a preferred solution of the distributed energy sharing method based on the hybrid game model, wherein:

[0034] The aggregator operation constraints include aggregator power balance constraints, energy storage equipment operation constraints, electric vehicle operation constraints, distribution network and aggregator transaction constraints, and aggregator point-to-point transaction constraints.

[0035] As a preferred solution of the distributed energy sharing method based on the hybrid game model, wherein:

[0036] The aggregator power balance constraints include:

[0037] Ignoring the peer-to-peer transactions between aggregators, the power balance constraint of each aggregator is:

[0038]

[0039] in, represents the photovoltaic output of aggregator i at time t, represents the electricity demand of aggregator i at time t, represents the load demand of electric vehicles in microgrid i at time t;

[0040] After taking into account the peer-to-peer transactions between aggregators, the power balance constraint of each aggregator is modified to:

[0041]

[0042] Energy storage equipment operation constraints:

[0043]

[0044] in, represents the state of charge of the energy storage device of aggregator i at time t, They represent the energy storage efficiency and energy release efficiency of the energy storage equipment of aggregator i, represents the rated capacity of the energy storage equipment of aggregator i, Δt represents the time interval, They represent the lower and upper limits of the state of charge of the energy storage equipment of aggregator i, They represent the charge state of the energy storage device of aggregator i at the initial and end times, They represent the upper limits of energy storage and energy release of energy storage equipment of aggregator i, represents the energy storage and release status of the energy storage equipment of aggregator i at time t;

[0045] Electric vehicle operation constraints:

[0046]

[0047] Among them, e represents electric vehicle, t a represents the attendance arrival time, t b represents the departure time, σ a and μ a represents the variance and mean of attendance arrival time, σ b and μ b represents the variance and mean of the departure time, represents the state of charge of electric vehicle e in microgrid i at the time of leaving and the initial time, η EV Indicates the charging efficiency of electric vehicles, E max Indicates the rated capacity of electric vehicles, represents the expected state of charge of electric vehicle e when leaving microgrid i, E e (i) represents the set of electric vehicles associated with the photovoltaic storage and charging microgrid i, represents the charging power and maximum charging power of electric vehicle e in microgrid i at time t;

[0048] Distribution network and aggregator transaction constraints:

[0049]

[0050] in, They represent the transaction status between aggregator i and distribution network at time t, They represent the upper limits of electricity purchase and sale from the distribution network by aggregator i, respectively;

[0051] Point-to-point easy constraints:

[0052]

[0053] in, They represent the electricity purchasing and selling status of aggregator i and aggregator j at time t, respectively, P2P,max Represents the upper limit of transactions between aggregators.

[0054] In a second aspect, an embodiment of the present invention provides a distributed energy sharing system based on a hybrid game model, comprising:

[0055] Operation parameter acquisition module, used to obtain the operation parameters of the distribution network and aggregator;

[0056] The power information acquisition module is used to obtain node power load information, aggregator power load information, and photovoltaic output information;

[0057] A module is established from the game model, which is used to establish a distribution network-aggregator master-slave game model based on the distribution network operation parameters and node power load information, with the maximization of the upper distribution network revenue as the first objective function, and the distribution network pricing constraints and distribution network operation constraints as constraints;

[0058] The hybrid game model establishment module is used to establish the aggregator alliance cooperation game model based on the aggregator operation parameters, aggregator power load and photovoltaic output information, taking the minimization of the lower-level photovoltaic storage integrated machine aggregator alliance operation cost as the second objective function and the aggregator operation constraints as the constraints, and combine the cooperation game model with the master-slave game model to form a distribution network-aggregator hybrid game model;

[0059] The solution module is used to solve the distribution network-aggregator mixed game model and obtain the optimal result of distributed energy sharing between the distribution network and the aggregator.

[0060] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0061] Memory and processor;

[0062] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed energy sharing method based on the hybrid game model as described in any embodiment of the present invention.

[0063] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the distributed energy sharing method based on a hybrid game model.

[0064] Beneficial effects of the invention: Based on the hybrid game model, the invention studies the coordinated operation of the distribution network and the aggregator alliance. By considering the hybrid game model in the distribution network-aggregator distributed energy sharing, the invention not only effectively improves the overall benefits of the aggregator cluster and the rationality of transactions, but also ensures the benefits of the aggregator in the alliance. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0066] Figure 1 It is an overall flow chart of the distributed energy sharing method based on the hybrid game model described in the first embodiment of the present invention;

[0067] Figure 2 It is a schematic diagram of prices set by a distribution network in a simulation example of a distributed energy sharing method based on a hybrid game model described in the second embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0070] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0071] Example 1

[0072] Reference Figure 1 , which is the first embodiment of the present invention, provides a distributed energy sharing method based on a hybrid game model, comprising:

[0073] S1: Obtain the operating parameters of the distribution network and aggregator;

[0074] S2: Obtain node power load information, aggregator power load information, and photovoltaic output information;

[0075] S3: Based on the distribution network operation parameters and node load information, the upper distribution network revenue maximization is taken as the first objective function, and the distribution network pricing constraints and distribution network operation constraints are taken as constraints to establish a distribution network-aggregator master-slave game model;

[0076] S4: Based on the aggregator operation parameters, aggregator power load and photovoltaic output information, the minimization of the operation cost of the lower-level photovoltaic storage integrated machine aggregator alliance is taken as the second objective function, and the aggregator operation constraints are taken as constraints. The aggregator alliance cooperation game model is established, and the cooperation game model and the master-slave game model are combined to form a distribution network-aggregator hybrid game model;

[0077] S5: Solve the distribution network-aggregator hybrid game model and obtain the optimal result of distributed energy sharing between distribution network and aggregator.

[0078] In the embodiment of the present application, the distribution network and aggregator operating parameters in the above step S1 include micro coal-fired power units, power lines, energy storage equipment and electric vehicle information.

[0079] In the embodiment of the present application, the first objective function in the above step S3 is expressed as:

[0080]

[0081] Where W represents the revenue of the distribution network, t represents the dispatch time, T represents the dispatch end time, i represents the aggregator number, and N represents the number of aggregators. They represent the electricity purchase and sales prices set by the distribution network for the aggregator at time t, They represent the electricity purchase and sales of aggregator i at time t, m represents the number of micro coal-fired units, M represents the number of micro coal-fired units, and λ m represents the unit power generation cost of micro coal-fired unit m, represents the active power generated by the micro coal-fired unit m at time t;

[0082] The distribution network pricing constraint is expressed as:

[0083]

[0084]

[0085] in, They represent the lower and upper limits of the electricity purchase price set by the distribution network for the aggregator at time t, They represent the lower and upper limits of the electricity price set by the distribution network for the aggregator at time t, They represent the upper limit of the average purchase price and the lower limit of the average sales price set by the distribution network for the aggregator at time t respectively;

[0086] The distribution network operation constraints are expressed as:

[0087]

[0088] Among them, r, l, n all represent power grid nodes, E m (l) represents the set of micro coal-fired units associated with node l, E i (l) represents the set of aggregators associated with node l, B n (l) represents the set of branches with node l as the first node, P rl,t and P ln,t represents the active power transmitted by branches rl and ln at time t, represents the amount of electricity purchased and sold by aggregator i to aggregator j, represents the active power demand of node l at time t, Q rl,t and Q ln,t represents the reactive power transmitted by branches rl and ln at time t, represents the reactive power generated by the micro coal-fired unit m at time t, represents the reactive power demand of node l at time t, V l,t and V r,t represents the voltage amplitude of node l and node r at time t, r rl and x rl Represents the resistance and reactance of branch rl, V 0 Indicates the rated voltage, and Indicates the upper limit of active power and reactive power transmitted by branch rl, represents the upper and lower limits of the voltage at node l, Indicates the upper and lower limits of the active power generated by the micro coal-fired unit m, and Indicates the upper and lower limits of the reactive power generated by the micro coal-fired unit m.

[0089] In the embodiment of the present application, the second objective function in the above step S4 is expressed as:

[0090]

[0091] Among them, C i represents the operating cost of aggregator i, C G i represents the transaction cost between aggregator i and distribution network, C tran i represents the transmission cost of peer-to-peer transactions between aggregator i and other aggregators, represents the energy storage equipment operating cost of aggregator i, λ tran represents the transmission cost per unit electric power, λ EESIt represents the cost of charging and discharging unit electric power of energy storage equipment. It represents the electric power released and stored by the energy storage device of aggregator i at time t.

[0092] The aggregator operation constraints include aggregator power balance constraints, energy storage equipment operation constraints, electric vehicle operation constraints, distribution network and aggregator transaction constraints, and aggregator point-to-point transaction constraints.

[0093] The aggregator power balance constraints include:

[0094] Ignoring the peer-to-peer transactions between aggregators, the power balance constraint of each aggregator is:

[0095]

[0096] in, represents the photovoltaic output of aggregator i at time t, represents the electricity demand of aggregator i at time t, represents the load demand of electric vehicles in microgrid i at time t;

[0097] After taking into account the peer-to-peer transactions between aggregators, the power balance constraint of each aggregator is modified to:

[0098]

[0099] Energy storage equipment operation constraints:

[0100]

[0101]

[0102] in, represents the state of charge of the energy storage device of aggregator i at time t, They represent the energy storage efficiency and energy release efficiency of the energy storage equipment of aggregator i, represents the rated capacity of the energy storage equipment of aggregator i, Δt represents the time interval, They represent the lower and upper limits of the state of charge of the energy storage equipment of aggregator i, They represent the charge state of the energy storage device of aggregator i at the initial and end times, They represent the upper limits of energy storage and energy release of energy storage equipment of aggregator i, represents the energy storage and release status of the energy storage equipment of aggregator i at time t;

[0103] Electric vehicle operation constraints:

[0104]

[0105] Among them, e represents electric vehicle, ta represents the attendance arrival time, t b represents the departure time, σ a and μ a represents the variance and mean of attendance arrival time, σ b and μ b represents the variance and mean of the departure time, represents the state of charge of electric vehicle e in microgrid i at the time of leaving and the initial time, η EV Indicates the charging efficiency of electric vehicles, E max Indicates the rated capacity of electric vehicles, represents the expected state of charge of electric vehicle e when leaving microgrid i, E e (i) represents the set of electric vehicles associated with the photovoltaic storage and charging microgrid i, represents the charging power and maximum charging power of electric vehicle e in microgrid i at time t;

[0106] Distribution network and aggregator transaction constraints:

[0107]

[0108] in, They represent the transaction status between aggregator i and distribution network at time t, They represent the upper limits of electricity purchase and sale from the distribution network by aggregator i, respectively;

[0109] Point-to-point easy constraints:

[0110]

[0111] in, They represent the electricity purchasing and selling status of aggregator i and aggregator j at time t, respectively, P2P,max In the embodiment of the present application, solving the distribution network-aggregator hybrid game model in the above step S5 includes:

[0112] Based on the dichotomy method and Nash bargaining theory, CPLEX and IPOPT solvers are used to solve the distribution network-aggregator mixed game model.

[0113] Specifically, the dichotomy and Nash bargaining theory are as follows:

[0114] 1) Dichotomy

[0115] If the kth result is the same as the k-2th result during the iteration, the iteration may fail to converge. Therefore, this embodiment iteratively solves the purchase and sale price of electricity based on the dichotomy method to ensure the convergence of the model. G,s k,t For example, if the above situation occurs, proceed as follows:

[0116]

[0117] Among them, λ G,sk,t represents the electricity selling price at time t obtained by the kth iteration.

[0118] if Then a boundary update is required. Then update the boundary of the next iteration to:

[0119]

[0120] If the price obtained through iterative solution satisfies the following formula, the iteration is terminated.

[0121]

[0122] Among them, λ G,sk,t represents the electricity selling price at time t obtained by the kth iteration, and δ represents the convergence coefficient.

[0123] 2) Nash Bargaining Theory

[0124] In order to solve the problem of uneven benefit distribution among multiple integrated energy service providers under the cooperative model, this embodiment considers introducing the Nash bargaining theory, and then decomposes the benefit distribution problem among aggregators into two sub-problems, namely: the optimal operation problem of a single aggregator before cooperation (P0) and the benefit distribution problem after cooperation (P1).

[0125] Optimal operation problem of a single aggregator before cooperation (P0):

[0126] min(A-14)

[0127] st(A-18),(A-20)-(A-25),(A-28)-(A-34)

[0128] Profit distribution problem after cooperation (P1):

[0129]

[0130] st(A-19)-(A-25),(A-28)-(A-38)

[0131] Among them, C0 i represents the optimal operating cost of a single aggregator i before cooperation, π i,j Represents the electricity transaction fee paid by aggregator i to aggregator j.

[0132] Since the original problem (P1) is a non-convex nonlinear programming problem, there may be multiple local optimal solutions, which will increase the difficulty of finding the global optimal solution. This embodiment considers converting it into two sub-problems: the social cost minimization problem (P2) and the bargaining transfer problem (P3).

[0133] Social cost minimization problem (P2):

[0134]

[0135] st(A-19)-(A-25),(A-28)-(A-38)

[0136] Bargaining transfer problem (P3):

[0137]

[0138] in, represents the optimal cost of aggregator i under the cooperative model.

[0139] In another possible implementation, an improved genetic algorithm (GA) may be used to solve the distribution network-aggregator mixed game model:

[0140] Encoding: The topology of the distribution network, load distribution, and the aggregator's electricity trading strategy are represented as chromosomes.

[0141] Fitness function: A fitness function is designed to evaluate the overall benefits of the system, such as economic benefits, user satisfaction, and utilization of renewable energy.

[0142] Selection and crossover: Select individuals with high fitness for crossover to generate new power distribution configurations and trading strategy combinations, and gradually evolve better solutions.

[0143] By simulating natural selection, this method can effectively explore multiple strategy combinations and improve the synergy between power grids and aggregators.

[0144] In another possible implementation, particle swarm optimization (PSO) can also be used to solve the distribution network-aggregator mixed game model:

[0145] Particle swarm optimization can be used to solve the problems of load distribution in distribution networks and price setting for aggregators. The application steps are as follows:

[0146] Initialization: Randomly generate initial load distribution strategy and electricity price settings.

[0147] Fitness function: The fitness of each particle is calculated based on the total cost, the user's electricity price response, and the supply and demand balance.

[0148] Update particles: Dynamically adjust the speed and position of particles according to current market demand and supply conditions to find better load distribution and electricity price strategies.

[0149] This approach is able to adapt to changes in real-time market conditions, thereby quickly finding effective strategies in a dynamic environment and improving overall operational efficiency.

[0150] In another possible implementation, simulated annealing (SA) may be used to solve the distribution network-aggregator mixed game model:

[0151] In the distribution network-aggregator model, simulated annealing can be used to optimize the aggregator's power dispatch strategy. The specific steps include:

[0152] Initial solution: Set the initial power dispatch plan.

[0153] Generate new solutions: Based on the current scheduling plan, randomly perturb and generate a new scheduling plan.

[0154] Evaluate new solutions: Calculate the economic benefits and user responses of new options, including potential demand response options.

[0155] Accept new solutions: According to the principles of the annealing algorithm, accept worse scheduling solutions with a certain probability to avoid falling into local optimality.

[0156] This random search strategy can explore a wider range of solution spaces, is suitable for dynamically changing electricity market environments, and improves system flexibility.

[0157] The above is a schematic scheme of the distributed energy sharing method based on the hybrid game model of this embodiment. It should be noted that the technical scheme of the distributed energy sharing system based on the hybrid game model and the technical scheme of the distributed energy sharing method based on the hybrid game model belong to the same concept. The details of the technical scheme of the distributed energy sharing system based on the hybrid game model in this embodiment that are not described in detail can be found in the description of the technical scheme of the distributed energy sharing method based on the hybrid game model.

[0158] The distributed energy sharing system based on the hybrid game model in this embodiment includes:

[0159] Operation parameter acquisition module, used to obtain the operation parameters of the distribution network and aggregator;

[0160] The power information acquisition module is used to obtain node power load information, aggregator power load information, and photovoltaic output information;

[0161] A module is established from the game model, which is used to establish a distribution network-aggregator master-slave game model based on the distribution network operation parameters and node power load information, with the maximization of the upper distribution network revenue as the first objective function, and the distribution network pricing constraints and distribution network operation constraints as constraints;

[0162] The hybrid game model establishment module is used to establish the aggregator alliance cooperation game model based on the aggregator operation parameters, aggregator power load and photovoltaic output information, taking the minimization of the lower-level photovoltaic storage integrated machine aggregator alliance operation cost as the second objective function and the aggregator operation constraints as the constraints, and combine the cooperation game model with the master-slave game model to form a distribution network-aggregator hybrid game model;

[0163] The solution module is used to solve the distribution network-aggregator mixed game model and obtain the optimal result of distributed energy sharing between the distribution network and the aggregator.

[0164] This embodiment further provides a computing device, which is applicable to the case of a distributed energy sharing method based on a hybrid game model, including:

[0165] A memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distributed energy sharing method based on the hybrid game model proposed in the above embodiment.

[0166] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the distributed energy sharing method based on the hybrid game model proposed in the above embodiment is implemented.

[0167] The storage medium proposed in this embodiment and the distributed energy sharing method based on the hybrid game model proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0168] Example 2

[0169] Reference Figure 2 Table 1 is an embodiment of the present invention, which provides a distributed energy sharing method based on a hybrid game model. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0170] This embodiment adopts a 33-node distribution network example. The distribution network is equipped with 3 micro coal-fired units, which are associated with nodes 4, 15, and 26 respectively to meet the load demand of the distribution network and the load demand of the aggregator. In addition, the three aggregators are associated with nodes 6, 16, and 20 respectively. All three aggregators are equipped with energy storage and electric vehicles. Aggregators 2 and 3 are equipped with photovoltaics, and aggregator 1 is not equipped with photovoltaics. In addition, this embodiment sets the mean value σ of the electric vehicle attendance arrival timea =8.6, variance μ a =3.2, the mean value of the time of leaving work σ a =17.6, variance μ a =3.34, each aggregator is equipped with 30 electric vehicles, and the arrival time and departure time are generated based on equations (A-26) and (A-27). This embodiment is solved by CPLEX and IPOPT solvers on the GAMS platform.

[0171] like Figure 2 As shown in Figure 1, the prices set by the distribution network are given; as shown in Table 1, the operating costs of the aggregator under different modes are given. Figure 2 It can be seen that for the distribution network, the purchase and sale prices show a trend of "buy low and sell high", and such a price trend ensures the maximization of the benefits of the distribution network. At the same time, affected by price constraints, the distribution network shall not set too high a selling price or too low a purchase price for aggregators to obtain high benefits, which also protects the interests of aggregators to a certain extent. As can be seen from Table 1, compared with the individual model, after the benefit distribution, the total operating cost of aggregators in the cooperative model is lower. In addition, before the benefit distribution, the operating costs of aggregators 2 and 3 in the cooperative model are greater than those in the individual model. This phenomenon will cause the failure of the cooperation of the aggregator alliance. After the introduction of the Nash bargaining mechanism, the operating costs of each aggregator were reduced by 958 yuan compared with the individual model, realizing the fair distribution of the benefits of each aggregator, thereby avoiding the problem of cooperation failure caused by uneven benefit distribution in the cooperative model.

[0172] Therefore, considering the hybrid game model in the distribution network-aggregator distributed energy sharing can effectively improve the overall benefits of the aggregator cluster and the rationality of transactions, and can also ensure the benefits of the aggregator in the alliance.

[0173] Table 1 Operating costs of aggregators under different models

[0174] Cost (Yuan) Aggregator 1 Aggregator 2 Aggregator 3 total Individual Mode 11852 4704 3220 19776 Before benefit distribution under cooperative model 8169 5109 3624 16902 Aggregator Direct Trading 2725 -1363 -1362 / After the benefits are distributed under the cooperative model 10894 3746 2262 16902

[0175] Compared with the distribution network-aggregator collaborative distributed energy sharing, the present invention is based on a hybrid game model, which effectively improves the overall benefits of the aggregator cluster and the rationality of transactions, and can also ensure the benefits of the aggregators in the alliance.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed energy sharing method based on a hybrid game model, characterized in that: include: Obtaining distribution network and aggregator operating parameters; Obtain node power load information, aggregator power load information, and photovoltaic output information; Based on the distribution network operation parameters and node load information, the distribution network revenue maximization is taken as the first objective function, and the distribution network pricing constraints and distribution network operation constraints are taken as constraints to establish a distribution network-aggregator master-slave game model. According to the aggregator's operating parameters, aggregator's electrical load and photovoltaic output information, the minimization of the operating cost of the lower-level photovoltaic storage aggregator alliance is taken as the second objective function, and the aggregator's operating constraints are taken as constraints. The aggregator alliance cooperation game model is established, and the cooperation game model and the master-slave game model are combined to form a distribution network-aggregator hybrid game model. The distribution network-aggregator mixed game model is solved to obtain the optimal result of distributed energy sharing between distribution network and aggregator.

2. The distributed energy sharing method based on the hybrid game model according to claim 1, characterized in that: The distribution network and aggregator operating parameters include micro coal-fired power units, power lines, energy storage equipment and electric vehicle information.

3. The distributed energy sharing method based on the hybrid game model according to claim 2, characterized in that: The solution to the distribution network-aggregator hybrid game model includes: Based on the dichotomy method and Nash bargaining theory, the mathematical programming solver is used to solve the distribution network-aggregator hybrid game model.

4. The distributed energy sharing method based on the hybrid game model according to claim 3, characterized in that: The first objective function is expressed as: Where W represents the revenue of the distribution network, t represents the dispatch time, T represents the dispatch end time, i represents the aggregator number, and N represents the number of aggregators. and They represent the electricity purchase and sales prices set by the distribution network for the aggregator at time t, and They represent the electricity purchase and sales of aggregator i at time t, m represents the number of micro coal-fired units, M represents the number of micro coal-fired units, and λ m represents the unit power generation cost of micro coal-fired unit m, represents the active power generated by the micro coal-fired unit m at time t; The distribution network pricing constraint is expressed as: in, and They represent the lower and upper limits of the electricity purchase price set by the distribution network for the aggregator at time t, and They represent the lower and upper limits of the electricity price set by the distribution network for the aggregator at time t, and They represent the upper limit of the average purchase price and the lower limit of the average sales price set by the distribution network for the aggregator at time t respectively; The distribution network operation constraints are expressed as: V l,t =V r,t -(P rl,t r rl +Q rl,t x rl ) / V 0 In l min ≤V l,t ≤V l max Among them, r, l, n all represent power grid nodes, E m (l) represents the set of micro coal-fired units associated with node l, E i (l) represents the set of aggregators associated with node l, B n (l) represents the set of branches with node l as the first node, P rl,t and P ln,t represents the active power transmitted by branches rl and ln at time t, and represents the amount of electricity purchased and sold by aggregator i to aggregator j, represents the active power demand of node l at time t, Q rl,t and Q ln,t represents the reactive power transmitted by branches rl and ln at time t, Q MT m,t represents the reactive power generated by the micro coal-fired unit m at time t, represents the reactive power demand of node l at time t, V l,t and V r,t represents the voltage amplitude of node l and node r at time t, r rl and x rl Represents the resistance and reactance of branch rl, V 0 Indicates the rated voltage, and Represents the upper limit of active power and reactive power transmitted by branch rl, V l max and V l min represents the upper and lower limits of the voltage at node l, and Indicates the upper and lower limits of the active power generated by the micro coal-fired unit m, and Indicates the upper and lower limits of the reactive power generated by the micro coal-fired unit m.

5. The distributed energy sharing method based on the hybrid game model according to claim 4, characterized in that: The second objective function is expressed as: Among them, C i represents the operating cost of aggregator i, represents the transaction cost between aggregator i and distribution network, represents the transmission cost of peer-to-peer transactions between aggregator i and other aggregators, represents the energy storage equipment operating cost of aggregator i, λ tran represents the transmission cost per unit electric power, λ EES It represents the cost of charging and discharging unit electric power of energy storage equipment. and It represents the electric power released and stored by the energy storage device of aggregator i at time t.

6. The distributed energy sharing method based on the hybrid game model according to claim 5, characterized in that: The aggregator operation constraints include aggregator power balance constraints, energy storage equipment operation constraints, electric vehicle operation constraints, distribution network and aggregator transaction constraints, and aggregator point-to-point transaction constraints.

7. The distributed energy sharing method based on the hybrid game model according to claim 6, characterized in that: The aggregator power balance constraints include: Ignoring the peer-to-peer transactions between aggregators, the power balance constraint of each aggregator is: in, represents the photovoltaic output of aggregator i at time t, represents the electricity demand of aggregator i at time t, represents the load demand of electric vehicles in microgrid i at time t; After taking into account the peer-to-peer transactions between aggregators, the power balance constraint of each aggregator is modified to: Energy storage equipment operation constraints: in, represents the state of charge of the energy storage device of aggregator i at time t, and They represent the energy storage efficiency and energy release efficiency of the energy storage equipment of aggregator i, represents the rated capacity of the energy storage equipment of aggregator i, Δt represents the time interval, and They represent the lower and upper limits of the state of charge of the energy storage equipment of aggregator i, and represents the charge state of the energy storage device of aggregator i at the initial and end time, P i EES,c,max and P i EES,d,max They represent the upper limits of energy storage and energy release of energy storage equipment of aggregator i, and represents the energy storage and release status of the energy storage equipment of aggregator i at time t; Electric vehicle operation constraints: Among them, e represents electric vehicle, t a represents the attendance arrival time, t b represents the departure time, σ a and μ a represents the variance and mean of attendance arrival time, σ b and μ b represents the variance and mean of the departure time, and represents the state of charge of electric vehicle e in microgrid i at the time of leaving and the initial time, η EV Indicates the charging efficiency of electric vehicles, E max Indicates the rated capacity of electric vehicles, represents the expected state of charge of electric vehicle e when leaving microgrid i, E e (i) represents the set of electric vehicles associated with the photovoltaic storage and charging microgrid i, and represents the charging power and maximum charging power of electric vehicle e in microgrid i at time t; Distribution network and aggregator transaction constraints: in, and They represent the transaction status between aggregator i and distribution network at time t, P i G,b,max and P i G,s,max They represent the upper limits of electricity purchase and sale from the distribution network by aggregator i, respectively; Point-to-point easy constraints: in, and They represent the electricity purchasing and selling status of aggregator i and aggregator j at time t, respectively, P2P,max Represents the upper limit of transactions between aggregators.

8. A system using the distributed energy sharing method based on the hybrid game model as claimed in any one of claims 1 to 7, characterized in that: include: Operation parameter acquisition module, used to obtain the operation parameters of the distribution network and aggregator; The power information acquisition module is used to obtain node power load information, aggregator power load information, and photovoltaic output information; A module is established from the game model, which is used to establish a distribution network-aggregator master-slave game model based on the distribution network operation parameters and node power load information, with the maximization of the upper distribution network revenue as the first objective function, and the distribution network pricing constraints and distribution network operation constraints as constraints; The hybrid game model establishment module is used to establish the aggregator alliance cooperation game model based on the aggregator operation parameters, aggregator power load and photovoltaic output information, taking the minimization of the lower-level photovoltaic storage integrated machine aggregator alliance operation cost as the second objective function and the aggregator operation constraints as the constraints, and combine the cooperation game model with the master-slave game model to form a distribution network-aggregator hybrid game model; The solution module is used to solve the distribution network-aggregator mixed game model and obtain the optimal result of distributed energy sharing between the distribution network and the aggregator.

9. A computing device comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distributed energy sharing method based on the hybrid game model described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distributed energy sharing method based on a hybrid game model as described in any one of claims 1 to 7.

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