A method and system for optimizing and controlling an electrothermal co-generation energy network

Abstract

The application discloses an electric-thermal collaborative energy network optimization control method and system, and the electric-thermal collaborative energy network optimization control method comprises the following steps: constructing a three-layer game architecture based on the energy relationship of an electric-thermal collaborative energy network; dividing the obtained three-layer game architecture into two-level master-slave games; performing optimization solving and outputting results according to the target functions and game constraint conditions of game participants; and realizing electric-thermal collaborative energy network optimization control based on the output results. In the technical scheme, a multi-layer game architecture of the electric-thermal collaborative network is constructed based on a multi-energy multi-agent network, and a two-level master-slave game model is established, so that the collaborative scheduling problem of multi-agent multi-energy, such as user side of the electric-thermal collaborative network, distributed new energy, low-grade heat conversion equipment and energy storage equipment, can be solved, and the economic index of the electric-thermal collaborative network can be improved.

Description

Technical Field

[0001] This invention belongs to the field of energy system optimization and control technology, and specifically relates to an optimization and control method and system for an electrothermal synergistic energy network. Background Technology

[0002] Faced with the dual challenges of carbon neutrality and high energy consumption in the power system, building a clean energy supply network that integrates electricity and heat to achieve synergy and cross-network mutual support has become the primary development path for low-carbon energy in the power system and high-energy-consuming industrial parks. Compared with existing integrated energy systems, the clean energy supply network that integrates electricity and heat is based on heat pump electrothermal conversion technology, which transforms low-grade heat energy such as industrial low-temperature waste heat into usable resources, and achieves synergy and complementarity between electricity and heat through digital and intelligent means.

[0003] With the continuous development of power-thermal co-generation networks, the demand side is playing an increasingly prominent role. Market transactions are no longer solely determined by the source side, but involve multiple parties, including the demand side and the source side. Demand options are becoming more diversified, and the interactive and competitive relationships among various source entities are becoming more complex. Simultaneously, power and thermal systems are being coupled and integrated. Low-grade thermal energy is being integrated into the system, and the extensive use of flexible thermal storage devices increases system flexibility, further complicating power-thermal co-generation scheduling. In summary, optimization control methods used for existing integrated energy systems are not suitable for power-thermal co-generation energy networks. How to coordinate the construction of multiple entities and multiple energy sources, reduce the overall energy consumption of the power-thermal co-generation network, and improve its economic indicators has become a pressing technical problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized control method and system for an electrothermal co-generation energy network to solve one or more of the aforementioned technical problems. The technical solution provided by this invention constructs a multi-layered game architecture for the electrothermal co-generation network based on a multi-energy, multi-entity network, and establishes a two-level master-slave game model. This can solve the coordinated scheduling problem among multiple entities and multiple energy sources in the electrothermal co-generation network, including the user side, distributed new energy sources, low-grade thermal energy conversion equipment, and energy storage equipment, thereby improving the economic indicators of the electrothermal co-generation network.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention discloses an optimized control method for an electrothermal synergistic energy network, comprising the following steps:

[0007] A three-layer game theory architecture is constructed based on the energy relationship of the electrothermal synergistic energy network. The participants in the three-layer game theory architecture include user aggregators, new energy operators, heat pump operators, battery energy storage operators, and water tank energy storage operators. The bottom layer of the three-layer game theory architecture is the new energy operator, the top layer is the user aggregator, and the middle layer consists of heat pump operators, new energy operators, battery energy storage operators, and water tank energy storage operators.

[0008] The obtained three-layer game architecture is divided into two levels of master-slave games. The first level of master-slave game involves the competition between producers and consumers in the electric-thermal co-generation energy network. The upper layer consists of the heat pump operator in the middle layer of the three-layer game architecture, and the lower layer consists of the new energy operator at the bottom layer. The heat pump operator adjusts the amount of electricity or heat it purchases from the new energy operator based on the new energy operator's electricity price. The new energy operator then adjusts its pricing again upon receiving the adjusted electricity or heat, thus establishing the game relationship. The second level of master-slave game involves the competition between producers and consumers in user energy consumption. The game involves a three-tiered game architecture where the top layer consists of user aggregators, and the bottom layer consists of all operators supplying energy to users in the middle layer. The user aggregators adjust the electricity purchased from new energy operators based on their electricity prices, adjust the electricity purchased from battery storage operators based on their electricity prices, and adjust the purchased heat power based on the heat prices of heat pump operators and water tank storage operators. Upon receiving the adjusted electricity or heat power from users, the new energy operators, battery storage operators, heat pump operators, and water tank storage operators adjust their pricing again, thus forming a game relationship.

[0009] Based on the two-level master-slave game obtained by partitioning, optimization is performed according to the objective functions of each player and the game constraints. When the game equilibrium and the optimization converge, the result is output. Based on the output result, the optimization control of the electrothermal synergistic energy network is realized.

[0010] A further improvement of the present invention is that, in the three-layer game architecture, the new energy operator and the battery energy storage operator respectively set their own electricity prices, and the heat pump operator and the water tank energy storage operator respectively set their own heat prices.

[0011] A further improvement of the present invention is that, in the electrothermal synergistic energy network,

[0012] User energy consumption includes electrical load and thermal load;

[0013] Power sources include the power grid and distributed renewable energy generation equipment;

[0014] The heat source includes a heat pump; the heat pump is an electrothermal coupling device used to convert electrical energy into heat energy, and its electrical load varies with the user's heat load.

[0015] Energy storage devices include battery energy storage and water tank energy storage devices;

[0016] In this electric-thermal co-generation energy network, the energy consumed by all equipment and users is preferentially provided directly or indirectly by distributed new energy power generation equipment. When the energy is insufficient, it is drawn from the power grid, and when there is a surplus, it is connected to the grid. For the electric-thermal co-generation energy network, users are energy consumers, distributed new energy power generation equipment is energy producer, heat pump is energy producer-consumer, and energy storage device is energy storage device. For users, distributed new energy power generation equipment, heat pump, and energy storage device are all energy producers.

[0017] A further improvement of the present invention is that the objective functions of each of the game participants include:

[0018] The objective function for new energy operators is maxC ne The objective function for heat pump operators' revenue is maxC. hp The objective function for battery energy storage operators is maxC. bt The objective function for water tank energy storage operators is maxC. tk The objective function for water tank energy storage operators is maxC. tk The objective function for user aggregation is minC. u The expressions are respectively,

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] In the formula, These are the electricity sales prices for new energy and battery storage operators, respectively. These are the sales prices for heat pumps and water tank energy storage operators, respectively. These are the grid connection tariff and the grid tariff, respectively. These refer to the power sold to the grid and the power purchased from the grid, respectively. This refers to the power output sold by new energy operators to the internal nodes of the energy network. Electricity sold by new energy sources to heat pump operators; The charging power for new energy sources to battery storage operators; Discharge power for battery energy storage operators; Selling heat capacity to heat pump operators; The heat pump provides heat storage capacity to water tank energy storage operators; For water tank energy storage operators, the heat release power; For user heat load; For user electrical load; The power output of battery energy storage operators to users; COP is the energy efficiency ratio of heat pumps.

[0025] A further improvement of the present invention is that the game constraints include:

[0026] Price constraint, expressed as:

[0027] In the formula, This is the lower limit for heat prices. This is the upper limit for heat prices;

[0028] Energy storage device constraints, expressed as follows:

[0029]

[0030] In the formula, i represents the water tank and the battery. This refers to the energy release power of the energy storage device; This represents the maximum energy release power of the energy storage device. Energy storage device charging power; E represents the maximum charging power of the energy storage device. i,t+1 E i,t These represent the capacity status of the energy storage device at time t+1 and time t, respectively. These refer to the charging and discharging efficiencies of the energy storage device, respectively. These are the minimum and maximum capacities of the energy storage device, respectively.

[0031] The heat pump power constraint is expressed as follows:

[0032]

[0033] In the formula, These are the upper and lower limits of the heat pump power, respectively.

[0034] The constraints of electrical energy balance and thermal energy balance are expressed as follows:

[0035]

[0036]

[0037] In the formula, This refers to the power output of wind power generation. This refers to the photovoltaic power generation capacity.

[0038] A further improvement of the present invention is that the optimization solution steps for the two-level master-slave game obtained based on partitioning, according to the objective functions of each player and the game constraints, include:

[0039] System parameter initialization;

[0040] An initial price population is randomly generated using a genetic algorithm; the price population includes the electricity and heat prices of each operator.

[0041] In the first-level master-slave game, the new energy operators receive the price. The mixed-integer linear programming solver solves the energy allocation optimization strategy under constraints based on the new energy operators' revenue and retains the optimized revenue.

[0042] Genetic algorithm population evolution generates new electricity and heat prices, and calculates fitness functions; among them, the objective function of the upper-level heat pump operator in the first-level master-slave game is to select the population before or after evolution based on the fitness functions before and after population evolution.

[0043] The population selected in the first-level master-slave game is passed to the second-level master-slave game. The multi-objective function of the multiple operators in the lower level of the second-level master-slave game is transformed by the entropy weight normalization method. The energy allocation optimization strategy is solved again by the solver for the transformed objective function, and the current payoff is retained.

[0044] New electricity and heat prices are generated again through population evolution using a genetic algorithm, and the fitness function is calculated. The objective function of the upper-level user aggregation quotient in the second-level master-slave game is used to select the population before or after evolution based on the fitness function before and after population evolution.

[0045] If the objective function values ​​of each game participant in two iterations are less than or equal to the convergence error, the final pricing and energy strategy decision is completed; otherwise, iterative calculation continues.

[0046] A further improvement of the present invention is that the system parameter initialization step includes:

[0047] Input the power generated by photovoltaic and wind power, the user's initial electrical load, the user's initial thermal load, the genetic population size, the population mutation rate, the crossover probability, and the game convergence error.

[0048] A further improvement of the present invention is that the step of transforming the multi-objective function of multiple operators in the lower layer of the second-level master-slave game using the entropy weight normalization method specifically includes:

[0049] Each objective function is written in matrix form, including: the lower level of the second-level master-slave game contains four participants: new energy, battery energy storage, heat pump, and water tank energy storage operators, with a total of m objective functions (m=4); after n random runs, n sets of objective function data are obtained, and the objective function is expressed as an n×m matrix C as follows:

[0050]

[0051] The elements of matrix C are c ij The matrix C is normalized using the maximum evaluation index to obtain a normalized matrix, the elements of which are x. ij x ij =0.1+(c ij -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c 1j ,c 2j ,…,c nj});

[0052] Calculate the information entropy S based on the elements of the normalized matrix. j With weighting coefficient w j The calculation expressions are respectively,

[0053]

[0054] in,

[0055] The original multi-objective function C j The multi-objective function F after normalization and entropy-weighted transformation is expressed as follows:

[0056] F = w ne C′ ne +w hp C′ hp +w bt C′ bt +w tk C′ tk ;

[0057] In the formula, C j ′=0.1+(C j -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c1j ,c 2j ,…,c nj}), j∈{new energy operators, battery energy storage operators, heat pump operators, water tank energy storage operators}.

[0058] A further improvement of the present invention lies in the fact that, in the process of determining the convergence of the game equilibrium and the optimization solution,

[0059] The game reaches equilibrium when the following convergence condition is met; where the convergence condition expression is:

[0060]

[0061] In the formula, k represents the k-th iteration, and ε is the convergence error.

[0062] This invention discloses an electrothermal synergistic energy network optimization control system, comprising:

[0063] The game theory architecture acquisition module is used to acquire a three-layer game theory architecture based on the energy relationship construction of the electric-thermal synergistic energy network. The game participants in the three-layer game theory architecture include user aggregators, new energy operators, heat pump operators, battery energy storage operators, and water tank energy storage operators. The bottom layer of the three-layer game theory architecture is the new energy operator, the top layer is the user aggregator, and the middle layer consists of heat pump operators, new energy operators, battery energy storage operators, and water tank energy storage operators.

[0064] The game theory segmentation module is used to divide the acquired three-layer game architecture into two levels of master-slave games. The first level of master-slave game involves the game between producers and producers / consumers in the electric-thermal co-generation energy network. The upper layer consists of the heat pump operators in the middle layer of the three-layer game architecture, and the lower layer consists of the new energy operators at the bottom layer. The heat pump operators adjust the electricity or heat power they purchase from the new energy operators based on the electricity price offered by the new energy operators. Upon receiving the adjusted electricity or heat power, the new energy operators then adjust their pricing again, thus establishing the game relationship. The second level of master-slave game involves producers in the user's energy consumption. The game between consumers and the upper layer consists of the top-level user aggregator in the three-layer game architecture, and the lower layer consists of all the operators supplying energy to users in the middle layer of the three-layer game architecture. The user aggregator adjusts the amount of electricity purchased from new energy operators based on the electricity sales price of new energy operators, adjusts the amount of electricity purchased from batteries based on the electricity sales price of battery storage operators, and adjusts the amount of heat purchased based on the heat prices of heat pump operators and water tank storage operators respectively. New energy operators, battery storage operators, heat pump operators, and water tank storage operators adjust their pricing again after receiving the adjusted electricity or heat from users, thus forming a game relationship.

[0065] The solution control module is used to perform optimization solutions based on the two-level master-slave game obtained from the partitioning, according to the objective functions of each player and the game constraints. When the game equilibrium and the solution optimization converge, the results are output, and the optimization control of the electrothermal synergistic energy network is realized based on the output results.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The technical solution provided by this invention constructs a multi-layer game architecture for an electric-thermal collaborative network based on a multi-energy, multi-entity network, and establishes a two-level master-slave game model. This can solve the collaborative scheduling problem among multiple entities and multiple energy sources in the electric-thermal collaborative network, including the user side, distributed new energy sources, low-grade thermal energy conversion equipment, and energy storage equipment, thereby improving the economic indicators of the electric-thermal collaborative network. Specific explanations of the technical solution include:

[0068] (1) In the prior art, there is a lack of reasonable and detailed optimization strategies for the coordinated scheduling of multiple entities and multiple energy sources such as user side, distributed new energy, low-grade thermal energy conversion equipment and energy storage equipment in complex electric-thermal coordinated networks. In view of this, the multi-level, multi-objective game optimization control method disclosed in this invention constructs a three-level game architecture for electric-thermal coordinated networks, and performs multi-energy linkage based on complex multi-energy networks to enhance the interaction and competition of multiple entities in electric-thermal coordinated networks, so as to achieve the energy control effect of coordinated mutual assistance between electric energy and thermal energy.

[0069] (2) Existing game theory in integrated energy systems is mostly single-layer or two-layer game with few game objects, lacking multi-layer and multi-objective game strategies, and is not suitable for the optimization control method of electrothermal co-generation energy networks. In view of this, the multi-level, multi-objective game optimization control method disclosed in this invention proposes a two-level multi-objective master-slave game. The first-level master-slave game is based on the electrothermal coupling equipment heat pump and distributed new energy, which is conducive to improving the enthusiasm of low-grade heat source conversion equipment and increasing the new energy consumption rate. The second-level master-slave game is based on the game between users and multiple energy supply sides, which helps to reduce users' economic expenditures and give users full choice, while taking into account the benefits of multiple operators. The above game method improves the economic benefits of a single complex energy network as a whole.

[0070] The multi-level, multi-objective game optimization control method disclosed in this invention solves the multi-level game based on a genetic algorithm, transforms the multi-objective function using an entropy weight normalization method, and proposes a method for judging game equilibrium and convergence, providing a fast and convenient solution for solving the multi-level game optimization of complex electrothermal collaborative networks. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art are briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0072] Figure 1 This is a schematic flowchart of an electrothermal synergistic energy network optimization control method disclosed in an embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram of the multi-layer game architecture of the electrothermal synergistic energy network in an embodiment of the present invention;

[0074] Figure 3 This is a schematic diagram of the two-level master-slave game process in the electrothermal synergistic energy network in an embodiment of the present invention;

[0075] Figure 4 This is a schematic diagram showing the results before and after multi-layer game optimization of the electrothermal synergistic energy network in an embodiment of the present invention. Detailed Implementation

[0076] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0077] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0078] The present invention will now be described in further detail with reference to the accompanying drawings:

[0079] Please see Figure 1The present invention provides an optimization control method for an electrothermal synergistic energy network, specifically a multi-level, multi-objective game-theoretic optimization control method for complex electrothermal synergistic energy networks, comprising the following steps:

[0080] Step 1: Construct a three-layer game theory architecture based on the energy relationship of the electrothermal synergistic energy network. The game participants include user aggregators, new energy operators, heat pump operators, battery storage operators, and water tank storage operators. The bottom layer of the game theory architecture consists of new energy operators, the top layer consists of user aggregators, and the middle layer consists of heat pump operators, new energy operators, battery storage operators, and water tank storage operators. New energy operators and battery storage operators set their own electricity prices, while heat pump operators and water tank storage operators set their own heat prices. Interactions between operators and user aggregators occur regarding electrical or thermal energy.

[0081] To further illustrate, in the aforementioned electrothermal co-generation energy network: the user's energy consumption includes electrical load and thermal load; the power source includes the power grid, distributed new energy power generation equipment such as photovoltaic and wind power; the heat source is a heat pump, which is an electrothermal coupling device that converts electrical energy into thermal energy, and its electrical load changes with the user's thermal load; the energy storage device includes battery energy storage and water tank energy storage devices; among them, new energy, heat pumps, and energy storage devices provide users with a variety of energy supplies; furthermore, in a single electrothermal co-generation energy network, the energy of all devices and users is mainly provided directly or indirectly by distributed new energy power generation equipment, and when insufficient or excessive, power is drawn from the power grid or connected to the power grid; for the electrothermal co-generation energy network, the user is the energy consumer, the new energy power generation equipment is the energy producer, the heat pump equipment is the energy producer-consumer (integrator of thermal energy production and electrical energy consumption), and the energy storage device is the energy storage device; for the user, the new energy power generation equipment, the heat pump equipment, and the energy storage device are all energy producers.

[0082] Step 2: Divide the three-level game architecture obtained in Step 1 into a two-level master-slave game; optimize and solve the two-level master-slave game to obtain the decision schemes of each game participant; the purpose of this game method is to reduce the energy consumer expenditure of complex energy networks, increase the net income of energy producers, consumers, and storage providers, that is, reduce the energy expenditure of users, increase the net income of heat pumps, and at the same time take into account the benefits of multiple operators supplying energy to users, thereby improving the overall economic efficiency of a single complex electrothermal co-working energy network.

[0083] The first level of the master-slave game involves a game between producers and consumers in a single energy network. The upper layer consists of heat pump operators in the middle layer of the game architecture, and the lower layer consists of new energy operators at the bottom. Heat pump operators adjust the amount of electricity they purchase from new energy sources based on the electricity prices offered by these operators. New energy operators, upon receiving the adjusted electricity or heat power from the heat pumps, then adjust their pricing again, thus forming the game relationship. The second level of the master-slave game involves a game between producers and consumers in user energy consumption. The upper layer consists of user aggregators at the top of the game architecture, and the lower layer consists of all operators supplying energy to users in the middle layer. This level includes multiple optimization objectives. User aggregators adjust the amount of electricity they purchase from new energy sources based on the electricity prices offered by new energy operators, and adjust the amount of electricity they purchase from batteries based on the electricity prices offered by battery storage operators. Similarly, they adjust the amount of heat power purchased based on the heat prices offered by heat pump and water tank storage operators. New energy, battery storage, and heat pump and water tank storage operators, upon receiving the adjusted electricity or heat power from users, then adjust their pricing again, thus forming the game relationship.

[0084] Step 3: Solve the two-level master-slave game divided in Step 2 based on the objective function models of each participant and the constraints of the game. When the game equilibrium and the optimization solution converge, the payoff or expenditure of each participant reaches the game equilibrium and the result is output. Optimization control is achieved based on the output result.

[0085] Further explanation is needed; the objective function for new energy operators is maxC. ne This includes the net revenue from selling electricity within the nodes of a complex electrothermal co-generation energy network, interacting with the power grid, and maintaining the new energy equipment.

[0086] The objective function for heat pump operators' revenue is maxC. hp This includes the net revenue from purchasing electricity from new energy operators and battery storage operators and selling heat energy to user aggregators and water tank aggregators.

[0087] The objective function for battery energy storage operators is maxC bt This includes net revenue from selling electricity to heat pump operators and user aggregators, and from purchasing electricity from renewable energy operators;

[0088] The objective function for water tank energy storage operators is maxC tk This includes the net revenue from selling heat energy to user aggregators and purchasing heat energy from heat pump operators;

[0089] The objective function for user aggregation is minC. u Total expenditure, including heating and electricity costs;

[0090] The expressions for the above objective functions are as follows:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] In the formula: These are the electricity sales prices for new energy and battery storage operators, respectively. These are the sales prices for heat pumps and water tank energy storage operators, respectively. These are the grid-connected electricity price and the grid electricity price, respectively. These refer to the power sold to the grid and the power purchased from the grid, respectively. This refers to the power output sold by a new energy operator to a single energy network node. Electricity sold by new energy sources to heat pump operators; The charging power for new energy sources to battery storage operators; Discharge power for battery energy storage operators; Selling heat capacity to heat pump operators; The heat pump provides heat storage capacity to water tank energy storage operators; For water tank energy storage operators, the heat release power; For user heat load; For user electrical load; The power output of battery energy storage operators to users; COP is the energy efficiency ratio of heat pumps.

[0097] In this embodiment of the invention, the constraints of the game include:

[0098] Electricity sales prices for new energy and battery storage operators are higher than grid connection prices but lower than grid electricity prices; heat pump and water tank storage operators' heat sales prices are higher than the lower limit of time-of-use heat prices. Below the time-sharing heating price ceiling The specific price constraints are as follows:

[0099]

[0100] Energy storage devices include batteries and water tanks; 'i' refers to both the water tank and the battery. This refers to the energy release power of the energy storage device; This represents the maximum energy release power of the energy storage device. Energy storage device charging power; E represents the maximum charging power of the energy storage device. i,t+1 E i,t These represent the capacity status of the energy storage device at time t+1 and time t, respectively. These refer to the charging and discharging efficiencies of the energy storage device, respectively. These are the minimum and maximum capacities of the energy storage device, respectively.

[0101] The constraints of energy storage devices are expressed as follows:

[0102]

[0103] The lower limit of heat pump power is The upper limit is The constraint is represented as,

[0104]

[0105] The constraints for electrical energy balance and thermal energy balance are as follows:

[0106]

[0107]

[0108] In the formula: This refers to the power output of wind power generation. This refers to the photovoltaic power generation capacity.

[0109] In this embodiment of the invention, the above-mentioned game is optimized and solved. The optimization solution method includes the following steps:

[0110] S1. System parameter initialization: Input parameters such as the power of photovoltaic and wind power generation, the initial electrical load of the user, the initial thermal load of the user, the number of genetic populations, the population mutation rate, the crossover probability, and the game convergence error.

[0111] S2. Use a genetic algorithm to randomly generate an initial price population, namely the electricity and heat prices of each operator;

[0112] S3, the price received by the new energy operators in the lower level of the first-level master-slave game, the mixed integer linear programming solver solves the energy allocation optimization strategy under the constraints based on the revenue of the new energy operators, and retains the optimized revenue;

[0113] S4. Genetic algorithm population evolution generates new electricity and heat prices, calculates the fitness function, which is the objective function of the upper-level heat pump operator in the first-level master-slave game, and selects the population before or after evolution based on the fitness function before and after population evolution.

[0114] S5. Pass the population selected in the first-level master-slave game to the second-level master-slave game. For the multi-objective function of multiple operators in the lower level of the second-level master-slave game, transform it by the entropy weight normalization method. Use the solver to solve the energy allocation optimization strategy again for the transformed objective function and retain the current payoff.

[0115] S6. Use the genetic algorithm to generate new electricity and heat prices again through population evolution. Calculate the fitness function, which is the objective function of the upper-level user aggregation quotient in the second-level master-slave game. Based on the fitness function before and after population evolution, select the population before or after evolution again.

[0116] S7. If the game reaches the convergence condition, that is, the objective function values ​​of each player in the previous two iterations are less than the convergence error, then the final pricing and energy strategy decision is completed; otherwise, the iterative calculation continues.

[0117] In step S5 of this embodiment of the invention, the entropy weight normalization transformation of the lower-level multi-objective function of the second-level master-slave game includes the following steps:

[0118] S5.1. Represent each objective function in matrix form: The lower level of the second-level master-slave game includes four participants: new energy, battery energy storage, heat pump, and water tank energy storage operators, resulting in m objective functions (m=4). The system is run n times randomly, yielding n sets of objective function data. Represent the objective functions as an n×m matrix C:

[0119]

[0120] S5.2. Normalize matrix C according to the extremely large evaluation index to obtain a normalized matrix, the elements of which are x. ij Calculate the information entropy S based on the normalized elements. j With weighting coefficient w j :

[0121] x ij =0.1+(c ij -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c 1j ,c 2j ,…,c nj});

[0122]

[0123]

[0124]

[0125]

[0126] S5.3, change the original multi-objective function C jThe multi-objective function F, after normalization and entropy weighting transformation of (j∈{new energy operators, battery energy storage operators, heat pump operators, water tank energy storage operators}), is expressed as:

[0127] F = w ne C′ ne +w hp C′ hp +w bt C′ bt +w tk C′ tk ;

[0128] In the formula, C′ j =0.1+(C j -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c 1j ,c 2j ,…,c nj}), j∈{new energy operators, battery energy storage operators, heat pump operators, water tank energy storage operators}.

[0129] The game strategies in this invention include pricing strategies for electricity and heat sales by operators, and energy allocation strategies for battery charging power, battery discharging power, water tank heating power, water tank heat dissipation power, heat pump power purchased from renewable energy sources, user power purchased from renewable energy sources, user power purchased from batteries, user power purchased from heat pumps, and user power purchased from water tanks.

[0130] In this embodiment of the invention, the method for determining game equilibrium and convergence in the optimization solution is as follows:

[0131] When the game reaches equilibrium, the objective functions of the user aggregator, new energy operator, battery energy storage operator, heat pump operator, and water tank energy storage operator in the current k-th iteration are equal to the objective function of the previous (k-1)-th iteration:

[0132]

[0133] For ease of computation during optimization, the game reaches equilibrium when the following convergence condition is met:

[0134]

[0135] In the formula: ε is the convergence error.

[0136] To further explain the technical solution of this invention, game theory is a mathematical method for studying decision-making when multiple parties have conflicting or related interests. Currently, game theory has been studied in areas such as optimal scheduling of integrated energy systems. However, for the coordinated scheduling of multiple entities and multiple energy sources in complex electric-thermal co-working networks, such as user-side, distributed new energy sources, low-grade thermal energy conversion equipment, and energy storage equipment, there is a lack of more reasonable and detailed planning. At the same time, most applications of game theory in integrated energy systems are single-layer or two-layer games, and the number of target objects participating in the game is small. It is only suitable for analyzing relatively simple energy systems and game problems, lacks multi-level and multi-objective game research, and is not suitable for the optimal control of electric-thermal co-working energy networks.

[0137] Further explanation is needed. Energy trading exists in the electrothermal co-generation energy network, but the demand side struggles to purchase cost-effective energy, while the source side cannot guarantee maximum profit. Economic optimization control of the electrothermal co-generation energy network is beneficial for achieving a win-win situation for both the demand and source sides. Currently, single-layer or two-layer game theory is used for optimization control in integrated energy systems, but the number of target objects in the game is small, and the importance and level of the participants are not well differentiated. As the participants in the energy trading market become more diversified, the trading relationships become more complex, requiring multi-layer and multi-objective game theory control methods to plan market transactions with different priorities.

[0138] This invention discloses a multi-level, multi-objective game-theoretic optimization control method for complex electrothermal synergistic energy networks. The method includes: constructing a three-level game architecture and a two-level master-slave game optimization solution for the electrothermal synergistic network system. Game participants include user cluster operators and operators of new energy sources, heat pumps, battery storage, and water tank storage. A genetic algorithm is used to solve the multi-level game. The multi-objective function composed of new energy sources, heat pumps, battery storage, and water tank storage operators is transformed using entropy weight normalization, and convergence conditions for the optimization solution are proposed. This invention can help reduce user economic expenditures and provide users with ample platform choices, increase the incentive for low-grade heat source conversion equipment, enhance new energy consumption, achieve flexible allocation of electricity and heat, and provide a solution for energy control optimization in electrothermal synergistic networks.

[0139] Please see Figure 2In a specific and exemplary embodiment of this invention, the multi-layered game architecture of the acquired complex electrothermal synergistic energy network includes user aggregators, new energy operators, heat pump operators, battery storage operators, and water tank storage operators. The new energy operator sets electricity price 'a', selling electricity to the heat pump operator, battery storage operator, and user aggregator, while simultaneously interacting with the power grid. The battery storage operator sets electricity price 'b', selling electricity to the heat pump operator and user aggregator. The heat pump operator sets heat price 'c', selling heat energy to the water tank storage operator and user aggregator. The water tank storage operator sets heat price 'c'... Price d, selling heat energy to user aggregators; user aggregators purchase electricity from new energy operators and battery storage operators, and heat energy from heat pump operators and water tank storage operators; the energy source of the complex electrothermal co-generation energy network is mainly provided directly by distributed new energy power generation equipment, or indirectly converted into electricity or heat energy by other operators; the game structure is divided into three layers based on the energy relationships of the energy network, with the bottom layer being new energy operators, the top layer being user aggregators, and the middle layer consisting of heat pump operators, new energy operators, battery storage operators, and water tank storage operators; the objective function of the new energy operators is maxC. ne The objective function for heat pump operators is maxC. hp The objective function for battery energy storage operators is maxC. bt The objective function for water tank energy storage operators is maxC. tk The objective function for user aggregation is minC. u .

[0140] Please see Figure 3 In a specific and exemplary embodiment of the present invention, the multi-layer game of the electrothermal co-energy network is a two-level master-slave game. In the first-level master-slave game, the upper layer is the heat pump operator and the lower layer is the new energy operator; in the second-level master-slave game, the upper layer is the user aggregator and the lower layer is the operator of the electric energy part and the thermal energy part.

[0141] The multi-level game process is as follows: Input system initialization parameters, including the power output of photovoltaic and wind power generation, initial user electrical load, initial user thermal load, genetic population size, population mutation rate, crossover probability, and game convergence error; then, use a genetic algorithm to generate the initial population for the operator's energy sales price; in the first-level master-slave game, the new energy operator receives the price, and the CPLEX solver is used to optimize the energy allocation of electricity, heat, and flexible energy storage under constraints based on the new energy operator's revenue, retaining the optimized revenue C. ne Genetic algorithm population evolution generates new electricity and heat prices, calculates the fitness function, i.e., the revenue C of the upper-level heat pump operator in the first-level master-slave game. hp The benefits of heat pumps before population evolution, C hp0 Compare, if C hp >C hp0If the evolved population is selected, the un-evolved population is selected; otherwise, the un-evolved population is selected. Based on the population selected in the first-level master-slave game, the lower level of the second-level master-slave game accepts the electricity and heat prices. The multi-objective functions of multiple operators are transformed using the entropy weight normalization method. The energy allocation is solved again using a solver on the transformed multi-agent objective function, and the current revenue of the transformed objective function is retained. New electricity and heat prices are generated again using population evolution through genetic algorithms, and C is calculated. u , compared with pre-evolutionary C u0 Compare, if C u >C u0 Select the evolved population; otherwise, select the un-evolved population. If the objective function values ​​of each operator and user aggregator are less than the convergence error ε in both iterations, the solution is complete; otherwise, continue iterative calculation.

[0142] The convergence condition is:

[0143] The constraints in solving the game include price constraints, energy storage device constraints, heat pump equipment constraints, and electrical and thermal balance constraints, expressed as:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] In the formula, This is the lower limit for heat prices. The upper limit of the heat price; i represents the battery or water tank, β dis β ch These are the discharge / heat and charge / heat indicators, respectively, P dis,max P ch,max These represent the upper limits for discharge / heat and charge / heat, respectively; E represents the energy storage device capacity; E min E max These represent the lower and upper limits of energy storage device capacity, respectively. These are the lower and upper limits of heat pump power, respectively. This refers to the power output of wind power generation. Photovoltaic power generation;

[0150] When performing entropy weight normalization transformation on the multi-objective function of the second-level master-slave game: The system is run 100 times randomly, and 100 sets of data are obtained for the four objective functions of the four participants: new energy, battery energy storage, heat pump, and water tank energy storage operators. Each objective function is written as an n×m matrix C, where m=4 and n=100:

[0151]

[0152] Normalizing matrix C yields a normalized matrix with elements x. ij Calculate the information entropy S j With weighting coefficient w j :

[0153] x ij =0.1+(c ij -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c 1j ,c 2j ,…,c nj})

[0154]

[0155]

[0156]

[0157]

[0158] The multi-objective function F after normalization and entropy weighting transformation is expressed as:

[0159] F = w ne C′ ne +w hp C′ hp +w bt C′ bt +w tk C′ tk ;

[0160] In the formula, C′ j =0.1+(C j -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c1j ,c 2j ,…,c nj}), j∈{new energy operators, battery energy storage operators, heat pump operators, water tank energy storage operators}.

[0161] Please see Figure 4 In this embodiment of the invention, the results of the multi-level game optimization of the electrothermal collaborative network system before and after optimization show that after optimization, the expenditure of the user aggregator decreased by 20.5%, the benefits of the heat pump operator increased by 29.4%, the benefits of the water tank energy storage operator increased by 6.3%, the benefits of the new energy operator increased slightly by 1.6%, and the benefits of the battery energy storage operator decreased by 8.1%.

[0162] It can be seen that, overall, the benefits or expenditures of all operators and user aggregators have been optimized. Although the benefits of battery energy storage operators have decreased, since battery energy storage operators are at the lower level of the first-level game and their objective function is incorporated into the objective function after entropy weight normalization transformation, the reduction in the benefits of individual operators is reasonable in order to reduce the expenditures of user aggregators at the upper level of the master-slave game while taking into account the benefits of other operators. It can be concluded that the multi-level game optimization of the electric-thermal collaborative network system can effectively improve the cost-effectiveness of users, reduce energy costs, and at the same time ensure the overall benefits of all operators.

[0163] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0164] An embodiment of the present invention discloses an electrothermal synergistic energy network optimization control system, comprising:

[0165] The game theory architecture acquisition module is used to acquire a three-layer game theory architecture based on the energy relationship construction of the electric-thermal synergistic energy network. The game participants in the three-layer game theory architecture include user aggregators, new energy operators, heat pump operators, battery energy storage operators, and water tank energy storage operators. The bottom layer of the three-layer game theory architecture is the new energy operator, the top layer is the user aggregator, and the middle layer consists of heat pump operators, new energy operators, battery energy storage operators, and water tank energy storage operators.

[0166] The game theory segmentation module is used to divide the acquired three-layer game architecture into two levels of master-slave games. The first level of master-slave game involves the game between producers and producers / consumers in the electric-thermal co-generation energy network. The upper layer consists of the heat pump operators in the middle layer of the three-layer game architecture, and the lower layer consists of the new energy operators at the bottom layer. The heat pump operators adjust the electricity or heat power they purchase from the new energy operators based on the electricity price offered by the new energy operators. Upon receiving the adjusted electricity or heat power, the new energy operators then adjust their pricing again, thus establishing the game relationship. The second level of master-slave game involves producers in the user's energy consumption. The game between consumers and the upper layer consists of the top-level user aggregator in the three-layer game architecture, and the lower layer consists of all the operators supplying energy to users in the middle layer of the three-layer game architecture. The user aggregator adjusts the amount of electricity purchased from new energy operators based on the electricity sales price of new energy operators, adjusts the amount of electricity purchased from batteries based on the electricity sales price of battery storage operators, and adjusts the amount of heat purchased based on the heat prices of heat pump operators and water tank storage operators respectively. New energy operators, battery storage operators, heat pump operators, and water tank storage operators adjust their pricing again after receiving the adjusted electricity or heat from users, thus forming a game relationship.

[0167] The solution control module is used to perform optimization solutions based on the two-level master-slave game obtained from the partitioning, according to the objective functions of each player and the game constraints. When the game equilibrium and the solution optimization converge, the results are output, and the optimization control of the electrothermal synergistic energy network is realized based on the output results.

[0168] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0170] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An optimal control method for an electric-thermal synergic energy network, characterized in that, The method comprises the following steps: An energy relationship based on the electric-thermal cooperative energy network is constructed to obtain a three-layer game architecture; the game participants of the three-layer game architecture include a user aggregator, a new energy operator, a heat pump operator, a battery energy storage operator, and a water tank energy storage operator; the bottom layer of the three-layer game architecture is the new energy operator, the top layer is the user aggregator, and the middle layer is the heat pump operator, the new energy operator, the battery energy storage operator, and the water tank energy storage operator; The obtained three-layer game architecture is divided into two levels of master-slave games; the first level of master-slave game is for the game between the producer and the consumer of the electric-thermal cooperative energy network, the upper layer is the heat pump operator in the middle layer of the three-layer game architecture, and the lower layer is the new energy operator in the bottom layer of the three-layer game architecture; the heat pump operator adjusts the electric power purchased from the new energy operator according to the electricity price of the new energy operator, and the new energy operator adjusts the pricing again after receiving the adjusted electric power, thereby forming a game relationship; the second level of master-slave game is for the game between the producer and the consumer in the user energy consumption, the upper layer is the user aggregator in the top layer of the three-layer game architecture, and the lower layer is all operators that supply energy to the user in the middle layer of the three-layer game architecture; the user aggregator adjusts the electric power purchased from the new energy operator according to the electricity price of the new energy operator, adjusts the electric power purchased from the battery energy storage operator according to the electricity price of the battery energy storage operator, and adjusts the heat power purchased from the heat pump operator and the water tank energy storage operator according to the heat prices of the heat pump operator and the water tank energy storage operator, respectively; the new energy operator, the battery energy storage operator, the heat pump operator, and the water tank energy storage operator adjust the pricing again after receiving the electric power or heat power adjusted by the user, thereby forming a game relationship; Based on the two levels of master-slave games obtained by division, the target functions of the game participants and the game constraint conditions are used for optimization solving, and the output results are obtained when the game equilibrium and the solving optimization converge, and the output results are used for realizing the optimization control of the electric-thermal cooperative energy network; The target functions of the game participants include: The optimization solving step of the two levels of master-slave games obtained by division, according to the target functions of the game participants and the game constraint conditions, includes: The new energy operator objective function is max C ne The heat pump operator revenue objective function is max C hp The battery energy storage operator objective function is max C bt The water tank energy storage operator objective function is max C tk The water tank energy storage operator objective function is max C tk And the user aggregation objective function is min C u The expressions are respectively, In the formula, respectively, the selling price of new energy, battery energy storage operator; respectively, the selling price of heat pump, water tank energy storage operator; respectively, the grid price, grid price; respectively, the selling power to the grid, the buying power from the grid; is the selling power of new energy operator to the energy network node; is the selling power of new energy to heat pump operator; is the charging power of new energy to battery energy storage operator; is the discharging power of battery energy storage operator; is the selling heat power of heat pump operator; is the heat storage power of heat pump to water tank energy storage operator; is the heat release power of water tank energy storage operator; is the user heat load; is the user electric load; is the power supply of battery energy storage operator to user; COP is the heat pump efficiency ratio; System parameter initialization; An initial price population is randomly generated by using a genetic algorithm; the price population includes the electricity prices and the heat prices of the operators; The new energy operator in the lower layer of the first level of master-slave game receives the price, and a mixed integer linear programming solver solves the energy distribution optimization strategy under the constraint condition according to the new energy operator's income and preserves the optimized income; A new electricity price and a new heat price are generated by population evolution of the genetic algorithm, and an adaptability function is calculated; the target function of the heat pump operator in the upper layer of the first level of master-slave game selects the population before evolution or the population after evolution according to the adaptability function before and after evolution. ​ The population selected by the first-level master-slave game is passed to the second-level master-slave game, the multi-objective functions of the lower multiple operators of the second-level master-slave game are transformed by an entropy weight normalization method, the transformed objective functions are solved by a solver to obtain energy distribution optimization strategies again, and the current revenue is retained; The population evolution of the genetic algorithm is used to generate new electricity selling prices and heat selling prices again, and an adaptability function is calculated; the objective function of the upper user aggregation merchant of the second-level master-slave game is selected again before or after population evolution according to the adaptability functions before and after population evolution; If the objective function values of each game participant before and after two iterations are less than or equal to a convergence error, the final pricing and energy strategy decision is completed, otherwise iteration calculation is continued. 2.The method of claim 1, wherein In the three-layer game architecture, the new energy operators and the battery energy storage operators respectively formulate their own electricity prices, and the heat pump operators and the water tank energy storage operators respectively formulate their own heat prices. 3.The method of claim 1, wherein In the electric-thermal collaborative energy network, The user energy consumption includes electric load and heat load; The power supply includes a power grid and distributed new energy power generation equipment; The heat source includes a heat pump; the heat pump is an electric-thermal coupled device for converting electric energy into heat energy, and the electric load changes with the change of the user heat load; The energy storage device includes a battery energy storage and a water tank energy storage device; In the electric-thermal collaborative energy network, the energy of all devices and user energy consumption is preferentially provided by the distributed new energy power generation equipment directly or indirectly, and when insufficient, electricity is taken from the power grid, and when excessive, electricity is connected to the grid; for the electric-thermal collaborative energy network, the user is an energy consumer, the distributed new energy power generation equipment is an energy producer, the heat pump is an energy producer and consumer, and the energy storage device is an energy storage device; for the user, the distributed new energy power generation equipment, the heat pump and the energy storage device are all energy producers. 4.The method of claim 1, wherein, The game constraint conditions include: The price constraint is expressed as wherein is a lower heat rate limit, is an upper heat rate limit; The energy storage device constraint is expressed as where i belongs to the water tank and the battery, is the discharging power of the energy storage device; is the maximum discharging power of the energy storage device; is the charging power of the energy storage device; is the maximum charging power of the energy storage device;E i,t+1 , E i,t are the capacity states of the energy storage device at time t+1 and time t, respectively; are the charging and discharging efficiencies of the energy storage device, respectively; are the minimum and maximum capacities of the energy storage device, respectively; The heat pump power constraint is expressed as In the formula, respectively upper and lower limits of the heat pump power; The electric energy balance and heat energy balance constraint is expressed as In the formula, is the wind power generation power; is the photovoltaic power generation power.

5. The method of claim 1, wherein, The system parameter initialization step includes: Input the power of photovoltaic and wind power generation, the initial electric load of the user, the initial heat load of the user, the number of genetic populations, the population mutation rate, the crossover probability and the game convergence error. 6.The method of Claim 1, wherein The step of transforming the multi-objective functions of the lower multiple operators of the second-level master-slave game by the entropy weight normalization method specifically includes: Each objective function is written in the form of a matrix, including: the lower level of the second-level master-slave game includes four participants, namely new energy, battery energy storage, heat pump and water tank energy storage operators, a total of m objective functions, m = 4; n random runs are performed to obtain n sets of objective function data, and the objective function is written in the form of an n x m matrix C as The elements of the matrix C are c ij The matrix C is normalized according to the maximum evaluation index to obtain a normalized matrix, and the elements of the matrix are x ij ij = 0.1 + (c ij -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c 1j ,c 2j ,…,c nj})​ The information entropy S is calculated from the elements of the normalized matrix j With the weight coefficient w j The calculation expressions are respectively wherein i = 1, 2,..., n; The original multi-objective function C j The multi-objective function F after normalization and entropy weight transformation is represented as, F = w ne C' ne + w hp C' hp + w bt C' bt + w tk C' tk ; In the formula, C' j = 0.1 + (C j -min{c 1j ,c 2j ,…,c nj}) / (max{c 1j ,c 2j ,…,c nj}-min{c 1j ,c 2j ,…,c nj}),j∈{new energy operator, battery energy storage operator, heat pump operator, water tank energy storage operator}.

7. The method of claim 6, wherein, In the game equilibrium and solution optimization convergence judgment process, When the following convergence condition is met, the game reaches equilibrium; wherein the convergence condition expression is Wherein, k represents the kth iteration, and ε is the convergence error.

8. An optimal control system for an electric-thermal synergic energy network, characterized in that, It includes: The game architecture obtaining module is configured to obtain a three-layer game architecture based on the energy relationship of the electric-thermal collaborative energy network; the game participants of the three-layer game architecture include a user aggregator, a new energy operator, a heat pump operator, a battery energy storage operator, and a water tank energy storage operator; the bottom layer of the three-layer game architecture is the new energy operator, the top layer is the user aggregator, and the middle layer is the heat pump operator, the new energy operator, the battery energy storage operator, and the water tank energy storage operator; The game division module is configured to divide the obtained three-layer game architecture into two-level master-slave games; the first-level master-slave game is for the game between the producer and the producer-consumer of the electric-thermal collaborative energy network, the upper layer is the heat pump operator in the middle layer of the three-layer game architecture, and the lower layer is the new energy operator in the bottom layer of the three-layer game architecture; the heat pump operator adjusts the electric power purchased from the new energy operator according to the electricity price of the new energy operator, and the new energy operator adjusts the pricing again after receiving the adjusted electric power, thereby forming a game relationship; the second-level master-slave game is for the game between the producer and the consumer in the user energy consumption, the upper layer is the user aggregator in the top layer of the three-layer game architecture, and the lower layer is all operators that supply energy to the user in the middle layer of the three-layer game architecture; the user aggregator adjusts the electric power purchased from the new energy operator according to the electricity price of the new energy operator, adjusts the electric power purchased from the battery energy storage operator according to the electricity price of the battery energy storage operator, and adjusts the heat power purchased from the heat pump operator and the water tank energy storage operator according to the heat prices of the heat pump operator and the water tank energy storage operator, respectively; the new energy operator, the battery energy storage operator, the heat pump operator, and the water tank energy storage operator adjust the pricing again after receiving the electric power or heat power adjusted by the user, thereby forming a game relationship; The solution control module is configured to, based on the two-level master-slave games obtained by division, perform optimization solution according to the target functions of the game participants and the game constraint conditions, output the results when the game equilibrium and the solution optimization converge, and realize the optimization control of the electric-thermal collaborative energy network based on the output results; The target functions of the game participants include: The optimization solution step of performing optimization solution according to the target functions of the game participants and the game constraint conditions based on the two-level master-slave games obtained by division includes: The new energy operator objective function is max C ne The heat pump operator revenue objective function is max C hp The battery energy storage operator objective function is max C bt The water tank energy storage operator objective function is max C tk The water tank energy storage operator objective function is max C tk And the user aggregation objective function is min C u The expressions are respectively, In the formula, respectively, the selling price of new energy, battery energy storage operator; respectively, the selling price of heat pump, water tank energy storage operator; respectively, grid-connected electricity price, grid electricity price; respectively, the selling power to the grid, the buying power from the grid; The new energy operator sells power to the energy network node; The new energy sells power to the heat pump operator; The new energy charges the battery energy storage operator; The battery energy storage operator discharges power; The heat pump operator sells heat power; The heat pump stores heat power to the water tank energy storage operator; The water tank energy storage operator discharges heat power; The user heat load; The user electric load; The battery energy storage operator supplies power to the user; COP is the heat pump energy efficiency ratio; System parameter initialization; An initial price population is randomly generated by using a genetic algorithm; the price population includes the electricity prices and heat prices of the operators; The new energy operator in the lower layer of the first-level master-slave game receives the price, an integer linear programming solver solves the energy distribution optimization strategy under the constraint condition according to the income of the new energy operator, and the optimized income is retained; A new electricity price and a new heat price are generated by population evolution of the genetic algorithm, and an adaptability function is calculated; the target function of the heat pump operator in the upper layer of the first-level master-slave game selects the population before evolution or the population after evolution according to the adaptability functions before and after evolution. ​ The population selected by the first level master-slave game is transmitted to the second level master-slave game, the multi-objective functions of the lower multiple operators of the second level master-slave game are transformed by an entropy weight normalization method, the transformed objective functions are solved by a solver to obtain energy distribution optimization strategies again, and the current income is kept; The population evolution of the genetic algorithm is used to generate new electricity and heat selling prices again, and an adaptability function is calculated; the objective function of the upper user aggregation merchant of the second level master-slave game is selected again before or after evolution according to the adaptability functions before and after evolution; If the objective function values of each game participant before and after two iterations are less than or equal to a convergence error, the final pricing and energy strategy decision is completed, otherwise iteration calculation is continued.

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