Multi-agent operation and dispatching method and system for integrated energy system based on mixed game

Abstract

A kind of integrated energy system multi-agent operation scheduling method and system based on mixed game, comprising: based on the characteristics of each energy subject in integrated energy system and energy conversion relationship, construct multi-energy collaborative system architecture, and establish each energy subject model;Based on the each energy subject model, design three-layer mixed game model, including upper master-slave game model, middle cooperative game model and lower non-cooperative game model;The three-layer mixed game model forms closed loop linkage through the information interaction between levels;Solve three-layer mixed game model, based on the solution result, the benefit distribution and settlement of each energy subject are carried out, and the operation scheduling of each energy subject is realized.The present application effectively solves the problem that traditional single game framework cannot cover multi-level demand, significantly improves multi-agent collaborative efficiency and market activity.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization scheduling technology, and particularly relates to a multi-entity operation scheduling method and system for integrated energy systems based on hybrid game theory. Background Technology

[0002] Integrated energy systems, through the coupling of multiple energy flows (electricity, heat, and cooling) and the synergy of distributed energy sources, have become a key pathway to improve energy efficiency and promote the consumption of renewable energy. Current system architectures are gradually shifting from a "centralized-dominated" model to a "distributed + energy hub" model, integrating distributed power sources such as wind turbines, photovoltaics, and combined cooling, heating, and power (CCHP) units, as well as electricity / heat / cooling energy storage devices. This also encompasses diverse loads from industry, commerce, and residential sectors, forming a complex energy network with multi-energy complementarity. However, the increased complexity of energy conversion relationships and the divergent interests of distributed entities brought about by multi-energy flow coupling (such as the conflict between the system operator's global optimization goals and the individual revenue goals of distributed energy sources) place higher demands on operation and scheduling methods.

[0003] Existing integrated energy system dispatching methods have three significant limitations: First, the multi-stakeholder interaction mechanism is simplistic, often employing a centralized model of "upper-level control + lower-level execution," neglecting the proactive decision-making capabilities of distributed energy, energy storage, and loads, making it difficult to motivate individuals to participate in market transactions. Second, the game theory model lacks adaptability; traditional single game frameworks (such as master-slave games or non-cooperative games) cannot cover the multi-level needs of "global control - meso-level coordination - micro-level transactions," especially in multi-energy flow coupling scenarios, making it difficult to balance efficiency and fairness. Third, the depth of multi-energy coordination is insufficient; quantitative modeling of key pathways such as CCHP waste heat utilization and the complementarity of energy storage and renewable energy is lacking, resulting in low renewable energy absorption rates and a rigid multi-energy market trading mechanism.

[0004] With the advancement of energy market reforms and the development of digital technologies, there is an urgent need to construct a hybrid game framework adapted to the characteristics of multiple stakeholders to address the aforementioned issues. Existing technologies such as blockchain smart contracts and cloud energy storage aggregation provide support for trusted transactions among multiple stakeholders, while multi-agent reinforcement learning and distributed optimization algorithms offer tools for solving complex game models. However, how to deeply integrate the hybrid game architecture with the coupling characteristics of multiple energy flows and the dynamic interaction needs of multiple stakeholders to form a coordinated scheduling method of "model-algorithm-mechanism" remains a critical technological bottleneck that urgently needs to be overcome. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-agent operation scheduling method and system for integrated energy systems based on hybrid game theory. By constructing a nested game model of "global control - meso-level coordination - micro-level trading", it systematically solves the core problems of existing technologies, such as the single multi-agent interaction mechanism, insufficient adaptability of game models, and lack of depth in multi-energy coordination.

[0006] The present invention adopts the following technical solution.

[0007] In a first aspect, this invention discloses a multi-agent operation scheduling method for an integrated energy system based on hybrid game theory, comprising the following steps: Based on the characteristics and energy conversion relationships of each energy source in the integrated energy system, a multi-energy collaborative system architecture is constructed, and models of each energy source are established. Based on the aforementioned energy entity models, a three-layer hybrid game model is designed, comprising an upper-layer master-slave game model, a middle-layer cooperative game model, and a lower-layer non-cooperative game model. The upper-layer master-slave game model outputs cooling, heating, and electricity load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer cooperative game model, the overall profit maximization of the alliance formed by the cooperative members is the objective; the total profit deviation of the alliance is calculated and transmitted to the upper layer, and the total tradable surplus energy of the alliance is calculated and transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the entity, with the objective of maximizing the profit of each microgrid itself, and conducts transactions, feeding the transaction results back to the middle layer. Solve the three-layer hybrid game model, allocate and settle the benefits of each energy entity based on the solution results, and realize the operation scheduling of each energy entity.

[0008] More preferably, The multi-energy collaborative system architecture includes a system operator, distributed energy resources, energy storage systems, energy hubs, and cooling, heating, and power loads. The system operator achieves the coupling of multiple energy flows, including electricity, heat, and cooling, through the energy hubs. The energy hubs include combined cooling, heating, and power units, gas-fired boilers, and electric chillers. The energy storage systems include electrochemical energy storage, thermal storage tanks, and cold storage tanks. The establishment of models for each energy source includes, when constructing the combined cooling, heating, and power (CCHP) unit model, a constraint must be met that the comprehensive utilization efficiency of residual heat must not be lower than a preset threshold. Specifically:

[0009] in, for t The combined cooling, heating and power unit generates total waste heat at all times. for t The heat storage device recovers waste heat at all times. for t The waste heat absorbed by the constant absorption chiller. fort The waste heat is constantly supplied directly to the heat load. A threshold is preset for the efficiency of comprehensive utilization of waste heat.

[0010] More preferably, The three-layer hybrid game model is specifically designed as follows: The aforementioned upper-level master-slave game model has a leader who is the system operator, whose decision variables include cooling, heating, and electricity load price signals and demand response subsidy coefficients. Followers include distributed energy aggregators, energy storage operators, combined cooling, heating, and power (CCHP) unit operators, and load aggregators. Based on the cooling, heating, and electricity load price signals and demand response subsidy coefficients output by the system operator, distributed energy aggregators, energy storage operators, and CCHP unit operators optimize their own output with the goal of maximizing their own revenue, while load aggregators optimize their load strategies. Based on the optimization results of the followers, the leader obtains the optimal cooling, heating, and electricity load price signals and demand response subsidy coefficients by iteratively solving the Stackelberg equilibrium. The mid-level cooperative game model includes distributed energy aggregators, energy storage operators, and combined cooling, heating, and power (CCHP) unit operators as cooperative members. With the goal of maximizing the overall revenue of the alliance formed by these members, the model quantifies the contribution of each member and allocates revenue based on an improved Shapley value method. This process calculates the output plan of each member, the total tradable surplus energy of the alliance, and the total revenue of the alliance. Based on the total revenue of the alliance, the deviation of the total revenue of the alliance is calculated. The underlying non-cooperative game model consists of various community micro-networks. Each entity adopts a P2P transaction mechanism to maximize its own profit and calculate the transaction results for each entity.

[0011] More preferably, In the aforementioned three-layer hybrid game model, the information interaction between layers specifically involves: In the upper-level master-slave game model, the system operator quantifies the deviation of the total alliance revenue fed back from the middle-level cooperative game model into a revenue adjustment coefficient and embeds it into its objective function to adjust the cooling, heating and power load price signals and demand response subsidy coefficients. The mid-level cooperative game model is based on the cooling, heating and power load price signals output by the upper-level master-slave game model, and the P2P transaction volume and transaction price in the transaction results submitted by the lower-level non-cooperative game model, and constructs an objective function with the goal of maximizing the overall profit of the alliance. In the lower-level non-cooperative game, each community micro-network initiates P2P transactions based on the total amount of tradable surplus energy of the alliance output by the middle-level cooperative game model, and feeds back the P2P transaction volume and transaction price to the middle-level cooperative game model.

[0012] More preferably, The system operator quantifies the deviation in the total alliance payout from the mid-level cooperative game model as a payout adjustment coefficient and embeds it into its objective function. Specifically, the system operator's objective function is:

[0013] in, The return adjustment coefficient is calculated based on the deviation of the total alliance return from the mid-level feedback. T This represents the total number of scheduling time slots. , , These are the price signals for electricity, heat, and cooling, respectively. , , Sales figures for electrical, heating, and cooling power, respectively; , These are the costs of purchasing electricity from the external grid and the operation and maintenance costs. This is the demand response subsidy coefficient. Reduce load in response to demand. This refers to subsidy income; The profit adjustment coefficient is determined as follows:

[0014]

[0015] in, The basic return adjustment coefficient; To adjust the sensitivity coefficient, it is dynamically adjusted according to the supply and demand tension of the system; for t The deviation in the total payoff of the alliance as reflected in the mid-level cooperative game model at each moment. for The actual total alliance payout in the mid-level cooperative game at any given moment; for In the mid-level cooperative game, the total revenue of the alliance plan is the sum of the revenues of each cooperative member operating independently.

[0016] More preferably, In the mid-level cooperative game model, the objective function constructed with the goal of maximizing the overall total payoff of all cooperative members is specifically as follows:

[0017] in, for t In the mid-level cooperative game model, the total electricity sold by the alliance at any given moment is... for t Total heat output of the Time Alliance for t Total cooling capacity of the Time Alliance; for t Total operating costs of the Time Alliance The total interaction cost of the alliance; The P2P transaction price between the alliance and its lower-level micronetworks is the average of the P2P transaction application quotes from each lower-level micronetwork. For the alliance's P2P electricity sales costs, For the first m P2P transaction volume of individual community micro-networks M The total number of community micro-networks participating in P2P transactions.

[0018] More preferably, The improved Shapley value method is specifically as follows: A dynamic adjustment factor is introduced, and after normalization, it is multiplied by the payoff value calculated by the traditional Shapley value method to obtain the adjusted payoff value of each subject. The traditional Shapley value method refers to the classic cooperative game allocation method that calculates the payoff of each cooperating subject based on marginal contribution. The dynamic adjustment factor is determined in the following manner:

[0019] in, For the first i The subject in the first t The dynamic adjustment factor at any given time; For the first i The subject in the first t The efficiency of multi-energy complementarity at all times; For the first i The subject in the first t The degree of participation in responding to needs at any given moment is the main focus. i exist t The ratio of the actual response adjustment at any given time to the theoretical maximum response potential; This is a weighted value for the efficiency of multi-energy complementarity. This is the weight value for the participation in demand response, and it satisfies... .

[0020] Secondly, this invention discloses a multi-entity operation scheduling system for a comprehensive energy system based on hybrid game theory, which is based on the aforementioned method. It includes a model construction module for each energy entity, a three-layer hybrid game model construction module, and an operation scheduling module for each energy entity. The module for constructing models for each energy entity builds a multi-energy collaborative system architecture based on the characteristics and energy conversion relationships of each energy entity in the integrated energy system, and establishes models for each energy entity. The three-layer hybrid game model construction module, based on the aforementioned energy entity models, designs a three-layer hybrid game model including an upper-layer master-slave game model, a middle-layer cooperative game model, and a lower-layer non-cooperative game model. The upper-layer master-slave game model outputs cooling, heating, and electricity load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer cooperative game model, the overall profit maximization of the alliance formed by the cooperative members is the objective; the total profit deviation of the alliance is calculated and transmitted to the upper layer, and the total tradable surplus energy of the alliance is calculated and transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the main entity, with the objective of maximizing the profit of each community microgrid itself, and conducts transactions, feeding back the transaction results to the middle layer. Each energy entity's operation scheduling module solves the three-layer hybrid game model, allocates and settles the benefits of each energy entity based on the solution results, and realizes the operation scheduling of each energy entity.

[0021] Thirdly, the present invention provides a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of the first aspects of the present invention.

[0022] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects of the present invention.

[0023] The beneficial effects of this invention are compared with those of the prior art: This invention employs a three-layer hybrid game theory architecture: "global regulation, meso-level collaboration, and micro-level trading." The upper-layer master-slave game model guides distributed entities to adjust their output strategies using dynamic price signals, addressing the issue of passive execution by distributed entities. The mid-layer cooperative game model quantifies the contributions of each entity's multi-energy complementarity using a Shapley value-sensitivity combination method, achieving fair revenue distribution and introducing a dynamic adjustment factor to overcome the limitations of static allocation in reflecting the time-series value of energy storage and the dynamic contribution of CCHP waste heat. The lower-layer non-cooperative game model supports direct trading and energy efficiency optimization between producers and consumers, compensating for the rigidity of the trading mechanism. This architecture effectively solves the problem that traditional single-game frameworks cannot cover multi-level needs, significantly improving the efficiency of multi-entity collaboration and market activity.

[0024] This invention establishes a dynamic pricing mechanism for electricity, heat, and cooling, incorporating parameters such as CCHP waste heat utilization rate and energy storage charging and discharging efficiency into the game objective function. It also employs the Alternating Direction Multiplier Method (ADMM) and a multi-objective genetic algorithm to collaboratively solve the hybrid game model, improving optimization accuracy and reliability and alleviating the pain point of low renewable energy absorption rate. By dynamically adjusting the penalty factor to adaptively optimize the Nash equilibrium state, and combining it with blockchain smart contracts to achieve reliable transaction data storage, a highly efficient scheduling scheme with a closed-loop "model-algorithm-mechanism" is ultimately formed, reducing total costs while increasing the renewable energy absorption rate. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the multi-agent operation scheduling method for integrated energy systems based on hybrid game theory according to the present invention. Figure 2 This is a schematic diagram of the multi-energy collaborative system architecture in Embodiment 1 of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0027] like Figure 1 As shown, this invention discloses a multi-agent operation scheduling method for an integrated energy system based on hybrid game theory, comprising the following steps: Step 1: Construct a multi-energy collaborative system architecture based on the characteristics and energy conversion relationships of each energy source in the integrated energy system, and establish models for each energy source; The multi-functional collaborative system architecture, such as Figure 2 As shown, it includes system operators, distributed energy sources, energy storage systems, energy hubs, and cooling, heating, and power loads. The system operators achieve multi-energy flow coupling of electricity, heat, and cooling through the energy hubs. The energy hubs include combined cooling, heating, and power units, gas-fired boilers, and electric chillers. The energy storage systems include electrochemical energy storage, thermal storage tanks, and cold storage tanks. The establishment of models for each energy source includes, when constructing the combined cooling, heating, and power (CCHP) unit model, a constraint must be met that the comprehensive utilization efficiency of residual heat must not be lower than a preset threshold. Specifically:

[0028] in, for t The combined cooling, heating and power unit generates total waste heat at all times. for tThe heat storage device recovers waste heat at all times. for t The waste heat absorbed by the constant absorption chiller. for t The waste heat is constantly supplied directly to the heat load. A threshold is preset for the efficiency of comprehensive utilization of waste heat.

[0029] Step 2: Based on the aforementioned energy entity models, design a three-layer hybrid game model comprising an upper-layer master-slave game model, a middle-layer cooperative game model, and a lower-layer non-cooperative game model. The upper-layer master-slave game model outputs cooling, heating, and electricity load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer cooperative game model, the overall profit maximization of the alliance formed by the cooperative members is the objective; the total profit deviation of the alliance is calculated and transmitted to the upper layer, and the total tradable surplus energy of the alliance is calculated and transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the entity, with the objective of maximizing the profit of each microgrid itself, and conducts transactions, feeding the transaction results back to the middle layer. The three-layer hybrid game model is specifically designed as follows: The aforementioned upper-level master-slave game model has a leader who is the system operator, whose decision variables include cooling, heating, and electricity load price signals and demand response subsidy coefficients. Followers include distributed energy aggregators, energy storage operators, combined cooling, heating, and power (CCHP) unit operators, and load aggregators. Based on the cooling, heating, and electricity load price signals and demand response subsidy coefficients output by the system operator, distributed energy aggregators, energy storage operators, and CCHP unit operators optimize their own output with the goal of maximizing their own revenue, while load aggregators optimize their load strategies. Based on the optimization results of the followers, the leader obtains the optimal cooling, heating, and electricity load price signals and demand response subsidy coefficients by iteratively solving the Stackelberg equilibrium. The mid-level cooperative game model includes distributed energy aggregators, energy storage operators, and combined cooling, heating, and power (CCHP) unit operators as cooperative members. With the goal of maximizing the overall revenue of the alliance formed by these members, the model quantifies the contribution of each member and allocates revenue based on an improved Shapley value method. This process calculates the output plan of each member, the total tradable surplus energy of the alliance, and the total revenue of the alliance. Based on the total revenue of the alliance, the deviation of the total revenue of the alliance is calculated. The underlying non-cooperative game model consists of various community micro-networks. Each entity adopts a P2P transaction mechanism to maximize its own profit and calculate the transaction results for each entity.

[0030] In the aforementioned three-layer hybrid game model, the information interaction between layers specifically involves: In the upper-level master-slave game model, the system operator quantifies the deviation of the total alliance revenue fed back from the middle-level cooperative game model into a revenue adjustment coefficient and embeds it into its objective function to adjust the cooling, heating and power load price signals and demand response subsidy coefficients. The mid-level cooperative game model is based on the cooling, heating and power load price signals output by the upper-level master-slave game model, and the P2P transaction volume and transaction price in the transaction results submitted by the lower-level non-cooperative game model, and constructs an objective function with the goal of maximizing the overall profit of the alliance. In the lower-level non-cooperative game, each community micro-network initiates P2P transactions based on the total amount of tradable surplus energy of the alliance output by the middle-level cooperative game model, and feeds back the P2P transaction volume and transaction price to the middle-level cooperative game model.

[0031] The system operator quantifies the deviation in the total alliance payout from the mid-level cooperative game model as a payout adjustment coefficient and embeds it into its objective function. Specifically, the system operator's objective function is:

[0032] in, The return adjustment coefficient is calculated based on the deviation of the total alliance return from the mid-level feedback. T This represents the total number of scheduling time slots. , , These are the price signals for electricity, heat, and cooling, respectively. , , Sales figures for electrical, heating, and cooling power, respectively; , These are the costs of purchasing electricity from the external grid and the operation and maintenance costs. This is the demand response subsidy coefficient. Reduce load in response to demand. This refers to subsidy income; The profit adjustment coefficient is determined as follows:

[0033]

[0034] in, The basic return adjustment coefficient; To adjust the sensitivity coefficient, it is dynamically adjusted according to the supply and demand tension of the system; for t The deviation in the total payoff of the alliance as reflected in the mid-level cooperative game model at each moment. for The actual total alliance payout in the mid-level cooperative game at any given moment; for In the mid-level cooperative game, the total revenue of the alliance plan is the sum of the revenues of each cooperative member operating independently.

[0035] In the mid-level cooperative game model, the objective function constructed with the goal of maximizing the overall total payoff of all cooperative members is specifically as follows:

[0036] in, for t In the mid-level cooperative game model, the total electricity sold by the alliance at any given moment is... for t Total heat output of the Time Alliance for t Total cooling capacity of the Time Alliance; for t Total operating costs of the Time Alliance The total interaction cost of the alliance; The P2P transaction price between the alliance and its lower-level micronetworks is the average of the P2P transaction application quotes from each lower-level micronetwork. For the alliance's P2P electricity sales costs, For the first m P2P transaction volume of individual community micro-networks M The total number of community micro-networks participating in P2P transactions.

[0037] The improved Shapley value method is specifically as follows: A dynamic adjustment factor is introduced, and the adjusted return value for each entity is obtained by multiplying the dynamic adjustment factor by the return value calculated using the traditional Shapley value method; wherein, the dynamic adjustment factor is specifically:

[0038] in, For the first i The subject in the first t The dynamic adjustment factor at any given time; For the first i The subject in the first t The efficiency of multi-energy complementarity at all times; For the first i The subject in the first t The degree of participation in responding to needs at any given moment is the main focus. i exist t The ratio of the actual response adjustment at any given time to the theoretical maximum response potential; This is a weighted value for the efficiency of multi-energy complementarity. This is the weight value for the participation in demand response, and it satisfies... .

[0039] Step 3: Solve the three-layer hybrid game model, allocate and settle the benefits of each energy entity based on the solution results, and realize the operation scheduling of each energy entity.

[0040] The present invention also discloses a multi-entity operation scheduling system for integrated energy systems based on hybrid game theory, which is based on the aforementioned method, including a model construction module for each energy entity, a three-layer hybrid game model construction module, and an operation scheduling module for each energy entity; The module for constructing models for each energy entity builds a multi-energy collaborative system architecture based on the characteristics and energy conversion relationships of each energy entity in the integrated energy system, and establishes models for each energy entity. The three-layer hybrid game model construction module, based on the aforementioned energy entity models, designs a three-layer hybrid game model including an upper-layer master-slave game model, a middle-layer cooperative game model, and a lower-layer non-cooperative game model. The upper-layer master-slave game model outputs cooling, heating, and electricity load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer cooperative game model, the overall profit maximization of the alliance formed by the cooperative members is the objective; the total profit deviation of the alliance is calculated and transmitted to the upper layer, and the total tradable surplus energy of the alliance is calculated and transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the main entity, with the objective of maximizing the profit of each community microgrid itself, and conducts transactions, feeding back the transaction results to the middle layer. Each energy entity's operation scheduling module solves the three-layer hybrid game model, allocates and settles the benefits of each energy entity based on the solution results, and realizes the operation scheduling of each energy entity.

[0041] Example 1: like Figure 1 As shown, this invention discloses a multi-agent operation scheduling method for an integrated energy system based on hybrid game theory, specifically including the following steps: Step 1: Construct a multi-energy collaborative system architecture based on the characteristics and energy conversion relationships of each energy source in the integrated energy system, and establish models for each energy source; Specifically, the multi-energy collaborative system architecture includes system operators, wind turbines, photovoltaics, CCHP, energy storage (electricity, heat, and cooling), and cooling, heating, and power loads. It achieves multi-energy flow coupling of electricity, heat, and cooling through energy hubs and clarifies the energy conversion relationships of each device.

[0042] The system adopts an "energy hub + distributed power source" design pattern, in which the energy hub includes CCHP units, gas boilers, and electric chillers; energy storage equipment includes electrochemical energy storage, thermal storage tanks, and cold storage tanks; distributed nodes cover wind turbines and photovoltaic power generation; and load nodes include industrial, commercial, and residential buildings.

[0043] The main components of the system and their functions are as follows: ①System Operator: With the goal of minimizing overall operating costs and maximizing renewable energy consumption, formulate electricity, heat, and cooling pricing strategies and demand response subsidies, and execute transaction settlements through blockchain smart contracts.

[0044] ② Distributed energy (wind turbines, photovoltaics): pursuing the maximization of individual benefits, optimizing power output plans, and participating in multi-energy market transactions.

[0045] ③ Energy storage: Smooth out the fluctuations of renewable energy through charging and discharging strategies and provide peak-shaving auxiliary services; cloud energy storage platforms aggregate user-side distributed energy storage resources.

[0046] ④ Cooling, heating and electricity loads: Based on the Multi-Path Hybrid Expert (MCME) network, the balance between comfort and energy costs is quantified, and the transferable / reducible loads are dynamically adjusted.

[0047] The objective function of the system operator is:

[0048] In the formula, Expenses for system operators, express t The cost of purchasing electricity from external sources at all times. Indicates maintenance costs. Revenue from electricity sales This indicates revenue from heat sales. Indicates revenue from cold chain sales. Indicates demand response revenue. T This represents the total number of scheduling moments.

[0049] The objective function for wind turbine photovoltaic systems is:

[0050] In the formula, For wind turbine and solar power revenue, express t Real-time user-side electricity price express t The actual output of wind turbines and solar power at all times. express t Constant maintenance costs This represents the energy curtailment penalty coefficient. express t The wind turbines and photovoltaics are expected to generate power at any time.

[0051] The objective function of the CCHP system is:

[0052] In the formula, For the benefit of the CCHP system, express t Real-time user-side hot price, for t User-side cooling price at all times for t The CCHP system generates heat at all times. for t The cooling capacity of the CCHP system at all times for t The power consumption of the CCHP system at any time for t CCHP maintenance costs at all times.

[0053] The constraints of the CCHP system are:

[0054] In the formula, For the thermal efficiency of the CCHP system, The cooling efficiency of the CCHP system.

[0055] The formula for the state of charge of electrochemical energy storage in an energy storage system is:

[0056] In the formula, , They are respectively t time, t State of charge of electrical energy storage at time -1 for t Constant charging power, for t Discharge power at all times For charging efficiency, For discharge efficiency, Indicates the rated capacity of energy storage. The step size is [not specified].

[0057] The objective function for electrochemical energy storage is:

[0058] In the formula, For the benefits of electrochemical energy storage, for t Peak shaving subsidy price at all times for t Constant operation and maintenance costs, for t Peak-valley subsidy prices for the power grid at all times. Indicates discharge power. This indicates the charging power.

[0059] The state formula for a thermal storage system is:

[0060] In the formula, , They represent t time, t The amount of heat stored in the thermal storage tank at time -1 Indicates the heat loss rate. , They represent t The constant charging and releasing of heat, , These represent the heat charging efficiency and the heat releasing efficiency, respectively.

[0061] The objective function of the thermal storage system is:

[0062] In the formula, For the benefit of the thermal storage system, express t Real-time user-side hot price, , They represent t The constant charging and releasing of heat, Indicates thermal storage system t The cost of operation and maintenance at all times.

[0063] The state equation of the cold storage system is:

[0064] In the formula, , They represent t time, t The cooling capacity of the cold storage tank at time -1. Indicates the cooling loss rate. , They represent t The amount of cooling charged and released at any given time. , These represent the charging efficiency and the discharging efficiency, respectively.

[0065] The objective function of the cold storage system is:

[0066] In the formula, For the benefit of the cold storage system, express t User-side cooling price at all times Indicates refrigeration operating costs, , They represent t The amount of cooling charged and released at any given moment.

[0067] Based on this, considering CCHP-energy storage synergistic optimization, the utilization rate of CCHP waste heat in charging the thermal storage tank and absorption cooling is quantified using the waste heat utilization efficiency formula to ensure that the comprehensive utilization efficiency of waste heat is not lower than the preset threshold. At the same time, a synergistic operation strategy is determined, which prioritizes CCHP waste heat to meet heat / cooling loads, with the remaining heat energy stored in the thermal storage tank; photovoltaic / wind turbine output is prioritized for local consumption, and surplus electricity is used to smooth out fluctuations through energy storage or participate in external grid transactions.

[0068] Specifically:

[0069] In the formula, for t The waste heat absorbed by the constant absorption chiller. This indicates the total waste heat generated by the combined cooling, heating and power unit. express t The heat storage device recovers waste heat at all times. Waste heat is directly supplied to the combined cooling, heating and power (CCHP) units to meet the heat load. A threshold is preset for the comprehensive utilization efficiency of waste heat; in this embodiment, it is preferably 85%.

[0070] Step 2: Based on the aforementioned energy entity models, design a three-layer hybrid game model comprising an upper-layer master-slave game model, a middle-layer cooperative game model, and a lower-layer non-cooperative game model. The upper-layer master-slave game model outputs cooling, heating, and electricity load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer cooperative game model, the overall profit maximization of the alliance formed by the cooperative members is the objective; the total profit deviation of the alliance is calculated and transmitted to the upper layer, and the total tradable surplus energy of the alliance is calculated and transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the entity, with the objective of maximizing the profit of each microgrid itself, and conducts transactions, feeding the transaction results back to the middle layer. Specifically, in the three-layer hybrid game model of this invention, each operating entity is a market representative corresponding to each energy entity mentioned in step 1; The aforementioned upper-level master-slave game model has a leader who is the system operator, whose decision variables include cooling, heating, and electricity load price signals and demand response subsidy coefficients. Followers include distributed energy aggregators, energy storage operators, combined cooling, heating, and power (CCHP) unit operators, and load aggregators. Based on the cooling, heating, and electricity load price signals and demand response subsidy coefficients output by the system operator, distributed energy aggregators, energy storage operators, and CCHP unit operators optimize their own output with the goal of maximizing their own revenue, while load aggregators optimize their load strategies. Based on the optimization results of the followers, the leader obtains the optimal cooling, heating, and electricity load price signals and demand response subsidy coefficients by iteratively solving the Stackelberg equilibrium. The mid-level cooperative game model includes distributed energy aggregators, energy storage operators, and combined cooling, heating, and power (CCHP) unit operators as cooperative members. With the goal of maximizing the overall revenue of the alliance formed by these members, the model quantifies the contribution of each member and allocates revenue based on an improved Shapley value method. This process calculates the output plan of each member, the total tradable surplus energy of the alliance, and the total revenue of the alliance. Based on the total revenue of the alliance, the deviation of the total revenue of the alliance is calculated. The underlying non-cooperative game model consists of various community micro-networks. Each entity adopts a P2P transaction mechanism to maximize its own profit and calculate the transaction results for each entity.

[0071] Specifically, the collaborative relationship of the three-layer hybrid game model is a dynamic coupling closed-loop architecture with "upper-layer global guidance - middle-layer collaborative foundation building - lower-layer micro-execution - bidirectional feedback optimization" as its core.

[0072] The specific interaction method of the three-layer hybrid game model is as follows: In the upper-level master-slave game model, the system operator quantifies the deviation of the total alliance revenue fed back from the middle-level cooperative game model into a revenue adjustment coefficient and embeds it into its objective function to adjust the cooling, heating and power load price signals and demand response subsidy coefficients. The mid-level cooperative game model is based on the cooling, heating and power load price signals output by the upper-level master-slave game model, and the P2P transaction volume and transaction price in the transaction results submitted by the lower-level non-cooperative game model, and constructs an objective function with the goal of maximizing the overall profit of the alliance. In the lower-level non-cooperative game, each community micro-network initiates P2P transactions based on the total amount of tradable surplus energy of the alliance output by the middle-level cooperative game model, and feeds back the P2P transaction volume and transaction price to the middle-level cooperative game model.

[0073] The three-layer hybrid game model is as follows: 1) Deep modeling of upper-level master-slave game (Stackelberg game) ① Game participants and decision-making sequence Leader: System Operator (SO), decision variables are cooling, heating and electricity load price signals and demand response subsidy coefficient, decision timing is the price curve of the next 24 hours after the date of release at 12:00 noon.

[0074] Followers: Distributed Energy Aggregators (DERA), Energy Storage Operators (ESO), Load Aggregators (LA), and Combined Cooling, Heating and Power (CCHP) System Operators, optimize output / load strategies based on price signals, and submit the next day's plan at 2 PM the day before.

[0075] ② Objective function and equilibrium condition The objective function of the system operator is:

[0076] In the formula: These are the prices of electricity, heating, and cooling, respectively, i.e., the price signals for heating, cooling, and electricity. , , These are the sales figures corresponding to the heating and cooling power of the electric heating system; , These are the costs of purchasing electricity from the external network and the operation and maintenance fees. This is the demand response subsidy coefficient. Reduce load in response to demand. This refers to subsidy income; The profit adjustment coefficient is calculated based on the deviation of the total alliance profit from the mid-level feedback. T This represents the total number of scheduling time slots. Specifically, the profit adjustment coefficient is determined in the following manner:

[0077]

[0078] In the formula: The basic return adjustment coefficient is preferably 0.8; To adjust the sensitivity coefficient, the value range is [0.3, 0.8], and it is dynamically adjusted according to the supply and demand tension of the system. When the supply and demand are unbalanced, a larger value is taken. for The deviation of the total alliance payoff in the mid-level cooperative game model at each moment; for The actual total payoff of the alliance in the mid-level cooperative game at any given moment; for In the mid-level cooperative game, the total revenue of the alliance plan is the sum of the revenues of each member operating independently.

[0079] The goal of distributed energy aggregators is:

[0080] In the formula, Distributed energy aggregator t Always put in actual effort, express t Electricity prices at all times This represents the output deviation penalty coefficient. Indicates predicted output. express t The operation and maintenance costs of distributed energy aggregators at all times. Distributed energy aggregator t Electricity sales volume at any given time.

[0081] The goal of energy storage operators is:

[0082] In the formula, , For energy storage operators t The discharge and charge power at any time For energy storage operators t The power required to respond at any given moment.

[0083] The goal of the load aggregator is:

[0084] In the formula, For load aggregators in t Reduce load at any time The user comfort loss coefficient can be specifically set according to user type, such as for residential users (who are sensitive to living loads such as air conditioning and lighting). The value is relatively large, around 0.8; for commercial users (such as shopping malls), the load can be flexibly adjusted. The value is moderate, around 0.4; for industrial users (interruptible loads). The value is relatively small, at 0.2.

[0085] The goal of combined cooling, heating and power (CCHP) unit operators is:

[0086] In the formula, , , CCHP combined cooling, heating and power unit operators t The electrical energy output, waste heat output, and cold energy output at any given time; The operation and maintenance costs of CCHP combined cooling, heating and power units.

[0087] Stackelberg equilibrium condition:

[0088] In the formula, This represents the optimal decision variable for the system operator. This represents the decision variables of the system operator that are continuously adjusted during the optimization process. This represents the objective function of the upper-level leader. Represents the optimal response of the lower-level objective function. P value, P This represents the set of decision variables for lower-level followers.

[0089] ③ Solution Algorithm (MADDPG) The state space includes real-time electricity price, renewable energy output, load demand, and energy storage SOC; the action space includes SO price adjustments and follower output / load adjustments; the reward function is used to incentivize consumption and achieve economic balance, and the reward function is as follows:

[0090] In the formula, for t The objective function value of the time system operator. This indicates the change in renewable energy output. Indicates the amount of abandoned electricity. To express one's ideal efforts , These are the weights for the adjustment of renewable energy output and the weights for items related to the curtailment rate.

[0091] 2) Mid-level cooperative game In mid-level cooperative game theory, cooperating members include DRA (including wind turbines and solar power), ESO (including electric / thermal energy storage), and CCHP system operators. They form a temporary alliance through contracts, sharing power output plans and load information. For cooperation to be feasible, the total revenue from cooperation must be greater than the sum of the revenues of each member operating independently.

[0092] ①The cooperative payoff function is:

[0093] In the formula, for t Total electricity sales of the Moment Alliance This indicates the total heat sales capacity of the alliance. express t Total cooling capacity of the Time Alliance; express t Total operating costs of the Time Alliance This represents the total interaction cost of the alliance; The P2P transaction price between the alliance and its lower-level micronetworks is the average of the P2P transaction application quotes from each lower-level micronetwork. For the alliance's P2P electricity sales costs, For P2P transaction volume, M The total number of community micro-networks participating in P2P transactions; where the total operating cost of the alliance is... This includes routine operating costs such as equipment maintenance, fuel consumption, and depreciation; the total interaction cost of the alliance. This includes additional costs such as line loss, heat loss, and cold loss when transmitting energy between alliance members; that is, the additional costs incurred by the alliance for cooperation.

[0094] Actual total payout of the alliance in the mid-level cooperative game That is, what is obtained The optimal solution; Total Profit of Alliance Plan in Mid-Level Cooperative Game This is the sum of the total revenue generated by each member operating independently.

[0095] ② Improved Shapley value assignment The traditional Shapley value method is a classic cooperative game theory allocation method that calculates the revenue of each cooperating entity based on marginal contributions. The traditional Shapley value method has two key drawbacks: First, it is static, calculating allocation weights only based on marginal contributions before the alliance is formed, failing to reflect real-time contribution differences across different time periods (e.g., the peak-shaving value of energy storage is much higher during peak hours than during off-peak hours, and the dynamic fluctuations in CCHP waste heat utilization efficiency with load changes); second, it has a single dimension, measuring contribution only through electricity trading, ignoring the implicit value of multi-energy complementarity (e.g., waste heat recovery, combined cooling and power) and demand response, which is incompatible with the multi-energy flow synergy characteristics of integrated energy systems. Furthermore, the traditional Shapley value method is often combined with sensitivity analysis, but sensitivity analysis only supplements the influence of a single factor, ultimately allocating revenue through fixed weights. It is a static linear combination, ignoring the dynamic and interactive nature of multi-energy flow synergy, and cannot adapt to complex scenarios in integrated energy systems such as CCHP waste heat utilization and the time-series value differences of energy storage. Therefore, the improved Shapley value method, by introducing a dynamic adjustment factor (integrating multi-energy complementary efficiency and demand response participation), achieves real-time contribution quantification and temporal fairness, which not only adapts to the dynamic operation scenario of the system, but also accurately incentivizes multi-dimensional collaborative behavior, thereby improving the stability of cooperation and the overall benefits.

[0096] Basic Shapley value assignment:

[0097] In the formula, Representing the subject i Shapley value, Indicates the weighting coefficient. This represents the characteristic function value of the new alliance. Let S represent the characteristic function value of subset S, where S represents the set of all cooperating members in the large alliance N that does not contain the current participant. i Any subset of.

[0098] Dynamic adjustment factor: Introducing a weighted combination of "multi-energy complementarity contribution" and "demand response participation", the formula is as follows:

[0099] In the formula, Indicates the first i The subject in the first tThe efficiency of multi-energy complementarity at any moment is used to quantify the subject. i exist t It constantly participates in the actual effect of the synergy of multiple energy flows such as electricity, heat and cold, and comprehensively considers the operating efficiency of the main core equipment and the synergistic contribution between the main entities, highlighting the complementary value in the multi-energy flow coupling scenario; among them, distributed energy aggregators focus on wind and solar consumption efficiency, energy storage operators focus on charging and discharging efficiency, CCHP operators focus on waste heat utilization efficiency, and then combines the weighted integration of the ratio of the actual synergistic effect between the main entities to the theoretical maximum synergistic potential. No. i The subject in the first t Multi-energy complementarity efficiency at all times The calculation formula is as follows:

[0100] in: as the main body i exist t The core equipment efficiency at any given time; specifically, the core equipment efficiency of a distributed energy aggregator is the ratio of the actual wind and solar power consumption to the total wind and solar power generation; the core equipment efficiency of an energy storage operator is its own charging and discharging efficiency, that is, when the energy storage system in the energy storage operator is in a charging state, its core equipment efficiency is the charging efficiency of the energy storage system, and when the energy storage system in the energy storage operator is in a discharging state, its core equipment efficiency is the discharging efficiency of the energy storage system; the core equipment efficiency of a CCHP operator is the residual heat utilization efficiency. as the main body i exist t The efficiency of auxiliary equipment coordination at all times. , for t Total waste heat generated by the CCHP unit at any given time (kWh). for t The heat loss of the waste heat exchanger at any given time (kWh); specifically, This measure primarily evaluates entities with clearly defined energy recovery or cascade utilization processes, such as CCHP's waste heat recovery system. Therefore, for entities without such processes, the weight of this measure can be set to 0. as the main body i exist t The efficiency of multi-energy conversion at all times , Let t be the output energy of the multi-energy conversion at time t (kWh). The input energy (kWh) for multi-energy conversion at time t. as the main body i exist tThe coordination coefficient at time t specifically represents the degree to which subject i completes energy interaction with other subjects at time t. , The complementary energy that subject i actually supplies to other subjects or receives from other subjects at time t. The maximum collaborative energy that subject i can theoretically participate in at time t is usually determined by the device's capacity and operating status. , , The weighting coefficients for each indicator satisfy the following conditions: It can be set according to the system's multi-functional collaboration priority.

[0101] For the first i The subject in the first t Moment-by-moment demand response participation is used to measure the subject's engagement. i exist t The degree of responsiveness to upper-level regulatory signals is quantified by the ratio of the actual adjustment amount of the subject to the theoretical maximum response potential under technical constraints. The calculation formula is as follows:

[0102] in, as the main body i exist t The actual response adjustment at any given moment. as the main body i exist t The theoretical maximum response potential at time t. The closer the value is to 1, the higher the enthusiasm for demand response.

[0103] This is a weighted value for the efficiency of multi-energy complementarity. This is the weight value for the participation in demand response, and it satisfies... Typically, the weighting coefficient for multi-functional complementarity is larger.

[0104] The final allocation formula based on dynamic factor adjustment is:

[0105] In the formula, Indicates the improved main body i Shapley value, This represents the sum of dynamic adjustment factors for all entities within the alliance, ensuring that the allocation weights are normalized.

[0106] The middle layer improves upon the traditional Shapley value method by introducing a dynamic adjustment factor that integrates multi-energy complementarity efficiency and demand response participation. This overcomes the limitations of static allocation in reflecting the time-series value of energy storage and the dynamic contribution of CCHP waste heat, thereby enhancing cooperation stability and total benefits.

[0107] 3) Lower-level non-cooperative game model ① Trading entities and market rules In the lower-level non-cooperative game model, each microgrid adopts a peer-to-peer (P2P) trading mechanism. The trading entities include community microgrids A, B, and C (each containing DER, energy storage, and load), and surplus energy is mutually supported through a distributed trading platform. The trading rules employ a bilateral pricing mechanism, with the seller's price being... The buyer's quoted price is The average transaction price is taken. .

[0108] ②Solving for Nash equilibrium The objective function for each microgrid is as follows (taking microgrid A as an example):

[0109] In the formula, Indicates transaction costs, Indicates the power purchased. This indicates the power output.

[0110] The equilibrium condition is , This represents the optimal strategy for the microgrid. This is the optimal strategy for other microgrids.

[0111] ③Block Management When the power flow of line z exceeds the limit, congestion management is triggered:

[0112] In the formula, Indicates the upper limit of the trend. Indicates the overall current flow after adjustment. This represents the cumulative adjustment of output at each moment. Indicates the first i Individual entities t The amount of output adjustment at any given moment.

[0113] Preferably, based on the above hybrid game model, a cloud energy storage aggregation model can be introduced. This model acts as a "resource link" connecting the middle-level cooperative game and the lower-level non-cooperative game. Its core function is to aggregate user-side distributed energy storage resources, providing energy storage support for the collaborative operation of the two-layer game. It is used to aggregate user-side distributed energy storage resources to form a cloud-based virtual energy storage pool. Users purchase cloud-based battery capacity on demand, and realize energy storage resource sharing and revenue distribution through smart contracts. The cloud energy storage aggregation model is specifically as follows:

[0114] In the formula, express t Total power of the cloud energy storage aggregate m Indicates the number of participating users. express t Time of the first i Individual user energy storage capacity.

[0115] Step 3: Solve the three-layer hybrid game model, allocate and settle the benefits of each energy entity based on the solution results, and realize the operation scheduling of each energy entity.

[0116] Specifically, the upper-level master-slave game is solved using the MADDPG reinforcement learning algorithm, which achieves dynamic adaptation of electricity price signals and output strategies through multi-agent collaborative training (the system operator and each follower are independent agents). The policy update formula is:

[0117] In the formula, Indicates policy network parameters, Represents the policy gradient. Indicates the degree of learning. Indicates an immediate reward. Indicates the discount factor. This represents the target value network prediction. The maximum fluctuation (<0.5%) of the cooling, heating, and electricity price signals and the demand response subsidy coefficient over consecutive (N=20) iterations is used to avoid strategy oscillations.

[0118] The mid-level cooperative game adopts a multi-objective genetic algorithm for collaboration. Its participants are an alliance composed of DRA, ESO and CCHP operators. The goal is not only to maximize the total alliance revenue, but also to take into account the fairness of revenue distribution and the efficiency of multi-energy complementarity. In particular, it must meet the hard constraint of CCHP waste heat utilization rate ≥85% and the fluctuation value (<2%) of continuous (N=15) iterations. The algorithm employs real-number encoding, with chromosome segments encompassing key decision variables such as CCHP heat / cold generation, energy storage charging / discharging power, and dynamic adjustment factor weights. A fitness function constructs a multi-objective optimization system, simultaneously considering total revenue, the allocation Gini coefficient, and waste heat utilization. Through genetic operations such as tournament selection, simulated binary crossover, and polynomial mutation, combined with the NSGA-II framework, the Pareto optimal solution is screened. Ultimately, a scheme that satisfies multi-energy synergy constraints and achieves fair revenue distribution is determined, solving the problem of traditional single algorithms failing to balance multiple objectives and ensuring the stability of alliance cooperation and improved total revenue. After obtaining the alliance's optimal output plan, total tradable surplus energy, and total alliance revenue, an improved Shapley value method is used based on each member's marginal contribution, combined with dynamic adjustment factors, to fairly distribute the total alliance revenue to cooperating members such as DRA, ESO, and the CCHP system operator, ensuring that each member's revenue reflects their contribution while maintaining cooperative stability.

[0119] The lower-level non-cooperative game model is solved using the ADMM algorithm. This model centers on the mutual energy sharing among community microgrids. Each microgrid, as an independent entity, pursues the minimum transaction cost and the minimum energy cost, while protecting privacy data and satisfying the constraint that line power flow does not exceed limits. ADMM decomposes the global optimization problem into local subproblems of each microgrid through a "decomposition-coordination" mechanism. Each microgrid only needs to input its own load demand, local output, and bid, without sharing privacy information. Then, the consistency between the local solution and the global solution is achieved through iterative formulas.

[0120] The game theory model of multi-energy collaborative system needs to cope with the challenges of "multi-subject dynamic interaction + high-dimensional uncertainty". Therefore, a hierarchical solution strategy is adopted to balance computational efficiency and optimization accuracy.

[0121] The ADMM iterative algorithm is a core tool in distributed optimization. It decomposes the global optimization problem into local subproblems for each agent (such as microgrid output optimization or energy storage charging / discharging plans) through a decomposition-coordination mechanism, and then achieves global consistency of solutions to the subproblems through iterative iteration of dual variables. Its iterative formula is:

[0122] In the formula, Indicates the first k Decision variables for local sub-problems in a community micronetwork t +1 step optimal solution; For a single entity, the objective function is a local subproblem of a single entity. For the local decision variables that need to be optimized, It is the penalty coefficient. This represents the globally unified value of the coordination variable. Indicates the adjustment of the first k The dual variable of the deviation between the "actual transaction electricity price" and the "global benchmark electricity price" of individual microgrids. This represents the objective function of the coordination layer.

[0123] During the calculation, the iteration is stopped when the rate of change of the transaction cost of each community micro-network is less than 0.5% for N=10 consecutive iterations, to ensure the stability of the transaction results.

[0124] This method is particularly suitable for non-cooperative game scenarios in P2P transactions, and can quickly converge to Nash equilibrium while protecting the privacy of each micro-network (typically with fewer than 50 iterations and a convergence accuracy of 10). -4 ).

[0125] The dynamic adjustment mechanism achieves a "plan-correction" closed loop through multi-timescale coupling: In the day-ahead phase (1-hour granularity), an initial strategy is generated based on forecast data; in the intraday phase (15-minute granularity), dynamic corrections are made based on real-time deviations (such as the difference between actual photovoltaic output and forecast). The formula is as follows:

[0126] In the formula, the correction coefficient k The value of ∈[0.6,0.9] needs to balance stability and response speed. When the load fluctuates drastically, a large value (such as 0.9) is taken to quickly track changes, while a small value (such as 0.6) is taken during stable periods to reduce strategy oscillations. Multi-timescale adjustment solves the problem of insufficient real-time deviation adaptation.

[0127] Compared with traditional optimization methods, this algorithm can better handle the randomness of renewable energy output (such as wind power prediction errors), and continuously optimizes decisions through policy gradient updates, thereby reducing system operating costs by 8%-12%.

[0128] The distribution and settlement of benefits among various energy entities specifically includes: The core of profit distribution is to achieve a closed loop throughout the entire process of "quantifying contributions, dynamically adjusting, and settling accounts with credibility," thereby ensuring the long-term stability of multi-party cooperation.

[0129] 1) Risk hedging mechanism and return adjustment The returns of multi-energy synergy systems are significantly affected by electricity price fluctuations and the uncertainty of renewable energy output, necessitating a risk hedging mechanism to balance return stability and flexibility. (Definition of return volatility) for:

[0130] In the formula, as the main body i exist t The actual benefits at any given moment For average returns, higher volatility indicates higher risk. T This represents the total number of scheduling moments within a control cycle.

[0131] At the same time, a risk factor is introduced. , The system's average volatility is used as the basis for compensation for high-risk participants. The compensation-based return is as follows:

[0132] In the formula, This is the hedging coefficient, and its value range is... This ensures that risks and returns are commensurate.

[0133] 2) Priority and weight allocation for cross-entity settlement Different entities have different settlement needs (e.g., users need real-time settlement, while operators can settle on a daily basis), and a priority mechanism is needed to optimize the efficiency of capital flow.

[0134] Define the settlement priority index:

[0135] In the formula, For the first i Settlement priority index for each entity, The liquidity demand coefficient is set at 1.0 for users and 0.6 for operators. To contribute weight to investment, As a weight for credit health, among which (generally Prioritize ensuring liquidity.

[0136] Settlement order is as follows Entities with higher priority (such as residential users) will receive settlement funds first, arranged in descending order.

[0137] 3) Blockchain smart contract settlement Based on the existing settlement mechanism, add dynamic triggering and exception handling logic for smart contracts: ①Dynamic trigger threshold: When the deviation between a subject's actual revenue and planned revenue is greater than or equal to a threshold, immediate settlement is triggered (instead of waiting for a fixed period). If the deviation does not reach the threshold, it continues to accumulate until the next detection node (detected at a 15-minute scheduling granularity within the day). The settlement trigger condition adaptively adjusts according to the system's operating status, and the threshold is calculated as follows:

[0138] In the formula, For dynamic trigger threshold, For real-time load fluctuation rate, The threshold is set to average load fluctuation. The greater the load fluctuation (such as morning and evening peak hours), the higher the threshold (maximum 0.08) to reduce resource consumption caused by frequent settlements.

[0139] ② Exception handling formula When the actual output deviates from the planned output by more than a threshold, a penalty mechanism is triggered.

[0140] In the formula, where It is the penalty coefficient, and =5, To ensure the maximum permissible deviation and strict compliance by the main entity, Punishment for abnormal exertion, Representing the subject i exist t Time-based basic weights Indicates a penalty price. express t The allowable output deviation threshold at all times This indicates a deviation from the planned output.

[0141] By introducing dynamic thresholds and anomaly penalties in blockchain smart contracts, the shortcomings of traditional transaction data, such as untraceability and high risk of default, can be overcome.

[0142] 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 protection scope of the claims of the present invention.

Claims

1. A multi-agent operation scheduling method for an integrated energy system based on hybrid game theory, characterized in that, Includes the following steps: A multi-energy collaborative system architecture is constructed based on the characteristics and energy conversion relationships of various energy entities in an integrated energy system, and models of each energy entity are established. This multi-energy collaborative system architecture includes a system operator, distributed energy sources, energy storage systems, energy hubs, and cooling, heating, and power loads. The system operator achieves coupling of electricity, heat, and cooling multi-energy flows through the energy hubs. The energy hubs include combined cooling, heating, and power (CCHP) units, gas-fired boilers, and electric chillers. The energy storage system includes electrochemical energy storage, thermal storage tanks, and cold storage tanks. When establishing models of each energy entity, the construction of the CCHP unit model must meet the constraint that the comprehensive utilization efficiency of residual heat must not be lower than a preset threshold. Specifically: in, for t The combined cooling, heating and power unit generates total waste heat at all times. for t The heat storage device recovers waste heat at all times. for t The waste heat absorbed by the constant absorption chiller. for t The waste heat is constantly supplied directly to the heat load. A threshold is preset for the comprehensive utilization efficiency of waste heat; Based on the aforementioned energy entity models, a three-layer hybrid game model is designed, comprising an upper-level master-slave game model, a middle-level cooperative game model, and a lower-level non-cooperative game model. In the upper-level game model, the leader is the system operator, and decision variables include cooling, heating, and electricity load price signals and demand response subsidy coefficients. Followers include distributed energy aggregators, energy storage operators, combined cooling, heating, and power (CCHP) unit operators, and load aggregators. Based on the cooling, heating, and electricity load price signals and demand response subsidy coefficients output by the system operator, distributed energy aggregators, energy storage operators, and CCHP unit operators optimize their own output to maximize their own revenue, while load aggregators optimize their load strategies. The upper-level master-slave game model outputs... The system outputs cooling, heating, and power load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer game model, cooperative members include distributed energy aggregators, energy storage operators, and combined cooling, heating, and power (CCHP) unit operators. With the goal of maximizing the overall revenue of the alliance formed by these cooperative members, the system calculates the output plans of each member, the total tradable surplus energy of the alliance, and the total revenue of the alliance. Based on the total revenue of the alliance, the system calculates the deviation of the total revenue of the alliance and transmits this deviation to the upper layer, while the total tradable surplus energy is transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the main body, with the goal of maximizing the revenue of each microgrid itself. The system calculates the transaction results of each entity and feeds these results back to the middle layer. In the aforementioned three-layer hybrid game model, the information interaction between layers specifically involves: In the upper-level master-slave game model, the system operator quantifies the deviation of the total alliance revenue fed back from the middle-level cooperative game model into a revenue adjustment coefficient and embeds it into its objective function to adjust the cooling, heating and power load price signals and demand response subsidy coefficients. The objective function of the system operator is as follows: in, The return adjustment coefficient is calculated based on the deviation of the total alliance return from the mid-level feedback. T This represents the total number of scheduling time slots. , , These are the price signals for electricity, heat, and cooling, respectively. , , Sales figures for electrical, heating, and cooling power, respectively; , These are the costs of purchasing electricity from the external grid and the operation and maintenance costs. This is the demand response subsidy coefficient. Reduce load in response to demand. This refers to subsidy income; The profit adjustment coefficient is determined as follows: in, The basic return adjustment coefficient; To adjust the sensitivity coefficient, it is dynamically adjusted according to the supply and demand tension of the system; for t The deviation in the total payoff of the alliance as reflected in the mid-level cooperative game model at each moment. for The actual total alliance payout in the mid-level cooperative game at any given moment; for In the mid-level cooperative game, the total revenue of the alliance plan is the sum of the revenues of each cooperative member operating independently. The mid-level cooperative game model is based on the cooling, heating and power load price signals output by the upper-level master-slave game model, and the P2P transaction volume and transaction price in the transaction results submitted by the lower-level non-cooperative game model, and constructs an objective function with the goal of maximizing the overall profit of the alliance. Its objective function is as follows: in, for t In the mid-level cooperative game model, the total electricity sold by the alliance at any given moment is... for t Total heat output of the Time Alliance for t Total cooling capacity of the Time Alliance; for t Total operating costs of the Time Alliance The total interaction cost of the alliance; The P2P transaction price between the alliance and its lower-level micronetworks is the average of the P2P transaction application quotes from each lower-level micronetwork. For the alliance's P2P electricity sales costs, For the first m P2P transaction volume of individual community micro-networks M The total number of community micro-networks participating in P2P transactions; In the lower-level non-cooperative game, each community micro-network initiates P2P transactions based on the total tradable surplus energy of the alliance output by the middle-level cooperative game model, and feeds back the P2P transaction volume and transaction price to the middle-level cooperative game model; P2P transactions are peer-to-peer transactions. Solve the three-layer hybrid game model, allocate and settle the benefits of each energy entity based on the solution results, and realize the operation scheduling of each energy entity.

2. The multi-entity operation and scheduling method for integrated energy systems according to claim 1, characterized in that: The aforementioned upper-level master-slave game model, based on the optimization results of the followers, obtains the optimal cooling, heating and power load price signal and demand response subsidy coefficient by iteratively solving the Stackelberg equilibrium. The mid-level cooperative game model quantifies the contribution of each cooperative member and allocates the payout based on the improved Shapley value method.

3. The multi-entity operation and scheduling method for integrated energy systems according to claim 2, characterized in that: The improved Shapley value method is specifically as follows: A dynamic adjustment factor is introduced, and after normalization, it is multiplied by the payoff value calculated by the traditional Shapley value method to obtain the adjusted payoff value of each subject. The traditional Shapley value method refers to the classic cooperative game allocation method that calculates the payoff of each cooperating subject based on marginal contribution. The dynamic adjustment factor is determined in the following manner: in, For the first i The subject in the first t The dynamic adjustment factor at any given time; For the first i The subject in the first t The efficiency of multi-energy complementarity at any moment is used to quantify the subject. i exist t Constantly participate in the actual effects of the synergistic interaction of electric, thermal, and cooling multi-energy flows; For the first i The subject in the first t The degree of participation in responding to needs at any given moment is the main focus. i exist t The ratio of the actual response adjustment at any given time to the theoretical maximum response potential; This is a weighted value for the efficiency of multi-energy complementarity. This is the weight value for the participation in demand response, and it satisfies... .

4. A multi-entity operation scheduling system for a comprehensive energy system based on hybrid game theory, according to the method of any one of claims 1-3, comprising a model construction module for each energy entity, a three-layer hybrid game model construction module, and an operation scheduling module for each energy entity, characterized in that: The module for constructing models for each energy entity builds a multi-energy collaborative system architecture based on the characteristics and energy conversion relationships of each energy entity in the integrated energy system, and establishes models for each energy entity. The three-layer hybrid game model construction module, based on the aforementioned energy entity models, designs a three-layer hybrid game model including an upper-layer master-slave game model, a middle-layer cooperative game model, and a lower-layer non-cooperative game model. The upper-layer master-slave game model outputs cooling, heating, and electricity load price signals and demand response subsidy coefficients, which are then transmitted to the middle layer. In the middle-layer cooperative game model, the overall profit maximization of the alliance formed by the cooperative members is the objective; the total profit deviation of the alliance is calculated and transmitted to the upper layer, and the total tradable surplus energy of the alliance is calculated and transmitted to the lower layer. The lower-layer non-cooperative game model uses each community microgrid as the main entity, with the objective of maximizing the profit of each community microgrid itself, and conducts transactions, feeding back the transaction results to the middle layer. Each energy entity's operation scheduling module solves the three-layer hybrid game model, allocates and settles the benefits of each energy entity based on the solution results, and realizes the operation scheduling of each energy entity.

5. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-3.

Images