Asynchronous decentration method for cooperative operation of multi-main-body energy system

Abstract

The invention discloses a multi-main-body energy system cooperative operation asynchronous decentration method, and relates to the technical field of multi-main-body energy system control cooperation. The asynchronous decentration method for cooperative operation of the multi-agent energy system comprises the following steps of building thermodynamic modeling, interaction of a wind power plant and a power grid, shared energy storage and load adjustment, bidirectional information interaction, construction of a Nash negotiation game model, consideration of fraudulent behaviors, dual judgment acceleration asynchronous decentration ADMM algorithm solving, asynchronous calculation and real-time communication. According to the method, a cooperation framework of the production and disappearing person community PComs, the wind power plant WF and the shared energy storage ESS system is established, resources are coordinated through two-way communication, the energy efficiency is improved, the thermal inertia of a building in the production and disappearing person community PComs, the independent power generation capacity of the wind power plant WF and the resource sharing capacity of the shared energy storage ESS system are considered in the framework, and the operation flexibility of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-agent energy system control collaboration, and specifically to an asynchronous decentralized method for collaborative operation of a multi-agent energy system. Background Art

[0002] With the continuous increase in global energy consumption, especially in the construction industry, improving energy efficiency and utilizing sustainable energy have become urgent challenges to be solved. The energy efficiency management of buildings is not only closely related to power consumption, but also involves complex factors such as the operation of air conditioning systems and building thermal inertia. At the same time, renewable energy sources, such as wind energy and solar energy, although crucial for future energy transformation, face significant volatility and uncertainty that cannot be ignored. How to find a balance between the volatility of renewable energy and building energy efficiency has become an important topic in the collaborative operation of multi-agent energy systems.

[0003] To address these challenges, the present invention proposes an asynchronous decentralized method for collaborative operation of a multi-agent energy system. By combining building thermal inertia, wind farm power generation capacity, and a shared energy storage system, a distributed game algorithm and an information exchange mechanism are used to coordinate resources among all parties, achieving optimal resource scheduling and balance of power supply and demand. Through this method, building energy efficiency can be improved, the consumption of renewable energy can be optimized, carbon emissions can be reduced, and the realization of a smart grid can be promoted, thereby providing technical support for the realization of green buildings and low-carbon communities.

[0004] Patent document CN112163698B discloses an optimization method for the operation strategy of a multi-energy collaborative integrated energy system during the heating period. The above patent realizes convenient solution, simple calculation, and easy application.

[0005] Patent document CN113591375B discloses an optimal collaborative operation method for a multi-energy system based on agents. The above patent overcomes the problem that traditional mathematical algorithms need to perform complex modeling of the coupling relationship between physical models, and expands the action space of general machine learning algorithms, enabling decisions to be closer to the optimal decision.

[0006] Patent document CN113673739B discloses a multi-temporal and multi-spatial scale collaborative optimization operation method for a distributed integrated energy system. The above patent effectively characterizes the influencing factors of system energy supply and demand, considers different influencing factors and regulation means at different temporal and spatial scales, gives play to the complementary advantages of multiple energies in the time dimension and space dimension, and effectively reduces the system operation cost.

[0007] The patent document CN112365108B discloses a multi-objective optimization and collaborative operation method for a park integrated energy system. The above patent realizes the minimization of the total operation cost of the park system and the maximization of the comprehensive energy use efficiency, thereby effectively improving the operation cost of the park integrated energy system and the utilization efficiency of various types of energy.

[0008] In summary, inspired by the above patent, this application has discovered a technical solution that can achieve the establishment of a cooperation framework for prosumer communities PComs, wind farms WF, and shared energy storage ESS systems, coordinate resources through two-way communication, improve energy efficiency, determine the transaction electricity volume and profit distribution of MAES-CWE based on a Nash bargaining model, and analyze fraud behaviors in the profit distribution process; and proposed a solution based on fraud equilibrium and a double-judgment accelerated asynchronous decentralized ADMM algorithm to ensure the privacy security of prosumer communities PComs, wind farms WF, and shared energy storage ESS systems, accelerate the solution speed, and achieve the distributed solution of the Nash bargaining model; It solves the technical problems of low resource scheduling efficiency, energy waste, large system volatility, and unbalanced interest distribution existing in the prior art; Therefore, this application proposes a multi-agent energy system collaborative operation asynchronous decentralized method that can achieve the establishment of a cooperation framework for prosumer communities PComs, wind farms WF, and shared energy storage ESS systems, coordinate resources through two-way communication, improve energy efficiency, determine the transaction electricity volume and profit distribution of MAES-CWE based on a Nash bargaining model, and analyze fraud behaviors in the profit distribution process. Summary of the Invention

[0009] The purpose of the present invention is to provide a multi-agent energy system collaborative operation asynchronous decentralized method to solve the technical problems of low resource scheduling efficiency, energy waste, large system volatility, and unbalanced interest distribution mentioned in the above background technology.

[0010] To achieve the above object, the present invention provides the following technical solution: A multi-agent energy system collaborative operation asynchronous decentralized method, the multi-agent energy system collaborative operation asynchronous decentralized method includes the following steps: Building thermodynamics modeling: Establish a building thermodynamics model based on considering thermal inertia; Interaction between wind farm and power grid: The wind farm WF decides the power output ratio to the prosumer community PComs, the shared energy storage ESS system, and the power grid according to the real-time wind speed and power generation capacity; Shared energy storage and load regulation: The shared energy storage ESS system provides power support during power demand and regulates the power grid fluctuations; during power surplus, the energy storage system optimizes power storage through charging; Two-way information interaction: The prosumer community PComs, the wind farm WF, and the shared energy storage ESS system share power demand, generation capacity, energy storage status, and grid power price information in real time through a two-way information interaction mechanism; Construct a Nash bargaining game model: Construct a cooperative game model based on Nash bargaining theory. In the model, the prosumer community PComs, the wind farm WF, and the shared energy storage ESS system negotiate and determine the transaction electricity volume, transaction price, and profit distribution according to their respective energy supply, demand, and resource sharing capabilities; Prevention of fraud behavior and fraud equilibrium; Use game theory to achieve reasonable profit distribution; Double judgment to accelerate the solution of the asynchronous decentralized ADMM algorithm: Use the double judgment to accelerate the asynchronous decentralized ADMM algorithm to perform distributed solution on the Nash bargaining game model; Asynchronous calculation and real-time communication: The prosumer community PComs, the wind farm WF, and the shared energy storage ESS system perform asynchronous calculation and update the transaction plan based on their own demands, generation capacity, and power market information.

[0011] Preferably, the building thermodynamic model adopts a resistance-capacitance network model, transfers cooling capacity through thermal resistance, stores cooling capacity with heat capacity, calculates the building cooling load, and dynamically adjusts the air-conditioning load by modeling the building envelope structure, air-conditioning system, and cooling load, and adjusts the air-conditioning operation strategy according to the actual demands of the building.

[0012] Preferably, the wind farm WF predicts power output according to the real-time wind speed and power generation, and adjusts the power distribution ratio between the prosumer community PComs and the grid through the grid to balance the volatility of wind power generation and grid demand; when the power demand of the grid exceeds the power generation capacity of the wind farm, the wind farm cooperates with the shared energy storage ESS system to adjust the energy storage power supply to the grid to stabilize the power supply.

[0013] Preferably, the shared energy storage ESS system determines the power charging and discharging time according to the fluctuation of the grid power price, predicted power demand, and the battery storage capacity of the system, and releases the energy storage power in a timely manner according to the change of power demand.

[0014] Preferably, the profit distribution plan is calculated by weighting based on the power resources, load demands, and market electricity prices provided by the prosumer community PComs, wind farm WF, and shared energy storage ESS system. In the Nash bargaining game model, the profit distribution takes into account the participation weights and resource sharing capabilities of the prosumer community PComs, wind farm WF, and shared energy storage ESS system, including symmetric and asymmetric profit distribution methods. When using the symmetric profit distribution method, all parties obtain the same benefits according to their respective contributions. The asymmetric profit distribution method, on the other hand, adjusts the profit distribution ratio based on actual factors such as the power generation capacity, energy storage capacity, and load demands of all parties to ensure the cooperation benefits of all parties.

[0015] Preferably, the fraud prevention and fraud equilibrium plan designs behavior constraints, incentive mechanisms, and punishment mechanisms to prevent the prosumer community PComs, wind farm WF, and shared energy storage ESS system from destroying the long-term cooperation stability of the system through malicious behaviors.

[0016] Preferably, the two judgment steps of the dual-judgment accelerated asynchronous decentralized ADMM algorithm are: local calculation and global coordination; In the local calculation stage, the prosumer community PComs, wind farm WF, and shared energy storage ESS system perform optimization calculations based on local information; In the global coordination stage, the prosumer community PComs, wind farm WF, and shared energy storage ESS system update the optimization plan through asynchronous communication, reducing the calculation time and ensuring the privacy and security of the system.

[0017] Preferably, the prosumer community PComs, wind farm WF, and shared energy storage ESS system share key decision-making information through a real-time communication mechanism. The asynchronous calculation process supports each participating party to perform independent calculations within the same time period, and adjusts the power trading plan and scheduling plan through real-time updates to ensure flexible response to power supply and demand.

[0018] Preferably, the following parameters are included in the construction of the building thermodynamics model: The heat balance constraint of wall ij; The trading model; The objective function.

[0019] Preferably, the information exchange process of the two-way information exchange mechanism is as follows: Information collection: The prosumer community PComs, wind farm WF, and shared energy storage ESS system collect current operating status data at predetermined time intervals: including current power demand and generation, the status of the energy storage system, load forecasting and power demand forecasting, the power generation capacity of the wind farm WF, and the grid electricity price; Information Sending: The prosumer community PComs, wind farm WF, and shared energy storage ESS system send the collected data to other participants in JSON format, and conduct two-way communication through the MQTT real-time transmission protocol; Information Processing and Analysis: The system that receives the information performs local calculations and analyses based on the received data, and evaluates and adjusts the operation strategy.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention establishes a cooperation framework for the prosumer community PComs, wind farm WF, and shared energy storage ESS system, coordinates resources and improves energy efficiency through two-way communication. This framework takes into account the thermal inertia of buildings in the prosumer community PComs, the independent power generation capacity of the wind farm WF, and the resource sharing capacity of the shared energy storage ESS system, improving the operation flexibility of the system; 2. The present invention determines the transaction electricity quantity and profit distribution of MAES-CWE through a model based on Nash negotiation, studies symmetric and asymmetric profit distribution methods, adapts to the demand differences of different cooperation parties, and ensures fairness and cooperation stability; 3. The present invention analyzes fraud behaviors in the profit distribution process and proposes a solution based on fraud equilibrium to ensure the stable cooperation of MAES-CWE and maintain the long-term stability of the system; 4. The present invention proposes a double-judgment accelerated asynchronous decentralized ADMM algorithm to ensure the privacy security of the prosumer community PComs, wind farm WF, and shared energy storage ESS system, speeds up the solution speed, realizes the distributed solution of the Nash negotiation model, enables the prosumer community PComs, wind farm WF, and shared energy storage ESS system to perform asynchronous calculations and direct communication, thereby reducing the solution time, fully ensuring privacy security, and taking into account both privacy protection and improvement of solution efficiency. Brief Description of the Drawings

[0021] Figure 1 It is a schematic diagram of the asynchronous decentralized method for the collaborative operation of the multi-agent energy system of the present invention. Detailed Embodiments

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1, an embodiment provided by the present invention: a multi-agent energy system collaborative operation asynchronous decentralized method, the multi-agent energy system collaborative operation asynchronous decentralized method includes the following steps: Building thermodynamics modeling: Establish a building thermodynamics model based on considering thermal inertia; Wind farm and power grid interaction: The wind farm WF decides the power output ratio to the prosumer community PComs, the shared energy storage ESS system and the power grid according to the real-time wind speed and power generation capacity; Shared energy storage and load regulation: The shared energy storage ESS system provides power support during power demand and regulates the power grid fluctuations; during power surplus, the energy storage system optimizes power storage through charging; Two-way information interaction: The prosumer community PComs, the wind farm WF and the shared energy storage ESS system share power demand, power generation capacity, energy storage status and power grid electricity price information in real time through a two-way information interaction mechanism; Construct a Nash bargaining game model: Construct a cooperative game model based on the Nash bargaining theory. In the model, the prosumer community PComs, the wind farm WF and the shared energy storage ESS system negotiate and determine the transaction electricity volume, transaction price and profit distribution according to their respective energy supply, demand and resource sharing capabilities; Fraud behavior prevention and fraud equilibrium; Use game theory to achieve reasonable profit distribution; Dual judgment to accelerate the solution of the asynchronous decentralized ADMM algorithm: Use the dual judgment to accelerate the asynchronous decentralized ADMM algorithm to perform distributed solution on the Nash bargaining game model; Asynchronous calculation and real-time communication: The prosumer community PComs, the wind farm WF and the shared energy storage ESS system calculate and update the transaction plan asynchronously based on their own demands, power generation capabilities and power market information; Furthermore, input the physical characteristics of the building such as enclosure structure materials, building airtightness, etc., calculate the temperature changes in different areas of the building, and predict the energy demand of each area through thermal inertia modeling, output the cold load prediction data of the building at different time periods for subsequent scheduling decisions, obtain the real-time wind speed data and power generation prediction data of the wind farm, calculate the power output ratio available to the prosumer community PComs and the shared energy storage ESS system according to the power demand and market electricity price, send the power supply plan to the prosumer community PComs and the shared energy storage ESS system, and adjust the output according to the demand, monitor the battery charging and discharging status, evaluate the current energy storage capacity, decide whether to charge or discharge according to the power demand prediction and grid price signal, adjust the energy storage charging and discharging strategy according to the power surplus or shortage situation, and send the updated charging plan to other systems; The prosumer community PComs sends its electricity demand and load forecasting information to the wind farm and the shared energy storage ESS system. The wind farm and the shared energy storage ESS system respectively feedback their power generation capabilities, energy storage status, and scheduling plans to the prosumer community PComs. The electricity market price information is pushed to all parties in real time by the power grid to help each system adjust its energy supply and demand strategy. Incorporate the electricity demand, power generation capacity, energy storage status, and electricity price of all parties into the game model, determine the transaction electricity volume and price through the Nash bargaining theory, and formulate a reasonable profit distribution plan according to the resource sharing capabilities of the participants. Set up an incentive mechanism through the game model to prevent participants from engaging in improper behaviors due to pursuing maximum profits, and set up a penalty mechanism to ensure that each participant acts according to the rules during the cooperation process; Use the ADMM algorithm to decompose the problem into multiple local sub-problems, which are calculated separately by each system. Accelerate the calculation process through a dual judgment mechanism to ensure that each system can calculate and update the solution asynchronously. Each participant independently performs asynchronous calculations to adapt to the real-time changing electricity demand and supply situation. Through the real-time communication mechanism, exchange the latest calculation results to ensure that all parties have the latest understanding of the current system state, and regularly adjust the trading plan to optimize power dispatching and resource allocation.

[0024] Please refer to Figure 1 , an embodiment provided by the present invention: A multi-agent energy system collaborative operation asynchronous decentralized method, the building thermodynamic model adopts a resistance-capacitance network model, transfers cooling capacity through thermal resistance, stores cooling capacity with heat capacity, calculates the building cooling load, and dynamically adjusts the air-conditioning load by modeling the building envelope structure, air-conditioning system, and cooling load, and adjusts the air-conditioning operation strategy according to the actual needs of the building; The construction of the building thermodynamic model includes the following parameters: The heat balance constraint of the wall ij; Transaction model; Objective function; The wind farm WF forecasts the power output according to the real-time wind speed and power generation, and adjusts the power distribution ratio between the prosumer community PComs and the power grid through the power grid to balance the volatility of wind power generation and the grid demand; when the power demand of the power grid exceeds the power generation capacity of the wind farm, the wind farm cooperates with the shared energy storage ESS system to adjust the energy storage power supply to the power grid to stabilize the power supply; The shared energy storage ESS system decides the power charging and discharging time according to the fluctuation of the power grid electricity price, the predicted power demand, and the battery storage capacity of the system, and releases the energy storage power in time according to the change of the power demand; Furthermore, a building thermodynamic model was established based on a resistance-capacitance network considering thermal inertia. This model transfers cooling capacity through thermal resistance and stores cooling capacity through heat capacity. The equipped air conditioner can meet the comfortable temperature requirements of users in the building cooling area. The enclosure structures of all areas in the building are the same. By adding up the air-conditioning loads of all areas, the total cooling load of the building can be obtained.

[0025] 1) The thermal balance constraint of wall ij is: (1) The thermal balance constraint of area n is: (2) The operating power of the air conditioner should meet the following requirements: (3) The comfortable temperature limit for users is: (4) 2) Transaction model The prosumer community PComs, wind farm WF, shared energy storage ESS system, and power grid conduct power transactions. Therefore, the prosumer community PComs, wind farm WF, and shared energy storage ESS system pay fees to the power grid, which can be expressed as: (5) The transmission charge of the prosumer community PComs can be expressed as: (6) The service fee that the prosumer community PComs pays to the shared energy storage ESS system can be expressed as: (7) The prosumer community PComs also needs to pay the maintenance cost of the power load equipment: (8) (9) The operation of the prosumer community PComs should meet the power balance constraint: (10) The electricity sold to the power grid and energy storage system should not be higher than the photovoltaic power generation output: (11) 3) Objective function With the goal of minimizing costs, the objective function of the prosumer community PComs can be expressed as follows: (12) 2. Wind farm 1) Transaction mode The wind farm WF sells electricity to the prosumer community PComs, the shared energy storage ESS system, and the power grid. Therefore, the wind farm WF receives payments from the prosumer community PComs, the shared energy storage ESS system, and the power grid, which can be expressed as: (13) The maintenance cost of the wind farm WF can be expressed as: (14) The transmission charge of the wind farm WF can be expressed as: (15) The wind farm WF pays the ESS service fee: (16) Power balance constraint: (17) (18) 2) Objective function With the goal of minimizing costs, the objective function of the wind farm WF can be expressed as follows: (19) 3. Shared energy storage 1) Equipment model The dynamic state of the state of charge (SoC) of the energy storage system is related to the at the end of the previous moment and the charging and discharging power at the current moment, as follows: (20) To ensure a normal life cycle, the of the shared energy storage ESS system should be maintained within this range: Initial state should not be higher than : (22) The charging and discharging power should not be higher than the upper limit, as follows: (23) 2) Transaction mode The shared energy storage ESS system conducts power transactions with the prosumer community PComs, the wind farm WF, and the power grid. The shared energy storage ESS system charges the prosumer community PComs and pays the wind farm WF and the power grid, which can be expressed as: (24) The shared energy storage ESS system charges service fees from the wind farm WF and the prosumer community PComs: The transmission charges of the shared energy storage ESS system can be expressed as: (25) The maintenance cost of the shared energy storage ESS system can be expressed as: (26) The operation of the energy storage system should satisfy the power balance constraint: (27) 3) Objective function Aiming at minimizing the cost, the objective function of the shared energy storage ESS system can be expressed as: (28).

[0026] Please refer to Figure 1 , an embodiment provided by the present invention: a cooperative operation asynchronous decentralized method for a multi-agent energy system. The scheme considering fraud behavior designs behavior constraints, incentive mechanisms and penalty mechanisms to prevent the prosumer community PComs, the wind farm WF and the shared energy storage ESS system from destroying the long-term cooperation stability of the system through malicious behaviors; The prosumer community PComs, the wind farm WF and the shared energy storage ESS system share key decision-making information through a real-time communication mechanism. The asynchronous calculation process supports each participant to calculate independently within the same time period, and adjusts the power trading plan and scheduling scheme through real-time update to ensure flexible response to power supply and demand; The profit distribution scheme is weighted and calculated according to the power resources, load demands and market power prices provided by the prosumer community PComs, the wind farm WF and the shared energy storage ESS system. In the Nash bargaining game model, the profit distribution considers the participation weights and resource sharing capabilities of the prosumer community PComs, the wind farm WF and the shared energy storage ESS system, including symmetric and asymmetric profit distribution methods; when the symmetric profit distribution method is adopted, each party obtains the same benefit according to its respective contribution; while the asymmetric profit distribution method adjusts the profit distribution ratio according to the actual factors such as the power generation capacity, energy storage capacity and load demand of each party to ensure the cooperation benefits of each party; Furthermore, the solution method 1) Nash bargaining with fraud behavior Suppose the power suppliers, electricity providers, and power supply parties belong to different stakeholders, and they can decide the amount of electricity to be sold or purchased and the fees to be paid or charged. As rational and independent entities, they seek to increase profits through cooperation. Nash negotiation can balance individual and collective interests. Therefore, we apply Nash negotiation in this study to enable power suppliers, electricity providers, and power supply parties to obtain optimal operation strategies. The standard Nash negotiation model of MAES-CWS can be expressed as follows: (29) (30) (31) (32) where , and are the cost reduction values after cooperation without considering internal transactions, which can be expressed as: (33) (34) (35) , , and are the costs after cooperation excluding internal transactions among the entities.

[0027] Equations (30), (31), and (32) can be rewritten as follows: (36) The Nash negotiation model is a non-convex and non-linear optimization problem, which is difficult to solve directly. Therefore, we can transform it into two sub-problems: the coalition cost minimization sub-problem (S1) and the profit allocation sub-problem (S2). In S2, the entities may not act honestly to maximize their profits. Therefore, fraud behavior is also discussed in this section.

[0028] ① Coalition cost minimization sub-problem (S1) S1 can be expressed as follows: (37) ② Profit allocation sub-problem (S2) a) Symmetric profit allocation sub-problem Symmetric S2 can be expressed as follows: (38) b) Asymmetric profit allocation sub-problem In the symmetric S2 in (38), the contributions of prosumer communities PComs, wind farms WF, and shared energy storage ESS systems are not considered. This is unfair to the entities with larger contributions. To make the profit distribution among each entity more fair, we introduce the contribution degree f to describe the degree of an entity's contribution to cooperation.

[0029] Since the connection among prosumer communities PComs, wind farms WF, and shared energy storage ESS systems is energy trading, we use the traded electricity volume to describe the contributions of the entities. f can be expressed as follows: (39) (40) (41) where , and are the total sum of the traded electricity volume, as shown below: (42) (43) (44) By introducing the contribution degree as the bargaining power, the asymmetric S2 can be obtained: (45) c) The profit distribution sub-problem with fraud The premise of the above discussion is that prosumer communities PComs, wind farms WF, and shared energy storage ESS systems are all honest. However, in reality, entities may commit fraud to pursue profit maximization.

[0030] According to the Nash bargaining model (29), for the prosumer community PComs, the optimal payment is as follows: (46) Similarly, for the wind farm WF and the shared energy storage ESS system, the best received payments are as follows: (47) (48) It can be seen from (46) that if the declared cost reduction value of the prosumer community PComs is lower than the actual cost reduction value, more profit may be obtained. Similarly, if the declared cost reduction values and of the wind farm WF and the shared energy storage ESS system are lower than the actual cost reduction values, more profit may be obtained.

[0031] , and can be described as follows: (49) (50) (51) wherein, , and are the fraud indices (CI) of the prosumer community PComs, the wind farm WF, and the shared energy storage ESS system, respectively.

[0032] To ensure the effectiveness of the Nash bargaining model, , , and the following conditions should also be satisfied: (52) After applying (49), (50), and (51) to (52), the upper limit of CI is as follows: (53) (54) (55) Obviously, to obtain more benefits, the subject is willing to apply the highest possible CI. However, it can be seen from (53), (54), and (55) that the upper limit is affected by the subject's behavior. Each subject must choose an appropriate CI.

[0033] We believe that when is positive and close to 0, the subject reaches the fraud equilibrium because at this time the subject cannot find a larger fraud index to satisfy (52). Here, we propose a relaxation algorithm as shown in Table 1, the steps of the relaxation algorithm, where , and are relaxation parameters. The stopping criterion is (56) Table 1 Steps of the relaxation algorithm

[0034] With the optimal CIs , and , the optimal , , and can be obtained. Then, the asymmetric S2 considering fraud behavior can be described as: (57) If the main body chooses symmetric profit distribution, then , and can be set to 1.

[0035] Please refer to Figure 1 , an embodiment provided by the present invention: a multi-agent energy system collaborative operation asynchronous decentralized method, the two judgment steps of the dual judgment accelerated asynchronous decentralized ADMM algorithm are respectively: local calculation and global coordination; In the local calculation stage, the prosumer community PComs, the wind farm WF, and the shared energy storage ESS system perform optimization calculations based on local information; In the global coordination stage, the prosumer community PComs, the wind farm WF, and the shared energy storage ESS system update the optimization scheme through asynchronous communication, reducing the calculation time and ensuring system privacy and security; Furthermore, the distributed solution method To protect the privacy of the prosumer community PComs, the wind farm WF, and the shared energy storage ESS system, and reduce the amount of information collected, this study adopts a distributed solution method based on ADMM.

[0036] Distributed model In S1, in order to decouple Equation (37) and achieve distributed problem solving, use and to represent the electricity quantity that the wind farm WF expects to trade with the prosumer community PComs and the electricity quantity that the PComs expects to trade with the wind farm WF, respectively. Use and to represent the electricity quantity that the shared energy storage ESS system expects to trade with the prosumer community PComs and the electricity quantity that the prosumer community PComs expects to trade with the shared energy storage ESS system, respectively. Use and to represent the electricity quantity that the wind farm WF expects to trade with the shared energy storage ESS system and the electricity quantity that the shared energy storage ESS system expects to trade with the wind farm WF, respectively. They will continue to exchange information until they reach an agreement, that is , , . Ignoring the constant , and , then the augmented Lagrangian function of Equation (37) can be expressed as: (58) where , and The formulas are as follows: (59) (60) (61) The distributed model that can be decomposed into prosumer communities PComs, wind farms WF, and shared energy storage ESS systems is shown in (62), (63), and (64) respectively: (62) (63) (64) In S2, to decouple (57) and achieve distributed problem solving, 、 and are the payments that the prosumer community PComs expects to pay to the wind farm WF and the shared energy storage ESS system, and the payment from the shared energy storage ESS system to the wind farm WF respectively. 、 and are the payments that the wind farm WF and the shared energy storage ESS system expect to receive from the prosumer community PComs and the payment that the wind farm WF expects to receive from the shared energy storage ESS system respectively. Then, the augmented Lagrangian function of equation (37) can be expressed as: (65) where , ,and The formulas of are as follows: (66) (67) (68) (65)The distributed model that can be decomposed into prosumer communities PComs, wind farms WF, and shared energy storage ESS systems is shown in (69), (70), and (71) respectively: (69) (70) (71) ② Parameter update based on double - judgment ADMM ADMM is used to solve the problem. Here, the double - judgment iterative parameter update method is explained by the trading behavior between the wind farm WF and the shared energy storage ESS system in S1.

[0037] The primal residual and dual residual are defined as: (72) (73) First judgment: Based on the difference between the original residual and the dual residual in two adjacent iterations obtained from (74) and (75), judge whether to enter the next judgment. If and is less than , the penalty factor remains unchanged. Otherwise, enter the second judgment and update the penalty factor.

[0038] (74) (75) Judgment two: Based on the magnitude relationship between the original residual and the dual residual, determine the update method of the penalty factor, specifically as follows: (76) Then the Lagrange multiplier is updated according to the following principle: (77) The stopping criterion is: (78) In the above formula expressions, ij represents nodes, n represents rooms, t represents time nodes, T represents the operation period, x represents the xth prosumer community PComs, X represents the set of prosumer communities PComs, and represent the wall and window areas, B represents the rated capacity of the shared energy storage ESS system, , represent the heat capacity of the room and the wall, , , represents the breakdown point of the negotiation, represents the energy efficiency ratio, represents the adjacent nodes of the wall, represents the adjacent nodes of the room, represents the number of prosumer communities PComs, represents the number of buildings of the prosumer community PComs, represents the number of rooms in the building of the prosumer community PComs, represents the maximum power of the air conditioner, , represents the maximum charge and discharge power, represents the electricity market price, represents the photovoltaic output of the prosumer community PComs at time t, represents the trading price of the main body selling electricity to the power grid, Represents the maximum wind power generation Represents the heat source within the area. If there are windows in this area, take 1, otherwise take 0 Represents the light intensity in the corresponding direction of the wall , Represents the thermal resistance of the wall and the window. If the wall is irradiated by sunlight, take 1, otherwise take 0 , Represents the minimum and maximum SoC of the ESS , Represents the temperature of the wall and node j Represents the heat absorption rate of the wall Represents the transmittance of the window Represents the time interval Represents the energy leakage coefficient Represents the service fee coefficient , Represents the charging and discharging efficiency , Represents the transmission fee coefficient , , , Represents the maintenance cost coefficient , Represents the accuracy , Represents the penalty factor , Represents the Lagrange multiplier Represents the fee paid by the shared energy storage ESS system to the power grid , Represents the payment received by the wind farm WF from the shared energy storage ESS system or the power grid , Represents the service fee paid by the prosumer community PComs and the wind farm WF to the shared energy storage ESS system , , Represents the transmission fees of the prosumer community PComs, the wind farm WF and the shared energy storage ESS system , , Represents the maintenance costs of the prosumer community PComs, the wind farm WF and the shared energy storage ESS system , , Represents the total costs of the prosumer community PComs, the wind farm WF and the shared energy storage ESS system , , Denote the payments made by the prosumer community PComs to the wind farm WF, the shared energy storage ESS system, and the power grid. Denote the air conditioner operating power of room n in the prosumer community PComs at time t. , Denote the air conditioner operating power and other loads of the buildings in the prosumer community PComs. , Denote the charge and discharge power at time t. Denote the electricity quantity traded between the prosumer community PComs and the shared energy storage ESS system at time t. Denote that the shared energy storage ESS system sells electricity to the prosumer community PComs. Denote that the prosumer community PComs sells electricity to the shared energy storage ESS system. , Denote the electricity quantity sold by and purchased from the power grid by the prosumer community PComs at time t. , Denote the electricity quantity sold by and purchased from the power grid by the shared energy storage ESS system at time t. Denote the power generated by the wind farm WF at time t. , , Denote the electricity quantity sold by the wind farm WF to the prosumer community PComs, the shared energy storage ESS system, and the power grid at time t. Denote the state of charge at time t. Denote the indoor temperature of room n at time t. , , Denote the fraud index.

[0039] ③ Asynchronous decentralized ADMM algorithm In traditional ADMM, there is a data center responsible for collecting information from the agents and broadcasting the iteration parameters. This mode can effectively solve the optimization problem, but may cause information leakage. To solve this problem, we propose a decentralized ADMM, where the agents can directly exchange information instead of through the data center.

[0040] Please refer to Figure 1 , an embodiment provided by the present invention: A method for asynchronous decentralized operation of a multi-agent energy system, and the information exchange process of the two-way information exchange mechanism is as follows: Information collection: The prosumer community PComs, wind farm WF, and shared energy storage ESS system collect current operating status data at predetermined time intervals, including current electricity demand and generation, the status of the energy storage system, load forecasting and electricity demand forecasting, the power generation capacity of the wind farm WF, and grid electricity prices; Information transmission: The prosumer community PComs, wind farm WF, and shared energy storage ESS system send the collected data to other participants in JSON format and conduct two-way communication through the MQTT real-time transmission protocol; Information processing and analysis: The system that receives the information performs local calculations and analysis based on the received data and evaluates and adjusts the operation strategy; Furthermore, each system collects current operating status data every 15 minutes, including current electricity demand and generation, the status of the energy storage system, load forecasting and electricity demand forecasting, the power generation capacity of the wind farm WF, and grid electricity prices. Each system sends the collected data to other participants in JSON format. To ensure the timely exchange of information, information is transmitted through the following methods: Real-time transmission protocols: Such as MQTT and WebSocket, which support real-time two-way communication; Regular scheduling and exchange: The systems regularly exchange the latest data, such as sending updates on load forecasting and power generation capacity every hour; The system that receives the information will perform local calculations and analysis based on the received data and evaluate whether it is necessary to adjust its operation strategy, such as adjusting the energy storage plan, changing the electricity trading plan, and optimizing the power generation strategy. The prosumer community PComs may adjust the air-conditioning load or electricity demand according to the power generation capacity of the wind farm WF; The wind farm WF judges whether it is necessary to increase or decrease the power output according to the electricity demand of the prosumer community PComs; The shared energy storage ESS system judges whether it is necessary to discharge or charge according to the grid electricity price and demand status; Based on the decision results of each system, the information feedback process will further adjust the energy supply and demand strategy. This process may need to be adjusted through multiple iterations to ensure the balance and optimization of the system.

[0041] Working principle: The air-conditioning system in the building relies on the thermal inertia characteristics of the building to optimize energy consumption. By establishing a building thermodynamics model, combining temperature changes with air-conditioning load demand, calculating the building's cooling load prediction, and matching it with the capabilities of the wind farm and energy storage system, energy waste is reduced; The power generation of the wind farm is affected by real-time wind speed and is unstable. Through cooperation with the shared energy storage system, the wind farm can store excess electrical energy and release electricity when the grid demand is high, providing a stable supply. The energy storage system also charges and discharges according to grid demand and market electricity prices to regulate the balance of power supply and demand; Among various energy units such as prosumer communities PComs, wind farms WF, and shared energy storage ESS systems, a real-time two-way information exchange mechanism is adopted to transmit their respective energy demands, power generation capabilities, and energy storage statuses. Such information sharing ensures that all parties can make immediate power dispatching decisions based on the latest data, achieving mutual benefit and win-win results as well as flexible energy allocation.

[0042] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for asynchronous decentralization of collaborative operation of a multi-agent energy system, characterized by: The method for collaborative operation of a multi-agent energy system with asynchronous decentralization includes the following steps: Building thermodynamic modeling: Establishing building thermodynamic model based on thermal inertia; Interaction between wind farms and the grid: The wind farm WF determines the proportion of power output to the prosumer community PComs, shared energy storage ESS system and the grid based on the real-time wind speed and power generation capacity; Shared energy storage and load regulation: The shared energy storage ESS system provides power support when power is in demand and regulates grid fluctuations; when there is excess power, the energy storage system optimizes power storage by charging; Two-way information interaction: The prosumer community PComs, wind farm WF and shared energy storage ESS system share power demand, power generation capacity, energy storage status and grid power price information in real time through a two-way information interaction mechanism; Constructing Nash negotiation game model: Constructing a cooperative game model based on Nash negotiation theory, in which the prosumer community PComs, wind farm WF and shared energy storage ESS system negotiate and determine the transaction volume, transaction price and profit distribution according to their respective energy supply, demand and resource sharing capabilities; Fraud prevention and fraud balance; Use game theory to achieve reasonable profit distribution; Double judgment accelerates asynchronous decentralized ADMM algorithm solution: Double judgment accelerates asynchronous decentralized ADMM algorithm to perform distributed solution of Nash negotiation game model; Asynchronous computing and real-time communication: The prosumer community PComs, wind farm WF and shared energy storage ESS system asynchronously calculate and update the transaction plan based on their own needs, power generation capacity and power market information.

2. According to claim 1, a method for asynchronous decentralization of coordinated operation of a multi-agent energy system is characterized by: The building thermodynamic model adopts a resistor-capacitor network model, transfers cold energy through thermal resistance and stores cold energy through thermal capacity, calculates the building's cooling load, and dynamically adjusts the air-conditioning load by modeling the building's envelope, air-conditioning system, and cooling load, and adjusts the air-conditioning operation strategy according to the actual needs of the building.

3. According to claim 1, a method for asynchronous decentralization of coordinated operation of a multi-agent energy system is characterized by: The wind farm WF predicts power output based on real-time wind speed and power generation, and adjusts the power distribution ratio between the prosumer community PComs and the power grid through the power grid to balance the volatility of wind power generation and power grid demand; when the power demand of the power grid exceeds the power generation capacity of the wind farm, the wind farm cooperates with the shared energy storage ESS system to adjust the energy storage power supply to the power grid to stabilize the power supply.

4. The method for asynchronous decentralization of collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The shared energy storage ESS system determines the power charging and discharging time according to the fluctuation of power grid power price, predicted power demand and battery storage capacity of the system, and releases the stored energy in time according to the change of power demand.

5. The method for asynchronous decentralization of collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The profit distribution scheme is weighted and calculated according to the power resources, load demand and market power price provided by the prosumer community PComs, wind farm WF and shared energy storage ESS system. In the Nash negotiation game model, the profit distribution takes into account the participation weights and resource sharing capabilities of the prosumer community PComs, wind farm WF and shared energy storage ESS system, including symmetric and asymmetric profit distribution methods. When the symmetrical profit distribution method is adopted, each party obtains the same benefits according to their respective contributions; while the asymmetrical profit distribution method adjusts the profit distribution ratio according to the actual factors of each party's power generation capacity, energy storage capacity and load demand to ensure the cooperative interests of all parties.

6. The method for asynchronous decentralization of a multi-agent energy system collaborative operation according to claim 1, characterized in that: The fraud prevention and fraud equilibrium scheme prevents the prosumer community PComs, wind farm WF and shared energy storage ESS system from destroying the long-term cooperative stability of the system through malicious behavior by designing behavioral constraints, incentive mechanisms and punishment mechanisms.

7. The method for asynchronous decentralization of collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The two judgment steps of the dual judgment accelerated asynchronous decentralized ADMM algorithm are: local calculation and global coordination; In the local calculation stage, the prosumer community PComs, wind farm WF and shared energy storage ESS system perform optimization calculations based on local information; In the global coordination stage, the prosumer community PComs, wind farm WF and shared energy storage ESS system update the optimization scheme through asynchronous communication to reduce the calculation time and ensure the privacy and security of the system.

8. The method for asynchronous decentralization of collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The prosumer community PComs, wind farm WF and shared energy storage ESS system share key decision-making information through a real-time communication mechanism. The asynchronous calculation process supports each participant to perform independent calculations in the same time period, and adjusts the power trading plan and scheduling plan through real-time updates to ensure flexible response to power supply and demand.

9. The method for asynchronous decentralization of a multi-agent energy system collaborative operation according to claim 1, characterized in that: The following parameters are included in the construction of the building thermodynamic model: Thermal balance constraints of wall ij; Trading models; Objective function.

10. The method for asynchronous decentralization of collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The information exchange process of the two-way information exchange mechanism is as follows: Information collection: The prosumer community PComs, wind farm WF and shared energy storage ESS system collect current operating status data at predetermined time intervals: including current power demand and generation, the status of the energy storage system, load forecast and power demand forecast, wind farm WF generation capacity and grid power price; Information transmission: The prosumer community PComs, wind farm WF and shared energy storage ESS system send the collected data to other participants in JSON format, and conduct two-way communication through the MQTT real-time transmission protocol; Information processing and analysis: The system that receives the information performs local calculations and analysis based on the received data, and evaluates and adjusts the operating strategy.

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