A method for asynchronous decentralization of collaborative operation of multi-agent energy systems

Abstract

The present invention discloses an asynchronous decentralized method for the coordinated operation of a multi-agent energy system, relating to the technical field of coordinated control of multi-agent energy systems. The method comprises the following steps: thermodynamic modeling of buildings, interaction between wind farms and power grids, shared energy storage and load regulation, two-way information exchange, construction of a Nash negotiation game model, consideration of fraudulent behavior, dual-judgment accelerated solution of an asynchronous decentralized ADMM algorithm, and asynchronous computing and real-time communication. The present invention establishes a collaborative framework among prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESS) systems, coordinating resources and improving energy efficiency through two-way communication. This framework considers the thermal inertia of buildings in the PComs community, the independent power generation capacity of the wind farms (WFs), and the resource sharing capabilities of the shared energy storage (ESS) system, thereby improving system operational flexibility.

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, particularly in the building sector, improving energy efficiency and utilizing sustainable energy have become pressing challenges. Building energy efficiency management is closely linked not only to electricity consumption but also involves complex factors such as the operation of air conditioning systems and building thermal inertia. Meanwhile, renewable energy sources such as wind and solar, while key to future energy transitions, face significant volatility and uncertainty. Balancing the volatility of renewable energy with building energy efficiency has become a crucial issue in the coordinated operation of multi-agent energy systems.

[0003] To address these challenges, this paper proposes an asynchronous, decentralized approach for the coordinated operation of multi-agent energy systems. Combining building thermal inertia, wind farm generation capacity, and shared energy storage systems, this approach employs a distributed game algorithm and information exchange mechanism to coordinate resources, achieving optimal resource scheduling and balancing power supply and demand. This approach can improve building energy efficiency, optimize renewable energy consumption, reduce carbon emissions, and promote the implementation of smart grids, providing technical support for the realization of green buildings and low-carbon communities.

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

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

[0006] Patent document CN113673739B discloses a multi-time and space scale collaborative optimization operation method for a distributed integrated energy system. The above patent effectively characterizes the influencing factors of the system's energy supply and demand, considers different influencing factors and control measures at different time and space scales, and gives full play to the complementary advantages of multiple energy sources in the time and space dimensions, effectively reducing the system's operating costs.

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

[0008] In summary, inspired by the aforementioned patents, this application has developed a technical solution that can establish a collaborative framework for prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESS) systems. This framework coordinates resources through two-way communication, improves energy efficiency, and uses a Nash negotiation model to determine the transaction power and profit distribution of MAES-CWE, as well as analyze fraudulent behavior in the profit distribution process.

[0009] A fraud equilibrium-based solution and a dual-judgment accelerated asynchronous decentralized ADMM algorithm were proposed to ensure the privacy and security of the prosumer community PComs, wind farm WF, and shared energy storage ESS system, speed up the solution, and realize the distributed solution of the Nash negotiation model.

[0010] It solves the technical problems of low resource scheduling efficiency, energy waste, large system volatility and uneven benefit distribution in existing technologies;

[0011] To this end, this application proposes a collaborative framework for the establishment of a prosumer community PComs, a wind farm WF, and a shared energy storage ESS system, an asynchronous decentralized method for the collaborative operation of a multi-agent energy system through two-way communication to coordinate resources, improve energy efficiency, determine the transaction power and profit distribution of MAES-CWE based on a Nash negotiation model, and analyze fraudulent behavior in the profit distribution process. Summary of the Invention

[0012] The purpose of the present invention is to provide an asynchronous decentralized method for the coordinated operation of a multi-agent energy system to solve the technical problems of low resource scheduling efficiency, energy waste, large system volatility and uneven benefit distribution raised in the above background technology.

[0013] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for collaborative operation of a multi-agent energy system in an asynchronous and decentralized manner, the method comprising the following steps:

[0014] Building thermodynamic modeling: Establishing a building thermodynamic model based on thermal inertia;

[0015] 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;

[0016] 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 through charging.

[0017] Two-way information exchange: Prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) use a two-way information exchange mechanism to share information on power demand, generation capacity, energy storage status, and grid power prices in real time.

[0018] Constructing a Nash negotiation game model: A cooperative game model based on Nash negotiation theory is constructed. In this model, the prosumer community PComs, wind farm WF, and shared energy storage ESS system negotiate to determine the transaction volume, transaction price, and profit distribution based on their respective energy supply, demand, and resource sharing capabilities.

[0019] Fraud prevention and fraud balance;

[0020] Use game theory to achieve reasonable profit distribution;

[0021] Dual Judgment Accelerated Asynchronous Decentralized ADMM Algorithm Solution: Dual judgment accelerated asynchronous decentralized ADMM algorithm is used to perform distributed solution of Nash negotiation game model;

[0022] Asynchronous computing and real-time communication: Prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) asynchronously compute and update trading plans based on their own needs, generation capacity, and electricity market information.

[0023] Preferably, 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 cooling load, and dynamically adjusts the air conditioning load by modeling the building envelope, air conditioning system and cooling load, and adjusts the air conditioning operation strategy according to the actual needs of the building.

[0024] Preferably, 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.

[0025] Preferably, the shared energy storage ESS system determines the power charging and discharging timing according to the fluctuation of power grid electricity 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.

[0026] Preferably, the profit distribution plan is calculated based on a weighted basis based on 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, 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 symmetric profit distribution method is adopted, each party obtains the same benefit based on their respective contributions. When the asymmetric profit distribution method is adopted, the profit distribution ratio is adjusted based on the actual factors of the power generation capacity, energy storage capacity, and load demand of each party to ensure the cooperative interests of all parties.

[0027] Preferably, the fraud prevention and fraud balance 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.

[0028] Preferably, the two judgment steps of the dual judgment accelerated asynchronous decentralized ADMM algorithm are: local calculation and global coordination;

[0029] 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;

[0030] 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 computing time and ensure the privacy and security of the system.

[0031] 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 computing process supports each participant to perform independent calculations within the same time period, and adjusts the power trading plan and scheduling scheme through real-time updates to ensure flexible response to power supply and demand.

[0032] Preferably, the building thermodynamic model construction includes the following parameters:

[0033] Thermal balance constraints of wall ij;

[0034] Trading models;

[0035] Objective function.

[0036] Preferably, the information exchange process of the two-way information exchange mechanism is as follows:

[0037] 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, energy storage system status, load forecast and power demand forecast, wind farm WF generation capacity, and grid power prices;

[0038] 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 via the MQTT real-time transmission protocol;

[0039] 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.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. This invention establishes a collaborative framework among prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs). This framework coordinates resources and improves energy efficiency through two-way communication. This framework considers the thermal inertia of buildings in the PComs, the independent power generation capacity of the wind farms (WFs), and the resource sharing capabilities of the shared energy storage (ESSs), thereby improving system operational flexibility.

[0042] 2. This paper uses a Nash negotiation-based model to determine the transaction volume and profit distribution of MAES-CWE. It studies symmetric and asymmetric profit distribution methods to adapt to the different needs of different partners and ensure fairness and cooperation stability.

[0043] 3. This paper analyzes fraudulent behavior in the profit distribution process and proposes a solution based on fraud equilibrium to ensure stable cooperation between MAES and CWE and maintain the long-term stability of the system;

[0044] 4. The present invention proposes a dual-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, accelerate the solution speed, and realize the distributed solution of the Nash negotiation model. It enables asynchronous calculation and direct communication between the prosumer community PComs, wind farm WF, and shared energy storage ESS system, thereby reducing the solution time and fully ensuring privacy security, taking into account both privacy protection and improved solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of the asynchronous decentralized method for collaborative operation of a multi-agent energy system of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] See also Figure 1 The present invention provides an embodiment of a method for collaborative operation of a multi-agent energy system in an asynchronous decentralized manner, the method comprising the following steps:

[0048] Building thermodynamic modeling: Establishing a building thermodynamic model based on thermal inertia;

[0049] 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;

[0050] 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 through charging.

[0051] Two-way information exchange: Prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) use a two-way information exchange mechanism to share information on power demand, generation capacity, energy storage status, and grid power prices in real time.

[0052] Constructing a Nash negotiation game model: A cooperative game model based on Nash negotiation theory is constructed. In this model, the prosumer community PComs, wind farm WF, and shared energy storage ESS system negotiate to determine the transaction volume, transaction price, and profit distribution based on their respective energy supply, demand, and resource sharing capabilities.

[0053] Fraud prevention and fraud balance;

[0054] Use game theory to achieve reasonable profit distribution;

[0055] Dual Judgment Accelerated Asynchronous Decentralized ADMM Algorithm Solution: Dual judgment accelerated asynchronous decentralized ADMM algorithm is used to perform distributed solution of Nash negotiation game model;

[0056] Asynchronous computing and real-time communication: Prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) asynchronously compute and update trading plans based on their own needs, generation capacity, and electricity market information.

[0057] Furthermore, the system inputs the physical properties of the building, such as the building envelope material and building sealing, to calculate the temperature changes in different areas of the building, and predicts the energy demand of each area through thermal inertia modeling. It outputs the cooling load forecast data of the building in different time periods for subsequent scheduling decisions, obtains the real-time wind speed data and power generation forecast data of the wind farm, and calculates the current power output ratio available to the prosumer community PComs and shared energy storage ESS system based on power demand and market electricity prices. It sends a power supply plan to the prosumer community PComs and shared energy storage ESS system and adjusts the output according to demand. It monitors the battery charging and discharging status, evaluates the current storage capacity, and decides whether to charge or discharge based on the power demand forecast and grid price signals. It adjusts the energy storage charging and discharging strategy according to the power surplus or shortage, and sends the updated charging plan to other systems.

[0058] The prosumer community (PComs) sends its power demand and load forecast information to the wind farm and shared energy storage (ESS). The wind farm and shared energy storage (ESS) system then feed back their generation capacity, energy storage status, and scheduling plan to the PComs. The power grid pushes power market price information to all parties in real time, helping each system adjust its energy supply and demand strategy. The power demand, generation capacity, energy storage status, and power price of each party are incorporated into the game model. The Nash negotiation theory is used to determine the transaction volume and price. A reasonable profit distribution plan is developed based on the resource-sharing capabilities of the participants. The game model establishes an incentive mechanism to prevent participants from engaging in improper behavior in pursuit of maximum profit. A penalty mechanism is also established to ensure that each participant abides by the rules during the cooperation process.

[0059] The ADMM algorithm is used to decompose the problem into multiple local sub-problems, which are calculated separately by each system. The calculation process is accelerated through a double judgment mechanism to ensure that each system can asynchronously calculate and update the solution. Each participant performs asynchronous calculations independently to adapt to the real-time changes in electricity demand and supply conditions. Through the real-time communication mechanism, the latest calculation results are exchanged to ensure that all parties have the latest understanding of the current system status, regularly adjust the transaction plan, and optimize power scheduling and resource allocation.

[0060] See also Figure 1 The present invention provides an embodiment of an asynchronous decentralized method for collaborative operation of a multi-agent energy system, wherein 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 cooling load, and dynamically adjusts the air conditioning load by modeling the building envelope, air conditioning system, and cooling load, and adjusts the air conditioning operation strategy according to the actual needs of the building;

[0061] The following parameters are included in the construction of the building thermodynamic model:

[0062] Thermal balance constraints of wall ij;

[0063] Trading models;

[0064] Objective function;

[0065] 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 grid through the grid to balance the volatility of wind power generation and grid demand. When the grid's power demand exceeds the wind farm's power generation capacity, the wind farm collaborates with the shared energy storage system ESS to adjust the stored energy supply to the grid to stabilize power supply.

[0066] The shared energy storage ESS system determines the charging and discharging times based on fluctuations in grid electricity prices, predicted electricity demand, and the system's battery storage capacity, and releases stored energy in a timely manner according to changes in electricity demand;

[0067] Furthermore, a building thermodynamic model was established based on a resistor-capacitor network that accounts for thermal inertia. This model transfers cooling energy through thermal resistance and stores cooling energy through thermal capacity. The installed air conditioners can meet the comfortable temperature requirements of users in the building's cooling zones. All areas within the building have the same building envelope. By summing the air conditioning loads for all areas, the total cooling load of the building can be calculated.

[0068] 1) The thermal balance constraint of wall ij is:

[0069] (1)

[0070] The heat balance constraint for region n is:

[0071] (2)

[0072] The operating power of the air conditioner should meet the following requirements:

[0073] (3)

[0074] The user's comfort temperature limits are:

[0075] (4)

[0076] 2) Trading Model

[0077] The prosumer community PComs, wind farm WF, and shared energy storage ESS system trade electricity with the grid. Therefore, the payment made by the prosumer community PComs, wind farm WF, shared energy storage ESS system and the grid can be expressed as:

[0078] (5)

[0079] The network access cost of the prosumer community PComs can be expressed as:

[0080] (6)

[0081] The service fee paid by the prosumer community PComs to the shared energy storage ESS system can be expressed as:

[0082] (7)

[0083] Prosumer communities (PComs) also need to pay for the maintenance of electrical load equipment:

[0084] (8)

[0085] (9)

[0086] The operation of the prosumer community PComs should meet the power balance constraints:

[0087] (10)

[0088] The amount of electricity sold to the grid and energy storage system should not be higher than the amount of electricity generated by photovoltaic power generation:

[0089] (11)

[0090] 3) Objective function

[0091] With the goal of minimizing cost, the objective function of the prosumer community PComs can be expressed as follows:

[0092] (12)

[0093] 2. Wind farms

[0094] 1) Trading Model

[0095] The wind farm WF sells electricity to the prosumer community PComs, the shared energy storage ESS system and the grid. Therefore, the wind farm WF receives payments from the prosumer community PComs, the shared energy storage ESS system and the grid, which can be expressed as:

[0096] (13)

[0097] The maintenance cost of wind farm WF can be expressed as:

[0098] (14)

[0099] The grid access cost of wind farm WF can be expressed as:

[0100] (15)

[0101] Wind farm WF pays ESS service fee:

[0102] (16)

[0103] Power balance constraints:

[0104] (17)

[0105] (18)

[0106] 2) Objective function

[0107] With the goal of minimizing cost, the objective function of the wind farm WF can be expressed as follows:

[0108] (19)

[0109] 3. Shared energy storage

[0110] 1) Device Model

[0111] The state of charge (SoC) of the energy storage system is dynamic and the state of charge (SoC) at the end of the previous moment ... and the current moment The charging and discharging powers are related as follows:

[0112] (20)

[0113] In order to ensure the normal life cycle, the shared energy storage ESS system Should stay within this range:

[0114] (twenty one)

[0115] Initial state Should not be higher than :

[0116] (twenty two)

[0117] The charging and discharging power should not be higher than the upper limit as shown below:

[0118] (twenty three)

[0119] 2) Trading Model

[0120] The shared energy storage ESS system trades electricity with the prosumer community PComs, the wind farm WF, and the grid. The shared energy storage ESS system collects fees from the prosumer community PComs and pays fees to the wind farm WF and the grid, which can be expressed as:

[0121] (twenty four)

[0122] The shared energy storage ESS system charges service fees to wind farms WF and prosumer communities PComs:

[0123] The grid access cost of the shared energy storage ESS system can be expressed as:

[0124] (25)

[0125] The maintenance cost of the shared energy storage ESS system can be expressed as:

[0126] (26)

[0127] The operation of the energy storage system should meet the power balance constraints:

[0128] (27)

[0129] 3) Objective function

[0130] With the goal of minimizing cost, the objective function of the shared energy storage ESS system can be expressed as:

[0131] (28).

[0132] See also Figure 1 The present invention provides an embodiment of an asynchronous decentralized method for collaborative operation of a multi-agent energy system. The fraudulent behavior scheme prevents prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESS) systems from destroying the long-term cooperative stability of the system through malicious behavior by designing behavioral constraints, incentive mechanisms, and penalty mechanisms.

[0133] 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 computing process supports independent calculations by each participant in the same time period, and adjusts the power trading plan and scheduling scheme through real-time updates to ensure flexible response to power supply and demand.

[0134] The profit distribution scheme is weighted based on the power resources provided by the prosumer community PComs, wind farm WF, and shared energy storage ESS system, load demand, and market power prices. In the Nash negotiation game model, 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. Both symmetric and asymmetric profit distribution methods are available. With a symmetric profit distribution method, each party receives equal benefits based on their respective contributions. With an asymmetric profit distribution method, the profit distribution ratio is adjusted based on the actual factors of each party's power generation capacity, energy storage capacity, and load demand to ensure the cooperative interests of all parties.

[0135] Further, the solution method

[0136] 1) Nash Negotiations with Fraudulent Practices

[0137] Assume that electricity suppliers, power suppliers, and power suppliers are different stakeholders who decide how much electricity to sell or buy and how much to pay or charge. As rational and independent entities, they seek to increase profits through cooperation. Nash negotiation can balance individual and collective interests. Therefore, in this study, we apply Nash negotiation to enable electricity suppliers, power suppliers, and power suppliers to obtain optimal operating strategies. The standard Nash negotiation model of MAES-CWS can be expressed as follows:

[0138] (29)

[0139] (30)

[0140] (31)

[0141] (32)

[0142] in, , and The cost reduction value after cooperation without considering internal transactions can be expressed as:

[0143] (33)

[0144] (34)

[0145] (35)

[0146] , ,and It refers to the costs other than the internal transactions between the entities after the cooperation.

[0147] (30), (31) and (32) can be rewritten as follows:

[0148] (36)

[0149] The Nash negotiation model is a nonconvex, nonlinear optimization problem that is difficult to solve directly. Therefore, we can transform it into two subproblems: the coalition cost minimization subproblem (S1) and the profit allocation subproblem (S2). In S2, agents may not act honestly to maximize their profits. Therefore, this section also discusses fraudulent behavior.

[0150] ① Alliance cost minimization sub-problem (S1)

[0151] S1 can be expressed as follows:

[0152] (37)

[0153] ② Profit distribution sub-problem (S2)

[0154] a) Symmetric profit distribution subproblem

[0155] Symmetry S2 can be expressed as follows:

[0156] (38)

[0157] b) Asymmetric profit distribution sub-problem

[0158] In symmetry S2 in (38), the contributions of the prosumer community PComs, wind farm WF, and shared energy storage ESS are not considered. This is unfair to the entities that contribute more. To make the profit distribution of each entity more fair, we introduce the contribution degree f to describe the degree of contribution of the entity to the cooperation.

[0159] Since the connection between the prosumer community PComs, wind farm WF, and shared energy storage ESS is energy trading, we use the amount of electricity traded to describe the contribution of the subject. f can be expressed as follows:

[0160] (39)

[0161] (40)

[0162] (41)

[0163] in , and is the sum of transaction power, as shown below:

[0164] (42)

[0165] (43)

[0166] (44)

[0167] Introducing contribution as bargaining power, we can get asymmetric S2:

[0168] (45)

[0169] c) The sub-problem of profit distribution involving fraudulent activities

[0170] The above discussion assumes that the prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) are honest. However, in reality, entities may commit fraud in pursuit of profit maximization.

[0171] According to the Nash negotiation model (29), for the prosumer community PComs, the optimal payment is as follows:

[0172] (46)

[0173] Similarly, for the wind farm WF and shared energy storage ESS system, the optimal received payment is as follows:

[0174] (47)

[0175] (48)

[0176] From (46) it can be seen that if the cost reduction value declared by the prosumer community PComs If the cost reduction value of the wind farm WF and the shared energy storage ESS system is lower than the actual cost reduction value, more profit may be obtained. and If the reduction is lower than the actual cost, more profit may be obtained.

[0177] , and It can be described as follows:

[0178] (49)

[0179] (50)

[0180] (51)

[0181] in, , and They are the fraud index (CI) of prosumer communities PComs, wind farms WF and shared energy storage ESS systems.

[0182] In order to ensure the effectiveness of the Nash negotiation model, , ,and The following conditions should also be met:

[0183] (52)

[0184] After applying (49), (50), and (51) to (52), the upper bound of CI is as follows:

[0185] (53)

[0186] (54)

[0187] (55)

[0188] Obviously, in order to obtain more benefits, the subject is willing to apply the highest possible CI. However, from (53), (54) and (55), it can be seen that the upper limit is affected by the subject's behavior. Each subject must choose an appropriate CI.

[0189] We believe that when When is positive and close to 0, the subject reaches the fraud equilibrium, because at this time the subject cannot find a larger fraud indicator to satisfy (52). Here, we propose a relaxation algorithm as shown in the relaxation algorithm steps in Table 1 to achieve the fraud equilibrium, where , and is the relaxation parameter. The stopping criterion is

[0190] (56)

[0191] Table 1 Relaxation algorithm steps

[0192]

[0193] With the optimal CIs , and , we can get the optimal , ,and Then the asymmetric S2 considering fraudulent behavior can be described as:

[0194] (57)

[0195] If the entity chooses to distribute profits symmetrically, then , and Can be set to 1.

[0196] See also Figure 1 , an embodiment provided by the present invention: a method for asynchronous decentralization of collaborative operation of a multi-agent energy system, wherein the two judgment steps of the dual judgment acceleration asynchronous decentralization ADMM algorithm are: local calculation and global coordination;

[0197] 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;

[0198] In the global coordination phase, 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;

[0199] Furthermore, the distributed solution method

[0200] In order to protect the privacy of the prosumer community PComs, wind farm WF and shared energy storage ESS system and reduce the amount of information collected, this study adopts a distributed solution method based on ADMM.

[0201] Distributed Model

[0202] In S1, in order to decouple Equation (37) and realize distributed problem solving, we use and They represent the amount of electricity that the wind farm WF expects to trade with the prosumer community PComs and the amount of electricity that PComs expects to trade with the wind farm WF. and They represent the amount of electricity that the shared energy storage ESS system expects to trade with the prosumer community PComs and the amount of electricity that the prosumer community PComs expects to trade with the shared energy storage ESS system. and They represent the amount of electricity that the wind farm WF expects to trade with the shared energy storage ESS system and the amount of electricity that the shared energy storage ESS system expects to trade with the wind farm WF. They will continue to exchange information until they reach an agreement, that is, 、 、 Ignore the constant , and , then the augmented Lagrangian function of formula (37) can be expressed as:

[0203] (58)

[0204] in , and The formula is as follows:

[0205] (59)

[0206] (60)

[0207] (61)

[0208] (58) can be decomposed into a distributed model of prosumer communities PComs, wind farms WF, and shared energy storage ESS systems, as shown in (62), (63), and (64), respectively:

[0209] (62)

[0210] (63)

[0211] (64)

[0212] In S2, in order to decouple (57), distributed problem solving is achieved. 、 and They 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 payments that the shared energy storage ESS system expects to pay to the wind farm WF. 、 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:

[0213] (65)

[0214] in , ,and The formula is as follows:

[0215] (66)

[0216] (67)

[0217] (68)

[0218] (65) can be decomposed into a distributed model of prosumer communities PComs, wind farms WF, and shared energy storage ESS systems, as shown in (69), (70), and (71), respectively:

[0219] (69)

[0220] (70)

[0221] (71)

[0222] ② Parameter update based on dual judgment ADMM

[0223] ADMM is used to solve the problem. Here, the transaction behavior between the wind farm WF and the shared energy storage ESS system in S1 is used to explain the dual judgment iterative parameter update method.

[0224] The primal and dual residuals are defined as:

[0225] (72)

[0226] (73)

[0227] First judgment: According to the difference between the original residual and the dual residual in two adjacent iterations obtained by (74) and (75), judge whether to proceed to the next step. and Less than , then the penalty factor remains unchanged. Otherwise, enter the second judgment and update the penalty factor.

[0228] (74)

[0229] (75)

[0230] Judgment 2: Based on the relationship between the original residual and the dual residual, determine the update method of the penalty factor, as follows:

[0231] (76)

[0232] The Lagrange multiplier is then updated according to the following principle:

[0233] (77)

[0234] The stopping criteria are:

[0235] (78)

[0236] In the above formula, ij represents the node, n represents the room, t represents the time node, T represents the operation cycle, x represents the xth prosumer community PComs, and X represents the set of prosumer community PComs. and represents the area of walls and windows, B represents the rated capacity of the shared energy storage ESS system, , represents the room and wall heat capacity, , , Indicates the breaking point of the negotiations. 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 in the prosumer communities PComs, represents the number of rooms in the building of the prosumer community PComs, Indicates the maximum power of the air conditioner. , Indicates the maximum charge and discharge power, represents the electricity market price, represents the photovoltaic output of the prosumer community PComs at time t, Indicates the transaction price of electricity sold by the entity to the grid, represents the maximum wind power generation, Indicates the heat source in the area. If there is a window in the area, Take 1, otherwise take 0, Indicates the light intensity in the corresponding direction of the wall. , Indicates the thermal resistance of the wall and the window. If the sun shines on the wall, 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 window transmittance, Indicates the time interval, represents the energy leakage coefficient, represents the service fee coefficient, , represents the charging and discharging efficiency, , Indicates the network fee coefficient, , , , represents the maintenance cost coefficient, , Indicates accuracy, , represents the penalty factor, , represents the Lagrange multiplier, represents the fee paid by the shared energy storage ESS system to the grid, , represents the payment received by the wind farm WF from the shared energy storage ESS system or the grid, , It means that the prosumer community PComs and wind farm WF pay for the service of the shared energy storage ESS system, , , represents the transmission fee of the prosumer community PComs, wind farm WF and shared energy storage ESS system, , , represents the maintenance cost of the prosumer community PComs, wind farm WF and shared energy storage ESS system, , , represents the total cost of the prosumer community PComs, wind farm WF and shared energy storage ESS system, , , Represents the payments made by the prosumer community PComs to the wind farm WF, the shared energy storage ESS system and the grid, represents the air conditioning operating power of room n in the prosumer community PComs at time t, , represents the air conditioning operating power and other loads of buildings in the prosumer community PComs, , represents the charge and discharge power at time t, represents the amount of electricity traded between the prosumer community PComs and the shared energy storage ESS system at time t, The shared energy storage ESS system sells electricity to the prosumer community PComs, It means that the prosumer community PComs sells electricity to the shared energy storage ESS system, , represents the amount of electricity sold to and purchased from the grid by the prosumer community PComs at time t, , represents the amount of electricity sold to and purchased from the grid by the shared energy storage ESS system at time t, represents the power generated by the wind farm WF at time t, , , represents the amount of electricity sold by the wind farm WF to the prosumer community PComs, the shared energy storage ESS system and the grid at time t, represents the state of charge at time t, represents the indoor temperature of room n at time t, , , Denotes the fraud index.

[0237] ③Asynchronous decentralized ADMM algorithm

[0238] In traditional ADMMs, a central data center collects information from agents and broadcasts iterative parameters. This model effectively solves optimization problems, but can lead to information leakage. To address this, we propose a decentralized ADMM in which agents exchange information directly, rather than through a central data center.

[0239] See also Figure 1 The present invention provides an embodiment of a method for asynchronous decentralization of a multi-agent energy system collaborative operation. The information exchange process of the two-way information exchange mechanism is as follows:

[0240] 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, energy storage system status, load forecast and power demand forecast, wind farm WF generation capacity, and grid power prices;

[0241] 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 via the MQTT real-time transmission protocol;

[0242] Information processing and analysis: The system that receives the information performs local calculations and analysis based on the received data, and evaluates and adjusts its operating strategy;

[0243] Furthermore, each system collects current operating status data every 15 minutes, including current power demand and generation, energy storage system status, load forecast and power demand forecast, wind farm WF generation capacity, and grid power prices. Each system sends the collected data to other participants in JSON format. To ensure timely information exchange, information is transmitted through the following methods:

[0244] Real-time transport protocols: such as MQTT and WebSocket, which support real-time two-way communication;

[0245] Regular scheduling exchanges: The system regularly exchanges the latest data, such as sending updates on load forecasts and generation capacity every hour;

[0246] The systems that receive the information perform local calculations and analysis based on the data received, assessing whether adjustments to their operating strategies are needed, such as adjusting energy storage plans, changing power trading plans, and optimizing power generation strategies. Prosumer communities (PComs) may adjust air conditioning loads or electricity demand based on the wind farm's WF generation capacity. Wind farms (WFs) determine whether to increase or decrease power output based on the PComs' power needs. Shared energy storage (ESS) systems determine whether to discharge or recharge based on grid electricity prices and demand conditions.

[0247] Based on the decision-making results of each system, the information feedback process will further adjust the energy supply and demand strategy. This process may require multiple iterations to ensure the balance and optimization of the system.

[0248] Working principle: The air conditioning system in a building relies on the thermal inertia characteristics of the building to optimize energy consumption. By establishing a building thermodynamic model, combining temperature changes with air conditioning load demand, the building's cooling load forecast is calculated and matched with the capacity of the wind farm and energy storage system to reduce energy waste.

[0249] Wind farm power generation is affected by real-time wind speed and is unstable. By collaborating with shared energy storage systems, wind farms can store excess power and release it when grid demand is high, ensuring a stable supply. The energy storage system also charges and discharges according to grid demand and market electricity prices, regulating the balance between power supply and demand.

[0250] Energy units such as prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) communicate their energy needs, generation capacity, and storage status through a real-time, two-way information exchange mechanism. This information sharing ensures that all parties can make timely power dispatch decisions based on the latest data, achieving mutually beneficial and flexible energy allocation.

[0251] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

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 a 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 through charging. Two-way information exchange: Prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) use a two-way information exchange mechanism to share information on power demand, generation capacity, energy storage status, and grid power prices in real time. Constructing a Nash negotiation game model: A cooperative game model based on Nash negotiation theory is constructed. In this model, the prosumer community PComs, wind farm WF, and shared energy storage ESS system negotiate to determine the transaction volume, transaction price, and profit distribution based on their respective energy supply, demand, and resource sharing capabilities. Fraud prevention and fraud balance; Use game theory to achieve reasonable profit distribution; Dual Judgment Accelerated Asynchronous Decentralized ADMM Algorithm Solution: Dual judgment accelerated asynchronous decentralized ADMM algorithm is used to perform distributed solution of Nash negotiation game model; Asynchronous computing and real-time communication: Prosumer communities (PComs), wind farms (WFs), and shared energy storage (ESSs) asynchronously compute and update trading plans based on their own needs, generation capacity, and electricity market information.

2. The asynchronous decentralized method for collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The building thermodynamic model adopts a resistor-capacitor network model to transfer cold energy through thermal resistance and store cold energy through thermal capacity, calculates the building cooling load, and dynamically adjusts the air conditioning load by modeling the building envelope, air conditioning system and cooling load, and adjusts the air conditioning operation strategy according to the actual needs of the building.

3. The asynchronous decentralized method for collaborative operation of a multi-agent energy system according to claim 1, characterized in that: 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 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 system ESS to adjust the energy storage power supply to the grid to stabilize the power supply.

4. The method for asynchronous decentralization of a multi-agent energy system collaborative operation according to claim 1, characterized in that: The shared energy storage ESS system determines the timing of power charging and discharging based on fluctuations in grid electricity prices, predicted power demand, and the system's battery storage capacity, and releases stored energy in a timely manner according to changes in power demand.

5. The asynchronous decentralized method for collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The profit distribution scheme is weighted based on the power resources provided by the prosumer community PComs, wind farm WF, and shared energy storage ESS system, load demand, and market electricity prices. In the Nash negotiation game model, 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 a symmetrical profit distribution method is adopted, each party obtains the same benefits based on their respective contributions; while an asymmetrical profit distribution method adjusts the profit distribution ratio based on 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 asynchronous decentralized method for collaborative operation of a multi-agent energy system 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 asynchronous decentralized method for 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 computing time and ensure the privacy and security of the system.

8. The asynchronous decentralized method for 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 computing process supports independent calculations by each participant within the same time period, and adjusts power trading plans and scheduling schemes 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 asynchronous decentralized method for collaborative operation of a multi-agent energy system according to claim 1, characterized in that: The information exchange process of the two-way information interaction 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, energy storage system status, load forecast and power demand forecast, wind farm WF generation capacity, and grid power 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 via 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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