Multi-factor integrated multi-main-body energy system and collaborative operation negotiation method

Abstract

The invention discloses a multi-factor integrated multi-subject energy system and a collaborative operation negotiation method, and relates to the technical field of multi-subject energy systems, and the collaborative operation negotiation method comprises the steps: constructing an MAES-WHB model, constructing an NB model of the MAES-WHB model based on an NB theory, decomposing the NB model into a CPS and a PAS, and carrying out the negotiation of the CPS and the PAS. The method comprises the following steps: substituting transaction electric quantity obtained by solving a CPS into an NB model, introducing a contribution degree as a bargaining capability, further constructing an asymmetric PAS model, based on an ADMM theory, decomposing the CPS and the PAS, constructing a distributed model of each main body, improving a standard ADMM, and providing a TPCA-ADMM algorithm for solving the CPS and the PAS. According to the method, the electrical characteristics and the thermal characteristics are comprehensively considered, and the function of bidirectional interaction between the wind power plant and the hydrogen energy system and between the wind power plant and the building is achieved by setting the MAES-WHB model.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-agent energy systems, and in particular to a multi-factor integrated multi-agent energy system and a collaborative operation negotiation method. Background Art

[0002] Under the dual challenges of global climate change and resource depletion, the consumption of traditional fossil energy has risen sharply, leading to increased greenhouse gas emissions and increasingly severe climate warming problems. At the same time, the limited and non-renewable nature of fossil energy has also increased the pressure on energy supply. With the demand for global energy transformation and the rapid development of artificial intelligence technology, as well as the pursuit of intelligent, networked and digital management of energy systems, the traditional energy system can no longer meet the needs of sustainable development. Therefore, energy transformation has become a major challenge facing the world today, that is, the transformation from a traditional energy system with high pollution and high emissions to a new energy system that is clean, low-carbon and sustainable. In MAES, various entities are interconnected through information technology, communication technology and automation technology to form an intelligent energy network. Due to the variability and uncertainty of wind power, large-scale wind power grid connection will inevitably lead to a match between wind power output and power load, increasing the difficulty of system scheduling. Therefore, the problem of wind power absorption needs to be solved urgently. Due to the different stakeholders of wind power, hydrogen energy systems and buildings, the specific interest relationship of the subjects in MAES is usually complex, and it is difficult to reasonably divide the interests. At the same time, MAES lacks operational flexibility, and the use of the standard ADMM method for privacy protection leads to slow convergence of distributed optimization, which cannot take into account both privacy protection and high solution efficiency.

[0003] 1. Patent document CN113591375B discloses an optimal coordinated operation method for 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.

[0004] 2. Patent document CN113467398B discloses a distributed control method and system for an integrated energy system based on a consistency algorithm. The above patent realizes the construction of a networked integrated energy system, analyzes the dynamics of each intelligent agent, and uses the consistency control algorithm of the multi-agent system to design a distributed control system for the integrated energy system, so that the electric power load rate of the gas generators of all CCHP units in the integrated energy system and the indoor temperature of the cooling load tend to be consistent.

[0005] 3. The patent document CN113098036B discloses an operation method for an integrated energy system based on a hydrogen fuel cell. The above patent realizes flexible adjustment of energy supply, energy consumption, and energy storage, thereby achieving flexible interaction of integrated energy and vertical integration of supply, demand, and storage.

[0006] 4. The patent document CN107665384B discloses a dispatching method for a power-thermal integrated energy system containing multi-region energy stations. The above patent realizes, through the coordination of different types of regional energy stations, full exploration of the potential of complementary advantages of multiple energies to reduce the network loss of the distribution network, improve the peak load phenomenon of the distribution network, increase the renewable energy consumption rate, and at the same time effectively reduce the operation cost of the regional power-thermal system.

[0007] In summary, inspired by the above patents, this application has developed functions that can achieve two-way interaction between a wind farm and a hydrogen energy system and between a wind farm and a building, can achieve the function of asymmetric profit distribution, can achieve the function of improving the convergence speed and solution efficiency of distributed optimization, and can achieve the function of distributed energy management for each subject, so as to solve the problems of low energy utilization efficiency, unstable energy supply, low system flexibility, unreasonable profit distribution in the collaborative operation of MAES, slow system response speed, high consumption of computing resources, low system reliability, low efficiency, and poor user experience existing in the existing energy system; Therefore, this application proposes a multi-factor integrated multi-subject energy system and a collaborative operation negotiation method that can achieve the functions of two-way interaction between a wind farm and a hydrogen energy system and between a wind farm and a building, can achieve the function of asymmetric profit distribution, can achieve the function of improving the convergence speed and solution efficiency of distributed optimization, and can achieve the function of distributed energy management for each subject. Summary of the Invention

[0008] The purpose of the present invention is to provide a multi-factor integrated multi-subject energy system and a collaborative operation negotiation method to solve the technical problems of low energy utilization efficiency, unstable energy supply, low system flexibility, unreasonable profit distribution in the collaborative operation of MAES, slow system response speed, high consumption of computing resources, low system reliability, low efficiency, and poor user experience mentioned in the above background technology.

[0009] To achieve the above purpose, the present invention provides the following technical solution: A collaborative operation negotiation method for a multi-factor integrated multi-subject energy system, and the collaborative operation negotiation method is as follows: S1: Construct a MAES-WHB model; S2: Based on the NB theory, construct an NB model of the MAES-WHB model; S3: Decompose the NB model into CPS and PAS, substitute the transaction power obtained by solving CPS into the NB model, introduce the contribution degree as the bargaining power, and then construct an asymmetric PAS model; S4: Based on the ADMM theory, decompose CPS and PAS to construct the distributed models of each subject; S5: Improve the standard ADMM and propose the TPCA-ADMM algorithm for solving CPS and PAS; Among them, MAES-WHB is a multi-agent energy system integrating a wind farm, a hydrogen energy system, and a building, MAES is a multi-agent energy system, NB is Nash bargaining, ADMM is the alternating direction method of multipliers, and the TPCA-ADMM algorithm is a three-stage prediction-correction accelerated alternating direction method of multipliers algorithm improved based on the ADMM algorithm; The specific content of S1 is as follows: S11: Calculate the revenue and cost of the wind farm to construct the objective function of the wind farm; S12: Calculate the cost of the hydrogen energy system and consider the power balance constraint to construct the objective function of the hydrogen energy system; S13: Calculate the cost of the building and consider the power balance constraint to construct the objective function of the building; S14: Construct the MAES-WHB model based on the wind farm model, the hydrogen energy system model, and the building model.

[0010] Preferably, the objective function of the wind farm is: ; Where, is the overall profit of the wind farm, is the revenue obtained by the wind farm from selling electricity to the hydrogen energy system, is the revenue obtained by the wind farm from selling electricity to the building, is the revenue obtained by the wind farm from selling electricity to the PG, is the maintenance cost of the wind farm, is the transmission fee of the wind farm; The objective function of the hydrogen energy system is: ; Where, is the total cost of the hydrogen energy system, is the electricity fee paid by the hydrogen energy system to the PG, is the maintenance cost of the hydrogen energy system; The objective function of the building is: ; Where, is the total cost of the building, Pay the electricity bill of PG for the building, Maintenance costs for the building.

[0011] Preferably, the NB model is: ; ; Among them, is the profit of the wind farm when cooperating, is the cost of the hydrogen energy system when not cooperating, is the cost of the building when not cooperating; The CPS of the NB model is: .

[0012] Preferably, the specific S3 is: S31: Calculate the overall transaction power consumption during the operation period; S32: Construct a mapping function to quantify the contribution of each entity to the transaction; S33: Solve the CPS of the NB model to obtain the transaction power consumption; S34: Substitute the transaction power consumption into the NB model to construct an asymmetric PAS model; The overall transaction power consumption during the operation period is: ; ; Among them, is the operation period, t is the moment, is the power sold by the wind farm to the hydrogen energy system during the operation period, is the power sold by the wind farm to the hydrogen energy system at time t, is the power sold by the wind farm to the building during the operation period, is the power sold by the wind farm to the building at time t; The method of constructing a mapping function to quantify the contribution of each entity to the transaction is: ; ; ; Among them, is the contribution of the wind farm, is the contribution of the hydrogen energy system, is the contribution of the building, is a constant used to increase the contribution of the wind farm; The asymmetric PAS model is: .

[0013] Preferably, S4 is specifically as follows: S41: Introduce new variables , , and , and convert the CPS into a minimization problem; S42: Based on ADMM, decompose the augmented Lagrangian function of the CPS into distributed optimization models for the wind farm, hydrogen energy system, and building; S43: Introduce new variables , , and , take the logarithm of the asymmetric PAS model, and convert it into a minimization problem; S44: Based on ADMM, decompose the augmented Lagrangian function of the asymmetric PAS model into distributed optimization models for the wind farm, hydrogen energy system, and building; Wherein, represents the amount of electricity that the hydrogen energy system hopes to purchase from the wind farm, represents the amount of electricity that the building hopes to purchase from the wind farm, represents the amount of electricity that the wind farm hopes to sell to the hydrogen energy system, represents the amount of electricity that the wind farm hopes to sell to the building, represents the electricity price that the hydrogen energy system hopes to pay to the wind farm, represents the electricity price that the building hopes to pay to the wind farm, represents the electricity price that the wind farm hopes to collect from the hydrogen energy system, represents the electricity price that the wind farm hopes to collect from the building, is the electricity price between the wind farm and the hydrogen energy system at time t, is the electricity price between the wind farm and the building at time t.

[0014] Preferably, the augmented Lagrangian function of the CPS is: ; Wherein, and are Lagrange multipliers, and are penalty factors; The augmented Lagrangian function of the asymmetric PAS is:

[0015] Wherein, and are Lagrange multipliers, and are penalty factors.

[0016] Preferably, S5 is specifically as follows: S51: In the first stage, calculate the primal residual, the dual residual, and the difference between the primal residual and the dual residual; S52: Compare the difference with a set value. If the difference is less than the set value, the penalty factor remains unchanged. If the difference is not less than the set value, enter the second stage to update the penalty factor; S53: In the third stage, introduce a prediction-correction mechanism to update the Lagrange multiplier; S54: If the iteration termination condition is met, stop the solution.

[0017] Preferably, the formula for calculating the primal residual and the dual residual is: ; where is the primal residual, is the electricity quantity that the hydrogen energy system / building hopes to purchase from the wind farm, is the electricity quantity that the wind farm hopes to sell to the hydrogen energy system / building; ; where is the dual residual, the electricity quantity that the hydrogen energy system / building hopes to purchase from the wind farm; The formula for calculating the difference between the primal residual and the dual residual is: ; where is the primal residual; ; where is the dual residual.

[0018] Preferably, the updated penalty factor in S52 is expressed as: ; where and are penalty factors, is a constant; The updated Lagrange multiplier in S53 is expressed as: ; where is an intermediate parameter in the process of updating the Lagrange multiplier, is the penalty factor, is the electricity quantity that the hydrogen energy system / building hopes to purchase from the wind farm, is the Lagrange multiplier, is the electricity quantity that the wind farm hopes to sell to the hydrogen energy system / building; ; wherein, is a constant; , wherein, is a prediction correction factor, is a given constant.

[0019] Preferably, the iteration termination condition is: ; wherein, is the number of iterations, is the maximum number of iterations.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By setting up the MAES-WHB model, the present invention realizes the function of two-way interaction between the wind farm and the hydrogen energy system as well as between the wind farm and the building, solves the problems of low energy utilization efficiency, unstable energy supply and low system flexibility, comprehensively considers the electrical characteristics and thermal characteristics, enhances the flexibility of system operation, improves the user's living experience, improves the energy conservation and emission reduction effect, and improves the energy utilization rate; 2. By setting up the distribution strategy with MAES collaborative operation, the present invention realizes the function of asymmetric profit distribution, solves the problem of reasonable profit distribution in MAES collaborative operation, can determine the respective bargaining power through the contribution degrees of the wind farm, the hydrogen energy system and the building, takes into account both individual rationality and coalition rationality, promotes technological innovation and management innovation, and improves the economic benefits of the system; 3. By setting up the construction of the CPS distributed model and the PAS distributed model, the present invention realizes the function of improving the convergence speed and solution efficiency of distributed optimization, solves the problems of slow system response speed, high consumption of computing resources, low system reliability and poor user experience, can reduce the resource consumption during system operation, prolongs the service life of the equipment, improves the reliability, stability and accuracy of system calculation, and improves the adaptability of the system to the environment and demand; 4. By setting up the TPCA-ADMM algorithm, the present invention realizes the function of distributed energy management of each subject, solves the problems of low system processing efficiency and low flexibility, can protect the information privacy of the wind farm, the hydrogen energy system and the building, improves the security and reliability of the energy system, and promotes the sustainable development of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of the energy system of the present invention; Figure 2 is a schematic diagram of the CPS Lagrangian function decomposition of the present invention; Figure 3Schematic diagram of Lagrangian function decomposition of the asymmetric PAS model of the present invention; Figure 4 Schematic diagram of the distributed solution process of the present invention. Specific embodiments

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying 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 of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Embodiment 1: Please refer to Figure 1 , a collaborative operation negotiation method for a multi-factor integrated multi-agent energy system, and the collaborative operation negotiation method is as follows: S1: Construct the MAES-WHB model; S2: Based on the NB theory, construct the NB model of the MAES-WHB model; S3: Decompose the NB model into CPS and PAS, substitute the transaction power obtained by solving CPS into the NB model, introduce the contribution degree as the bargaining power, and then construct an asymmetric PAS model; S4: Based on the ADMM theory, decompose CPS and PAS, and construct a distributed model for each agent; S5: Improve the standard ADMM, and propose the TPCA-ADMM algorithm for solving CPS and PAS; Wherein MAES-WHB is a multi-agent energy system integrating a wind farm, a hydrogen energy system and a building, MAES is a multi-agent energy system, NB is Nash negotiation, ADMM is the alternating direction multiplier method, and the TPCA-ADMM algorithm is a three-stage prediction-correction accelerated alternating direction multiplier method algorithm improved based on the ADMM algorithm; The specific content of S1 is as follows: S11: Calculate the revenue and cost of the wind farm and construct the objective function of the wind farm; S12: Calculate the cost of the hydrogen energy system and consider the power balance constraint to construct the objective function of the hydrogen energy system; S13: Calculate the cost of the building and consider the power balance constraint to construct the objective function of the building; S14: Construct the MAES-WHB model based on the wind farm model, the hydrogen energy system model and the building model; The objective function of the wind farm is: ; Among them, is the overall profit of the wind farm, is the income obtained by the wind farm from selling electricity to the hydrogen energy system, is the income obtained by the wind farm from selling electricity to buildings, is the income obtained by the wind farm from selling electricity to the PG, is the maintenance cost of the wind farm, is the transmission fee of the wind farm; The objective function of the hydrogen energy system is: ; Among them, is the total cost of the hydrogen energy system, is the electricity fee paid by the hydrogen energy system to the PG, is the maintenance cost of the hydrogen energy system; The objective function of the building is: ; Among them, is the total cost of the building, is the electricity fee paid by the building to the PG, is the maintenance cost of the building; Furthermore, the revenue of the wind farm is calculated through , and where, is the on-grid electricity price of wind power, is the electricity quantity sold by the wind farm to the PG at time t. The maintenance cost and transmission fee of the wind farm are calculated through and where is the maintenance cost coefficient of the wind farm, is the wind power generation, and is the transmission fee coefficient of the wind farm. The constraints that the wind farm output needs to meet are and where is the maximum wind power generation. Combining with the objective function of the wind farm, a wind farm model is constructed. When the electrolyzer satisfies and where is the maximum operating power of the electrolyzer, is the ramp power. The hydrogen production during the operation of the electrolyzer is calculated through where is the hydrogen production rate, is the power consumed by the electrolyzer at time t, is the time interval. The operation mode of the compressor is and where is the specific heat capacity constant of hydrogen, is the hydrogen production at time t, is the compressor efficiency, is the isentropic exponent of hydrogen, / is the hydrogen compression ratio, is the power consumed by the compressor at time t, is the maximum operating power of the compressor. The formula for indicating the hydrogen storage amount through the internal air pressure of the hydrogen storage tank is 、 and , where is the internal pressure of the hydrogen storage tank, is the internal temperature of the hydrogen storage tank, is the volume of the hydrogen storage tank, is the molar mass of hydrogen, is the hydrogen load demand at time t, is the maximum air pressure of the hydrogen storage tank, is the minimum air pressure of the hydrogen storage tank. The operation model of the battery is: 、 、 、 and , where is the stored energy at time t, is the charging power at time t, is the charging efficiency, is the discharging power at time t, is the discharging efficiency, is the maximum energy storage capacity, is the minimum energy storage capacity, is a binary variable used to avoid simultaneous charging and discharging at time t. The cost of the hydrogen energy system includes the cost of purchasing electricity from the wind farm and the PG and the maintenance cost for the electrolyzer and the BS, which is calculated through and , where is the day-ahead market price, is the amount of electricity purchased by the hydrogen energy system from the PG, is the electrolyzer maintenance cost coefficient, is the energy storage maintenance cost coefficient. The operation of the hydrogen energy system should satisfy the following power balance constraint as . Combining with the objective function of the hydrogen energy system to construct the hydrogen energy system model, the thermal balance constraint of the wall ij is calculated through , where i and j are nodes, is the heat capacity of the wall, is the wall temperature, is the adjacent node of the wall, is the temperature of node j, is the thermal resistance. Since the thermal resistance values of the wall without window and the wall with window are different, it is necessary to analyze according to the actual situation. If the wall is irradiated by sunlight, then take 1, otherwise take 0. is the wall heat absorption rate. is the wall surface area. is the light intensity in the corresponding direction of the wall. Through calculate the heat balance of zone n, where n is the room index. is the zone heat capacity. is the indoor temperature. is the adjacent node of the zone. is the heat source in the zone. If there is a window in this zone, then take 1, otherwise take 0. is the window transmittance. is the surface area of the zone window. is the light intensity in the corresponding direction of the zone window. is the energy efficiency ratio. is the operating power of the air conditioner in room n at time t. is the window thermal resistance. The operating power of the air conditioner needs to satisfy , where is the maximum AC working power. The temperature limit for user comfort is , where is the lowest comfortable temperature. is the highest comfortable temperature. The trading model includes the building cost, including the cost of purchasing electricity from the wind farm and PG and the maintenance cost for the air conditioner and the base load. The calculation formula is , and , where is the electricity purchased by the building from PG. is the building maintenance cost. is the air conditioner maintenance cost coefficient. is the electricity consumed by the air conditioner. is the base load maintenance cost coefficient. is the electricity consumed by the base load. is the number of buildings. The power balance constraint that the buildings should satisfy is , a building model is constructed by combining with the objective function of the building. By combining the wind farm model, the hydrogen energy system model and the building model, the MAES-WHB model is constructed, realizing the two-way interaction function between the wind farm and the hydrogen energy system and between the wind farm and the building, solving the problems of low energy utilization efficiency, unstable energy supply and low system flexibility, comprehensively considering the electrical characteristics and thermal characteristics, enhancing the flexibility of system operation, improving the user's living experience, improving the effect of energy conservation and emission reduction, and improving the utilization rate of energy.

[0024] Embodiment 2: Please refer to Figure 1 , a collaborative operation negotiation method for a multi-factor integrated multi-agent energy system, and the NB model is: ; ; Among them, is the profit of the wind farm when cooperating, is the cost of the hydrogen energy system when not cooperating, is the cost of the building when not cooperating; The CPS of the NB model is: ; The specific content of S3 is: S31: Calculate the overall transaction power in the operation period; S32: Construct a mapping function to quantify the contribution degree of each subject to the transaction; S33: Solve the CPS of the NB model to obtain the transaction power; S34: Substitute the transaction power into the NB model to construct an asymmetric PAS model; The overall transaction power in the operation period is: ; ; Among them, is the operation period, t is the moment, is the power sold by the wind farm to the hydrogen energy system in the operation period, is the power sold by the wind farm to the hydrogen energy system at time t, is the power sold by the wind farm to the building in the operation period, is the power sold by the wind farm to the building at time t; The method for constructing a mapping function to quantify the contribution degree of each subject to the transaction is: ; ; ; Among them, is the contribution degree of the wind farm, is the contribution degree of the hydrogen energy system, is the contribution degree of the building, is a constant used to increase the contribution degree of the wind farm; The asymmetric PAS model is: ; Furthermore, NB is a cooperative game that can take into account both individual and collective interests. By applying the NB theory and combining the wind farm model, hydrogen energy system model, and building model, the NB model of the MAES-WHB model is obtained. As an alliance of the wind farm, hydrogen energy system, and building, the CPS of the MAES-WHB model is , and in the CPS, the transaction amounts I W2H and I W2B can cancel each other out. By solving the CPS, the optimal transaction power and the operating powers of the electrolyzer in the hydrogen energy system, BS, and air conditioner in the building are obtained. By calculating the overall transaction power within the operating cycle, a mapping function is constructed to quantify the contribution degrees of each entity to the transaction. Each entity participating in the power transaction will obtain a contribution degree, and the more power an entity trades, the higher its corresponding contribution degree. When the transaction powers are the same, the contribution degree of the power selling entity is higher than that of the power buying entity. By introducing the contribution degree as the bargaining power and substituting the optimal transaction power obtained from solving the CPS into the asymmetric NB model, the asymmetric PAS model is constructed , and the optimal transaction electricity price and are obtained by solving the PAS, and then the asymmetric distribution of profits is realized. In , the profit distribution is symmetric. This strategy can determine the bargaining power of each entity through the contribution degrees of the wind farm, hydrogen energy system, and building, and then solve the problem of reasonable profit distribution in the coordinated operation of MAES, taking into account both individual rationality and alliance rationality, promoting technological innovation and management innovation, and improving the economic benefits of the system.

[0025] Example 3: Please refer to Figure 1 , Figure 2 and Figure 3 , a coordinated operation negotiation method for a multi-factor integrated multi-agent energy system, and the NB model is: ; ; wherein, is the profit of the wind farm when cooperating, is the cost of the hydrogen energy system when not cooperating, is the cost of the building when not cooperating; The CPS of the NB model is: ; Specifically, S3 is as follows: S31: Calculate the overall transaction power of the operation cycle; S32: Construct a mapping function to quantify the contribution of each entity to the transaction; S33: Solve the CPS of the NB model to obtain the transaction power; S34: Substitute the transaction power into the NB model to construct an asymmetric PAS model; The overall transaction power of the operation cycle is: ; ; Among them, is the operation cycle, t is the moment, is the power sold by the wind farm to the hydrogen energy system during the operation cycle, is the power sold by the wind farm to the hydrogen energy system at time t, is the power sold by the wind farm to the building during the operation cycle, is the power sold by the wind farm to the building at time t; The method for constructing a mapping function to quantify the contribution of each entity to the transaction is: ; ; ; Among them, is the contribution of the wind farm, is the contribution of the hydrogen energy system, is the contribution of the building, is a constant used to increase the contribution of the wind farm; The asymmetric PAS model is: ; Specifically, S4 is as follows: S41: Introduce new variables , , and , and transform the CPS into a minimization problem; S42: Decompose the augmented Lagrangian function of the CPS into distributed optimization models of the wind farm, hydrogen energy system, and building based on ADMM; S43: Introduce new variables , , and , take the logarithm of the asymmetric PAS model and transform it into a minimization problem; S44: Based on ADMM, decompose the augmented Lagrangian function of the asymmetric PAS model into distributed optimization models for the wind farm, hydrogen energy system, and building; Among them, represents the electricity quantity that the hydrogen energy system hopes to purchase from the wind farm, represents the electricity quantity that the building hopes to purchase from the wind farm, represents the electricity quantity that the wind farm hopes to sell to the hydrogen energy system, represents the electricity quantity that the wind farm hopes to sell to the building, represents the electricity price that the hydrogen energy system hopes to pay to the wind farm, represents the electricity price that the building hopes to pay to the wind farm, represents the electricity price that the wind farm hopes to charge from the hydrogen energy system, represents the electricity price that the wind farm hopes to charge from the building, is the electricity price between the wind farm and the hydrogen energy system at time t, is the electricity price between the wind farm and the building at time t; The augmented Lagrangian function of the CPS is: ; Among them, and are Lagrange multipliers, and are penalty factors; The augmented Lagrangian function of the asymmetric PAS is:

[0026] Among them, and are Lagrange multipliers, and are penalty factors; Furthermore, the ADMM algorithm efficiently handles constrained optimization problems in a distributed and parallel environment by alternately optimizing the primal variables and dual variables, combines the decomposability of the dual ascent method and the excellent convergence properties of the augmented Lagrangian multiplier method, decomposes the large global problem into multiple smaller and easier-to-solve local subproblems, and obtains the solution of the large global problem by coordinating the solutions of the subproblems. When and , the wind farm, hydrogen energy system, and building reach an agreement on the traded electricity quantity, convert the CPS into a minimization problem, and based on the ADMM algorithm, decompose the augmented Lagrangian function of the CPS into distributed optimization models for the wind farm, hydrogen energy system, and building. The distributed optimization model of the wind farm is , The distributed optimization model of the hydrogen energy system is , The distributed optimization model of the building is , When and , it is considered that each entity reaches an agreement on the electricity price. Take the logarithm of the asymmetric PAS model and transform it into a minimization problem. Based on ADMM, decompose the augmented Lagrangian function of the asymmetric PAS model into the distributed optimization models of the wind farm, hydrogen energy system and building. The optimization model of the wind farm is , The distributed optimization model of the hydrogen energy system is , The distributed optimization model of the building is , It realizes the function of improving the convergence speed and solving efficiency of distributed optimization, solves the problems of slow system response speed, high consumption of computing resources, low system reliability and poor user experience, can reduce the resource consumption during system operation, extend the service life of equipment, improve the reliability, stability and accuracy of system calculation, and improve the adaptability of the system to the environment and demand.

[0027] Example 4: Please refer to Figure 4 , a collaborative operation negotiation method for a multi-factor integrated multi-agent energy system, where S5 is specifically as follows: S51: Calculate the primal residual, dual residual and the difference between the primal residual and the dual residual in the first stage; S52: Compare the difference with the set value. If the difference is less than the set value, the penalty factor remains unchanged. If the difference is not less than the set value, enter the second stage to update the penalty factor; S53: Introduce a prediction-correction mechanism to update the Lagrange multiplier in the third stage; S54: Meet the iteration termination condition and stop solving; The formula for calculating the primal residual and the dual residual is: ; Among them, is the primal residual, is the electricity quantity that the hydrogen energy system / building hopes to purchase from the wind farm, is the electricity quantity that the wind farm hopes to sell to the hydrogen energy system / building; ; Among them, is the dual residual, The electricity quantity that the hydrogen energy system / building hopes to purchase from the wind farm; The formula for calculating the difference between the primal residual and the dual residual is: ; wherein, is the primal residual; ; wherein, is the dual residual; The updated penalty factor of S52 is expressed as: ; wherein, and are penalty factors, is a constant; The updated Lagrange multiplier of S53 is expressed as: ; wherein, is an intermediate parameter in the Lagrange multiplier update process, is a penalty factor, is the electricity quantity that the hydrogen energy system / building hopes to purchase from the wind farm, is the Lagrange multiplier, is the electricity quantity that the wind farm hopes to sell to the hydrogen energy system / building; ; wherein, is a constant; , wherein, is the prediction correction factor, is a given constant; The iteration termination condition is: ; wherein, is the number of iterations, is the maximum number of iterations; Furthermore, it is solved by the TPCA-ADMM algorithm improved based on the ADMM algorithm. In the first stage, the primal residual and the dual residual are calculated through and , and then the difference between and is calculated through and , and When it is less than the set value and the original residual and dual residual change in a decreasing or acceptable direction, the penalty factor remains unchanged and enters the third stage. By introducing a correction mechanism to update the Lagrange multiplier, if and is greater than or equal to the set value, it enters the second stage to update the penalty factor to prevent the current penalty factor from deteriorating the convergence efficiency. In the second stage, the penalty factor is updated according to the current original residual and dual residual values. After the penalty factor is updated, it enters the third stage to complete the update of the Lagrange multiplier. When the iteration termination condition is met, the update stops, realizing the function of distributed energy management of each entity, solving the problems of low system processing efficiency and low flexibility, being able to protect the information privacy of wind farms, hydrogen energy systems and buildings, improving the security and reliability of the energy system, and promoting the sustainable development of energy.

[0028] Example 5: Please refer to Figure 1 , a collaborative operation negotiation method for a multi-factor integrated multi-agent energy system, and the collaborative operation negotiation method is as follows: S1: Construct the MAES-WHB model; S2: Based on the NB theory, construct the NB model of the MAES-WHB model; S3: Decompose the NB model into CPS and PAS, substitute the traded electricity obtained by solving CPS into the NB model, introduce the contribution degree as the bargaining power, and then construct an asymmetric PAS model; S4: Based on the ADMM theory, decompose CPS and PAS to construct a distributed model for each entity; S5: Improve the standard ADMM and propose the TPCA-ADMM algorithm for solving CPS and PAS; where MAES-WHB is a multi-agent energy system integrating wind farms, hydrogen energy systems and buildings, MAES is a multi-agent energy system, NB is Nash negotiation, ADMM is the alternating direction multiplier method, and the TPCA-ADMM algorithm is a three-stage prediction-correction accelerated alternating direction multiplier method algorithm improved based on the ADMM algorithm; The specific content of the above S1 is as follows: S11: Calculate the revenue and cost of the wind farm to construct the objective function of the wind farm; S12: Calculate the cost of the hydrogen energy system and consider the power balance constraint to construct the objective function of the hydrogen energy system; S13: Calculate the cost of the building and consider the power balance constraint to construct the objective function of the building; S14: Based on the wind farm model, hydrogen energy system model and building model, construct the MAES-WHB model; The objective function of the wind farm is: ; wherein, is the overall profit of the wind farm, is the income obtained by the wind farm from selling electricity to the hydrogen energy system, is the income obtained by the wind farm from selling electricity to buildings, is the income obtained by the wind farm from selling electricity to PG, is the maintenance cost of the wind farm, is the transmission fee of the wind farm; The objective function of the hydrogen energy system is: ; wherein, is the total cost of the hydrogen energy system, is the electricity fee paid by the hydrogen energy system to PG, is the maintenance cost of the hydrogen energy system; The objective function of the building is: ; wherein, is the total cost of the building, is the electricity fee paid by the building to PG, is the maintenance cost of the building; Furthermore, by constructing a wind farm model, a hydrogen energy system model and a building model, the integration of wind power, hydrogen power and buildings is realized. The wind farm model sells electricity directly to the hydrogen energy system and buildings, and sells the excess electricity to PG. The hydrogen energy system model is constructed by calculating the consumption of the main equipment in the hydrogen production system, including the hydrogen production of the electrolyzer when it operates in a stable state, the constraint conditions that the electrolyzer operation needs to meet, the operation mode of the compressor when it compresses hydrogen into high-pressure hydrogen, the hydrogen storage capacity calculated by the internal air pressure when the hydrogen storage tank stores compressed hydrogen, and the operation mode of BS. The building model analyzes data on the heat balance constraint of wall ij, the heat balance of area n, the operating power of the air conditioner and the comfort temperature limit of users, realizes the function of detecting data in each link during the operation of the wind farm, hydrogen energy system and building, solves the problems of energy loss and interest disputes caused by untimely and inaccurate information collection of each part of the system, can accurately collect the real-time information of each part in the system, improves the flexibility and accuracy of the system, improves the working efficiency of the system, and reduces the contradictions among the main bodies.

[0029] Working principle: By constructing a wind farm model, a hydrogen energy system model, and a building model, the integration of wind power, hydrogen power, and buildings is achieved. The wind farm model sells electricity directly to the hydrogen energy system and buildings, and the excess electricity is sold to the PG. By calculating and solving the revenue of the wind farm, the maintenance cost of the wind farm, and the transmission fees, combined with the constraints that the wind farm output needs to meet, with the goal of improving the overall profit of the wind farm, the objective function of the wind farm is obtained as , The construction of the hydrogen energy system model includes the hydrogen production volume under the stable operation state of the electrolyzer, the operation mode of the compressor, indicating the hydrogen storage volume through the internal air pressure of the hydrogen tank, and the operation mode of the BS. The cost of the hydrogen energy system model includes the cost of purchasing electricity from the wind farm and the PG and the maintenance cost mainly for the electrolyzer and the BS. Analyze the power balance constraint that the hydrogen energy system operation satisfies, and with the goal of reducing the overall cost, the objective function of the hydrogen energy system is obtained as , The building model analyzes the heat balance constraint of the wall ij, the heat balance of the region n, the operating power of the air conditioner, and the temperature limit for user comfort. The cost of the building model includes the cost of purchasing electricity from the wind farm and the PG and the maintenance cost mainly for the air conditioner and the base load. Combining the power balance constraint that the building operation needs to meet, with the goal of reducing the overall cost, the objective function of the building is obtained as , Construct the wind farm model, the hydrogen energy system model, and the building model; Through the application of the NB theory, combined with the wind farm model, the hydrogen energy system model, and the building model, the NB model of the MAES-WHB model is obtained. As an alliance of the wind farm, the hydrogen energy system, and the building, its CPS is , In the CPS, the transaction amount I W2H and I W2B can cancel each other out. By solving the CPS, the optimal transaction electricity quantity is obtained, as well as the operating power of the electrolyzer, the BS in the hydrogen energy system, and the air conditioner in the building. By calculating the overall transaction electricity quantity within the operation period, a mapping function that can quantify the contribution degree of each subject to the transaction is constructed. Each subject participating in the electricity transaction will obtain a contribution degree. The more electricity the subject trades, the higher its corresponding contribution degree. When the transaction electricity quantities are the same, the contribution degree of the electricity selling subject is higher than that of the electricity purchasing subject. By introducing the contribution degree as the bargaining power and substituting the optimal transaction electricity quantity obtained from solving the CPS into the NB model, an asymmetric PAS model is constructed , By solving the PAS, the optimal transaction electricity price and In case, the profit distribution is symmetric; Convert the CPS into a minimization problem. Based on ADMM, decompose the augmented Lagrangian function of the CPS into distributed optimization models for the wind farm, hydrogen energy system, and building. When and hold, it is considered that the wind farm, hydrogen energy system, and building reach an agreement on the traded electricity volume. Take the logarithm of the asymmetric PAS model and convert it into a minimization problem. Based on ADMM, decompose the augmented Lagrangian function of the asymmetric PAS model into distributed optimization models for the wind farm, hydrogen energy system, and building. When and hold, it is considered that each entity reaches an agreement on the electricity price; Solve it through the TPCA - ADMM algorithm improved based on the ADMM algorithm. In the first stage, calculate the original residual and dual residual through and . Then calculate and through and . When and are less than the set values, and the original residual and dual residual change in a decreasing or acceptable direction, the penalty factor remains unchanged and enters the third stage. Update the Lagrange multiplier by introducing a correction mechanism. If and are greater than or equal to the set values, then enter the second stage to update the penalty factor to prevent the current penalty factor from deteriorating the convergence efficiency. In the second stage, update the penalty factor according to the current original residual and dual residual values. After the penalty factor is updated, enter the third stage to complete the update of the Lagrange multiplier and stop the update when the iteration termination condition is met.

[0030] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above - mentioned 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 point of view, the embodiments should be regarded as exemplary and non - restrictive. 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 regarded as limiting the claimed rights.

Claims

1. A collaborative operation negotiation method for a multi-factor integrated multi-agent energy system, characterized by: The collaborative operation negotiation method is: S1: Construct the MAES-WHB model; S2: Based on NB theory, the NB model of MAES-WHB model is constructed; S3: Decompose the NB model into CPS and PAS, substitute the transaction power obtained by solving CPS into the NB model, introduce contribution as bargaining power, and then construct an asymmetric PAS model; S4: Based on ADMM theory, CPS and PAS are decomposed to construct the distributed model of each subject; S5: Improve the standard ADMM and propose the TPCA-ADMM algorithm to solve CPS and PAS; Among them, MAES-WHB is a multi-agent energy system integrating wind farms, hydrogen energy systems and buildings, MAES is a multi-agent energy system, NB is Nash negotiation, ADMM is the alternating direction multiplier method, and the TPCA-ADMM algorithm is a three-stage prediction-correction accelerated alternating direction multiplier method algorithm based on the improved ADMM algorithm; The S1 is specifically: S11: Calculate the benefits and costs of the wind farm to construct the objective function of the wind farm; S12: Calculate the cost of the hydrogen energy system and consider the power balance constraint to construct the objective function of the hydrogen energy system; S13: Calculate the cost of the building and consider the power balance constraint to construct the objective function of the building; S14: Construct the MAES-WHB model based on the wind farm model, hydrogen energy system model and building model.

2. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 1 is characterized by: The objective function of the wind farm is: ； in, For the overall profit of the wind farm, The revenue that wind farms earn from selling electricity to hydrogen energy systems. The revenue earned by the wind farm from selling electricity to buildings. The income obtained by the wind farm from selling electricity to PG. is the maintenance cost of the wind farm, Grid access fees for wind farms; The objective function of the hydrogen energy system is: ； in, is the total cost of the hydrogen energy system, Pay PG's electricity bill for the hydrogen system, The maintenance cost of the hydrogen energy system; The objective function of the building is: ； in, is the total cost of the building, Pay PG electricity bill for the building, The cost of maintaining the building.

3. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 1 is characterized by: The NB model is: ； ； in, For the profit of wind farm during cooperation, is the cost of the hydrogen energy system without cooperation, The cost of the building if there is no cooperation; The CPS of the NB model is: 。 4. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 1 is characterized by: The S3 is specifically: S31: Calculate the total transaction power during the operation period; S32: Construct a mapping function to quantify the contribution of each subject to the transaction; S33: Solve the CPS of the NB model to obtain the transaction power; S34: Bring the transaction volume into the NB model to build an asymmetric PAS model; The total transaction power during the operation cycle is: ； ； in, is the operation cycle, t is the time, The amount of electricity sold by the wind farm to the hydrogen energy system during its operation cycle. is the amount of electricity sold by the wind farm to the hydrogen energy system at time t, The amount of electricity sold by the wind farm to the building during its operation cycle. is the amount of electricity sold by the wind farm to the building at time t; The method to construct a mapping function to quantify the contribution of each subject to the transaction is: ； ； ； in, is the contribution of the wind farm, Contribution to the hydrogen energy system, Contribution to the building, is a constant used to improve the contribution of wind farms; The asymmetric PAS model is: 。 5. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 1 is characterized by: The S4 is specifically: S41: Introducing new variables , , and , transform CPS into a minimization problem; S42: Decomposing the augmented Lagrangian function of CPS into distributed optimization models of wind farms, hydrogen energy systems and buildings based on ADMM; S43: Introducing new variables , , and , take the logarithm of the asymmetric PAS model and transform it into a minimization problem; S44: Decomposing the augmented Lagrangian function of the asymmetric PAS model into a distributed optimization model of wind farms, hydrogen energy systems and buildings based on ADMM; in, represents the amount of electricity the hydrogen energy system wishes to purchase from the wind farm, represents the amount of electricity the building wishes to purchase from the wind farm, represents the amount of electricity the wind farm wishes to sell to the hydrogen system, represents the amount of electricity the wind farm wishes to sell to the building, represents the price that the hydrogen energy system wishes to pay to the wind farm for electricity, represents the price the building wishes to pay to the wind farm for electricity, represents the price the wind farm would like to receive for electricity from the hydrogen system, represents the price the wind farm wishes to charge for electricity from the building, is the electricity price between the wind farm and the hydrogen energy system at time t, is the electricity price between the wind farm and the building at time t.

6. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 5 is characterized by: The augmented Lagrangian function of the CPS is: ； in, and is the Lagrange multiplier, and is the penalty factor; The augmented Lagrangian function of asymmetric PAS is: in, and is the Lagrange multiplier, and is the penalty factor.

7. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 1 is characterized by: The S5 is specifically: S51: In the first stage, the original residual and the dual residual as well as the difference between the original residual and the dual residual are calculated; S52: Compare the difference with the set value. If the difference is less than the set value, the penalty factor remains unchanged. If the difference is not less than the set value, enter the second stage to update the penalty factor. S53: In the third stage, the prediction correction mechanism is introduced to update the Lagrange multiplier; S54: The iteration termination condition is met and the solution stops.

8. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 7 is characterized by: The formula for calculating the original residual and the dual residual is: ； in, is the original residual, The amount of electricity you wish to purchase from the wind farm for the hydrogen system / building, The amount of electricity the wind farm wishes to sell to the hydrogen system / building; ； in, is the dual residual, The amount of electricity the hydrogen system / building wishes to purchase from the wind farm; The formula for calculating the difference between the primal residual and the dual residual is: ； in, is the original residual; ； in, is the dual residual.

9. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 7 is characterized by: The update penalty factor of S52 is expressed as: ； in, and is the penalty factor, is a constant; The updated Lagrange multiplier of S53 is expressed as: ； in, is an intermediate parameter in the Lagrange multiplier update process, is the penalty factor, The amount of electricity you wish to purchase from the wind farm for the hydrogen system / building, is the Lagrange multiplier, The amount of electricity the wind farm wishes to sell to the hydrogen system / building; ； in, is a constant; , in, is the prediction correction factor, is a given constant.

10. The collaborative operation negotiation method of a multi-factor integrated multi-agent energy system according to claim 7, characterized in that: The iteration termination condition is: ； in, is the number of iterations, is the maximum number of iterations.

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