A multi-microgrid operation optimization method considering comprehensive contribution degree of shared energy storage
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA THREE GORGES UNIV
- Filing Date
- 2026-04-27
- Publication Date
- 2026-08-07
AI Technical Summary
[0003]然而，现有基于共享储能的多微网运行优化方法仍存在若干局限
1，本发明通过构建微网租赁共享储能的多目标优化模型，量化了净负荷波动平抑效果与储能租赁成本之间的权衡关系，显著提升了储能资源配置的合理性与适应性，为后续多微网协同运行奠定了可靠的数据与策略基础。该方法引入了改进的多目标粒子群算法对充放电策略与容量配置进行协同优化，有效协调了储能效果与成本之间的矛盾，提升了储能资源的利用效率与微网运行的平稳性。
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of multi-microgrid operation optimization and shared energy storage technology, and in particular relates to a method for optimizing the operation of multi-microgrids with shared energy storage that takes into account the comprehensive contribution. Background Technology
[0002] Common technical approaches in the field of multi-microgrid operation optimization mainly focus on improving energy utilization efficiency to support the consumption of new energy sources and the low-carbon operation of the system. The industry generally adopts a shared energy storage model to aggregate distributed energy storage resources, using centralized configuration and unified scheduling to serve the main entities of the multi-microgrid, thereby addressing the problems of low utilization rate and high investment cost of traditional distributed energy storage. In recent years, a shared energy storage optimization framework based on cooperative game theory has gradually become a research hotspot. This method introduces a point-to-point trading mechanism and a Nash negotiation model to achieve power sharing and collaborative operation among multi-microgrids, thereby improving the overall economic efficiency of the system.
[0003] However, existing optimization methods for multi-microgrid operation based on shared energy storage still have several limitations. First, most studies, when leasing shared energy storage to microgrids, fail to effectively coordinate the contradiction between the energy storage's effect on mitigating net load fluctuations and leasing costs, lacking a multi-objective collaborative optimization mechanism. This results in energy storage resource allocation failing to meet the individualized needs of each microgrid. Second, existing research on multi-microgrid cooperative operation mainly focuses on the electricity sharing level, failing to fully integrate the electricity-carbon collaborative trading mechanism, thus limiting the system's potential for carbon emission reduction. Furthermore, traditional benefit allocation methods often employ the Shapley value method or symmetric Nash negotiation models, making it difficult to quantify the actual contribution of each microgrid in the interaction between electricity and carbon quotas. This leads to a lack of fairness and incentive in benefit allocation, affecting the stability of the alliance. In summary, the problems with existing technologies are: 1. When microgrids lease and share energy storage, there is an inherent contradiction between the net load fluctuation smoothing effect and the energy storage leasing cost. There is a lack of a multi-objective collaborative optimization mechanism, making it difficult to achieve on-demand leasing and efficient resource utilization.
[0004] 2. The cooperative operation of multiple microgrids is mostly limited to electricity sharing and has failed to establish an electricity-carbon joint trading mechanism, making it difficult to balance the economic efficiency and environmental protection of the system.
[0005] 3. The benefit distribution method fails to quantify the comprehensive contribution of each microgrid in the interaction between electricity and carbon quotas, lacks a fair and differentiated distribution mechanism, and affects the enthusiasm of various stakeholders to participate in cooperation.
[0006] Solving problems such as multi-objective coordination of microgrid leasing and shared energy storage, the construction of an electricity-carbon joint trading mechanism, and benefit distribution based on comprehensive contribution requires establishing a multi-objective optimization model to balance energy storage effectiveness and cost, designing an electricity-carbon collaborative trading framework to couple electricity and carbon quota resources, and constructing an asymmetric Nash bargaining model based on a nonlinear energy mapping function to quantify the comprehensive contribution of each microgrid. This involves the deep integration of multiple technical fields, including multi-objective optimization algorithms, cooperative game theory, and electricity-carbon coupling mechanism design. Simultaneously, it is necessary to ensure the model's generalization ability and solution efficiency under different microgrid types and different renewable energy output characteristics. The entire research process not only requires solid optimization theory, game analysis, and energy system modeling skills, but also needs to consider computational efficiency and scalability verification in engineering practice. It is a systematic and interdisciplinary challenge involving model innovation, algorithm design, and empirical analysis.
[0007] Effectively addressing the aforementioned issues can significantly improve the utilization efficiency of shared energy storage resources, achieving synergistic optimization of economic and low-carbon aspects in multi-microgrid systems. This enhances the grid's ability to perceive and regulate new energy fluctuations, reducing system operating costs and carbon emission levels. High-precision operation optimization can provide a reliable basis for day-ahead scheduling, real-time balancing, and ancillary service markets for multi-microgrid systems, supporting the safe grid connection and consumption of a high proportion of new energy sources and promoting clean energy substitution. Furthermore, the proposed two-stage optimization framework, which considers comprehensive contribution, provides a referable technical path for other energy system optimization problems characterized by multi-entity collaboration and multi-resource coupling, possessing significant theoretical value and engineering application prospects. Therefore, it is necessary to propose an operation optimization method for multi-microgrids with shared energy storage that considers comprehensive contribution to address the aforementioned problems. Summary of the Invention
[0008] The technical problem to be solved by the present invention is to provide an operation optimization method for multi-microgrids with shared energy storage that takes into account the comprehensive contribution, and achieves efficient utilization of energy storage resources and fair distribution of benefits among multiple stakeholders through a two-stage optimization mechanism.
[0009] To achieve the above technical solution, the technical solution adopted by the present invention is as follows: A method for optimizing the operation of multi-microgrids with shared energy storage, taking into account the overall contribution, includes: S1, constructing a multi-microgrid system architecture with shared energy storage: Establish a system architecture that includes multiple microgrid entities and a shared energy storage operator; each microgrid is equipped with wind power, photovoltaics, gas boilers, thermal storage devices, and CHP-CCS-P2G coupling equipment; microgrids exchange electricity through interconnection buses, and carbon allowances are transferred between microgrids through point-to-point trading. S2, Establish a multi-objective optimization model for microgrid leasing and shared energy storage: Construct a multi-objective optimization model with the objectives of minimizing the net load variance and minimizing energy storage leasing costs; S3, establish an optimized configuration model for shared energy storage operators; Shared energy storage operators aggregate the charging and discharging power requirements of each microgrid and optimize energy storage capacity configuration with the goal of minimizing their own operating costs; S4, Establish a multi-micro-network cooperative game model: Based on the optimization results in step S2, an electricity-carbon joint trading mechanism is introduced to establish a cooperative game model with the goal of minimizing the operating costs of each microgrid. S5, Construct an asymmetric Nash bargaining model based on comprehensive contribution; S6 quantifies the overall contribution of each microgrid; S7 uses the alternating direction multiplier method to solve the model in a distributed manner.
[0010] Preferably, in step S1, the CHP-CCS-P2G unit model is established as follows: (1); In the formula, For micro-network indexing; For time indexing; For micro-network CHP in China Power generation during a given time period; , and These are the electrical powers supplied to CHP, CCS, and P2G, respectively. This refers to natural gas consumption. For CHP power generation efficiency; The calorific value of natural gas; and These are the upper and lower limits of CHP power generation capacity, respectively. and These are the upper and lower limits of CSS power consumption, respectively. and These are the upper and lower limits of P2G power consumption, respectively. For P2G gas production capacity; For electro-gas conversion efficiency; This represents the amount of CO2 captured. The coupling coefficient between P2G electrical power and CO2 capture rate; For CCS capture efficiency; and These are the minimum and maximum values of the CHP electrothermal conversion coefficient, respectively. For the heat output of CHP; The linear supply slope of the CHP thermoelectric power; For micro-network The thermal power corresponding to the minimum power generation of the CHP.
[0011] Preferably, in step S1, the GB unit model is constructed as follows: (2); In the formula, For micro-network GB in Heat production capacity during a given time period; GB natural gas consumption; It is the GB heat production efficiency; and GB upper and lower limits for heat production capacity respectively; The TES unit model is constructed as follows: (3); In the formula, and microgrids TES in The power of heat charging and releasing during the time period; and Both the charging and releasing states are 0-1 variables; and These are the maximum charge / discharge power and maximum heat storage capacity of the TES, respectively. for Thermal storage capacity for different time periods; for arrive The time interval; , The charging and releasing efficiency respectively; The carbon emission model is constructed as follows: (4); In the formula, and respectively for the carbon trading market to micro-network Allocated carbon emission allowances and actual carbon emissions; Carbon emission quota coefficient for gas-fired equipment; For carbon emission quota coefficients of wind power and photovoltaic units; and microgrids PV and WT in Output power during the time period; and These are the carbon emission coefficients of CHP and GB, respectively.
[0012] Preferably, in step S2, constructing a multi-objective optimization model with the objectives of minimizing the net load variance and minimizing energy storage leasing costs includes: (5); (6); In the formula, For micro-network Net load variance after using energy storage; , and They are respectively The load power during the time period and the charging and discharging power of energy storage; This is the average value of the equivalent load; and microgrids PV and WT in Output power during the time period; For micro-network The cost of shared energy storage leasing; and microgrids The unit cost of leasing shared energy storage capacity and charging / discharging power; and These are the charges for charging and discharging capacity of units using SESO services; To determine the unit cost of charging and discharging, an improved multi-objective particle swarm optimization algorithm is used to solve the above model, obtaining the charging and discharging strategies and energy storage capacity configurations for each microgrid.
[0013] Preferably, in step S3, the shared energy storage operator aggregates the charging and discharging power requirements of each microgrid and optimizes the energy storage capacity configuration with the goal of minimizing its own operating costs, including: (7); In the formula, For SESO's operating costs; Japanese investment cost; Costs associated with interacting with the upper-level power grid; and These are the power cost and capacity cost of the energy storage power station, respectively, in yuan / kW and yuan / (kWh); and These are the maximum charging and discharging power and capacity of the energy storage power station, respectively. This refers to the expected number of days of use for the energy storage power station. Daily maintenance costs; and SESO in Electricity purchased and sold to the upper-level power grid during different time periods; and These are the purchase and sale prices of electricity from the superior power grid, respectively. The total number of microgrids is 3; The constraints include the state of charge constraints, charge / discharge power constraints, and power balance constraints of the energy storage power station: (8); (9); In the formula, and SESO in Charge and discharge power during specific time periods; and These represent the charge and discharge states of SESO, both of which are 0-1 variables; For SESO in Energy storage capacity for different time periods; and The charging and discharging efficiencies of SESO are respectively.
[0014] Preferably, in step S4, based on the optimization results of step S2, an electricity-carbon joint trading mechanism is introduced, and a cooperative game model is established with the goal of minimizing the operating costs of each microgrid, including: (10); The operating cost of a CHP unit is: (11); The costs of interacting with the upstream power grid and purchasing gas are: (12); The cost of energy exchange is: (13); The cost of negotiated carbon trading is: (14); The tiered carbon emission cost is: (15); (16); The constraints include power balance constraints for each microgrid, point-to-point electricity trading constraints, and carbon emission trading constraints. (17); (18); (19); In the formula, For micro-network exist The equivalent electrical load after wind and solar power is smoothed by energy storage during the period; , , , and These are the operating costs of CHP, the costs of interacting with the upper-level power grid and purchasing gas, the costs of electricity interaction, the costs of negotiated carbon trading, and the costs of carbon emissions. , and Operation and maintenance coefficients for CHP, P2G, and CCS respectively; This refers to the unit price of gas. , and microgrids exist Gas purchase volume during specific time periods and electricity purchase and sale with the upstream power grid; and microgrids With micro-network exist The unit price and exchanged volume of electricity trading during the specified time period; and microgrids With micro-network Carbon trading unit price and carbon trading volume; This refers to actual carbon emissions; The benchmark price for carbon trading; For the rate of price increase; This represents the length of the carbon emission range.
[0015] Preferably, step S5 specifically includes the following steps: The multi-micronet Nash negotiation model is established as follows: (20); The above model is decomposed into two sub-problems for solving: Subproblem P1, minimizing the cost of alliances: (twenty one); Subproblem P2, maximizing the distribution of benefits: (twenty two); In the formula, For micro-network The cost of operating independently, i.e., the point at which the Nash negotiations broke down; This refers to the revenue generated by MicroNetwork through its participation in the collaboration.
[0016] Preferably, step S6 specifically includes: Establish a quantitative model for the power contribution factor: Calculation of electrical energy contribution factor based on nonlinear energy sharing mapping model: (twenty three); In the formula, , microgrids Total electrical energy supplied and received; , These represent the maximum power supplied and received in each microgrid; Establish a quantitative model for carbon emission contribution factors: (twenty four); In the formula, For micro-network The reduction in carbon emissions after cooperation; , microgrids Carbon emissions before and after the cooperation; Calculate the overall contribution rate: (25); In the formula, , The weights for the contributions of electricity and carbon emissions are respectively determined to satisfy... Considering the principle of prioritizing safety, ensuring a reliable power supply is given top priority; therefore, the Analytic Hierarchy Process (AHP) is used to quantify the priority of objectives and determine... It is 0.7. It is 0.3.
[0017] Preferably, step S7 specifically includes the following steps: To address the coupling problem of energy trading prices between cooperative microgrids in subproblem P2, auxiliary variables are introduced for decoupling: (26); Constructing the augmented Lagrangian function: (27); In the formula, and These are the Lagrange multipliers related to electricity trading prices and carbon trading prices in subproblem 2, respectively. This is a penalty factor.
[0018] Preferably, the solution process includes: S701, Initialize the prediction model parameters, set the maximum number of iterations, penalty factor and convergence threshold; S702, Input the charging and discharging strategies and energy storage capacity configuration results of each microgrid obtained in step S2; S703, initializes electricity trading price, carbon trading price, and Lagrange multipliers; S704, each microgrid independently solves subproblem P1 and updates local decision variables; S705, exchange boundary variable information between adjacent microgrids; S706, update the Lagrange multipliers; S707: Determine if the convergence condition is met. If it is, terminate the iteration and output the electricity and carbon trading prices and optimized scheduling schemes among the microgrids; otherwise, return to S704 to continue the iteration.
[0019] The beneficial effects of this invention are as follows: 1. This invention quantifies the trade-off between net load fluctuation mitigation and energy storage leasing costs by constructing a multi-objective optimization model for microgrid leasing and shared energy storage. This significantly improves the rationality and adaptability of energy storage resource allocation, laying a reliable data and strategy foundation for subsequent multi-microgrid collaborative operation. The method introduces an improved multi-objective particle swarm optimization algorithm to collaboratively optimize charging and discharging strategies and capacity configuration, effectively coordinating the contradiction between energy storage performance and cost, and improving the utilization efficiency of energy storage resources and the stability of microgrid operation.
[0020] 2. This technical solution, by introducing an electricity-carbon joint trading mechanism, fully integrates electricity sharing and carbon quota transfer within a multi-microgrid cooperative game model, achieving synergistic optimization of system economics and environmental protection. Simultaneously, by combining tiered carbon pricing and peer-to-peer trading models, it constructs an electricity-carbon coupled negotiation framework, effectively reducing system operating costs and total carbon emissions, and enhancing the model's adaptability and control accuracy in the context of low-carbon transformation.
[0021] 3. This technical solution fully explores the actual contributions of each microgrid in electricity interaction and carbon emission reduction by constructing an asymmetric Nash bargaining model based on a nonlinear energy mapping function, quantifies the comprehensive contribution, and achieves differentiated and fair distribution of cooperative benefits based on this. Simultaneously, the model is solved in a distributed manner using the alternating direction multiplier method, ensuring the privacy and decision-making autonomy of each microgrid entity, and improving the scalability and convergence efficiency of the model in engineering applications.
[0022] 4. This invention not only significantly improves the operational economy and low-carbon benefits of multi-microgrid systems with shared energy storage, but also provides reliable technical support for multi-entity collaborative operation under conditions of high proportion of renewable energy grid connection. This method has significant engineering application value in improving energy storage resource utilization efficiency, reducing system operating costs, and promoting carbon emission reduction. It also provides a generalizable technical path for optimizing other multi-entity, multi-resource coupled energy systems, demonstrating its broad prospects in the construction of new power systems and the energy internet. Attached Figure Description
[0023] Figure 1 This is a schematic diagram of the system operation architecture of the present invention; Figure 2 This is a flowchart of the model solution process for this invention; Figure 3 This is a comparison chart of the net load optimization of each microgrid in the embodiments of the present invention; Figure 4 This is a diagram showing the energy interaction volume and transaction price of each microgrid in an embodiment of the present invention; Figure 5 This is a diagram showing the microgrid power optimization scheduling results in an embodiment of the present invention; Figure 6 This is a diagram showing the microgrid thermal energy optimization scheduling results in an embodiment of the present invention. Detailed Implementation
[0024] Example 1: like Figure 1 and Figure 2 As shown, a method for optimizing the operation of multi-microgrids with shared energy storage, taking into account the overall contribution, includes: S1, constructing a multi-microgrid system architecture with shared energy storage: Establish a system architecture that includes multiple microgrid entities and a shared energy storage operator; each microgrid is equipped with wind power, photovoltaics, gas boilers, thermal storage devices, and CHP-CCS-P2G coupling equipment; microgrids exchange electricity through interconnection buses, and carbon allowances are transferred between microgrids through point-to-point trading. S2, Establish a multi-objective optimization model for microgrid leasing and shared energy storage: Construct a multi-objective optimization model with the objectives of minimizing the net load variance and minimizing energy storage leasing costs; S3, establish an optimized configuration model for shared energy storage operators; Shared energy storage operators aggregate the charging and discharging power requirements of each microgrid and optimize energy storage capacity configuration with the goal of minimizing their own operating costs; S4, Establish a multi-micro-network cooperative game model: Based on the optimization results in step S2, an electricity-carbon joint trading mechanism is introduced to establish a cooperative game model with the goal of minimizing the operating costs of each microgrid. S5, Construct an asymmetric Nash bargaining model based on comprehensive contribution; S6 quantifies the overall contribution of each microgrid; S7 uses the alternating direction multiplier method to solve the model in a distributed manner.
[0025] Preferably, in step S1, the CHP-CCS-P2G unit model is established as follows: (1); In the formula, For micro-network indexing; For time indexing; For micro-network CHP in China Power generation during a given time period; , and These are the electrical powers supplied to CHP, CCS, and P2G, respectively. This refers to natural gas consumption. For CHP power generation efficiency; The calorific value of natural gas; and These are the upper and lower limits of CHP power generation capacity, respectively. and These are the upper and lower limits of CSS power consumption, respectively. and These are the upper and lower limits of P2G power consumption, respectively. For P2G gas production capacity; For electro-gas conversion efficiency; This represents the amount of CO2 captured. The coupling coefficient between P2G electrical power and CO2 capture rate; For CCS capture efficiency; and These are the minimum and maximum values of the CHP electrothermal conversion coefficient, respectively. For the heat output of CHP; The linear supply slope of the CHP thermoelectric power; For micro-network The thermal power corresponding to the minimum power generation of the CHP.
[0026] Preferably, in step S1, the GB unit model is constructed as follows: (2); In the formula, For micro-network GB in Heat production capacity during a given time period; GB natural gas consumption; It is the GB heat production efficiency; and GB upper and lower limits for heat production capacity respectively; The TES unit model is constructed as follows: (3); In the formula, and microgrids TES in The power of heat charging and releasing during the time period; and Both the charging and releasing states are 0-1 variables; and These are the maximum charge / discharge power and maximum heat storage capacity of the TES, respectively. for Thermal storage capacity for different time periods; for arrive The time interval; , The charging and releasing efficiency respectively; The carbon emission model is constructed as follows: (4); In the formula, and respectively for the carbon trading market to micro-network Allocated carbon emission allowances and actual carbon emissions; Carbon emission quota coefficient for gas-fired equipment; For carbon emission quota coefficients of wind power and photovoltaic units; and microgrids PV and WT in Output power during the time period; and These are the carbon emission coefficients of CHP and GB, respectively.
[0027] Preferably, in step S2, constructing a multi-objective optimization model with the objectives of minimizing the net load variance and minimizing energy storage leasing costs includes: (5); (6); In the formula, For micro-network Net load variance after using energy storage; , and They are respectively The load power during the time period and the charging and discharging power of energy storage; This is the average value of the equivalent load; and microgrids PV and WT in Output power during the time period; For micro-network The cost of shared energy storage leasing; and microgrids The unit cost of leasing shared energy storage capacity and charging / discharging power; and These are the charges for charging and discharging capacity of units using SESO services; To determine the unit cost of charging and discharging, an improved multi-objective particle swarm optimization algorithm is used to solve the above model, obtaining the charging and discharging strategies and energy storage capacity configurations for each microgrid.
[0028] Preferably, in step S3, the shared energy storage operator aggregates the charging and discharging power requirements of each microgrid and optimizes the energy storage capacity configuration with the goal of minimizing its own operating costs, including: (7); In the formula, For SESO's operating costs; Japanese investment cost; Costs associated with interacting with the upper-level power grid; and These are the power cost and capacity cost of the energy storage power station, respectively, in yuan / kW and yuan / (kWh); and These are the maximum charging and discharging power and capacity of the energy storage power station, respectively. This refers to the expected number of days of use for the energy storage power station. Daily maintenance costs; and SESO in Electricity purchased and sold to the upper-level power grid during different time periods; and These are the purchase and sale prices of electricity from the superior power grid, respectively. The total number of microgrids is 3; The constraints include the state of charge constraints, charge / discharge power constraints, and power balance constraints of the energy storage power station: (8); (9); In the formula, and SESO in Charge and discharge power during specific time periods; and These represent the charge and discharge states of SESO, both of which are 0-1 variables; For SESO in Energy storage capacity for different time periods; and The charging and discharging efficiencies of SESO are respectively.
[0029] Preferably, in step S4, based on the optimization results of step S2, an electricity-carbon joint trading mechanism is introduced, and a cooperative game model is established with the goal of minimizing the operating costs of each microgrid, including: (10); The operating cost of a CHP unit is: (11); The costs of interacting with the upstream power grid and purchasing gas are: (12); The cost of energy exchange is: (13); The cost of negotiated carbon trading is: (14); The tiered carbon emission cost is: (15); (16); The constraints include power balance constraints for each microgrid, point-to-point electricity trading constraints, and carbon emission trading constraints. (17); (18); (19); In the formula, For micro-network exist The equivalent electrical load after wind and solar power is smoothed by energy storage during the period; , , , and These are the operating costs of CHP, the costs of interacting with the upper-level power grid and purchasing gas, the costs of electricity interaction, the costs of negotiated carbon trading, and the costs of carbon emissions. , and Operation and maintenance coefficients for CHP, P2G, and CCS respectively; This refers to the unit price of gas. , and microgrids exist Gas purchase volume during specific time periods and electricity purchase and sale with the upstream power grid; and microgrids With micro-network exist The unit price and exchanged volume of electricity trading during the specified time period; and microgrids With micro-network Carbon trading unit price and carbon trading volume; This refers to actual carbon emissions; The benchmark price for carbon trading; For the rate of price increase; This represents the length of the carbon emission range.
[0030] Preferably, step S5 specifically includes the following steps: The multi-micronet Nash negotiation model is established as follows: (20); The above model is decomposed into two sub-problems for solving: Subproblem P1, minimizing the cost of alliances: (twenty one); Subproblem P2, maximizing the distribution of benefits: (twenty two); In the formula, For micro-network The cost of operating independently, i.e., the point at which the Nash negotiations broke down; This refers to the revenue generated by MicroNetwork through its participation in the collaboration.
[0031] Preferably, step S6 specifically includes: Establish a quantitative model for the power contribution factor: Calculation of electrical energy contribution factor based on nonlinear energy sharing mapping model: (twenty three); In the formula, , microgrids Total electrical energy supplied and received; , These represent the maximum power supplied and received in each microgrid; Establish a quantitative model for carbon emission contribution factors: (twenty four); In the formula, For micro-network The reduction in carbon emissions after cooperation; , microgrids Carbon emissions before and after the cooperation; Calculate the overall contribution rate: (25); In the formula, , The weights for the contributions of electricity and carbon emissions are respectively determined to satisfy... Considering the principle of prioritizing safety, ensuring a reliable power supply is given top priority; therefore, the Analytic Hierarchy Process (AHP) is used to quantify the priority of objectives and determine... It is 0.7. It is 0.3.
[0032] Preferably, step S7 specifically includes the following steps: To address the coupling problem of energy trading prices between cooperative microgrids in subproblem P2, auxiliary variables are introduced for decoupling: (26); Constructing the augmented Lagrangian function: (27); In the formula, and These are the Lagrange multipliers related to electricity trading prices and carbon trading prices in subproblem 2, respectively. This is a penalty factor.
[0033] Preferably, the solution process includes: S701, Initialize the prediction model parameters, set the maximum number of iterations, penalty factor and convergence threshold; S702, Input the charging and discharging strategies and energy storage capacity configuration results of each microgrid obtained in step S2; S703, initializes electricity trading price, carbon trading price, and Lagrange multipliers; S704, each microgrid independently solves subproblem P1 and updates local decision variables; S705, exchange boundary variable information between adjacent microgrids; S706, update the Lagrange multipliers; S707: Determine if the convergence condition is met. If it is, terminate the iteration and output the electricity and carbon trading prices and optimized scheduling schemes among the microgrids; otherwise, return to S704 to continue the iteration.
[0034] Example 2: This embodiment selects three different types of microgrids in a certain region as case study objects. The system operation architecture is as follows: Figure 1 As shown; the overall process of the multi-microgrid operation optimization method considering comprehensive contribution and including shared energy storage is as follows. Figure 2 As shown.
[0035] To verify the effectiveness and rationality of the proposed method in terms of shared energy storage resource utilization, system economy, environmental protection, and the rationality of benefit distribution, using Scenario I as the baseline, other scenarios are successively superimposed with shared energy storage optimization, electricity cooperation, carbon trading cooperation, and a comprehensive contribution allocation mechanism. The following five comparative scenarios are set up for simulation analysis: 1. Scenario I: Each microgrid operates independently, without leasing or sharing energy storage, and without engaging in any electricity or carbon trading cooperation. Each microgrid configures its own energy storage to meet its own needs, i.e., independent operation mode.
[0036] 2. Scenario II: Each microgrid adopts the multi-objective optimization model of the first stage of this paper to lease shared energy storage in order to smooth net load fluctuations, but does not carry out electricity and carbon trading cooperation between microgrids, that is, only the shared energy storage optimization mode.
[0037] 3. Scenario III: Based on the optimization of shared energy storage in Scenario II, each microgrid further develops cooperation in electricity trading, but does not consider carbon trading, i.e., the shared energy storage + electricity cooperation model.
[0038] 4. Scenario IV: Based on the shared energy storage optimization in Scenario II, each microgrid simultaneously carries out electricity and carbon trading cooperation, but does not consider the distribution of benefits based on comprehensive contribution. Instead, it adopts the symmetric Nash negotiation model for revenue distribution, i.e., the shared energy storage + electricity-carbon cooperation model.
[0039] 5. Scenario V: Based on the shared energy storage optimization in Scenario II, each microgrid carries out electricity-carbon joint trading cooperation and uses the asymmetric Nash bargaining model based on comprehensive contribution proposed in this paper for revenue distribution, which is the method proposed in this paper.
[0040] This example uses the improved multi-objective particle swarm optimization algorithm IMOPSO and the alternating direction multiplier method ADMM for a phased solution. The specific steps are as follows: S301, Initialize the parameters of the multi-objective particle swarm optimization algorithm, and set the population size, maximum number of iterations, learning factor and inertia weight range; S302, input the wind and solar power output, load data and shared energy storage related cost parameters of each microgrid; S303 uses IMOPSO to solve the multi-objective optimization model of microgrid leasing and shared energy storage, obtains the Pareto front of each microgrid, and selects the optimal compromise solution based on the fuzzy membership function, and records the charging and discharging strategy and the required leased energy storage capacity of each microgrid. S304, Shared energy storage operators aggregate the charging and discharging demands of each microgrid and optimize the energy storage capacity configuration with the goal of minimizing their own operating costs, thereby obtaining the optimal power and capacity configuration of shared energy storage; S305, using the charging and discharging strategies of each microgrid obtained in step S303 as known parameters, enter the cooperative game stage; initialize the ADMM algorithm parameters, and set the maximum number of iterations, penalty factor and convergence threshold; S306, each microgrid independently solves the subproblem P1 of minimizing the alliance cost and updates local decision variables, including CHP output, purchased and sold electricity volume, P2P transaction volume, etc. S307, exchange boundary variable information between adjacent microgrids, including electricity trading volume and carbon trading volume; S308, update the Lagrange multipliers; S309, determine whether the convergence condition is met, and whether both the original residual and the dual residual are less than the threshold. If the condition is met, terminate the iteration and output the electricity and carbon trading prices and the optimized scheduling scheme among the microgrids; otherwise, return to S306 to continue the iteration. S310. Based on the comprehensive contribution of each microgrid calculated in step S6, solve the revenue distribution scheme in subproblem P2 to obtain the final cooperative operating cost of each microgrid.
[0041] The multi-objective optimization results of each microgrid are shown in Table 1. The net load curves after optimization by shared energy storage are compared below. Figure 3 As shown, taking MG1 as an example, through the first stage optimization of this invention, the net load mean square error decreased by 62.8%, effectively smoothing out net load fluctuations. The net load mean square errors of MG2 and MG3 decreased by 42.9% and 50.5% respectively, indicating that the method of this invention can effectively smooth the net load curves of each microgrid and improve the local absorption capacity of renewable energy.
[0042] Table 1: Results of multi-objective optimization;
[0043] The optimized configuration results of shared energy storage are shown in Table 2. When the three microgrids independently purchase energy storage, the total required energy capacity is 5165.07 kWh, and the total power capacity is 1177.78 kW. After adopting the method of this invention, the actual energy capacity in the shared energy storage mode is reduced to 3666.81 kWh, and the power capacity is reduced to 650.44 kW, saving 29% in energy capacity and 44.7% in power capacity, significantly improving the utilization efficiency of energy storage resources.
[0044] Table 2: Results of Optimized Configuration of Shared Energy Storage;
[0045] Table 3 shows a comparison of the operating costs of multi-microgrid systems under different scenarios. As can be seen from Table 3, the method of this invention (Scenario V) can significantly reduce the total operating cost of the system. Table 4 shows a comparison of carbon emissions, demonstrating that the method of this invention also has a significant advantage in achieving carbon emission reduction. The total costs for Scenarios II to IV are RMB 38,105.60, RMB 35,772.22, and RMB 35,012.04, respectively. Scenario V, while maintaining the carbon emission reduction effect, further optimizes the cost allocation of each microgrid.
[0046] Table 3: Comparison of operating costs of multi-microgrid systems;
[0047] Table 4: Comparison of carbon emissions;
[0048] The comprehensive contribution results of each microgrid are shown in Table 5. The comprehensive contribution rates of MG1, MG2, and MG3 are 0.44, 0.31, and 0.25, respectively, which are consistent with the actual contributions of each microgrid in power interaction and carbon emission reduction. MG1 has the highest power contribution factor, indicating that it makes the greatest contribution to the alliance in power interaction; MG2 has the highest carbon emission contribution factor, indicating that it makes a significant contribution to carbon emission reduction.
[0049] Table 5: Calculation results of overall contribution;
[0050] The profit distribution results for Scenario IV and Scenario V are compared in Tables 6 and 7. Scenario IV did not consider overall contribution, resulting in roughly equal profit increases for each micronetwork. MG1, MG2, and MG3 saw profit increases of RMB 1030.61, RMB 1031.59, and RMB 1031.37 respectively, failing to reflect the actual contributions of each micronetwork. In Scenario V, after distributing profits based on overall contribution, MG1, with the highest contribution rate, received the highest profit increase of RMB 1363.34, while MG3, with the lowest contribution rate, received the smallest increase of RMB 760.40, achieving a differentiated and fair distribution of profits. This distribution mechanism effectively incentivizes micronetworks to actively participate in cooperation and enhances the stability of the alliance.
[0051] Table 6: Benefit Distribution Results for Scenario IV;
[0052] Table 7: Scenario V Benefit Distribution Results;
[0053] The amount of electricity exchanged and the transaction price of each microgrid are as follows: Figure 4 As shown, the transaction price between microgrids always falls between the purchase price and the sales price of the upper-level power grid. This pricing mechanism effectively enhances the enthusiasm of each microgrid to participate in cooperation. Figure 4 It can be seen that microgrid 1 has surplus power during the periods of 00:00-08:00 and 16:00-24:00, and provides power to other microgrids; microgrid 2 has insufficient power generation capacity during many periods and needs to purchase power from external sources; microgrid 3 needs to purchase power from other microgrids during peak load periods, but can supply power to other microgrids during periods of high wind power output.
[0054] The results of P2P carbon trading for each microgrid are shown in Table 8. Microgrid 1 purchased 760.76 kg of carbon allowances from Microgrid 2, while Microgrid 3 purchased 134.44 kg and 856.31 kg of carbon allowances from Microgrid 1 and Microgrid 2, respectively. The carbon trading prices among the microgrids all fell between the purchase and sale prices in the carbon market (between RMB 0.1 / kg and RMB 0.75 / kg), verifying the effectiveness of the carbon trading mechanism.
[0055] Table 8: P2P carbon trading results of each microgrid;
[0056] Taking MG1 as an example, the power optimization scheduling results are as follows: Figure 5 As shown, the thermal energy optimization scheduling results are as follows: Figure 6 As shown, MG1 is a wind power generation system, and its wind power output exhibits distinct time-of-day characteristics. During the periods 00:00-06:00 and 19:00-24:00, wind power output exceeds load demand, and the system prioritizes utilizing energy storage charging to absorb excess wind power. If there is still surplus power, it is absorbed through P2G-CCS, sold to MG2 and MG3, and sold to the grid. During the period 07:00-18:00, wind power output is less than load demand, and the system prioritizes releasing energy storage to compensate for the shortfall. If there is still surplus load, it is met by CHP units, purchasing electricity from other microgrids, and purchasing electricity from the grid.
[0057] In terms of thermal energy optimization, the MG1 has a lower heat load demand during the periods of 00:00-07:00 and 18:00-24:00, and maintains thermal balance mainly through the output of GB and CHP. During the period of 08:00-17:00, in order to meet the higher heat load demand, the heat output of CHP is increased, and the thermal energy storage absorbs and replenishes heat energy through charging and discharging heat, realizing multi-energy synergistic optimization.
[0058] To further verify the generalization ability of the proposed method, data from another region were used for testing. The results also showed that the proposed method has significant advantages in reducing operating costs and carbon emissions, which will not be elaborated upon here due to space limitations.
[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for optimizing the operation of multi-microgrids with shared energy storage, taking into account comprehensive contribution, characterized in that, include: S1, constructing a multi-microgrid system architecture with shared energy storage: Establish a system architecture that includes multiple microgrid entities and a shared energy storage operator; each microgrid is equipped with wind power, photovoltaics, gas boilers, thermal storage devices, and CHP-CCS-P2G coupling equipment; microgrids exchange electricity through interconnection buses, and carbon allowances are transferred between microgrids through point-to-point trading. S2, Establish a multi-objective optimization model for microgrid leasing and shared energy storage: Construct a multi-objective optimization model with the objectives of minimizing the net load variance and minimizing energy storage leasing costs; S3, establish an optimized configuration model for shared energy storage operators; Shared energy storage operators aggregate the charging and discharging power requirements of each microgrid and optimize energy storage capacity configuration with the goal of minimizing their own operating costs; S4, Establish a multi-micro-network cooperative game model: Based on the optimization results in step S2, an electricity-carbon joint trading mechanism is introduced to establish a cooperative game model with the goal of minimizing the operating costs of each microgrid. S5, Construct an asymmetric Nash bargaining model based on comprehensive contribution; S6 quantifies the overall contribution of each microgrid; S7 uses the alternating direction multiplier method to solve the model in a distributed manner.
2. The method for optimizing the operation of a multi-microgrid with shared energy storage, taking into account comprehensive contribution, as described in claim 1, is characterized in that... In step S1, the CHP-CCS-P2G unit model is established as follows: （1）； In the formula, For micro-network indexing; For time indexing; For micro-network CHP in China Power generation during a given time period; , and These are the electrical powers supplied to CHP, CCS, and P2G, respectively. This refers to natural gas consumption. For CHP power generation efficiency; The calorific value of natural gas; and These are the upper and lower limits of CHP power generation capacity, respectively. and These are the upper and lower limits of CSS power consumption, respectively. and These are the upper and lower limits of P2G power consumption, respectively. For P2G gas production capacity; For electro-gas conversion efficiency; This represents the amount of CO2 captured. The coupling coefficient between P2G electrical power and CO2 capture rate; For CCS capture efficiency; and These are the minimum and maximum values of the CHP electrothermal conversion coefficient, respectively. For the heat output of CHP; The linear supply slope of the CHP thermoelectric power; For micro-network The thermal power corresponding to the minimum power generation of the CHP.
3. The method for optimizing the operation of a multi-microgrid with shared energy storage, taking into account comprehensive contribution, as described in claim 2, is characterized in that... In step S1, the GB unit model is constructed as follows: （2）； In the formula, For micro-network GB in Heat production capacity during a given time period; GB natural gas consumption; It is the GB heat production efficiency; and GB upper and lower limits for heat production capacity respectively; The TES unit model is constructed as follows: （3）； In the formula, and microgrids TES in The power of heat charging and releasing during the time period; and Both the charging and releasing states are 0-1 variables; and These are the maximum charge / discharge power and maximum heat storage capacity of the TES, respectively. for Thermal storage capacity for different time periods; for arrive The time interval; , The charging and releasing efficiency respectively; The carbon emission model is constructed as follows: （4）； In the formula, and respectively for the carbon trading market to micro-network Allocated carbon emission allowances and actual carbon emissions; Carbon emission quota coefficient for gas-fired equipment; For carbon emission quota coefficients of wind power and photovoltaic units; and microgrids PV and WT in Output power during the time period; and These are the carbon emission coefficients of CHP and GB, respectively.
4. The method for optimizing the operation of a multi-microgrid with shared energy storage, taking into account comprehensive contribution, as described in claim 1, is characterized in that... In step S2, constructing a multi-objective optimization model with the objectives of minimizing the net load mean square error and minimizing energy storage leasing costs includes: （5）； （6）； In the formula, For micro-network Net load variance after using energy storage; , and They are respectively The load power during the time period and the charging and discharging power of energy storage; This is the average value of the equivalent load; and microgrids PV and WT in Output power during the time period; For micro-network The cost of shared energy storage leasing; and microgrids The unit cost of leasing shared energy storage capacity and charging / discharging power; and These are the charges for charging and discharging capacity of units using SESO services; To determine the unit cost of charging and discharging, an improved multi-objective particle swarm optimization algorithm is used to solve the above model, obtaining the charging and discharging strategies and energy storage capacity configurations for each microgrid.
5. The method for optimizing the operation of a multi-microgrid with shared energy storage, taking into account comprehensive contribution, as described in claim 1, is characterized in that... In step S3, the shared energy storage operator aggregates the charging and discharging power requirements of each microgrid and optimizes the energy storage capacity configuration with the goal of minimizing its own operating costs, including: （7）； In the formula, For SESO's operating costs; Japanese investment cost; Costs associated with interacting with the upper-level power grid; and These are the power cost and capacity cost of the energy storage power station, respectively, in yuan / kW and yuan / (kWh); and These are the maximum charging and discharging power and capacity of the energy storage power station, respectively. This refers to the expected number of days of use for the energy storage power station. Daily maintenance costs; and SESO in Electricity purchased and sold to the upper-level power grid during different time periods; and These are the purchase and sale prices of electricity from the superior power grid, respectively. The total number of microgrids is 3; The constraints include the state of charge constraints, charge / discharge power constraints, and power balance constraints of the energy storage power station: （8）； （9）； In the formula, and SESO in Charge and discharge power during specific time periods; and These represent the charge and discharge states of SESO, both of which are 0-1 variables; For SESO in Energy storage capacity for different time periods; and The charging and discharging efficiencies of SESO are respectively.
6. The method for optimizing the operation of a multi-microgrid with shared energy storage, taking into account comprehensive contribution, as described in claim 4, is characterized in that... In step S4, based on the optimization results of step S2, an electricity-carbon joint trading mechanism is introduced to establish a cooperative game model with the goal of minimizing the operating costs of each microgrid, including: （10）； The operating cost of a CHP unit is: （11）； The costs of interacting with the upstream power grid and purchasing gas are: （12）； The cost of energy exchange is: （13）； The cost of negotiated carbon trading is: （14）； The tiered carbon emission cost is: （15）； （16）； The constraints include power balance constraints for each microgrid, point-to-point electricity trading constraints, and carbon emission trading constraints. （17）； （18）； （19）； In the formula, For micro-network exist The equivalent electrical load after wind and solar power is smoothed by energy storage during the period; , , , and These are the operating costs of CHP, the costs of interacting with the upper-level power grid and purchasing gas, the costs of electricity interaction, the costs of negotiated carbon trading, and the costs of carbon emissions. , and Operation and maintenance coefficients for CHP, P2G, and CCS respectively; This refers to the unit price of gas. , and microgrids exist Gas purchase volume during specific time periods and electricity purchase and sale with the upstream power grid; and microgrids With micro-network exist The unit price and exchanged volume of electricity trading during the specified time period; and microgrids With micro-network Carbon trading unit price and carbon trading volume; This refers to actual carbon emissions; The benchmark price for carbon trading; For the rate of price increase; This represents the length of the carbon emission range.
7. The method for multidimensional assessment and early warning of the research value of GCN astronomical transient events according to claim 1, characterized in that, Step S5 specifically includes the following steps: The multi-micronet Nash negotiation model is established as follows: （20）； The above model is decomposed into two sub-problems for solving: Subproblem P1, minimizing the cost of alliances: （21）； Subproblem P2, maximizing the distribution of benefits: （22）； In the formula, For micro-network The cost of operating independently, i.e., the point at which the Nash negotiations broke down; This refers to the revenue generated by MicroNetwork through its participation in the collaboration.
8. The method for multidimensional assessment and early warning of the research value of GCN astronomical transient events according to claim 1, characterized in that, Step S6 specifically includes: Establish a quantitative model for the power contribution factor: Calculation of electrical energy contribution factor based on nonlinear energy sharing mapping model: （23）； In the formula, , microgrids Total electrical energy supplied and received; , These represent the maximum power supplied and received in each microgrid; Establish a quantitative model for carbon emission contribution factors: （24）； In the formula, For micro-network The reduction in carbon emissions after cooperation; , microgrids Carbon emissions before and after the cooperation; Calculate the overall contribution rate: （25）； In the formula, , The weights for the contributions of electricity and carbon emissions are respectively determined to satisfy... Considering the principle of prioritizing safety, ensuring a reliable power supply is given top priority; therefore, the Analytic Hierarchy Process (AHP) is used to quantify the priority of objectives and determine... It is 0.
7. It is 0.
3.
9. A method for multidimensional assessment and early warning of the research value of GCN astronomical transient events according to claim 8, characterized in that, Step S7 specifically includes the following steps: To address the coupling problem of energy trading prices between cooperative microgrids in subproblem P2, auxiliary variables are introduced for decoupling: （26）； Constructing the augmented Lagrangian function: （27）； In the formula, and These are the Lagrange multipliers related to electricity trading prices and carbon trading prices in subproblem 2, respectively. This is a penalty factor.
10. A method for multidimensional assessment and early warning of the research value of GCN astronomical transient events according to claim 9, characterized in that, The solution process includes: S701, Initialize the prediction model parameters, set the maximum number of iterations, penalty factor and convergence threshold; S702, Input the charging and discharging strategies and energy storage capacity configuration results of each microgrid obtained in step S2; S703, initializes electricity trading price, carbon trading price, and Lagrange multipliers; S704, each microgrid independently solves subproblem P1 and updates local decision variables; S705, exchange boundary variable information between adjacent microgrids; S706, update the Lagrange multipliers; S707: Determine if the convergence condition is met. If it is, terminate the iteration and output the electricity and carbon trading prices and optimized scheduling schemes among the microgrids; otherwise, return to S704 to continue the iteration.