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Aiming at the problems of low energy efficiency and unstable operation in the optimal allocation of optical storage capacity in rural new energy microgrids, this paper proposes an optimization method based on two-layer multi-objective collaborative decision-making. First, an outer optimization objective function containing constraints on capacity allocation, line transmission security, charging and discharging power of the energy storage system, microgrid security, and power supply reliability was constructed, and an inner optimization objective function containing constraints on energy storage self-discharge correction and power balance was constructed. The quantum-behaved particle swarm optimization algorithm is used to solve the optimal solution set of the objective function, and the interactive multi-criteria decision-making method is used to select the compromise solution to realize efficient optimal allocation of optical storage capacity. The test results show that this method can obtain the Pareto optimal solution set of multi-objective functions in the model. Comprehensive prospect value calculation results are obtained according to each configuration scheme, and a compromise scheme is obtained. The total energy consumption, power deviation rate, light rejection rate, and load loss cost were significantly reduced, with maximum values of 90.5%, 2.03% and 114,700 yuan, respectively, and the load loss probability was lower than 2.2%. The results show that the proposed method can effectively improve the total energy consumption utilization of the microgrid, reduce the power deviation rate and light abandonment rate, and provide significant advantages in different lighting and load demand scenarios. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the problems of low energy efficiency and unstable operation in the optimal allocation of optical storage capacity in rural new energy microgrids, this paper proposes an optimization method based on two-layer multi-objective collaborative decision-making. First, an outer optimization objective function containing constraints on capacity allocation, line transmission security, charging and discharging power of the energy storage system, microgrid security, and power supply reliability was constructed, and an inner optimization objective function containing constraints on energy storage self-discharge correction and power balance was constructed. The quantum-behaved particle swarm optimization algorithm is used to solve the optimal solution set of the objective function, and the interactive multi-criteria decision-making method is used to select the compromise solution to realize efficient optimal allocation of optical storage capacity. The test results show that this method can obtain the Pareto optimal solution set of multi-objective functions in the model. Comprehensive prospect value calculation results are obtained according to each configuration scheme, and a compromise scheme is obtained. The total energy consumption, power deviation rate, light rejection rate, and load loss cost were significantly reduced, with maximum values of 90.5%, 2.03% and 114,700 yuan, respectively, and the load loss probability was lower than 2.2%. The results show that the proposed method can effectively improve the total energy consumption utilization of the microgrid, reduce the power deviation rate and light abandonment rate, and provide significant advantages in different lighting and load demand scenarios.