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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.

Since the 20th century, human society has developed rapidly, and with the continuous development of urbanization, serious environmental pollution problems have followed. Actively studying the development of new energy industry has become an important issue around the world. Rural energy is an important foundation of rural economic and development. Promoting green energy transformation and development in rural areas is an inherent requirement to meet people’s needs for a better life and an important part of building a modern energy system. This study constructs a tripartite evolutionary game model of the government, new energy enterprises and farmers, and analyzes the evolutionary path and stable strategy of the three participants in the process of energy industry transformation. The results show that (1) moderate or high level of government subsidies for farmers use of new energy can promote the development of new energy industry, while low level of subsidies is not conducive to the development of rural new energy industry. (2) When the government’s investment in new energy infrastructure construction is low, farmers will choose to use new energy passively. When the government’s investment in new energy infrastructure construction is moderate or high, farmers will choose to actively use new energy, and new energy enterprises will evolve to actively produce new energy under the circumstance of active use by farmers. (3) Under the current development situation of rural new energy industry, the smaller the proportion coefficient of pollution control costs borne by new energy enterprises formulated by the government, the more conducive the evolution of new energy enterprises to actively produce new energy. Perfect farmers use of new energy countermeasures for the benign development of new energy industry is of great significance. Therefore, from the perspective of evolutionary game, we should promote the transformation of environmental governance to public governance mode. A basic framework for a tripartite coordination mechanism among farmers, the government and enterprises will be established. At the same time, the establishment of specific systems and policies will guide the interests of farmers, the government and new energy enterprises, so as to truly realize the positive development of the new energy industry driven by the participation of three parties.