An Optimal Allocation Strategy for Multienergy Networks Based on Double-Layer Nondominated Sorting Genetic Algorithms

Aiming at the problems of complex structures, variable loads, and †uctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double-layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second-layer optimization is based on the optimization result of the Žrst layer. e Žrst layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage o’set, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the Žrst layer and the characteristics of di’erent DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental beneŽts of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of di’erent DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. e double-layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides e’ective guidance for the optimal allocation and reliable operation of multienergy networks.