Abstract
Aiming at the problems of complex structures, variable loads, and fluctuation 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 first layer. The first 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 offset, 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 first layer and the characteristics of different 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 benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double‐layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.
Highlights
Energy is the driving force and foundation for social and economic development
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 improve the system economy
Since wind resources and solar resources are clean energy, the power generation of the micro gas turbine (MT) is stable and controllable, and the battery can suppress system power uctuations. e node diagram of a typical multienergy network is shown in Figure 1. erefore, the multienergy network studied in this paper includes a wind turbine generator (WTG), a photovoltaic (PV) panel, an MT, and a storage battery (SB)
Summary
Energy is the driving force and foundation for social and economic development. As traditional fossil energy sources are increasingly exhausted, improving energy e ciency, developing new energy sources, and strengthening multienergy complementarity have become inevitable choices to solve the contradiction between energy demand growth and energy shortage in the process of social and economic development. Literature study [3] applies a novel improved fruit y optimization algorithm-based multiobjective optimization method to optimize the annual total cost and the pollutant emission of the stand-alone hybrid photovoltaic panelwind generator-diesel-battery system. In view of the power characteristics of various DGs and the optimization results of the rst-layer algorithm, the secondlayer algorithm takes into account the economic cost, power supply reliability, and environmental bene ts and combines the multiobjective nondominated sorting genetic algorithm to optimize the allocation of various DG capacities. E results show that the operation performance, economy, reliability, and environmental protection of the multienergy network optimized by the double-layer nondominated sorting genetic algorithm are greatly improved The proposed algorithm is applied to a multienergy network in a certain area. e results show that the operation performance, economy, reliability, and environmental protection of the multienergy network optimized by the double-layer nondominated sorting genetic algorithm are greatly improved
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