Implementation of hybrid energy system (HES) is generally considered as a promising way to satisfy the electrification requirements for remote areas. In the present study, a novel decision making methodology is proposed to identify the best compromise configuration of HES from a set of feasible combinations obtained from HOMER. For this purpose, a multi-objective function, which comprises four crucial and representative indices, is formulated by applying the weighted sum method. The entropy weight method is employed as a quantitative methodology for weighting factors calculation to enhance the objectivity of decision-making. Moreover, the optimal design of a stand-alone PV/wind/battery/diesel HES in Yongxing Island, China, is conducted as a case study to validate the effectiveness of the proposed method. Both the simulation and optimization results indicate that, the optimization method is able to identify the best trade-off configuration among system reliability, economy, practicability and environmental sustainability. Several useful conclusions are given by analyzing the operation of the best configuration.

This paper explores the techno-economic feasibility of renewable power generation in a remote South China Sea island, seeking to substitute the existing diesel generator with a PV-winddiesel-battery hybrid energy system (HES). First, the authors assessed the availability of local renewable energy resources, elaborated the dispatch strategy of the HES, and introduced the load curve, system configuration, techno-economic specifications of the major components. Then, the Hybrid Optimization of Multiple Energy Resources (HOMER) software was employed to simulate and optimize the HES. In order to identify the optimal configuration of the system, different combinations of the HES were simulated before comparing the economic and operation information of the feasible alternatives. Through the simulation and optimization, it is concluded that the HES can provide sufficient and reliable power to the study area. Finally, the economic and resources sensitivity analysis was performed to identify how the optimal design is to change with the sensitivity variables.