Selecting the Optimal Micro-Grid Planning Program Using a Novel Multi-Criteria Decision Making Model Based on Grey Cumulative Prospect Theory

Haoran Zhao,Huiru Zhao,Sen Guo

doi:10.3390/en11071840

Abstract

As useful supplements and effective support for large-scale electric power networks, micro-grid systems are the development tendency of future electric power systems. The planning performance of a micro-grid not only affects its security, reliability and economy, but also has a profound influence on the stable operation of large-scale electric power networks with the increasing penetration of micro-grids. Hence, studies related to micro-grid planning program evaluation are of great significance. This paper established a novel multi-criteria decision making (MCDM) model combining the best-worst method (BWM), the entropy weighting approach, and grey cumulative prospect theory for optimum selection of micro-grid planning programs. Firstly, an evaluation index system containing 18 sub-criteria was built from the perspectives of economy, electricity supply reliability and environmental protection. Secondly, the weights of sub-criteria were calculated integrating the subjective weights judged by the BWM and the objective weights computed by the entropy weighting method. Then, the cumulative prospect theory (CPT) combined with grey theory was employed to select the optimal micro-grid planning program. The empirical result indicates that the program with 100 kWp photovoltaic power generation unit, 200 kW wind power generation unit and 600 kWh NaS battery energy storage system is the optimal micro-grid planning program. To verify the robustness of obtained result, a sensitivity analysis related to values change of parameters under different risk preferences was conducted, and the result indicates that the selected optimal micro-grid planning program will not be influenced by various risk preferences of decision makers (DMs) and investors. The novel MCDM proposed in this paper is applicable and feasible in the micro-grid planning programs evaluation and selection.

Highlights

With the growth of national economies, the demand for electricity has increased sharply, which brings about the expansion of the electricity grid scale
Three micro-grid planning programs optimized by HOMER software are treated as the evaluated objects in this research and the planning program with 100 kWp photovoltaic power generation unit, 200 kW wind power generation unit, and kWh NaS battery energy storage system is identified to be the optimum program evaluated by the established novel multi-criteria decision making (MCDM) model
For the integrated weights integrating the subjective weights determined by the best-worst method (BWM) and the objective weights computed by the entropy weighting approach, the distribution network infrastructure investment delayed income, net present value (NPV) of micro-grid construction, annual electricity generation amount to network, micro-grid total investment, the electric power quality qualification rate, and the proportion of renewable energy installed are the most significant sub-criteria

Summary

Introduction

With the growth of national economies, the demand for electricity has increased sharply, which brings about the expansion of the electricity grid scale. The resulting ultra-large-scale electricity systems are becoming increasingly complicated and difficult to operate, so it is difficult to meet the increasing demand for safe and reliable electricity supply. Distributed power generation can utilize the renewable energy existing in decentralized forms, reduce the transmission and distribution distance, and improve the reliability and sustainability of electric power systems [5]. The concept of micro-grids is gradually proposed to guarantee the reliable connection and effective use of the distributed generation as well as maximize the advantages of distributed generation technologies in the fields of economy, energy, and environment [6]. Many countries incorporate micro-grids into their future electricity grids [7,8]

Methods

Discussion

Conclusion