Abstract

This paper designs the integrated charging station of PV and hydrogen storage based on the charging station. The energy storage system includes hydrogen energy storage for hydrogen production, and the charging station can provide services for electric vehicles and hydrogen vehicles at the same time. To improve the independent energy supply capacity of the hybrid charging station and reduce the cost, the components are reasonably configured. To minimize the configuration cost of the integrated charging station and the proportion of power purchase to the demand of the charging station, the energy flow strategy of the integrated charging station is designed, and the optimal configuration model of optical storage capacity is constructed. The NSGA-II algorithm optimizes the non-inferior Pareto solution set, and a fuzzy comprehensive evaluation evaluates the optimal configuration.

Highlights

  • China is an enormous energy-consuming country, and most fossil fuels such as oil depend on imports [1]

  • Renewable energy provides us with convenience, they have some shortcomings

  • As the power grid purchase of charging stations indirectly causes high carbon emissions, charging stations that consider the combination of distributed renewable energy have a bright future

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Summary

Introduction

China is an enormous energy-consuming country, and most fossil fuels such as oil depend on imports [1]. Cheng et al [11] proposed a double objective mathematical model that comprehensively minimizes charging station cost and the highest photovoltaic utilization rate, constrained by the range of decision variables, power balance, and energy storage variables. Zhang et al [12] takes the load demand of electric vehicles as the premise, combined with the advantages of quasi energy storage performance of electric vehicle power station switching mode. It considers the multi-objective of minimum construction cost and minimum power shortage loss cost.

Component Mathematical Model
Optimization Model
Fuzzy Comprehensive Evaluation
Capacity Optimization Configuration Results

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