Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system

The development and utilization of renewable energy sources can not only effectively reduce carbon dioxide emissions, but also provide access to electricity to more parts of the world. A standalone hybrid system based on renewable sources is a promising way to supply reliable and continuous power in remote areas to which the grid has not extended. This paper designs and investigates a photovoltaics (PV)-wind-hydropower station with pumped-storage installation (HSPSI) hybrid energy system in Xiaojin, Sichuan, China as case of study. HSPSI can use the available flow of the river and store surplus energy generated from wind and PV by pumping water from the lower reservoir to the upper one. From the perspective of the investors, the techno-economic index is usually used to design the PV-wind-HSPSI hybrid energy system which aims to find the optimal configure with maximum power supply reliability and minimum investment cost. The trade-off analysis between the two objectives is based on Pareto optimality theory by means of Multi-Objective Particle Swarm Optimization (MOPSO). Besides, this paper takes the curtailment rate (CR) of the wind and PV power into consideration due to policy requirements. The relationship between the two objectives under various CR are analyzed and compared. Several results can be obtained as follows: 1) Comparing with the PV-HSPSI and wind-HSPSI hybrid energy system, the levelized cost of energy (LCOE) of the PV-wind-HSPSI hybrid energy system can reduce by 32.8% and 45.0% respectively. 2) For the PV-wind-HSPSI hybrid energy system, the LCOE can be as low as 0.091 $/kWh when 5% LPSP can be acceptable. 3) The policy of CR is unfavorable for the investors which leads to a higher investment cost. 4) Particle Swarm Optimization (PSO) performs better than genetic algorithm (GA) and Simulated Annealing method (SA) with a least LCOE. 5) both MOPSO and weighted sum approach (WSA) have a good performance to find the Pareto fronts and its hypervolume indicator (HV) is calculated. (c) 2019 Elsevier Ltd. All rights reserved.