Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

Abstract

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (Eexcess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.