Multi-optimal design and dispatch for a grid-connected solar photovoltaic-based multigeneration energy system through economic, energy and environmental assessment

Abstract

Solar photovoltaic-based multigeneration energy systems (SPVMES) which can use the excess energy of photovoltaic (PV) systems for heating and hydrogen production to improve the self-consumption of solar PV, have gained wide attention as a promising solution for clean and efficient production. To reliably meet diverse energy demands, gird system is integrated into a SPVMES as a backup energy source. In this paper, a grid-connected SPVMES is established to meet the electricity, heating and hydrogen loads, including PV, electrolysis, solid oxide fuel cell, electric heater for small-scale centralized heating and energy storage devices for electricity, heating and hydrogen storage, respectively. The emphasis to focus on when designing and dispatching the SPVMES lies in system performance evaluation. The innovation of this paper is to design and dispatch such a grid-connected SPVMES in a preliminary design stage through economic, energy and environmental assessment by considering cost of energy (COE), energy rate (ER) and renewable fraction (RF). Simultaneously, to explore which single objective/multi-objective optimization has better performance in comprehensive system evaluation, four different scenarios named COE optimization, RF optimization, ER optimization and multi-objective optimization are analyzed and compared. The specific application processes of component sizing and system dispatching with different optimization objectives are presented in detail. The results show that the proposed system has the COE of 0.0343$ in COE optimization scenario, the ER of 0.9128 in ER optimization scenario and the RF of 0.8811 in RF optimization scenario, which shows its significant potential in economic, energy efficiency and environmental performance. By comprehensively scoring the four optimization scenarios, it can be found that the multi-objective optimization scenario has better performance in system performance evaluation compared to the other three single objective optimization scenarios. Additionally, the multi-objective optimization could balance the tradeoff of COE, RF and ER and compromise the results of the single objective optimization scenarios. Potentially, sensitivity analysis is conducted to point out the effect of discharging depth of battery, PV subsidy, linear increasement of loads and seasonality effect on system performances.