Photovoltaic (PV) and Wind turbine (WT) systems are the most promising options for rural energy delivery. In the literature, numerous studies have simply considered only on economic efficiency, while maximizing dependability by a constant value for the given demand load. Moreover, a single set Pareto optimal front (PF) has been attained for the multi-objective optimization problem. Furthermore, the primary challenge of various the off-grid multiple renewable energy sources is excessive energy loss, resulting over/under sizing and a highly total cost for the system. Therefore, this paper presents a unique improved bi-archive approach to develop the diversity and convergence independently in order to find four optimum sets of PF solutions. In addition, a new integration based on best worst method (BWM) and preference ranking organization for enrichment evaluations (PROMETHEE II) method along with group decision making (GDM) mechanism are addressed to sort and rank the optimal sets PF solutions with full consistency ratios of the three experts opinions and high degree of stability in ranking configurations. The theoretical results based on actual hourly meteorological data in Malaysia and South Africa indicated that the suggested improved two-archive-BWM- PROMETHEE II-GDM method is not only able to construct a uniform set of Pareto optimal solutions with fast convergence and high diversity, but can also rank and select the most desired design for the PV/WT/Battery system. The optimum configurations of the two cases studies are composed of a single WT, 105 PV modules, and 69 batteries storages at zero LLP, 60185 ($) of LLC, and 13548018 (KWh) of Pdump for Malaysia case study, meanwhile it composes of 16 WTs, 80 PV modules, and 69 batteries storage at zero of LLP, 66073 ($) of LCC, and 31371 (KWh) of Pdump. The proposed standalone hybrid renewable energy sources system is verified and assessed in a comparison with existing hybrid methods in terms of increasing the reliability, lowering overall cost, and reducing wasted energy.
- Decision Making
- Renewable energy sources
- Rural energy
- Triple-objective optimization