The share of renewable energy based power system (REP) are increasing gradually. The changes caused by these sources are becoming a major obstacle to grid operation. So, the economic and technological optimization of REP becomes inevitable. This paper propose an energy management system of hybrid micro grid system (HMS) considering the actual climate and demand data of Yanbu, Saudi Arabia to meet the city’s load demand. HMS consists of different renewable energy resources (RES) which are wind turbines (WT), solar photovoltaic (PV), and batteries as a storage devices. The network performance is studied in the presence of some uncertainties related to RES and load to get closer to real model. The suggested stand-alone PV/WT/battery hybrid system was optimized and analyzed in this work using the multi-objective water cycle algorithm (MOWCA) considering the loss of power supply probability (LPSP) and the cost of electricity (COE). The point estimate method is utilized for modeling the solar and wind power uncertainties. Nine case studies are examined using three distinct scenarios, which include the impact of load and uncertainty resulting from RES, as well as varying numbers of homes (5 and 15 houses). The recommended method consistently yielded a collection of solutions that constituted a Pareto front (PF). The designer might choose the best compromise option from the PF by taking into account a number of factors. Optimizing the size of HMS components, supplying all loads at the lowest possible energy cost with the highest level of dependability is achieved by MOWCA. The obtained results are then compared with those achieved by multi-objective particle swarm optimization (MOPSO). Their ideal number of component units, LPSP, and COE are better than the others, and the accepted MOWCA is more robust than the other method since it has the lowest variation.
Saleh et al. (2025) studied this question.
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