Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in smart home. The contradictory nature of these two objective functions (cost and level) requires multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered cost satisfaction, but there lack research considers both objectives simultaneously. Our work proposes an optimum home appliance scheduling method obtain level with minimum energy. To achieve this goal, first, management system (EMS) developed using rule-based algorithm reduce by efficient utilization renewable resources storage system. second part involves development optimization for optimal based on level, involving their time device-based preferences. For purpose, grey wolf accretive (MGWASA) developed, aim provide load patterns per unit index (Cs_index) percentage (%S). MGWASA evaluated grid-connected model EMS. ensure accuracy numerical simulations, actual climatological data preferences are considered. Cs_index derived six different cases simulating (a) load, (b) ideal (c) base (random) without results benchmarked against other state-of-the-art algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), particle swarm (MOBPSO), Multi-objective artificial bee colony (MOABC), evolutionary (MOEA). With proposed technique, % reduction annual achieved. yields at 0.049$ %S 97%, comparison NSGAII, MOBPSO, MOABC, MOEA, which yield 95%, 90%, 92%, 94% 0.052$, 0.048$, 0.0485$, 0.050$, respectively. Moreover, various related aspects, including balance, PV utilization, cost, net present cash payback period, also analyzed. Lastly, sensitivity analysis carried out demonstrate impact any future uncertainties inputs.