## 8 Conclusions  
In this paper, a HEMS model was proposed for residential electricity consumers using multiple appliances with an ESS and PV generation including the option of a H2G energy exchange. A new optimization technique called HGWGA was proposed and implemented to solve the residential consumer appliance scheduling problem in the presence of an ESS and PV. The proposed hybrid algorithm was developed by combining the attributes of GWO and the GA to reduce the consumer electricity consumption cost and the PAR while considering RTP and CPP tariff schemes. To substantiate the performance, the strength and effectiveness of the proposed technique, three scenarios were considered, and the obtained results were compared with GWO, the GA, and unscheduled cases. From Table 7, it is clear that for all three scenarios, the total electricity consumption cost was reduced by 14.93%, 24.39%, and 43.22%, respectively, through the optimal appliance scheduling using the proposed HGWGA in case of just the RTP signal being present. Similarly, when the CPP signal was incorporated, the electricity consumption cost decreased by 25.15%, 39.73%, and 62.45%, and the PAR was reduced by 30%, 31.25%, and 38.5%, respectively. For all three scenarios, the percentage reduction in cost and the PAR for both the RTP and CPP tariffs displayed the effectiveness of the proposed hybrid technique. The results of the execution time demonstrated that the convergence rate of the proposed HGWGA for the scheduling of appliances was comparatively fast, and therefore, this technique can be a better choice in real-time applications in SMSUs for load scheduling. The proposed algorithm for the optimal appliance scheduling strategy can be applied to actual data when and where they are provided. It not only reduced the energy cost, but also increased the stability and reliability of the grid. In addition, the contribution of scheduling results for several HEMSs can be important for an aggregator to manage its resources for a cost-effective and reliable operation of a microgrid. This work focused on residential loads; however, an increase in the number of appliances and the incorporation of loads of other energy sectors, i.e., industrial and commercial, is planned for future work. In future, RERs including PV, wind, biogas, etc., will be integrated with the conventional energy generation resources to develop a hybrid energy generation system. Battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV) will be considered for the ESS. To improve the performance of the proposed methodology and to diminish the effects of uncertainties, stochastic models of DR strategies can be used. A hybrid of the proposed heuristic technique and fuzzy techniques can be used to further enhance the performance in the future. Conceptualization, M.M.I. and M.I.A.S. ; methodology, M.M.I. and M.I.A.S. ; software, M.M.I. ; validation, M.M.I., M.I.A.S., and S.S.H. ; formal analysis, M.M.I. and M.I.A.S. ; data curation, M.M.I. ; writing--original draft preparation, M.M.I. ; writing--review and editing, I.A.S, S.A., S.S.H., R.L., M.F.N.K., M.W., and M.A.S. ; visualization, S.A., S.S.H., R.L., M.F.N.K., M.W., and M.A.S. ; supervision, M.I.A.S. ; project administration, M.I.A.S., S.A. and S.S.H. No external funding has been availed for the completion of this research. The authors declare no conflict of interest. In this paper, following acronyms and symbols are used.