Cakil, FatihTekdemir, Ibrahim Gursu2026-02-082026-02-0820250378-77881872-6178https://doi.org/10.1016/j.enbuild.2025.115783https://hdl.handle.net/20.500.12885/5658Residential consumers have a significant share in total global energy demand. By adjusting operating hours of electrical appliances at residences has a potential to get economic, technical and environmental benefits. In this study, an optimal load shifting strategy that is based on particle swarm optimization algorithm is developed considering this potential. It is applied both in a single-objective and in a multi-objective form. The multi-objective optimization approach is realized by using weight factors and effects of different weight values are also demonstrated. Rooftop photovoltaic panels are integrated into 300 virtual residences, which are formed by using statistical models. Also, energy buying, selling and dynamic pricing mechanisms are involved in the analyses. Having photovoltaic panels and energy selling mechanism, optimization process has also realized a consideration of energy market. After analyses obtained for revealed optimization problems in the study, electricity bills, peak-to-average ratio and utilization of solar panels in residential power demand is calculated for a single month. It is seen that a significant improvement is reached when compared to the case without any load shifting approach and to the one with a novel load shifting strategy: electricity bills are reduced by up to 37.61 %, and carbon dioxide emission is reduced by 32.05 kg per residence when the proposed method is used, which are far better than the others. Although the technical parameter that is relevant to the system operation cannot be improved, it can be prevented from reaching undesirable extreme values by using the proposed participation in load shifting index.eninfo:eu-repo/semantics/closedAccessDemand responseLoad shifting strategiesDynamic priceRooftop photovoltaic panelsParticle swarm optimizationArticle10.1016/j.enbuild.2025.115783341WOS:0015087481000012-s2.0-105004395950Q1Q1