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    A novel probabilistic load shifting approach for demand side management of residential users
    (Elsevier Science Sa, 2024) Cakil, Fatih; Tekdemir, Ibrahim Gursu
    Demand side management is a beneficial set of techniques which leads improving consumption profile of residential users. Practice of dynamic pricing and appropriate shifting of electrical appliances at home are important tasks at that point. In this study, a novel probabilistic load shifting strategy is proposed and its beneficial effects on economic and technical parameters are demonstrated. For that purpose, a survey on energy consumption is applied for residential users and its results are used for creating a probabilistic model. 300 virtual residents are created in this way and a probabilistic simulation technique is developed. Next, the developed simulation technique is applied for creation of the monthly consumption data. Finally, it is demonstrated that electricity bills are decreased significantly when the proposed approach is applied. Besides that, peak-to-average ratio which is calculated by considering all residential users is also decreased as a result. In conclusion, when the results are compared with the ones of conventional constant tariff, time-of-use tariff with three time zones and a deterministic load shifting strategy, it is observed that the best results of economic and technical parameters in question are achieved by using the proposed probabilistic load shifting strategy together with dynamic pricing mechanism involved.
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    An optimal load shifting strategy for residential energy consumers considering economic, technical and environmental impacts
    (Elsevier Science Sa, 2025) Cakil, Fatih; Tekdemir, Ibrahim Gursu
    Residential 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.