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In recent years, demand-side management (DSM) strategies have become an indispensable tool in the operation and planning of modern electricity grids (EGs). One effective way of ensuring the economical and reliable operation of an EG is through assessing its load factor (LF), while considering different types of electrical equipment (e.g., residential, commercial, and industrial). Toward this end, this paper proposes a mixed-integer quadratically constrained programming (MIQCP) model to deal with the LF assessment problems in a modified power distribution system through optimal scheduling of electrical equipment. This MIQCP model aims to minimize the total costs of purchasing energy by the electric utility via an iterative process, in which the difference between the energy consumption in each period and the average consumption is reduced. In the proposed model, the uncertainties in the consumption habits of different consumers, information related to each electrical equipment, energy prices, and the grid’s technical constraints are considered. A modified 34-node EG, differentiated by consumer type, is implemented to evaluate the proposed model. Results show that the LF value is related to the optimal scheme of the electrical equipment that meets the operational and economic requirements of the power grid.

In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.