A MILP model to relieve the occurrence of new demand peaks by improving the load factor in smart homes

Abstract Demand response (DR) programs based on pricing options allow residential customers to achieve a financial reduction in their energy bill due to changes in their consumption patterns, especially during peak periods. However, when a large number of consumers adopt this energy management program, the demand shifted to periods with low energy prices can generate new demand peaks. As a result, the quality of the power supply service may be compromised. To address this concern, this paper proposes a mixed-integer linear programming (MILP) model that aims to improve the load factor (LF) related to the demand profile of customers. To achieve this goal, an intelligent scheduling strategy for household appliances that considers flexibility in customer comfort, here called customer hourly preferences, is developed. Based on these preferences, the strategy seeks the efficient daily usage of smart appliances, mainly those with higher average power, to avoid its coincident consumption in periods with lower energy rates, thus mitigating the appearance of new peaks. In the proposed model, the operating expenses of both customers and the electricity company (ECO) are minimized. A set of technical and operational constraints such as the average power, number of times utilized, and average time of usage of home appliances, as well as the charging rate, average time for charging, and initial state-of-charge (SoC) of the plug-in electric vehicle (PEV) battery, are considered. Uncertainties related to the periods of the day when a given appliance (including PEV) is turned-on for consumption are modeled using a Monte Carlo Method (MCM). The MILP model is solved using a commercial solver CPLEX that makes use of classical optimization techniques to ensure the optimal solution to this problem. The performance of the MILP model was tested through two case studies. Case study 1 considers a group of consumers with the same income, while case study 2 triples the number of consumers in the previous case considering different incomes. The results show the importance of the proposed tool for analyzing and evaluating prospective scenarios that guarantee the efficient usage of electric energy with the lowest financial expense for both consumers and the ECO.