A Multi-objective Mathematical Model for Optimal Energy Management of Smart Residential Areas Considering Uncertainty

Iran's current electricity generation systems rely on large-scale power plants situated far from consumers, utilizing fossil fuels as their primary source. However, as demand for electricity surges, greenhouse gas emissions, electricity losses, and reliability issues have become prevalent. Smart cities have emerged as a promising solution to these challenges, with smart grids being a critical component of their energy systems. Efficient energy management necessitates the optimization of resource usage, minimization of costs and environmental risks, and maximization of reliability. This study proposes a multi-objective optimization model for managing a smart grid's energy. The model aims to minimize costs, pollution, and peak consumption while simultaneously maximizing reliability. To account for uncertainties such as renewable energy output, demand, and price, a stochastic approach is utilized. The problem is formulated as a mixed integer linear programming model, and the Pareto solutions are obtained using the epsilon constraint method. In order to achieve the most optimal Pareto solutions, we have employed the use of the fuzzy satisfiability method. Results indicate that the smart energy grid can effectively reduce energy consumption, costs, and pollution while significantly increasing system reliability.