Optimized Energy Management Considering Renewable Energy and Demand Response in Smart Grids

The escalating demand for electricity, driven by rapid population growth, technological advancements, and economic development, poses significant challenges in balancing energy production and consumption. The arrival of smart grids (SGs) empowers consumers to optimize their energy usage through demand response (DR) programs. This paper presents an optimization energy management framework that utilizes generation from utility and photovoltaic-battery systems to minimize electricity costs, reduce carbon emissions, and mitigate peak to average ratio (PAR). Fur this purpose, the genetic algorithm with crossover variants (GACV) is introduced to solve the complete scheduling problem for scenarios: without photovoltaic-battery systems, with photovoltaic systems, and with photovoltaic-battery systems. To evaluate the performance of the proposed framework based on the GACV algorithm and its different variants, an extensive evaluation is conducted. The objective functions are evaluated and compared for each variant. The highest reduction in cost and carbon emissions are achieved by 48.79 % and 42.7 %, respectively, in scenario II by variant roulette wheel with uniform crossover (RWUC), and PAR is reduced by 41.6 % in scenario III by variant roulette wheel with single-point crossover (RWSC).