An Efficient Energy Management in Smart Grid Considering Demand Response Program and Renewable Energy Sources

Abstract

The advancement of the smart grids (SGs) is enabling consumers to schedule home appliances to respond to demand response programs (DRs) offered by distribution system operators (DSOs). This way, not only will customers save money on their energy bills and be more comfortable, but the utility company will also be able to regulate peak-hour demand and reduce carbon emissions (CE). Designing an optimization scheme to reduce the electricity bill cost, peak-to-average ratio (PAR), CO2 emission, wait time, and enhance the user comfort in terms of delay, luminance, and thermal comfort is not only the aim of this work but also the need of demand-side management. This research focuses on energy usage, scheduling, and management under the DR program of an electric utility, as well as renewable energy sources integration, i.e., solar energy (SE), thermal, controllable heat and power (CHP), and wind energy (WE). Moreover, the integration of renewable energy sources will reduce electricity bills and also lower the environmental impact of CE. In this context, a smart appliances scheduler and energy management controller (ASEMC) is proposed which is based on heuristic algorithms, i.e., genetic algorithm (GA), wind-driven optimization (WDO), particle swarm optimization (PSO), bacterial foraging optimization (BFO) and our proposed hybrid of GA, PSO, and WDO (HGPDO) algorithm. The performance of the proposed scheme and heuristic algorithms is evaluated via simulations. Results show that in Scenario 1, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 25.7%, 36.39%, and 20.74%, respectively, while in Scenario 2, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 35.25%, 31.72%, and 36.30%, respectively. Furthermore, in Scenario 1, user comfort in terms of cumulative delay, indoor air freshness quality, thermal, and visual comfort improves by 26.77%, 3.28%, 13.33%, and 31.66%, whereas in Scenario 2, user comfort improves by 23.33%, 3.30%, 10%, and 45%, respectively.