Investigating Heuristic and Optimization Energy Management Algorithms to Minimize Residential Electricity Costs

Recently, renewable and distributed resources have been emerging to fulfill the ever-increasing energy demand to promote sustainable development globally. An energy management system (EMS) is used to integrate these resources cost-effectively. The intermittent nature of Solar energy, however, might have an impact on the energy security and stability of power systems. To guarantee the effectiveness, dependability, and quality of electricity provided, an optimal control approach is crucial. In this research, a Heuristic and Optimization-based EMS is developed to deliver energy from available resources. The supply system of the microgrid consists of Solar PV, and Energy Storage Systems (ESS) in addition to the main utility grid to serve the residential loads through smart EMS with optimal cost. Inside the Heuristic EMS logical decisions like - when to utilize the main grid or, cut it down are made based on the accessibility of Solar PV and battery state-of-charge (SoC). While, an optimization objective function is formulated within the EMS, aiming at reducing grid intake, utilizing off-peak hours for charging ESS from the grid, maximizing renewable penetration, and potentially exploiting these on-site generators during on-peak hours. Manipulating some practical datasets two plausible cases have been studied to test and validate the performance of the proposed model. Finally, the simulation outcomes of the two methods are compared with each other. As the electricity price significantly changes, the optimization-based EMS was found effective to manage the battery charge-discharge operation and can minimize the residential electricity bill consequently.