Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

Abstract

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

Design of a PV‐fed electric vehicle charging station with a combination of droop and master‐slave control strategy

“…14 The published work on PV-based charging systems covers system design, vehicle-to-grid (V2G) operations, power management, and electricity bill reduction in smart homes. [15][16][17][18][19][20][21] Because all these methods involve a greater standard over a long duration, they cannot tolerate unexpected system perturbations such as rapid irradiance shifts. It has been demonstrated by Thang et al 22 that a sliding-mode control can be used to enhance the robustness of the output voltage of the DC bus to bridge this gap.…”

“…The published work on PV‐based charging systems covers system design, vehicle‐to‐grid (V2G) operations, power management, and electricity bill reduction in smart homes 15‐21 . Because all these methods involve a greater standard over a long duration, they cannot tolerate unexpected system perturbations such as rapid irradiance shifts.…”