Optimization of the home energy system in presence of price fluctuation and intermittent renewable energy sources in grid-connected and islanded modes

The home energy management (HEM) system is a critical tool for managing energy consumption and minimizing the related costs in a building. A smart renewable-based HEM system that participates in the demand response program is modeled in this work and its operations in both grid-connected and islanded modes are studied. This HEM receives its energy from wind turbines in islanded mode and upstream grid in grid-connected mode. This system also includes a diesel generator in the case of wind generation insufficiency. In the connected mode, electricity price is uncertain and in islanded mode, wind generation is uncertain, which are taken into account in modeling. The goal of this paper is the minimization of the electricity bill cost for the households. The studied system also contains a controllable appliance to simulate participation in the demand response program. Moreover, the electric vehicles (EVs) in this system operate in vehicle-to-home (V2H) mode which means the battery in the EV can supply some of the needed energy if needed. Also, the kernel search optimization algorithm is utilized to improve system operation in presence of some uncertainties. The impact of applying the optimization is assessed to validate this approach. The optimization is also done in the grid-connected because the price fluctuates throughout the day and proper management of power purchase from the grid can greatly reduce the cost of energy. The system behavior is studied separately in four seasons with both operation modes, and with and without the optimization. The results indicate that using the optimization has decreased the energy costs in connected mode by 13.18 % and islanded mode by 9.55 %, which proves the effectiveness of the proposed modeling and optimization algorithm.

Automated summary

The home energy management system plays a critical role in managing energy consumption and costs. This study introduces a smart renewable-based system that participates in the demand response program and examines its operations in grid-connected and islanded modes. The use of optimization algorithms helps reduce energy costs.