Abstract
An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.
Highlights
Energy demand is increasing rapidly around the globe with population growth and economic development
In [2], a smart grid (SG) is defined as ‘‘it is an electricity supply network that can smartly incorporate the actions of all users linked to it like generators, consumers and prosumers.’’ SGs works with different kinds of devices such as smart meters (SMs), smart appliances, RESs, and batteries
The exogenic grid signals (RTP, forecasted temperature, solar irradiance) used in the proposed heuristic-based programmable energy management controller (HPEMC) are illustrated in figures 12, 13 and 14
Summary
Energy demand is increasing rapidly around the globe with population growth and economic development. To reach the drastically increasing energy demand with lower carbon emissions, researchers have proposed new methods of energy generation using renewable energy sources (RESs) To effectively utilize these sources, we have to transform existing power grids into smart grids (SG). 3) In addition to cost, PAR, UC objectives, carbon emission is formulated and investigated to solve energy management problem by power usage scheduling of smart appliances under hybrid generation to improve sustainability. 7) A practical optimization model for energy management is formulated for power usage scheduling under hybrid generation utilizing AMI and price-based DR programs. This is real-time pricing (RTP) of the SG.
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