Abstract
In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission.
Highlights
The traditional power system is inefficient because it entirely depends on fossil fuels, and having centralized generation that is far away from consumers
The proposed system model was developed for a residential smart home with three types of load: Electrically Controllable-Appliances (ECA), Thermostatically Controllable-Appliances (TCA), and Optically Controllable-Appliances (OCA), which communicate with an EMC based on a Ant colony optimization (ACO) algorithm via the Internet, and the EMC schedule operation of appliances and charging/discharging of electric vehicles (EVs) per the RTP tariff received from the utility provider
A prediction model, Artificial Neural Network (ANN)-modified Enhanced Differential Evolution (mEDE), is developed for accurate electricity generation estimation of the microgrid to contribute to efficient energy management
Summary
The traditional power system is inefficient because it entirely depends on fossil fuels, and having centralized generation that is far away from consumers. Smart grids are power grids with advanced communication and control technologies between consumers and generating stations, delivering optimized power usage, clean energy at reduced cost, and improving the quality of energy and efficiency of the power grid They provide reductions in technical losses and greenhouse gas emissions, and solve the high carbon emissions problem, where 23% and 41% pollution emission is caused by transport sector, and energy sector, respectively, around the globe [3]. The users reduce electricity cost and import a minute amount of energy from the commercial power grid by adopting the optimal schedule In this manner, efficient energy management ensures stable and reliable microgrid operation. Adapted ACO algorithm successfully solves the presented problem, allowing a high monetary reduction in the energy cost paid by consumers, alleviating the peak formation in electricity demand, minimizing carbon emission, and improving the comfort of the users.
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