Abstract
This study presents a new two-stage hybrid optimization algorithm for scheduling the power consumption of households that have distributed energy generation and storage. In the first stage, non-identical home energy management systems (HEMSs) are modeled. HEMS may contain distributed generation systems (DGS) such as PV and wind turbines, distributed storage systems (DSS) such as electric vehicle (EV), and batteries. HEMS organizes the controllable appliances considering user preferences, amount of energy generated/stored and electricity price. A group of optimum consumption schedules for each HEMS is calculated by a Genetic Algorithm (GA). In the second stage, a neighborhood energy management system (NEMS) is established based on Bayesian Game (BG). In this game, HEMSs are players and their pre-determined optimal schedules are their actions. NEMS regulates the total power fluctuations by allowing the energy transfer among households. In the proposed algorithm, HEMS decreases the electricity cost of the users, while NEMS flats the load curve of the neighborhood to prevent overloading of the distribution transformer. The proposed HEMS and NEMS models are implemented from scratch. A survey of 250 participants was conducted to determine user habits. The results of the survey and the proposed system were compared. In conclusion, the proposed hybrid energy management system saves power by up to 25% and decreases cost by 8.7% on average.
Highlights
The purpose of this study is to develop an energy management strategy that diminish the total cost of electricity, flatten the load curve in a smart home and prevent overloading of the distribution transformer in the neighborhood of smart homes
The home energy management systems (HEMSs) are designed in the first phase
Five possible outcomes were produced by Genetic Algorithm (GA)
Summary
With the advancement of technology, increase in consumer comfort level and widespread use of electric vehicles cause more electricity consuming products to be introduced into our daily lives. As electrical loads in the existing power grid increase, the demand and supply gaps widen. Smart grids which integrated with distributed production and storage systems, information technologies and advanced control algorithms, can overcome this problem [1]. Energy efficiency in smart grids is achieved by managing demand side, distributed generation and storage systems [2,3,4]. It is possible to supply these demands by utilizing existing energy efficiently In this context, HEMS has been developed to control household appliances, manage distributed production and storage systems, monitor energy usage, and reduce electricity costs [9]. Observing the demands and supplies of each smart home, NEMS coordinates all assets in the distribution system from a center [19,20]
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