研究目的
To present an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider.
研究成果
The proposed hybrid grey wolf genetic algorithm (HGWGA) performs well in comparison with different meta-heuristic techniques available in the literature in reducing the peak-to-average ratio and energy cost. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
研究不足
The study focuses on residential loads; however, an increase in the number of appliances and the incorporation of loads of other energy sectors, i.e., industrial and commercial, is planned for future work. The integration of renewable energy resources including PV, wind, biogas, etc., with the conventional energy generation resources to develop a hybrid energy generation system is also planned for future work.
1:Experimental Design and Method Selection:
The study employs a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm (HGWGA) to model HEMS for residential consumers.
2:Sample Selection and Data Sources:
The study considers a residential consumer load comprised of multiple appliances with an energy storage system (ESS) and photovoltaic (PV) generation.
3:List of Experimental Equipment and Materials:
The proposed architecture of a HEMS consists of an energy management controller (EMC), smart meter, display system, smart scheduler unit (SMSU), PV system, ESS, power electronic converter, and a set of appliances.
4:Experimental Procedures and Operational Workflow:
The smart appliances, PV system, and ESS are connected to the EMC and SMSU through a wireless system. The smart meter is connected to the SMSU and receives real-time pricing (RTP) and critical peak pricing (CPP) tariff signals through an advanced metering infrastructure (AMI).
5:Data Analysis Methods:
The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering RTP and CPP tariff signals.
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