A smart home energy management system methodology for techno-economic optimal sizing of standalone renewable-storage power systems under uncertainties

• Published in: Journal of energy storage Vol. 85; p. 111072
• Main Authors: Abu Gunmi, Mohammad, Hu, Feihu, Abu-Ghunmi, Diana, Abu-Ghunmi, Lina
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.04.2024
• Subjects: Battery; Electrolyzer; Fuel cell; Photovoltaic; Residential standalone microgrid; Smart home energy management system; Supercapacitor; Techno-economic optimal sizing; Wind turbine; Smart home energy management system; Techno-economic optimal sizing; Photovoltaic; Battery; Supercapacitor; Wind turbine; Residential standalone microgrid; Fuel cell; Electrolyzer
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2024.111072

This paper develops a novel smart home energy management system methodology (SHEMS) to incorporate in techno-economic optimal sizing (TEOS) of residential standalone microgrid (RSMG). The SHEMS approach is based on the state of charge of battery, supercapacitor and hydrogen tank as well as day-ahead forecast of solar irradiation, wind speed and household load. Fuzzy logic decision making technique is the core of the SHEMS strategy for deciding power-side operation mode (power source selection) and demand-side consumption mode (appliances and electrolyzer operation strategies) of the RSMG. Minimizing hydrogen consumption and battery usage are considered in the SHEMS strategy. The RSMG consists of photovoltaic (PV) and wind turbine (WT) as the main power source while the backup source is formed by supercapacitor (SC), battery (Ba), fuel cell (FC), electrolyzer (El) and hydrogen tank (HT). The optimization model uses total net present value of costs (NPC) as the objective function. The considered residential household load profile consists of ten appliances, classified into four consumption categories (shiftable, adjustable, curtailable and critical) and applied to four different occupancy patterns (four classes), in addition to electrolyzer unit. The day-ahead prediction is obtained for hourly power generation and consumption using SVM-PSO model. The results, which are compared with previous researches, indicate the effectiveness of the proposed strategy to minimize the cost of the zero-emission power system balancing among cost effectiveness, technical feasibility and residents' comfort. The simulation is performed under Amman weather conditions using GAMS-MATLAB-Excel platform. •Integrated SHEMS strategy for optimal sizing and optimal operation of standalone RHMG.•Improvement of energy system efficiency, self-sufficiency, cost-effectiveness, reliability, and resilience.•Green renewable-storage power system.•Green standalone home nanogrid.