Demand response-based cost mitigation strategy in renewable energy connected microgrid using intelligent energy management system

• Published in: Electrical engineering Vol. 106; no. 1; pp. 1033 - 1052
• Main Authors: Vaikund, Harini, Srivani, S. G.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2024; Springer Nature B.V
• Subjects: Controllability; Controllers; Datasets; Distributed generation; Economics and Management; Electric power demand; Electric power systems; Electrical Engineering; Electrical loads; Electrical Machines and Networks; Electricity; Energy costs; Energy management; Energy Policy; Energy resources; Energy sources; Energy storage; Engineering; Industrial development; Model accuracy; Original Paper; Photovoltaic cells; Power Electronics; Power flow; Renewable energy; Renewable resources; Benchmark dataset; DNN controller; Microgrid; Energy management system; Cost reduction
• ISSN: 0948-7921; 1432-0487
• DOI: 10.1007/s00202-023-02034-8

A microgrid was a mixed device of distributed energy resources that contain renewable energy resources, power storage devices and loads and has the capacity to operate locally in a single controllable entity. However, rising electricity costs and rising consumer electricity demand were major problems in worldwide. An energy management system (EMS) was integrated into the system to address these problems. Yet, the managing between load and source and economic problems were a challenging task for the power system industry. Several approaches were developed to manage the EMS to overcome these issues, but it consumes too much time in energy reporting and difficult to solve the energy challenges. So, a novel energy management system was proposed to manage the power flows to reduce the electricity cost. A standard microgrid was designed like IEEE 6 bus system as per the guidelines of IEEE Standard 1547-2018. PV, grid, and battery were chosen for sources, and in the load, home uses were taken. According to the behaviour of individual person and the accompanying appliances activation power requirement, an actual-time standard dataset was constructed. Using this dataset, the intelligent controller was built to anticipate when the sources will be turned ON and OFF. EMS forecasts the load demand and checks the trained value of an intelligent model to produce a command signal of source’s CB. The suggested intelligent-based EMS system performance was analysed at both islanded and grid disconnected mode. The proposed model provides 97% accuracy, 0.059% FPR, and 99.8% specificity. The results show that the proposed intelligent controller provides better prediction performance in both conditions and is therefore more suitable for real-time estimation.