Abstract
The installation of grid-connected microgrids (μGs) is considered a suitable solution to enhance the modernization of distributed generation systems into smart grids. This realization has raised the need for the development of an energy management system (EMS) for achieving efficient monitoring, control, and management of the energy flows in the system. In this article, the fuzzy inference system (FIS) based EMS synthesis is proposed to efficiently control the distribution of energy flows of μG in real time. The FIS is further optimized using a genetic algorithm approach to achieve faster evaluation and robust EMS development. In the development process of the EMS, the graph theory-based representation of the μGs is proposed for efficient representation of the energy demand and energy generation with the energy systems connected in the μG. To assess the performance of the proposed EMS, a photovoltaic generation of 19.95 kWp and an aggregated load of 8 kWp are characterized along with a battery energy storage system to form a μG. The results identified the benefits of the proposed approach regarding profit generated and battery usage during the μG operation.
| Original language | English |
|---|---|
| Pages (from-to) | 1582-1591 |
| Journal | IEEE Systems Journal |
| DOIs | |
| Publication status | Published - Mar 2022 |
| Externally published | Yes |
Keywords
- Battery energy storage system
- distributed power generation
- energy management systems
- fuzzy reasoning
- genetic algorithms
- graph theory
- photovoltaic power systems
- power generation control
- smart power grids