article · IEEE Access
This research evaluates a dual-layer strategy designed to optimise the operation and control of an AC microgrid connected to the utility grid. The microgrid incorporates solar photovoltaic panels, a wind turbine system, and battery storage. The framework splits tasks between a control layer and an energy management system layer. The control layer uses a Particle Swarm Optimisation algorithm to maintain high power quality through refined set point tracking. Concurrently, the energy management layer applies a Sparrow Search Algorithm operating across 15-minute intervals over a 24-hour cycle to minimise overall operating costs. This configuration dynamically responds to fluctuations in energy generation, environmental conditions, and user demand. Testing with real-world operational data confirms that the Sparrow Search Algorithm achieves substantial cost reductions over traditional genetic algorithms, particle swarm optimisation, and unoptimised setups, while explicitly factoring in battery health constraints.
Integrating renewable power sources such as wind and solar into local grids introduces unpredictability that can raise operational costs and degrade battery lifespans. By combining rapid local control with smart economic scheduling, this approach stabilises power delivery and lowers running expenses. This provides grid operators with an effective method to make hybrid renewable power systems both reliable and cost-effective.
The framework is relevant to microgrid operators, industrial energy managers, and utility providers seeking to integrate renewable generation with battery storage. Because the methodology has been validated through experimental analysis using real-world data, it represents applied and tested research. Implementing it in commercial settings would likely require embedding the control and scheduling algorithms into commercial energy management system software platforms and microgrid controllers.
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This paper presents a dual-layer approach for managing and controlling an AC Microgrid (MG). The MG integrates a Photovoltaic System (PVS), Wind Turbine System (WTS), a Battery Storage System (BSS), all interconnected with the utility grid. The dual-layer is structured into a Control Layer (CL) and an Energy Management System Layer (EMS-L). The CL proposes an efficient model coupled with a system control, ensuring high power quality for the AC microgrid. This system employs a Particle Swarm Optimization (PSO) algorithm to optimize the set point tracking performance. Simultaneously, the EMS-L utilizes a Sparrow Search Algorithm (SSA) with the objective of minimizing the total operating costs of the AC microgrid. This is achieved by employing a 15-minutes step time over a 24-hours period, enhancing both precision and rapidity of the system’s operation. This enhanced temporal resolution effectively models and responds to fluctuations in energy demand, supply variability and environmental factors. This synergistic integration allows for nuanced and efficient energy flow management in order to optimise the MG performance. A significant aspect of the research is the comparative analysis of the proposed SSA-EMS with PSO and Genetic Algorithm (GA), alongside an unoptimized system. This analysis highlights the hybrid methodology’s superior efficiency and effectiveness. The robust framework, combining SSA at the EMS layer with PSO at the control layer, has been empirically tested using a real world data to confirm its effectiveness and resilience in practical scenarios. These tests provide solid evidence of the methodology’s potential in boosting the sustainable and reliable operation of microgrids. Significantly, the SSA-EMS showcases notable cost efficiency, achieving reductions of 33.34% and 41.18% compared to PSO and GA. Impressively, compared to an unoptimized system, the SSA-EMS demonstrates an even more remarkable cost reduction of 49.43% and 59.10% while considering the impact on battery health constraints.
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DOI: 10.1109/access.2024.3370681
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