Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing

Abstract

The current microgrid (MG) needs alternatives to raise the management level and avoid waste. This approach is important for developing the modern electrical system, as it allows for better integration of distributed generation (DG) and battery energy storage systems (BESSs). Using algorithms based on artificial intelligence (AI) for the energy management system (EMS) can help improve the MG operation to achieve the lowest possible cost in buying and selling electricity and, consequently, increase energy conservation levels. With this, the research proposes two strategies for managing energy in the MG to determine the instants of charge and discharge of the BESS. A heuristic method is employed as a reference point for comparison purposes with the fuzzy logic (FL) operation developed. Furthermore, other algorithms based on artificial neural networks (ANNs) are proposed using the non-linear autoregressive technique to predict the MG variables. During the research, the developed algorithms were evaluated through extensive case studies, with simulations that used data from the PV system, load demands, and electricity prices. For all cases, the AI algorithms for predictions and actions managed to reduce the cost and daily consumption of electricity in the main electricity grids compared with the heuristic method or with the MG without using BESSs. This indicates that the developed power management strategies can be applied to reduce the costs of grid-connected MG operations. It is important to highlight that the simulations were executed in an adequate time, allowing the use of the proposed algorithms in dynamic real-time situations to contribute to developing more efficient and sustainable electrical systems.

Smart Home Powered by New Energy: Energy Management Systems (EMS) Technologies and Future Prospects

“…Modern energy solutions now heavily rely on microgrids (MGs), as shown Figure 3, particularly when coupled with resources like photovoltaic (PV) panels, wind turbines, diesel engines, energy storage systems (ESS), and controllable loads [8]. People who live in rural places notably benefit from microgrids since they offer a more decentralized, efficient, and dependable power supply than conventional centralized power producing systems.…”

“…Energy management systems (EMS) play a particularly important function in this situation. As shown in Figure 4, the article goes into great length about an artificial intelligence-based emergency management system (EMS) that uses fuzzy logic (FL) programming for decision-making, nonlinear autoregressive models of prediction, and artificial neural network (ANN) algorithms for prediction [8]. This EMS can dynamically decide whether to buy or sell electricity based on numerous criteria such as energy costs, load demand, and battery state of charge.…”

“…The article also underlines the significance of integrating RES into microgrids, which, when combined with ESS and controllable loads, such as PV panels and wind turbines, offer local consumers dependable and sustainable power. This variety of microgrids, however, also presents difficulties, particularly with regard to non-dispatchable renewable energy sources (NDRS), bidirectional energy flows, and more engaged users [8].…”

Multi-microgrids system’s resilience enhancement

“…3, June 2024: 1399-1409 1400 system (EMS) [6]. In EMS for MG, several approaches are used, ranging from the employment of heuristic algorithms to the application of optimization techniques [7]- [9]. However, in some situations, reducing the load is very critical.…”