Microgrid Energy Management Strategy Based on MAS

Chalah, Samira; Belaidi, Hadjira; Merrad, Lydia; Alili, Thiziri

doi:10.1109/ihsh57076.2022.10092010

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…From this perspective, how MG has been widely accepted through the combinati of various resources, such as PV panels, wind turbines, diesel generators, and energy st age systems (ESS), and controllable loads, which operate together to provide reliable a sustainable electricity to local consumers [6]. It is an alternative to centralized power ge eration systems, which depend on large power plants and high-voltage transmission lin to supply electricity to remote areas.…”

Section: Figurementioning

confidence: 99%

“…To avoid increasing simulation processing, incorporate a battery model that does n include the simulation degradation process. However, it is essential to emphas that the battery model used in the simulation is realistic enough to provide accur results in the management of the purchase and sale of electricity, quality of volta levels, and frequency in the bars; • Consider the variable and uncertain nature of PV generation, the unpredictable puts and outputs of loads at the distribution level, and the price of electricity in dynamic scenario for a daily operation; • Present simultaneous solutions for multiple desired objectives, with the minimi tion of daily operating costs, the improvement of the use of generated PV energy, a From this perspective, how MG has been widely accepted through the combination of various resources, such as PV panels, wind turbines, diesel generators, and energy storage systems (ESS), and controllable loads, which operate together to provide reliable and sustainable electricity to local consumers [6]. It is an alternative to centralized power generation systems, which depend on large power plants and high-voltage transmission lines to supply electricity to remote areas.…”

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confidence: 99%

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

2023

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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.

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Section: Figurementioning

confidence: 99%

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confidence: 99%

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

2023

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“…However, in some situations, reducing the load is very critical. Hence, the MG has to require extra power from its neighboring MGs; thus, involves a very high resilience [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-microgrids system’s resilience enhancement

Chalah,

Belaidi

2024

IJEECS

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Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.

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Microgrid Energy Management Strategy Based on MAS

Cited by 2 publications

References 13 publications

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

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

Multi-microgrids system’s resilience enhancement

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