Decentralized Energy Management System in Microgrid Considering Uncertainty and Demand Response

Wynn, Sane Lei Lei; Boonraksa, Terapong; Boonraksa, Promphak; Pinthurat, Watcharakorn; Marungsri, Boonruang

doi:10.3390/electronics12010237

Cited by 28 publications

(8 citation statements)

References 46 publications

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“…Wynn et al 74 presented decentralized EMS to diminish the operation cost of the system under the generation of renewable sources and load uncertainties. At first, the PV, wind, and power demand was forecasted by the autoregressive T A B L E 1 Analysis of the centralized and decentralized energy management systems (EMS) schemes available.…”

Section: Decentralized Emsmentioning

confidence: 99%

Supply energy management methods for a direct current microgrid: A comprehensive review

Varshnry,

Lather,

Dahiya

2024

Energy Storage

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The revolution of microgrids (MGs) in the modern world has impacted the power systems to deal with the loads with optimal use of renewable energy resources (RERs). In the models consisting of energy management systems (EMS) and generation sources, there will be power fluctuations between the source and the load under certain conditions due to the unpredictable behavior of RERs. Therefore, a reliable scheme has to be implemented between the source and the load to ensure optimal performance of direct current (DC)‐MGs. This article has presented a review of the EMS models introduced by various researchers to enable optimal power supply and efficient operation of the DC‐MGs. A thorough illustration of the schemes adopted to provide efficient operations under grid‐connected and islanded mode of operations are provided. The effects and advantages of centralized and decentralized EMSs are discussed. The role of a battery energy storage system (BESS) in microgrid scheduling is presented and compared with a few other energy storage systems. The scheduling strategies implemented for optimal EMS using different algorithms and techniques are elaborated. In addition, the effects of battery degradation and the formulated solutions to improve the degraded models with improved battery life are also provided with cycle life and calendar life. Then, the lithium ion and lead acid BESS effect on microgrid EMS is presented. Finally, the energy management methods for DC‐MGs end with challenges, research gaps, and future direction.

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Section: Decentralized Emsmentioning

confidence: 99%

Supply energy management methods for a direct current microgrid: A comprehensive review

Varshnry,

Lather,

Dahiya

2024

Energy Storage

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“…Second, an optimal generation scheduling procedure is intended to reduce system operating expenses. The simulation results indicated that the optimal generation scheduling could minimize operating expenses under the worst-case scenario [31]. In [32], combining two models, the ARMA and nonlinear auto regressive with exogenous input (NARX), a novel method was presented for predicting solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Cavus,

Ugurluoglu,

Ayan

et al. 2023

Applied Sciences

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Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model’s accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.

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“…The results of the proposed study indicate a significant reduction in energy cost bills and an improvement in renewable power generation. Wynn et al [34] modelled a decentralized energy management system to reduce operational costs under RES. The model applies PSO for optimal RES generation and load demand schedule.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Smart Energy Management in Hybrid Microgrids: Evaluating Operational Modes, Resources Optimization, and Environmental Impacts

Billah,

Yousif,

Numan

et al. 2023

IEEE Access

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Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system's performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.INDEX TERMS Renewable energy sources, particle swarm optimization, energy management system, multi agent system, microgrid. NOMENCLATURE RES Renewable energy sources. DER Distributed energy resources. PV Photovoltaic. MAS Multi agent system. PSO Particle swarm optimization. AI Artificial intelligence. MILP Mixed integer linear programming.The associate editor coordinating the review of this manuscript and approving it for publication was Xiwang Dong.

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Decentralized Energy Management System in Microgrid Considering Uncertainty and Demand Response

Cited by 28 publications

References 46 publications

Supply energy management methods for a direct current microgrid: A comprehensive review

Supply energy management methods for a direct current microgrid: A comprehensive review

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Decentralized Smart Energy Management in Hybrid Microgrids: Evaluating Operational Modes, Resources Optimization, and Environmental Impacts

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