A systematic review of energy management system based on various adaptive controllers with optimization algorithm on a smart microgrid

Behera, Sadasiva; Choudhury, Nalin B. Dev

doi:10.1002/2050-7038.13132

Cited by 34 publications

(4 citation statements)

References 120 publications

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“…Many researchers investigated control algorithms tailored to the characteristics of diverse energy storage technologies to reduce power fluctuations in microgrids, employing methods like fuzzy-logic control (FLC), [9][10][11][12][13] filter-based method (FBM), [14][15][16][17] coordinated control strategies, 18 and other optimization-based methods. 19,20 This study concentrates on filter-based current sharing, active power control, and power splitting in a HESS in a standalone DC microgrid, utilizing a fully active architecture, dual-loop system, and hybrid capacitor control for bus voltage regulation and battery current tracking.…”

Section: Introductionmentioning

confidence: 99%

A new control method of hybrid energy storage system for DC microgrid application

Taye,

Choudhury

2024

Energy Storage

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Energy storage system play a crucial role in safeguarding the reliability and steady voltage supply within microgrids. While batteries are the prevalent choice for energy storage in such applications, their limitation in handling high‐frequency discharging and charging necessitates the incorporation of high‐energy density and high‐power density storage devices to enhance power quality, maintain power balance, and reduce fluctuations. In this study, we introduce a hybrid energy storage system (HESS) solution, combining a battery and a supercapacitor, to address intermittent power supply challenges. The effective management of this HESS is pivotal for constant DC voltage and sustaining microgrid stability. To achieve this, we propose an innovative control strategy called the Adaptive Filter‐Based Method (AFBM) for DC microgrid operation, which prioritizes stable and smooth performance while addressing safety and degradation concerns related to the storage devices. Our control strategy determines the distribution of charging and discharging currents for each storage device within the DC microgrid based on their state of charge. We conducted simulations in MATLAB/Simulink, comparing the proposed AFBM method with the conventional filter‐based approach using identical configurations, parameters, and operational modes. The results demonstrate the superior performance of our proposed system, showcasing improved voltage stability, more equitable load distribution, and efficient charging and discharging of storage devices compared to the traditional method. This research contributes to the advancement of microgrid energy storage solutions, enhancing their reliability and performance.

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Section: Introductionmentioning

confidence: 99%

A new control method of hybrid energy storage system for DC microgrid application

Taye,

Choudhury

2024

Energy Storage

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“…For ensuring supply reliability, fuel savings, lesser emissions, voltage security, full exploitation of renewable potential, and coordinated output of multiple DGs, there is a need for energy management and optimal dispatch of microgrids. Two different approaches that have widely been used in the literature for the optimal operation of MG are (a) deterministic approaches and (b) heuristic optimization approaches 2 – 8 .…”

Section: Introductionmentioning

confidence: 99%

Data-driven optimization for microgrid control under distributed energy resource variability

Mathur,

Kumari,

Meena

et al. 2024

Sci Rep

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The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

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“…Thirunavukkarasu et al 1 offered a comprehensive review, yet the broad scope may overlook specific limitations of individual methods. Behera and Choudhury 2 conducted a systematic review, but might not sufficiently address the technical nuances of optimization algorithms. Leonori et al 3 introduced Genetic Algorithms, effective but computationally intensive, posing challenges in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Machine learning optimization for hybrid electric vehicle charging in renewable microgrids

Hassan

2024

Sci Rep

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Renewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.

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A systematic review of energy management system based on various adaptive controllers with optimization algorithm on a smart microgrid

Cited by 34 publications

References 120 publications

A new control method of hybrid energy storage system for DC microgrid application

A new control method of hybrid energy storage system for DC microgrid application

Data-driven optimization for microgrid control under distributed energy resource variability

Machine learning optimization for hybrid electric vehicle charging in renewable microgrids

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