Optimum control of power flow management in PV, wind, and battery-integrated hybrid microgrid systems by implementing in real-time digital simulator-based platform

Patel, Swadhin Kumar; Ghosh, Arnab; Ray, Pravat Kumar

doi:10.1007/s00500-023-07838-1

Cited by 17 publications

(9 citation statements)

References 30 publications

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“…Other cascaded controllers are PDF(1 + PI) [54], 2-DOF-FOPID-PI [65], PO-PID + DD [26] and other controllers have been studied. The advantages, disadvantages, and application scope of different cascade controllers are shown in table 7.…”

Section: Cascade Controllersmentioning

confidence: 99%

“…Through a comprehensive analysis of various performance metrics, it was ultimately concluded that the controller exhibited remarkable robustness [67]. Degree of freedom controllers can also be used in cascade control such as 2-DOF-FOPID-PI [65]. It is used in the design of controllers with filters and fractional orders such as 2-DOF-PI-FOPDN [35].…”

Section: Degree Of Freedom Controllersmentioning

confidence: 99%

“…There are more intelligent optimization algorithms applied in renewable energy-containing power systems, which are briefly listed next for reference. The sine cosine algorithm (SCA) [24,29,63,65], the grasshopper optimization algorithm (GOA) [20,54,73], and the particle swarm optimisation algorithm (PSO) [21,42,47]. Other optimisation algorithms are grey wolf optimisation algorithm (GWO) [18,35], quasi-oppositional optimisation algorithm (QOLOA) [53], intensity pareto evolutionary algorithm [32], adaptive distributed auctions algorithm (ADAA) [33], imperial competition algorithm (ICA) [23,72], lightning search algorithm [56], whale optimisation algorithm (WOA) [57,58], firefly algorithm (FA) [16], water cycle algorithm (WCA) [61,87], dragonfly algorithm (DA) [67], teaching learning optimisation (TLBO) [50], butterfly optimisation algorithm (BOA) [62], in harmony algorithm (IHA) [36], spotted hyena algorithm (SHO) [28], crow search algorithm (CSA) [59,66], moth flame algorithm (MFO) [37], moth swarm algorithm (MSA) [38], genetic algorithm (GA) [44,49], differential evolution algorithm (DE) [39], biogeographic optimisation algorithm (BBO) [46], volleyball league algorithm (VPL) [60], yellow saddlefish (YSGA) [64], wild goat algorithm (WGA)…”

Section: Intelligent Optimisation Algorithmmentioning

confidence: 99%

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A review of control strategies for automatic generation control in power systems with renewable energy

Kuang,

Tian,

Liu

et al. 2024

Prog. Energy

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This review presents a state-of-the-art literature review of Automatic Generation Control (AGC) control strategies for power systems containing renewable energy sources. The incorporation of renewable energy into the power system has a large impact on the stability, reliability, economy and security of the power system. To mitigate these effects, it is important to choose a suitable control strategy for AGC. However, there is a limited amount of literature available on the review of AGC in renewable energy power systems., so a review of AGC control strategies for renewable energy-containing power systems is necessary. The investigation of this paper focuses on all kinds of different AGC control strategies for renewable energy-containing power systems, such as Proportional Integral Derivative (PID) control, fuzzy control, artificial neural network control, etc., and compares and considers these different control methods, while this paper summarises the power system models with/without renewable energy. In addition, this paper summarises and discusses the application of intelligent optimization algorithms and energy storage systems to control strategies. The problems and future research directions of the current research on power systems with renewable energy sources are also discussed.

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Section: Cascade Controllersmentioning

confidence: 99%

Section: Degree Of Freedom Controllersmentioning

confidence: 99%

Section: Intelligent Optimisation Algorithmmentioning

confidence: 99%

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A review of control strategies for automatic generation control in power systems with renewable energy

Kuang,

Tian,

Liu

et al. 2024

Prog. Energy

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“…Suchismita Patel et al used battery storage in renewable energy systems to reduce power fluctuations caused by the intermittent behaviour of renewable energy sources and proposed that a controller is applied to a voltage-controlled loop, which is important for the proposed renewable energy system. Energy stabilization strategies are proven [ 34 ]. This is also our future research direction and research content.…”

Section: Problem Formulation and Microgrid Modelmentioning

confidence: 99%

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm

Zhang,

Zhou

2024

Biomimetics

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In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm’s search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results.

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“…A hybrid PV-Wind-Battery energy storage system is simulated in MATLAB/Simulink to test the state of charge (SOC) control [6].The aim of this study is to design and build a small-scale PV-Wind-Battery stand-alone renewable energy system. An EMS is presented to maintain power balance in a standalone system and to enable flexible and configurable control for various wind speed and solar irradiance fluctuations in renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Energy Management System for Hybrid PV-Wind-Battery Based Fuzzy Logic Controller System

2023

IRJMETS

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This research proposes an Energy Management System (EMS) for a small-scale Hybrid PV-Wind-Battery based standalone system. PV energy and wind energy are used as primary energy sources, with the battery serving as a backup supply. The Solar Energy Conversion System (SECS) and the Wind Energy Conversion System (WECS) are modeled and simulated with the help of different boost and buck converters. This work also discusses the design, simulation, and implementation of a multi-source renewable energy system MPPT control technique based on a Fuzzy Logic Controller (FLC).Renewable sources are varied according to the solar irradiance and wind speeds while loads are kept constant. The DC load is connected directly to the DC bus, while AC load is connected via inverter. After the addition of the both system, if the power cannot sustain the load due no wind and foggy atmosphere, the battery system is incorporated to support the system. It incorporates the EMS using fuzzy logic for the power balance of the system. For the better performance optimization, operational efficiency and the reliability of the system, hybrid PV-WT-Battery system in modeled and simulated in MATLAB/Simulink. This system is for standalone mode, it provides the foundation for further study with interface to the grid and many other problems.

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Optimum control of power flow management in PV, wind, and battery-integrated hybrid microgrid systems by implementing in real-time digital simulator-based platform

Cited by 17 publications

References 30 publications

A review of control strategies for automatic generation control in power systems with renewable energy

A review of control strategies for automatic generation control in power systems with renewable energy

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm

Energy Management System for Hybrid PV-Wind-Battery Based Fuzzy Logic Controller System

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