Adaptive Dynamic Control Based Optimization of Renewable Energy Resources for Grid-Tied Microgrids

Majeed, Muhammad Asghar; Asghar, Furqan; Hussain, M. Imtiaz; Amjad, Waseem; Munir, Anjum; Armghan, Hammad; Kim, Jun-Tae

doi:10.3390/su14031877

Cited by 7 publications

(5 citation statements)

References 24 publications

(26 reference statements)

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“…It enables the modeling of the battery's behavior and the optimization of its operation within the microgrid [14], [28].…”

Section: Battery Energy Storage Systemmentioning

confidence: 99%

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Majeed,

Phichaisawat,

Asghar

et al. 2023

IEEE Access

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Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.INDEX TERMS Adaptive Genetic Algorithm, grid-tied microgrid, bidirectional converter, cost optimization, renewable energy resources, peak average ratio, load scheduling, resource allocation.

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“…It enables the modeling of the battery's behavior and the optimization of its operation within the microgrid [14], [28].…”

Section: Battery Energy Storage Systemmentioning

confidence: 99%

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Majeed,

Phichaisawat,

Asghar

et al. 2023

IEEE Access

Self Cite

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“…A double fundamental signal extracter is used to improve the quality of wind energy generation system and solar photovoltaic generation system in [12]. An adaptive technique is studied to develop the optimization of renewable energy resources in [13], the proposed system provides parameters optimization of frequency and voltages. An adaptive model predictive current controller for DFIG has been designed in [14], and the energy management strategies based on fuzzy coordinated controller with the 20 kVA energy router simulation platform in [15].…”

Section: Introductionmentioning

confidence: 99%

Research on operational characteristics and methods for new energy nearby connected converters

zhang,

sun,

wang

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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As an important means of carbon reduction, the application scale of new energy power generation is gradually expanding. With the diversity of new energy access to grid, many wind or photovoltaic converters have directly encountered issues such as circulating current or resonance, so the operational characteristics and methods for new energy nearby connected converters are crucial for the safe and stable operation of enterprise power grids. Firstly, the current research achievements and main technical characteristics were introduced; Secondly, topology design and architecture are explained, then centralized complementary architecture principles are presented based on independent port constraints; Thirdly, energy release stability control based on active protection and system regulation strategy and constraints are studied. Finally, the conclusion summarizes the main research content, the line parameters in the application of nearby connected converters is the main performance influencing factors.

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“…Based on this, artificial intelligence algorithms such as adaptive dynamic programming [21] and robust model predictive control (MPC) [22], which predict the output of a system based on historical information and future inputs, are gradually applied to control multiple microgrids [23]. The latter is also able to transform the frequency control process into solving an optimisation problem and is thus well adapted to the random output constraints of EVs.…”

Section: Introductionmentioning

confidence: 99%

Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control

Fan,

Yang,

et al. 2023

IET Generation Trans & Dist

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System reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally.

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Adaptive Dynamic Control Based Optimization of Renewable Energy Resources for Grid-Tied Microgrids

Cited by 7 publications

References 24 publications

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Optimal Energy Management System for Grid-Tied Microgrid: An Improved Adaptive Genetic Algorithm

Research on operational characteristics and methods for new energy nearby connected converters

Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control

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