Hierarchical control technique-based harmony search optimization algorithm versus model predictive control for autonomous smart microgrids

Sedhom, Bishoy E.; El‐Saadawi, Magdi M.; Hatata, Ahmed Y.; Alsayyari, Abdulaziz

doi:10.1016/j.ijepes.2019.105511

Cited by 49 publications

(17 citation statements)

References 28 publications

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“…This structure consists of independent advanced controllers, which are connected and therefore are aware of the mutual situation. A distributed control structure is particularly efficient for the multiple sub-grid topologies [94]. This structure enables some level of cooperation between different control entities, but the main issue is how to share data and define the access level of information.…”

Section: Distributedmentioning

confidence: 99%

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Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

et al. 2021

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This work analyzes interlinking converter control in hybrid AC/DC microgrids. The paper addresses the state-of-the-art general hybrid microgrid structure. The key power electronics topologies are used as bidirectional interface converters in the AC and DC parts. Different control structures of hybrid microgrids are categorized, followed by the classification of the main control functions, their control strategies, and the control techniques and a summary of their positive and negative aspects and applications. Control functions, their strategies and techniques are classified in the interlinking-converter based. Finally, overall control objectives, time-scaled control structures, and their strategies are outlined. The prospects, main challenges, research gaps, and the trend of the hybrid microgrid structure and control are reviewed and summarized in the conclusions.

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Section: Distributedmentioning

confidence: 99%

“…MPC in DG and ES application is studied in [121]- [126]. [97], [98], [85], [99], [82], [86], [100], [94], [93], [95], [96] Distributed [121] provides a review of model predictive current control with the SVM modulation technique for the DGs. [122] uses MPC for PV applications.…”

Section: A Power-sharingmentioning

confidence: 99%

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

et al. 2021

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“…This facilitates the algorithm to be used to optimize systems of continuous as well as discrete variables. HS algorithm has been widely used in solving problems of multi-parameters and multi-extreme optimization functions and engineering applications, and has achieved good application in the study of power grid optimization [20]- [22]. A Pareto-based grouping discrete harmony search algorithm is proposed to solve the multi-objective flexible job shop scheduling problem (FJSP) [23].…”

Section: Introductionmentioning

confidence: 99%

Optimum Location and Parameter Setting of STATCOM Based on Improved Differential Evolution Harmony Search Algorithm

Zhang

et al. 2020

IEEE Access

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Identification of the optimum location and capacity of Static Synchronous Compensator (STAT-COM) in power grids has been given much attention to maximize its technical performance while minimizing operational cost. In this regard, a new multi-objective optimization model that minimizes power loss, enhances system stability and reduces operational cost of STATCOMs is presented in this paper. To overcome the issue of the harmony search (HS) optimization algorithm in solving high-dimensional multi-objective optimization problem, an improved differential evolution harmony search (DEHS) algorithm is proposed. In this algorithm, mutation and crossover operations are adopted instead of the original pitch adjustment operation adopted in the HS optimization algorithm, which enhances the algorithm global search ability. Moreover, opposition-based learning technique is incorporated to the process to broaden the diversity of variables and hence improving the search efficiency of the algorithm. The proposed algorithm is employed to identify the optimal allocation and sizing of multiple STATCOMs within the IEEE 30-bus system. Results reveal the superiority of the proposed optimization algorithm over the conventional multi-objectives adaptive harmony search algorithm. INDEX TERMS Flexible ac transmission systems, static synchronous compensator, system optimization, harmony search algorithm.

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“…In solving complex optimization problems, metaheuristic methods show high efficiency. Common metaheuristic methods include artificial bee colony algorithm (ABC) [1], locust swarms (LS) [2], cuckoo search (CS) [3], particle swarm optimization (PSO) [4], harmony search algorithm (HS) [5], [6], fruit fly optimizer algorithm (FOA) [7], tree seed algorithm (TSA) [8], biogeography-based optimization (BBO) [9], and differential evolution algorithm (DE) [10], etc. The simplicity and efficiency of metaheuristic methods making it popular for solving many engineering and scientific problems, such as shop scheduling [11], [12], medical diagnosis [13], equation solving [14], etc.…”

Section: Introductionmentioning

confidence: 99%

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

Zhao

Chen

et al. 2020

IEEE Access

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To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets. INDEX TERMS Firefly algorithm, full attraction model, level-based attracting model, variable step size.

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Hierarchical control technique-based harmony search optimization algorithm versus model predictive control for autonomous smart microgrids

Cited by 49 publications

References 28 publications

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

Recent Contributions, Future Prospects and Limitations of Interlinking Converter Control in Hybrid AC/DC Microgrids

Optimum Location and Parameter Setting of STATCOM Based on Improved Differential Evolution Harmony Search Algorithm

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

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