A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation

Akter, Afifa; Zafir, Ehsanul Islam; Dana, Nazia Hasan; Joysoyal, Rahul; Sarker, Subrata K.; Li, Li; Muyeen, S M; Das, Sajal K.; Kamwa, Innocent

doi:10.1016/j.esr.2024.101298

Cited by 13 publications

(4 citation statements)

References 170 publications

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“…These algorithms offer robust solutions against the local optimum, ensuring that the global optimal solution is achieved. Their parallel and distributed nature enables scalable optimization, which can offer promising solutions for near-to-real-time decision-making in smart grids [13]. Due to their properties, they can be utilized to effectively allocate and oversee computational tasks across edge devices, considering factors like resource availability, latency, and response time for building edge-offloading decision-making processes with improved performance and efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…This is especially relevant in edge offloading, where there are various criteria to consider, such as resource availability, latency, and response time [12]. They are also effective for optimization problems in smart grid decentralization, such as computation resources allocation and scheduling, where traditional methods may struggle to find optimal solutions [13]. Incorporating heuristic algorithms into the decision-making process for edge offloading can boost the performance and efficiency of edge-cloud systems, characterized by rapid decision-making and a minimal use of computational resources [14].…”

Section: Introductionmentioning

confidence: 99%

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Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Arcas,

Cioara,

Anghel

2024

Biomimetics

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As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services’ computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Arcas,

Cioara,

Anghel

2024

Biomimetics

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“…Noteworthy is the work by Elsied et al [25], which employs a genetic algorithm to concurrently optimize cost and CO2 emissions, marking a significant advancement toward sustainable microgrid management. However, this approach does not fully incorporate the stochastic variability inherent in renewable energy sources and loads, as detailed in references [17,26].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids

Tabares,

Cortés

2024

Energies

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In recent years, the adoption of renewable energy sources has significantly increased due to their numerous advantages, which include environmental sustainability and economic viability. However, the management of electric microgrids presents complex challenges, particularly in the orchestration of energy production and consumption under the uncertainty of fluctuating meteorological conditions. This study aims to enhance decision-making processes within energy management systems specifically designed for microgrids that are interconnected with primary grids, addressing the stochastic and dynamic nature of energy generation and consumption patterns among microgrid users. The research incorporates stochastic models for energy pricing in transactions with the main grid and probabilistic representations of energy generation and demand. This comprehensive methodology allows for an accurate depiction of the volatile dynamics prevalent in the energy markets, which are critical in influencing microgrid operational performance. The application of the Stochastic Dual Dynamic Programming (SDDP) algorithm within a multi-stage adaptive framework for microgrids is evaluated for its effectiveness compared to deterministic approaches. The SDDP algorithm is utilized to develop robust strategies for managing the energy requirements of 1, 2, and 12 prosumers over a 24 h planning horizon. A comparative analysis against the precise solutions obtained from dynamic programming via Monte Carlo simulations indicates a strong congruence between the strategies proposed by the SDDP algorithm and the optimal solutions. The results provide significant insights into the optimization of energy management systems in microgrid settings, emphasizing improvements in operational performance and cost reduction.

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“…Techniques for enhancing algorithm parameters encompass meta-heuristic parameter optimization 86 , genetic algorithms, and grid search 87,88 . Optimize its variables to ensure MPPT's reliable functionality across a diverse range of situations.…”

Section: Optimizing and Adjusting Parametersmentioning

confidence: 99%

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

Bouguerra,

Badoud,

Mekhilef

et al. 2024

Sci Rep

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This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.

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A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation

Cited by 13 publications

References 170 publications

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

Using Stochastic Dual Dynamic Programming to Solve the Multi-Stage Energy Management Problem in Microgrids

Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments

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