Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review

Mel, Ishanki De; Klymenko, Oleksiy V.; Short, Michael

doi:10.1016/j.seta.2022.102066

“…While maintaining long-term stability is one concern, further refinement of the optical components' design and alignment may require specific requirements. For practical and widespread usage in signal and image processing applications, it is imperative to address the complexity of optical setups [91,92]. Furthermore, specialized optical systems designed for specific signal or image processing operations could be less versatile and adaptable than electrical processors with software installed.…”

Section: Optical Computing In Signal and Image Processingmentioning

confidence: 99%

Harnessing optical advantages in computing: a review of current and future trends

Kibebe,

Liu,

Tang

2024

Front. Phys.

0

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At the intersection of technological evolution and escalating computational demand, the role of optics is reemerging as a transformative force in the field of computing. This article examines the evolving landscape surrounding optical advantages in computing, focusing on current trends and prospects. Optical computing finds applications across various domains, such as parallel processing, high-speed signal processing, energy efficiency, quantum computing, machine learning, secure communication, and signal/image processing. This review synthesizes insights from scholarly articles, peer-reviewed journals, and academic papers to analyze the potential and challenges of leveraging optics for computational tasks. The literature review also critically examines the challenges of adopting optical computing solutions. The recommended multidimensional approach to overcoming adoption challenges involves holistically addressing integration challenges, manufacturing complexities, and infrastructure needs where collaboration will catapult optical computing into an era of computational power. Through a multidimensional exploration, this article provides a comprehensive understanding of the opportunities and challenges in harnessing optical advantages in computing, positioning optical computing as a revolutionary force with far-reaching consequences. Consequently, this review offers insight and guides researchers, industry professionals, and policymakers toward a computational future that maximizes the advantages of optical computing in specific and pivotal application areas, transcending existing boundaries.

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“…In order for more economic energy management, M5 also calculates the steady-state setting points such as (P 0 , Q 0 ) by solving optimal power flow [59]. On the other hand, it generates the supplementary signals for controller parameters and outputs [60] according to the targets of auxiliary service. Review papers regarding M5 can be seen in [61] - [62].…”

Section: Microgrid Control Frameworkmentioning

confidence: 99%

Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

She¹,

Li²,

Cui³

et al. 2023

IEEE Trans. Smart Grid

20

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Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A highlevel research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.

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“…In order for more economic energy management, M5 also calculates the steady-state setting points such as (P 0 , Q 0 ) by solving optimal power flow [47]. On the other hand, it generates the supplementary signals for controller parameters and outputs [48] according to the targets of auxiliary service. Review papers regarding M5 can be seen in [49] - [50].…”

Section: Auxiliary Service (Supplementary Signal)mentioning

confidence: 99%

Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

She¹,

Li²,

Cui³

et al. 2022

Preprint

View full text Add to dashboard Cite

Challenges and opportunities coexist in microgrids as a result of emerging large-scale distributed energy resources (DERs) and advanced control techniques. In this paper, a comprehensive review of microgrid control is presented with its fusion of model-free reinforcement learning (MFRL). A highlevel research map of microgrid control is developed from six distinct perspectives, followed by bottom-level modularized control blocks illustrating the configurations of grid-following (GFL) and grid-forming (GFM) inverters. Then, mainstream MFRL algorithms are introduced with an explanation of how MFRL can be integrated into the existing control framework. Next, the application guideline of MFRL is summarized with a discussion of three fusing approaches, i.e., model identification and parameter tuning, supplementary signal generation, and controller substitution, with the existing control framework. Finally, the fundamental challenges associated with adopting MFRL in microgrid control and corresponding insights for addressing these concerns are fully discussed.

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Balancing accuracy and complexity in optimisation models of distributed energy systems and microgrids with optimal power flow: A review

Cited by 9 publications

References 135 publications

Harnessing optical advantages in computing: a review of current and future trends

Harnessing optical advantages in computing: a review of current and future trends

Fusion of Microgrid Control With Model-Free Reinforcement Learning: Review and Vision

Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision

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