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Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

机译:配电网中最优潮流研究进展——问题制定与优化方法研究进展

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Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the "Dual-Carbon" target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.
机译:分布式发电机 (DG) 在配电网 (DN) 中具有很高的渗透率。了解它们对 DN 的影响对于实现最佳功率流 (OPF) 至关重要。已经建立了各种DG模型,例如随机模型和预测模型,并用于OPF。传统的OPF旨在最大限度地降低运营成本或电力损耗,而“双碳”目标则导致了碳减排目标的加入。此外,OPF 还采用了机器学习 (ML) 等最先进的优化技术。然而,目前大多数研究都集中在优化方法上,而不是OPF的问题表述。本文的目的是全面了解OPF问题,并提出潜在的解决方案。通过深入研究问题表述和不同的优化技术,为实际OPF问题选择合适的解决方案变得更加容易。此外,本文还全面概述了未来的进展,并对最佳潮流 (OPF) 领域采用的各种方法进行了比较分析。虽然数学方法提供了准确的解决方案,但它们的复杂性可能会带来挑战。另一方面,启发式算法表现出鲁棒性,但可能无法确保全局最优。此外,机器学习技术在处理大量数据集方面表现出熟练程度,但它们需要大量数据并且可能具有有限的可解释性。最后,本文对OPF未来的研究方向进行了展望,包括扩展DG的不确定性、电力市场的整合和分布式优化。本综述的主要目的是全面了解DN中DG对OPF的影响。本文旨在探讨OPF的问题表述,并提出潜在的解决方案。通过深入了解问题的表述和不同的优化技术,可以在配电网中实现最佳和可持续的电力流,从而实现更高效、更可靠和更具成本效益的电力系统。这为寻求优化电力系统运行的研究人员和从业人员提供了巨大的好处。

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