四线低压电网中的Volt-VAr-Watt优化：精确非线性模型与光滑近似——扩展版
Volt-VAr-Watt Optimization in Four-Wire Low-Voltage Networks: Exact Nonlinear Models and Smooth Approximations - Extended Version
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中文总结 AI 辅助
针对低压配电网电压越限问题，提出三种无整数变量的非线性VVWO模型，含精确编码与光滑逼近，在真实四线不平衡网络上验证，证明优化方法优于增量法。
中文摘要 AI 辅助
分布式能源的普及正在增加低压（LV）配电网络中过电压和欠电压现象的发生频率。智能逆变器功能，如Volt-VAr和Volt-Watt控制，可以在用户侧调节电压，但由于其不可微性，在优化模型中难以捕捉。本文提出了三种用于四线不平衡最优潮流的非线性模型，这些模型在不使用二进制或整数变量的情况下纳入了这些非光滑函数。第一种模型利用控制流将这些非光滑函数直接编码为用户自定义函数，这一功能得益于许多最先进的代数建模语言。第二种和第三种模型引入了具有可调逼近误差的定制光滑逼近，以解决由不可微性引起的潜在数值问题，在保持精度的同时提高可靠性。所有三种方法均在具有不同屋顶太阳能采用水平的真实四线不平衡低压网络上进行了评估，包括一个具有302个智能逆变器的539节点系统。本文不仅是首个在真实四线不平衡低压网络模型上展示准确、可靠且可处理的Volt-Var-Watt优化（VVWO）的研究，而且还证明了基于优化的非增量方法相比常用的增量（准稳态）方法具有更高的可靠性。
英文摘要
The proliferation of distributed energy resources is increasing the prevalence of both overvoltages and undervoltages in low-voltage (LV) distribution networks. Smart inverter functionalities, such as Volt-VAr and Volt-Watt control, can regulate voltage at the consumer level but are challenging to capture in optimization models due to their nondifferentiability. This paper proposes three nonlinear models for four-wire unbalanced optimal power flow that incorporate these nonsmooth functions without binary or integer variables. The first model encodes these nonsmooth functions directly as user-defined functions using control flow, a feat enabled by many state-of-the-art algebraic modeling languages. The second and third introduce bespoke smooth approximations with tunable approximation errors to address potential numerical issues arising from nondifferentiability, improving reliability while maintaining accuracy. All three methods are evaluated on real four-wire unbalanced LV networks with varying rooftop solar adoption levels, including a 539-bus system with 302 smart inverters. This paper is not only the first to demonstrate accurate, reliable, and tractable Volt-Var-Watt optimization (VVWO) on real four-wire unbalanced LV network models, but it also establishes that optimization-based, non-incremental methods offer superior reliability compared to commonly used incremental (quasi-steady-state) approaches.
发表机构
- The University of Melbourne（墨尔本大学）
- The University of Queensland（昆士兰大学）
机构由 AI 辅助整理，请以论文原文为准。
