.png){kind=icon}
返回
Data-driven stochastic AC-OPF using Gaussian process regression
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijepes.2023.109249.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
At present, electricity generation is responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating significant amounts of renewable energy sources into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly intermittent and thus bring a lot of uncertainty to power grid operation and challenge existing optimization and control policies. The chance-constrained (CC) alternating current optimal power flow (AC-OPF) framework finds the minimum cost of generation dispatch, maintaining the power grid operation within security limits with a prescribed probability. Unfortunately, the AC-OPF problem's chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and also needs additional assumptions to be made about the behavior of renewable generation probability distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach for solving the stochastic AC-OPF problem, based on Gaussian process regression (GPR) to close this gap. The Gaussian process (GP) approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertain inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is validated and illustrated using a number of standard IEEE test cases.
Keyword:
Chance constraints
Gaussian processes
Intermittency
Optimal power flow
Stochastic phenomena
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5
论文数:
1.1W
被引数:
3.1W
机构
引用论文
Distribution-free chance-constrained load balance model for the operation planning of hydrothermal power systems coupled with multiple renewable energy sources耦合多种可再生能源的水火电力系统运行规划的无分布机会约束负载平衡模型
PREAI
分享收藏CasADi: a software framework for nonlinear optimization and optimal controlCasADi: 非线性优化和最优控制的软件框架
PREAI
分享收藏The Radiocarbon Approach to Neanderthals in a Carnivore Den Site: a Well-Defined Chronology for Teixoneres Cave (Moià, Barcelona, Spain).png)
Radiocarbon
RadiocarbonIF0
PREAI
分享收藏Chance-Constrained Optimal Power Flow: Risk-Aware Network Control under UncertaintySIAM REVIEWOAAI
SIAM REVIEWIF6.1
OAAI分享收藏
查看更多内容