We present a method for joint phase identification and topology recovery in unbalanced three phase radial networks using only voltage measurements. By recovering phases and topology jointly, we utilize all three phase voltage measurements and can handle networks where some buses have a subset of three phases. Our method is theoretically justified by a novel linearized formulation of unbalanced three phase power flow and makes precisely defined and reasonable assumptions on line impedances and load statistics. We validate our method on three IEEE test networks simulated under realistic conditions in OpenDSS, comparing our performance to the state of the art. In addition to providing a new method for phase and topology recovery, our intuitively structured linearized model will provide a foundation for future work in this and other applications.
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is a challenging problem for existing methods, especially as the grid is subject to increasingly frequent reconfiguration due to the adoption of renewable energy. Further, running existing control mechanisms with incorrect network information may lead to unstable control. In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage li…
Operators can now remotely control switches and update the control settings for voltage regulators and distributed energy resources (DERs), thus unleashing the network reconfiguration opportunities to improve efficiency. Aligned to this direction, this work puts forth a comprehensive toolbox of optimization models leveraging the control capabilities of smart grid assets. We put forth detailed yet practical models to capture the operation of locally and remotely controlled regulators, and customize the watt-var DER control curves complying with the IEEE 1547.8 mandates. Maintaining radiality is…
The renewable energy proliferation calls upon the grid operators and planners to systematically evaluate the potential impacts of distributed energy resources (DERs). Considering the significant differences between various inverter-based resources (IBRs), especially the different capabilities between grid-forming inverters and grid-following inverters, it is crucial to develop an efficient and effective assessment procedure besides available co-simulation framework with high computation burdens. This paper presents a streamlined graph-based topology assessment for the integrated power system t…
Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNN-for-OPF framework innovatively exploits the locality property of locational marginal prices and voltage magnitude. Furthermore, we develop a physics-aware (ac-)flow feasibility regularization approach for general OPF learning. The advantages o…
This paper defines a Maximum Likelihood Estimator (MLE) for the admittance matrix estimation of distribution grids, utilising voltage magnitude and power measurements collected only from common, unsychronised measuring devices (Smart Meters). First, we present a model of the grid, as well as the existing MLE based on voltage and current phasor measurements. Then, this problem formulation is adjusted for phase-less measurements using common assumptions. The effect of these assumptions is compared to the initial problem in various scenarios. Finally, numerical experiments on a popular IEEE bench…
This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning i…
Delft University of Technology（代尔夫特理工大学）；TenneT TSO B.V.（TenneT输电系统运营公司）；Smart Wires Inc.（智能电线公司）；RTE（法国输电网络公司）
System operators are faced with increasingly volatile operating conditions. In order to manage system reliability in a cost-effective manner, control room operators are turning to computerised decision support tools based on AI and machine learning. Specifically, Reinforcement Learning (RL) is a promising technique to train agents that suggest grid control actions to operators. In this paper, a simple baseline approach is presented using RL to represent an artificial control room operator that can operate a IEEE 14-bus test case for a duration of 1 week. This agent takes topological switching …
Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures…
Improved steady-state stability of power grids with a communication infrastructure
Macedonian Academy of Sciences and Arts（马其顿科学院与艺术院）；Faculty of electrical engineering and information technologies, University “Ss Cyril and Methodius”（西里尔和梅托迪乌斯大学电气工程和信息技术学院）；Faculty of Computer Sciences and Engineering, University “Ss Cyril and Methodius”（西里尔和梅托迪乌斯大学计算机科学与工程系）；BioCircuits Institute, UC San Diego（加州大学圣地亚哥分校生物电路研究所）
To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact d…
The effective way of energy transmission plays a key factor in improving the overall transmission systems efficiency. Many methods are proposed to control the reactive power flow, voltage fluctuations and power factor improvement, The proposed converter topology gives a much significant improvement in transmission systems performance which includes multistage transformers control with the controlled converters along with the series active filters. The overall control strategy which involves the Multistage Voltage Re-Injection Transformer Controlled Converters (MSVRITCC) to reinject the voltage…
We consider a grid-connected voltage source converter (VSC) and address the problem of estimating the grid angle and frequency, information that is essential for an appropriate operation of the converter. We design phase-locked loop (PLL) algorithms with guaranteed stability properties, under the assumption that the grid is characterized by relatively high short-circuit-ratio (SCR) and inertia. In particular we derive, using passivity arguments, generalization of the conventional synchronous reference frame (SRF) and arctangent (ATAN) PLL, which are the standard solutions in power applications…
