Fully Distributed Peer-to-Peer Optimal Voltage Control with Minimal Model Requirements

This paper addresses the problem of voltage regulation in a power distribution grid using the reactive power injections of grid-connected power inverters. We first discuss how purely local voltage control schemes cannot regulate the voltages within a desired range under all circumstances and may even yield detrimental control decisions. Communication and, through that, coordination are therefore needed. On the other hand, short-range peer-to-peer communication and knowledge of electric distances between neighbouring controllers are sufficient for this task. We implement such a peer-to-peer controller and test it on a 400~V distribution feeder with asynchronous communication channels, confirming its viability on real-life systems. Finally, we analyze the scalability of this approach with respect to the number of agents on the feeder that participate in the voltage regulation task

DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility

Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the feasibility of the predicted solutions to physical system constraints. Due to the inherent approximation errors, the solutions predicted by DNNs may violate the operating constraints, e.g., the transmission line capacities, limiting their applicability in practice. To address this challenge, we develop DeepOPF+ as a DNN approach based on the so-called "preventive" framework. Specifically, we calibrate the generation and transmission line limits used in the DNN training, thereby anticipating approximation errors and ensuring that the resulting predicted solutions remain feasible. We theoretically characterize the calibration magnitude necessary for ensuring universal feasibility. Our DeepOPF+ approach improves over existing DNN-based schemes in that it ensures feasibility and achieves a consistent speed up performance in both light-load and heavy-load regimes. Detailed simulation results on a range of test instances show that the proposed DeepOPF+ generates 100% feasible solutions with minor optimality loss. Meanwhile, it achieves a computational speedup of two orders of magnitude compared to state-of-the-art solvers.Comment: 7 pages, SmarGridComm202

On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints

Power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal Power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converged DRL method for TSC-OPF is proposed in this paper. The Markov Decision Process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved Deep Deterministic Policy Gradient algorithm with Curriculum learning, Parallel exploration, and Ensemble decision-making (DDPG-CPEn) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.Comment: 10 pages, 11 figure

Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time

The Optimal power flow (OPF) problem contains many constraints. However, equality constraints along with a limited set of active inequality constraints encompass sufficient information to determine the feasible space of the problem. In this paper, a hybrid supervised regression and classification learning based algorithm is proposed to identify active and inactive sets of inequality constraints of AC OPF solely based on nodal power demand information. The proposed algorithm is structured using several classifiers and regression learners. The combination of classifiers with regression learners enhances the accuracy of active / inactive constraints identification procedure. The proposed algorithm modifies the OPF feasible space rather than a direct mapping of OPF results from demand. Inactive constraints are removed from the design space to construct a truncated AC OPF. This truncated optimization problem can be solved faster than the original problem with less computational resources. Numerical results on several test systems show the effectiveness of the proposed algorithm for predicting active and inactive constraints and constructing a truncated AC OPF. We have posted our code for all simulations on arxiv and have uploaded the data used in numerical studies to IEEE DataPort as an open access dataset.Comment: Pages:

Research on Algorithm Driven Intelligent Management and Control Technology for Future Power Grid

An ever-more crucial architecture for both present and future electrical systems is a Power Grid (PG) that spans multiple areas comprising interlinked transmission lines, which may effectively reallocate energy resources on an extensive level. Preserving system equilibrium and increasing operating earnings are largely dependent on how the PG dispatches power using a variety of resources. The optimization techniques used to solve this dispatch issue today are not capable of making decisions or optimizing online; instead, they require doing the entire optimization computation at every dispatch instant. Herein, a novel Mutable Galaxy-based Search-tuned Flexible Deep Convolutional Neural Network (MGS-FDCNN) is presented as an online solution to targeted coordinated dispatch challenges in future PG. System optimization can be achieved using this strategy using only past operational data. First, a numerical model of the targeted coordination dispatch issue is created. Next, to solve the optimization challenges, we construct the MGS optimization approach. The effectiveness and accessibility of the suggested MGS-FDCNN approach are validated by the presentation of experimental data relying on the IEEE test bus network