Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
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DOI：
10.1109/tpwrs.2023.3298735
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发表时间：
2022-11
影响因子：
6.6
通讯作者：
Seonho Park;Wenbo Chen;Dahyeon Han;Mathieu Tanneau;Pascal Van Hentenryck
中科院分区：
文献类型：
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作者：
Seonho Park;Wenbo Chen;Dahyeon Han;Mathieu Tanneau;Pascal Van Hentenryck
由于可再生能源发电在发电组合中所占比例较大以及预测误差增加，可靠性评估承诺 (RAC) 优化在电网运营中变得越来越重要。独立系统运营商 (ISO) 还致力于使用更精细的时间粒度、更长的时间范围以及可能的随机公式来获得额外的经济和可靠性优势。本文的目标是解决扩展 RAC 公式范围时出现的计算挑战。它提出 RACLearn 1) 使用基于图神经网络 (GNN) 的架构来预测发电机承诺和活动线路约束，2) 将置信值与每个承诺预测相关联，3) 选择高置信度预测的子集，4) 修复可行性，5) 为具有可行预测和活动约束的最先进优化算法提供种子。对中大陆独立系统运营商 (MISO) 使用的精确 RAC 公式和实际传输网络（8965 条传输线、6708 条总线、1890 台发电机和 6262 个负载单元）进行的实验结果表明，RACLearn 框架可以将 RAC 优化速度加快 2 到 4 倍，并且解决方案质量的损失可以忽略不计。
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors. Independent System Operators (ISOs) also aim at using finer time granularities, longer time horizons, and possibly stochastic formulations for additional economic and reliability benefits. The goal of this article is to address the computational challenges arising in extending the scope of RAC formulations. It presents RACLearn that 1) uses a Graph Neural Network (GNN) based architecture to predict generator commitments and active line constraints, 2) associates a confidence value to each commitment prediction, 3) selects a subset of the high-confidence predictions, which are 4) repaired for feasibility, and 5) seeds a state-of-the-art optimization algorithm with feasible predictions and active constraints. Experimental results on exact RAC formulations used by the Midcontinent Independent System Operator (MISO) and an actual transmission network (8965 transmission lines, 6708 buses, 1890 generators, and 6262 load units) show that the RACLearn framework can speed up RAC optimization by factors ranging from 2 to 4 with negligible loss in solution quality.