用贝叶斯优化高效筛选分布式能源接入的高风险场景
Selecting Critical Scenarios of DER Adoption in Distribution Grids Using Bayesian Optimization
- 基于多目标贝叶斯优化，以电网应力为黑箱函数进行高效搜索
- 在200-400节点配电网中实现比传统方法快10倍以上且具统计保证
- 适合电网规划人员评估光伏接入风险，避免盲目试错
我们提出一种新方法，用于筛选分布式能源（DER）接入配电网中最关键的风险场景。预测新增光伏接入可能引发的电压越限和线路过载风险，对电网投资规划至关重要，但现有方法仍依赖确定性或经验性场景选择。本文构建基于多目标贝叶斯优化的高效搜索框架，将电网应力指标视为计算代价高的黑箱函数，通过高斯过程代理模型逼近，并设计基于帕累托临界概率的采集函数。该方法提供统计保证，相较保守的穷举搜索提升一个数量级效率。在含200-400个节点的真实馈线案例中验证了其有效性和准确性。
原文摘要 · Abstract (English)
We develop a new methodology to select scenarios of DER adoption most critical for distribution grids. Anticipating risks of future voltage and line flow violations due to additional PV adopters is central for utility investment planning but continues to rely on deterministic or ad hoc scenario selection. We propose a highly efficient search framework based on multi-objective Bayesian Optimization. We treat underlying grid stress metrics as computationally expensive black-box functions, approximated via Gaussian Process surrogates and design an acquisition function based on probability of scenarios being Pareto-critical across a collection of line- and bus-based violation objectives. Our approach provides a statistical guarantee and offers an order of magnitude speed-up relative to a conservative exhaustive search. Case studies on realistic feeders with 200-400 buses demonstrate the effectiveness and accuracy of our approach.
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