发表机构
University of Michigan; University of Cape Coast; University of Michigan–Dearborn; Université Laval（密歇根大学; 开普海岸大学; 密歇根大学迪尔伯恩分校; 拉瓦尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出决策门控代理辅助随机优化框架，结合物理信息代理与独立认证，实现光伏接纳容量规划，显著加速并提升决策可靠性。
AI 中文摘要
高比例光伏（PV）渗透规划需要在不确定的辐照度和需求下进行重复的非线性光伏与潮流评估，尤其是在同时优化渗透率和总电损这两个相互竞争的目标并满足可靠性约束时。本文提出了一种决策聚焦的、代理辅助的随机多目标框架，其中校准的光伏模型与配电网仿真器耦合提供高保真参考，而匹配的纯数据代理和物理信息代理加速优化，并基于损失-渗透率权衡对决策可靠性进行实证评估。尽管两种代理在留出误差上几乎相同，但优化器现实筛选却产生了显著不同的运行有效性比率，分别为44.4%和93.4%，这促使根据决策性能而非测试精度选择物理信息模型。代理推理将60个场景设计评估的成本从286.0毫秒降至0.46毫秒，实现了约620倍的加速。一种决策门控的丰富策略进一步针对帕累托关键区域、约束边界区域和代理分歧区域，将预测误差降低了39.1%。独立的500场景认证验证了所有80个筛选候选方案，并识别出46个非支配设计；选定的折中方案实现了70.74%的预期渗透率和297.9千瓦的预期总电损。跨馈线迁移消除了85节点和69节点系统上的基线电压越限，但在33节点系统上产生了过电压和62.7%的馈线网络损耗增加，这表明认证的渗透率具有拓扑特异性，需要决策层面的审计、独立认证和馈线特定验证。
英文摘要
High photovoltaic (PV) penetration planning requires repeated nonlinear PV and power-flow evaluations under uncertain irradiance and demand, particularly when two competing objectives, penetration and total electrical loss, are optimized simultaneously subject to reliability constraints. This paper develops a decision-focused, surrogate-assisted stochastic multiobjective framework in which a calibrated PV model coupled with a distribution-network simulator provides the high-fidelity reference, while matched data-only and physics-informed surrogates accelerate optimization and are evaluated empirically for decision reliability across the loss-penetration tradeoff. Although the two surrogates exhibit nearly identical held-out errors, optimizer-realistic screening yields markedly different operational validity rates of 44.4% and 93.4%, respectively, motivating selection of the physics-informed model on decision performance rather than test accuracy. Surrogate inference cuts the cost of a 60-scenario design evaluation from 286.0 to 0.46 ms, an approximately 620 time speedup. A decision-gated enrichment strategy further targets Pareto-critical, constraint boundary, and surrogate-disagreement regions, reducing prediction error by 39.1%. Independent 500 scenario certification verifies all 80 screened candidates and identifies 46 non-dominated designs; the selected compromise attains 70.74% expected penetration with 297.9 kW expected total electrical loss. Cross-feeder transfer eliminates baseline voltage violations on the 85- and 69-bus systems, but produces overvoltage and a 62.7% increase in feeder-network loss on the 33-bus system, demonstrating that certified penetration is topology-specific and demands decision-level auditing, independent certification, and feeder-specific validation.
Comments17 pages
