考虑消费者保护的动态电价随机混合整数优化
Stochastic Mixed-Integer Optimization of Dynamic Electricity Tariffs with Consumer Protection
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中文总结 AI 辅助
本文针对日前居民电价设计问题，基于伦敦低碳家庭数据，用HiGHS求解随机混合整数规划，提出分段保护型随机电价，在限制账单涨幅的同时实现了小幅峰值削减，还对比了代表性家庭变体的效果。
中文摘要 AI 辅助
日前居民电价需在需求被观测前公布，激进的高价在模拟中可削减峰值，但也会提高用户账单。本文研究了当电价设计问题需将收入维持在固定电价基准附近并限制账单涨幅时，峰值削减会损失多少。我们使用来自5566户伦敦低碳家庭的每半小时数据（1.678亿条验证读数），估计相对于标准电价对照组的准实验价格响应，构建自助法需求场景，并使用HiGHS求解随机混合整数规划。在所有73个符合条件的保留测试日中，分段保护型随机电价平均降低模拟峰值需求2.29%（95%日自助法置信区间[2.11, 2.48]），收入变化为-2.26%，平均最差分段账单涨幅为2.48%。移除分段账单上限仅将峰值削减提升至2.37%，而最差分段账单涨幅升至6.81%：在该样本中，大量平均保护成本仅造成少量削峰性能损失。相同电价方案使家庭95分位账单涨幅为8.61%（CVaR95为14.76%），因此分段平均上限无法约束家庭尾部风险。我们报告了一致家庭模拟下的完整保护价格前沿，并比较了分段保护与代表性家庭（感知尾部）变体：后者将家庭95分位从8.61%降至7.05%，同时平均峰值削减仅从2.29%变为2.28%。所有优化结果均为自愿试验下的模型反事实；因无批发成本，我们未优化利润。
英文摘要
Day-ahead residential tariffs must be posted before demand is observed. Aggressive high prices can cut peaks in simulation, but they can also raise customer bills. This paper measures how much peak reduction is lost when a tariff design problem is required to keep revenue near a flat-tariff baseline and to limit bill increases. Using half-hourly data from 5,566 Low Carbon London households (167.8 million validated readings), we estimate quasi-experimental price response against the standard-tariff comparison group, form bootstrap demand scenarios, and solve stochastic mixed-integer programs with HiGHS. On all 73 eligible held-out test days, a segment-protected stochastic tariff reduces simulated peak demand by 2.29% on average (95% day-bootstrap CI [2.11, 2.48]), with revenue change -2.26% and mean worst-segment bill increase 2.48%. Removing the segment bill cap raises peak reduction only to 2.37%, while the worst segment bill increase rises to 6.81%: in this sample, substantial average protection costs little peak-shaving performance. The same schedules leave a household 95th-percentile bill increase of 8.61% (CVaR95 14.76%), so segment-average caps do not bound household tails. We report the full price-of-protection frontier under consistent household simulation and compare segment protection with a representative-household (tail-aware) variant: the latter cuts household p95 from 8.61% to 7.05% while changing mean peak reduction only from 2.29% to 2.28%. All optimized outcomes are model-based counterfactuals under an opt-in trial; wholesale costs are unavailable, so we do not optimize profit.
