AI 中文总结
针对区域供热网络热需求不确定性，本文将CC和CVaR优化适配到混合整数机组组合问题，结合贝叶斯模型生成的热需求预测分布，通过真实与合成数据评估了两种鲁棒优化方法的性能。
AI 中文摘要
区域供热网络是能源转型的关键组成部分，但由于热需求存在大量不确定性，其运行规划颇具挑战性，本文在不损害系统可靠性的前提下解决该波动问题。我们将机会约束规划（CC）和条件风险价值（CVaR）优化方法适配到区域供热网络的混合整数机组组合问题中，以此计算需求不确定性下的最优机组组合调度方案。我们采用贝叶斯模型生成热需求时间序列，该模型可明确量化预测不确定性，并生成预测分布作为网络优化的输入。为处理热需求的固有不确定性，我们应用两种鲁棒优化方法：机会约束规划限制未满足需求的概率，而CVaR优化则惩罚分布尾部的严重短缺。我们针对柏林区域供热网络的真实世界数据，以及一组不同规模、参数符合实际的合成基准实例，对这两种方法进行评估，从解的质量、风险暴露程度和计算工作量三个方面对两种不确定性处理方法进行比较。
英文摘要
While district heating networks are a key component of the energy transition, their operational planning is challenging due to substantial uncertainty in heat demand. We address this volatility without compromising system reliability. By adapting chance-constrained programming (CC) and conditional value-at-risk (CVaR) optimization to the mixed-integer unit-commitment problem for district-heating networks, we can compute optimal unit-commitment schedules under demand uncertainty. We generate heat demand time series using a Bayesian model, which explicitly quantifies forecasting uncertainty and yields predictive distributions as input to the network optimization. To handle the inherent uncertainty in heat demand, we apply both robust optimization approaches: chance-constrained programming limits the probability of unmet demand, while CVaR optimization penalizes severe shortfalls in the tail of the distribution. We evaluate the approaches both on real-world data from the Berlin district heating network and on a benchmark set of synthetic, realistically parameterized instances of varying sizes. The results compare the two uncertainty-handling methods with respect to solution quality, risk exposure, and computational effort.
