AI 中文总结
研究能源社区基于价格的灵活需求分布式调度，通过双层随机动态规划建模，得出阈值定价规则（TPR），该规则计算成本低、抗建模不确定性，能保证社区收益与成员理性，且随社区规模增长渐近最优。
AI 中文摘要
我们研究了能源社区中基于价格的灵活需求分布式调度问题，协调器广播电价，各家庭安排其用电。家庭需求包括可延迟和不可延迟负载。协调器代表社区成员按净能量计量电价与配电公司交易。我们将分布式需求调度制定为双层随机动态规划。上层优化协调器定价策略以最小化社区能源成本，下层是随机动态规划以最大化家庭消费收益。通过揭示最优集中调度结构，我们得出阈值定价规则（TPR），它计算成本低，对建模不确定性有鲁棒性，能保证社区收益充足和成员个体理性，且随着社区规模增长渐近最优。
英文摘要
We study price-based distributed scheduling of flexible demand in an energy community, where a coordinator broadcasts electricity prices and individual households schedule their consumption. Household demand includes deferrable and non-deferrable loads, such as electric vehicle charging with completion deadlines and thermostatically controlled loads. The coordinator transacts with a distribution utility on behalf of community members under the regulated Net Energy Metering tariff. We formulate distributed demand scheduling as a bilevel stochastic dynamic program. The upper level optimizes the coordinator's pricing policy to minimize the community's energy costs subject to operating, revenue adequacy, and individual rationality constraints. The lower level involves stochastic dynamic programs that maximize households' consumption benefits subject to the availability of renewable generation. The computational cost of such a distributed stochastic dynamic program is prohibitive in general. By uncovering the structure of optimal centralized scheduling, we derive Threshold Pricing Rule (TPR) -- a simple community pricing policy with linear computational costs for the upper- and lower-level optimizations. Being independent of parameters of the underlying stochastic dynamic program, TPR is robust against modeling uncertainties and is shown to guarantee revenue adequacy for the community and individual rationality for community members. As the community size grows, TPR is shown to be asymptotically optimal.
