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Autonomous Demand Response Using Stochastic Differential Games

机译：使用随机差分博弈的自主需求响应

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摘要

Demand response (DR) programs are implemented to encourage consumers to reduce their electricity demand when needed, e.g., at peak-load hours, by adjusting their controllable load. In this paper, our focus is on controllable load types that are associated with dynamic systems and can be modeled using differential equations. Examples of such load types include heating, ventilation, and air conditioning; water heating; and refrigeration. In this regard, we propose a new DR model based on a two-level differential game framework. At the beginning of each DR interval, the price is decided by the upper level (aggregator, utility, or market) given the total demand of users in the lower level. At the lower level, for each player (residential or commercial buildings that are equipped with automated load control systems and local renewable generators), given the price from the upper level, the electricity usage of air conditioning unit, and the battery storage charging/discharging schedules, are controlled in order to minimize the user’s total electricity cost. The optimal user strategies are derived using the stochastic Hamilton–Jacobi–Bellman equations. We also show that the proposed game can converge to a feedback Nash equilibrium. Based on the effect of real-time pricing on users’ daily demand profile, the simulation results demonstrate the properties of the proposed game and show how we can optimize consumers’ electricity cost in the presence of time-varying prices.
机译：实施了需求响应（DR）计划，以鼓励消费者在需要时（例如，在高峰时段）通过调整可控负载来减少用电需求。在本文中，我们的重点是与动态系统关联的可控负载类型，并且可以使用微分方程进行建模。这种负载类型的示例包括加热，通风和空调；水加热；和冷藏。在这方面，我们提出了一种基于两级差分博弈框架的新的灾难恢复模型。在每个灾难恢复间隔的开始，价格由上层（聚合器，公用事业或市场）决定，同时要考虑下层用户的总需求。在较低级别，对于每个参与者（配备有自动负载控制系统和本地可再生发电机的住宅或商业建筑物），从较高级别给出价格，空调单元的用电量以及电池存储的充电/放电控制时间表，以最大程度地减少用户的总电费。最佳的用户策略是使用随机的Hamilton–Jacobi–Bellman方程得出的。我们还表明，提出的博弈可以收敛到反馈纳什均衡。基于实时定价对用户每日需求状况的影响，仿真结果演示了所建议游戏的特性，并展示了在价格随时间变化的情况下如何优化消费者的电费。

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