By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network.

中文翻译：

---

用于智能电网安全验证的并行统计模型检查

通过使用部署在用户场所的小型计算设备，自主需求响应 (ADR) 使用户的电力消耗适应给定的与时间相关的电费。这允许最终用户节省他们的电费，并且配电系统运营商可以通过避免需求高峰来优化（通过合适的时间相关关税）电网管理。不幸的是，即使使用 ADR，用户的功耗也可能会偏离预期（最低成本），例如，因为 ADR 设备无法正确预测用户驻地的能源需求。结果，聚合的电力需求可能出现不希望的峰值。在本文中，我们通过介绍方法和实现它们的软件工具（APD-Analyser）来解决这个问题，使配电系统运营商能够有效验证给定的随时间变化的电价是否达到了预期目标，即使最终用户偏离了他们的预期行为。我们通过来自丹麦中压配电网络的现实场景展示了所提出方法的可行性。