[论文解读] Control and Optimization Meet the Smart Power Grid - Scheduling of Power Demands for Optimal Energy Management

Iordanis Koutsopoulos, Leandros Tassiulas|arXiv (Cornell University)|Aug 21, 2010

Smart Grid Energy Management参考文献 10被引用 17

一句话总结

本文提出了一种基于阈值的调度策略，用于智能电网中的最优能量管理，其中电网运营商通过调度用户电力需求以最小化运行成本——该成本被建模为瞬时负荷的凸函数。受控释放策略根据实时电力消耗动态激活或排队需求，随着截止时间增长，实现渐近最优。

ABSTRACT

The smart power grid aims at harnessing information and communication technologies to enhance reliability and enforce sensible use of energy. Its realization is geared by the fundamental goal of effective management of demand load. In this work, we envision a scenario with real-time communication between the operator and consumers. The grid operator controller receives requests for power demands from consumers, with different power requirement, duration, and a deadline by which it is to be completed. The objective is to devise a power demand task scheduling policy that minimizes the grid operational cost over a time horizon. The operational cost is a convex function of instantaneous power consumption and reflects the fact that each additional unit of power needed to serve demands is more expensive as demand load increases.First, we study the off-line demand scheduling problem, where parameters are fixed and known. Next, we devise a stochastic model for the case when demands are generated continually and scheduling decisions are taken online and focus on long-term average cost. We present two instances of power consumption control based on observing current consumption. First, the controller may choose to serve a new demand request upon arrival or to postpone it to the end of its deadline. Second, the additional option exists to activate one of the postponed demands when an active demand terminates. For both instances, the optimal policies are threshold based. We derive a lower performance bound over all policies, which is asymptotically tight as deadlines increase. We propose the Controlled Release threshold policy and prove it is asymptotically optimal. The policy activates a new demand request if the current power consumption is less than a threshold, otherwise it is queued. Queued demands are scheduled when their deadline expires or when the consumption drops below the threshold.

研究动机与目标

• 解决在智能电网环境中通过智能调度用户电力需求以最小化电网运行成本的挑战。
• 将电网运营商的决策过程建模为在与用户实时通信条件下的控制与优化问题。
• 开发能够适应动态需求到达的在线调度策略，同时保持系统可靠性和成本效率。
• 建立理论性能边界，并设计适用于长期平均成本最小化的渐近最优控制策略。

提出的方法

• 将电网运行成本建模为瞬时总功率消耗的凸函数，反映负荷增加带来的边际成本上升。
• 引入两种控制实例：一种允许将新需求推迟至截止时间再处理，另一种允许在负荷下降时重新激活已排队的需求。
• 提出受控释放阈值策略，仅当当前功率消耗低于阈值时才激活新需求。
• 使用随机模型分析长期平均成本，假设需求持续生成且在线调度决策实时作出。
• 通过动态规划和截止时间渐近增长下的性能分析，推导出基于阈值的最优策略。
• 建立所有策略的通用下界性能，受控释放策略在截止时间增长时渐近达到该下界。

实验结果

研究问题

• RQ1 电网运营商如何实时最优调度用户电力需求，以最小化凸运行成本？
• RQ2 当需求动态到达且必须在线决策时，最优调度策略的结构是什么？
• RQ3 基于阈值的策略性能如何随需求截止时间增加而变化？
• RQ4 能否推导出长期平均成本的通用下界，且该下界是否可渐近实现？
• RQ5 在不同运行约束下，哪些控制动作——推迟、激活或排队——能实现最优性能？

主要发现

• 受控释放阈值策略渐近最优，随着需求截止时间增加，达到通用下界性能。
• 在非抢占式调度情况下，问题退化为装箱问题，且为NP难问题，表明在一般情况下计算上不可行。
• 在抢占式情况下，迭代算法可提供最优解，有效解决负载均衡问题。
• 两种控制实例的最优控制策略均为基于阈值的，仅依赖于当前瞬时功率消耗。
• 受控释放策略中的阈值不依赖于队列大小，只要队列非空，这是由于功率消耗在阈值附近保持稳定。
• 该模型支持异构需求类型，包括可灵活调度、可变功率水平以及能量约束的需求（如电动汽车充电）。