[论文解读] Foresighted Demand Side Management

Yuanzhang Xiao, Mihaela van der Schaar|arXiv (Cornell University)|Jan 9, 2014

Smart Grid Energy Management参考文献 18被引用 5

一句话总结

本文提出了一种用于智能电网的去中心化、前瞻性需求侧管理框架，其中聚合商和ISO利用推测的未来价格进行长期决策。通过利用拉格朗日分解和随机次梯度更新，该框架在信息有限的情况下实现了社会最优，与短视策略相比总系统成本降低高达60%，与基于李雅普诺夫的方法相比降低30%。

ABSTRACT

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities.

研究动机与目标

• 设计一种去中心化的需求侧管理策略，即使在信息共享有限的情况下，也能实现全系统范围的社会最优。
• 解决前瞻性聚合商与ISO之间存在的耦合问题，其中每个实体的决策会影响其他实体的未来成本。
• 使每个聚合商仅使用本地信息和来自ISO的推测未来价格，即可最小化其长期成本。
• 通过在拉格朗日乘子上使用随机次梯度方法，确保收敛到最优解。
• 验证所提出的框架在成本降低方面优于短视和基于李雅普诺夫的前瞻性需求侧管理策略。

提出的方法

• ISO根据系统约束的估计拉格朗日乘子，向每个聚合商提供一个推测的未来价格。
• 每个聚合商使用其自身状态、需求和储能信息，结合推测价格，求解本地贝尔曼方程。
• 系统建模为具有凸成本函数的马尔可夫决策过程，可通过拉格朗日松弛实现分解。
• 拉格朗日乘子通过使用1/k步长的随机次梯度下降法进行更新，确保收敛到最优值。
• 每个聚合商的推测价格被推导为惩罚项关于其电能购买量的梯度。
• 由于成本函数的凸性，该框架确保无对偶间隙，从而保证去中心化解达到全局最优。

实验结果

研究问题

• RQ1 在具有前瞻性实体且信息共享有限的智能电网中，去中心化的需求侧管理框架能否实现社会最优？
• RQ2 每个聚合商如何在不了解其他聚合商或ISO状态的情况下，做出最优的长期决策？
• RQ3 推测的未来价格在实现未知实体间耦合下的去中心化优化中起到什么作用？
• RQ4 当仅能获得部分信息时，如何设计ISO的经济调度策略以最小化长期总系统成本？
• RQ5 所提出的框架在成本降低方面是否优于短视和基于李雅普诺夫的前瞻性需求侧管理策略？

主要发现

• 尽管采用去中心化决策且信息不完整，所提出的框架在智能电网中实现了社会最优。
• 与最优短视需求侧管理相比，总系统成本最高可降低60%。
• 与基于李雅普诺夫优化的前瞻性需求侧管理方法相比，成本最高可降低30%。
• 使用1/k步长的拉格朗日乘子更新可收敛到最优值，确保收敛到全局最优。
• 每个聚合商的推测价格被推导为惩罚项的梯度，从而实现精确的去中心化成本最小化。
• 通过拉格朗日松弛对贝尔曼方程进行分解，使每个实体仅使用本地信息即可计算其最优策略。