不确定网络存储操作的分布式在线改进贪婪算法
Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty
摘要：
间歇性和随机性可再生能源的整合需要增加电网运行的灵活性。从广义上讲，存储提供了随时间转移能源的灵活性;另一方面，网络提供了在地理位置上转移能源的灵活性。随机环境下存储网络的最优控制是一个重要的开放性问题。关键的挑战是，即使在小网络中，连续空间上的相应约束随机控制问题也会受到维数的影响，并且在一般情况下是难以处理的。对于大型网络，没有有效的算法是已知的，给这个问题的最佳或可证明接近最佳的性能。本文提供了一个有效的算法来解决这个问题的性能保证。我们研究存储网络的操作，即，通过电力网络互连的存储系统。针对相应的约束随机控制问题，提出了一种在线修正贪婪算法。一个次优界的算法推导出，并构造了一个半定规划，以尽量减少界。在许多情况下，边界接近零，因此算法接近最优。基于乘法器的交替方向法，提出了一种仅依赖局部信息和邻居通信的在线算法的分布式实现方法。数值算例验证了所建立的理论性能界，并证明了算法的可扩展性.
英文摘要：
The integration of intermittent and stochastic renewable energy resources requires increased flexibility in the operation of the electric grid. Storage, broadly speaking, provides the flexibility of shifting energy over time; network, on the other hand, provides the flexibility of shifting energy over geographical locations. The optimal control of storage networks in stochastic environments is an important open problem. The key challenge is that, even in small networks, the corresponding constrained stochastic control problems on continuous spaces suffer from curses of dimensionality, and are intractable in general settings. For large networks, no efficient algorithm is known to give optimal or provably near-optimal performance for this problem. This paper provides an efficient algorithm to solve this problem with performance guarantees. We study the operation of storage networks, i.e., a storage system interconnected via a power network. An online algorithm, termed Online Modified Greedy algorithm, is developed for the corresponding constrained stochastic control problem. A sub-optimality bound for the algorithm is derived, and a semidefinite program is constructed to minimize the bound. In many cases, the bound approaches zero so that the algorithm is near-optimal. A task-based distributed implementation of the online algorithm relying only on local information and neighbor communication is then developed based on the alternating direction method of multipliers. Numerical examples verify the established theoretical performance bounds, and demonstrate the scalability of the algorithm.
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