Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty

TL;DR

This paper introduces an efficient, scalable online algorithm for controlling storage networks in stochastic environments, providing performance guarantees and near-optimal solutions through distributed implementation.

Contribution

It develops a novel online modified greedy algorithm with provable near-optimality for large stochastic storage networks, including a distributed version using local information.

Findings

The algorithm achieves near-zero sub-optimality bounds in many cases.

Numerical results confirm the theoretical performance guarantees.

The distributed implementation scales effectively with network size.

Abstract

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.