Distributed Online Modified Greedy Algorithm for Networked Storage Operation Under Uncertainty

Abstract

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.

Online Modified Greedy Algorithm for Storage Control Under Uncertainty

“…Extending the algorithm to a setting with multiple storages that are connected via a power network will enable the algorithm to be applied to settings such as storage control in micro-grids. One possible way for such an extension is reported in [29].…”

Residential Energy Storage Management With Bidirectional Energy Control

“…Energy storage has been considered at the power grid operator or aggregator to combat the fluctuation of renewable generation, with many works in literature on storage control and assessment of its role in renewable generation [2], for power balancing with fixed load [3], [4] or flexible load control [6], [7], and for phase balancing [5]. Residential energy storage systems to reduce electricity cost have been considered without renewable [17] and with renewable integration [10]- [13], [18]- [24]. Only energy buying was considered in these works.…”

“…The idea of energy selling back or trading is considered in [26]- [28], where [26], [27] focus on demand-side management via pricing schemes using game approaches for load scheduling among customers, and [28] considers a microgrid operation and supply. In addition, although not explicitly modeled, the system considered in [24] can be generalized to include energy selling under a simplified model, provided that buying and selling prices are constrained such that the overall cost function is still convex. All these works consider the grid level operation and the cost associated with it, and use a simple battery storage model without considering degradation or operational cost.…”

A Two-Layer Decentralized Control Architecture for DER Coordination

“…A centralized MPC architecture that includes all of the network parameters would achieve high performance, but would be too large to be computationally feasible for real time control as discussed in [3]. Several recent papers address this issue by distributing the computation across nodes in the network [3], [6] [7], [8], [9] using message passing to achieve similar performance to a fully centralized control. These fully distributed control architectures, however, fail to address practical concerns surrounding device ownership, data privacy, and communication infrastructure.…”