Back to Search Start Over

Control of Generalized Energy Storage Networks

Authors :
Qin, Junjie
Chow, Yinlam
Yang, Jiyan
Rajagopal, Ram
Publication Year :
2015

Abstract

The integration of intermittent and volatile 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 general storage networks in uncertain environments is an important open problem. The key challenge is that, even in small networks, the corresponding constrained stochastic control problems with 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 near-optimal performance. This paper provides an efficient and provably near-optimal algorithm to solve this problem in a very general setting. We study the optimal control of generalized storage networks, i.e., electric networks connected to distributed generalized storages. Here generalized storage is a unifying dynamic model for many components of the grid that provide the functionality of shifting energy over time, ranging from standard energy storage devices to deferrable or thermostatically controlled loads. An online algorithm is devised for the corresponding constrained stochastic control problem based on the theory of Lyapunov optimization. We prove that the algorithm is near-optimal, and construct a semidefinite program to min- imize the sub-optimality bound. The resulting bound is a constant that depends only on the parameters of the storage network and cost functions, and is independent of uncertainty realizations. Numerical examples are given to demonstrate the effectiveness of the algorithm.<br />Comment: This report, written in January 2014, is a longer version of the conference paper [1] (See references in the report). This version contains a somewhat more general treatment for the cases with sub-differentiable objective functions and Markov disturbance. arXiv admin note: substantial text overlap with arXiv:1405.7789

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1504.05661
Document Type :
Working Paper