Decentralized optimal dispatch of distributed energy resources

In this paper, we address the problem of optimally dispatching a set of distributed energy resources (DERs) without relying on a centralized decision maker. We consider a scenario where each DER can provide a certain resource (e.g., active or reactive power) at some cost (namely, quadratic in the amount of resource), with the additional constraint that the amount of resource that each DER provides is upper and lower bounded by its capacity limits. We propose a low-complexity iterative algorithm for DER optimal dispatch that relies, at each iteration, on simple computations using local information acquired through exchange of information with neighboring DERs. We show convergence of the proposed algorithm to the (unique) optimal solution of the DER dispatch problem. We also describe a wireless testbed we developed for testing the performance of the algorithms.

[1]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[2]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[3]  M. Madrigal,et al.  An analytical solution to the economic dispatch problem , 2000 .

[4]  G. Joos,et al.  The potential of distributed generation to provide ancillary services , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[5]  Richard Han,et al.  TSync: a lightweight bidirectional time synchronization service for wireless sensor networks , 2004, MOCO.

[6]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[7]  Stephen P. Boyd,et al.  Optimal Scaling of a Gradient Method for Distributed Resource Allocation , 2006 .

[8]  Christoforos N. Hadjicostis,et al.  Coordination and Control of Distributed Energy Resources for Provision of Ancillary Services , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[9]  John N. Tsitsiklis,et al.  Weighted Gossip: Distributed Averaging using non-doubly stochastic matrices , 2010, 2010 IEEE International Symposium on Information Theory.

[10]  Asuman E. Ozdaglar,et al.  Constrained Consensus and Optimization in Multi-Agent Networks , 2008, IEEE Transactions on Automatic Control.

[11]  Christoforos N. Hadjicostis,et al.  Distributed algorithms for control of demand response and distributed energy resources , 2011, IEEE Conference on Decision and Control and European Control Conference.

[12]  Nitin H. Vaidya,et al.  Resilient Networked Control of Distributed Energy Resources , 2012, IEEE Journal on Selected Areas in Communications.