Distributed MPC for Efficient Coordination of Storage and Renewable Energy Sources Across Control Areas

Kyri Baker, Junyao Guo, Gabriela Hug, Xin Li

Research output: Contribution to journalArticle

69 Scopus Citations

Abstract

In electric power systems, multiple entities are responsible for ensuring an economic and reliable way of delivering power from producers to consumers. With the increase of variable renewable generation it is becoming increasingly important to take advantage of the individual entities' (and their areas') capabilities for balancing variability. Hence, in this paper, we employ and extend the approximate Newton directions method to optimally coordinate control areas leveraging storage available in one area to balance variable resources in another area with only minimal information exchange among the areas. The problem to be decomposed is a model predictive control problem including generation constraints, energy storage constraints, and AC power flow constraints. Singularity issues encountered when formulating the respective Newton-Raphson steps due to intertemporal constraints are addressed and extensions to the original decomposition method are proposed to improve the convergence rate and required communication of the method.
Original languageAmerican English
Pages (from-to)992-1001
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume7
Issue number2
DOIs
StatePublished - 2016

NREL Publication Number

  • NREL/JA-5D00-67328

Keywords

  • convergence
  • energy storage
  • generators
  • mathematical model
  • optimization
  • power systems
  • predictive control

Fingerprint

Dive into the research topics of 'Distributed MPC for Efficient Coordination of Storage and Renewable Energy Sources Across Control Areas'. Together they form a unique fingerprint.

Cite this