A robust solution to the load curtailment problem

H. P. Simão, H. B. Jeong, B. Defourny, Warren Buckler Powell, A. Boulanger, A. Gagneja, L. Wu, R. N. Anderson

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Operations planning in smart grids is likely to become a more complex and demanding task in the next decades. In this paper we show how to formulate the problem of planning short-term load curtailment in a dense urban area, in the presence of uncertainty in electricity demand and in the state of the distribution grid, as a stochastic mixed-integer optimization problem. We propose three rolling-horizon look-ahead policies to approximately solve the optimization problem: a deterministic one and two based on approximate dynamic programming (ADP) techniques. We demonstrate through numerical experiments that the ADP-based policies yield curtailment plans that are more robust on average than the deterministic policy, but at the expense of the additional computational burden needed to calibrate the ADP-based policies. We also show how the worst case performance of the three approximation policies compares with a baseline policy where all curtailable loads are curtailed to the maximum amount possible.

Original languageEnglish (US)
Article number6599009
Pages (from-to)2209-2219
Number of pages11
JournalIEEE Transactions on Smart Grid
Volume4
Issue number4
DOIs
StatePublished - Dec 1 2013

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Keywords

  • Approximate dynamic programming
  • Computer simulation
  • Demand response
  • Load management
  • Mathematical programming
  • Optimization methods
  • Power distribution
  • Power system management
  • Power system modeling
  • Smart grids

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