Model predictive control of electric power and reserve dynamic dispatch including demand response

Research output: Contribution to journalJournal articlepeer-review

Abstract

In this paper we formulate a dynamic economic emission and spinning reserve dispatch (DEESRD) problem which determines the optimal power and spinning reserve schedule by simultaneously minimizing the power and spinning reserve costs, and the amount of emission under some constraints. Demand response (DR) can improve the reliability and reduce the energy price. In this paper, we focus on Game Theory DR program which is one of the incentive-based DR programs. We incorporate DR into the DEESRD problem by formulating dynamic economic emission and spinning reserve dispatch with demand response (DEESRD-DR) problem. The objective of the DEESRD-DR is to minimize the energy and reserve costs, minimize the amount of emission and maximize the benefit of the generation company (GENCO). The optimal solutions of the DEESRD and DEESRD-DR problems can be obtained using artificial intelligent-based optimization techniques such as differential evolution, particle swarm optimization, artificial immune system, artificial bee colony, etc., however these methods give an open-loop optimal solution. The open-loop nature cannot deals with inaccuracies, modeling uncertainties and unexpected external disturbances where the power system components suffer from, therefore we have designed closed-loop solutions by model predictive control (MPC). The performance of the MPC has been investigated by applying the MPC strategy to the DEESRD and DEESRD-DR problems with test system consisting of five generating units and five customers.

Original languageEnglish
Pages (from-to)159-170
Number of pages12
JournalJournal of Intelligent and Fuzzy Systems
Volume37
Issue number1
DOIs
StatePublished - 2019

Keywords

  • Computational intelligent technique
  • demand response
  • dynamic dispatch
  • emission
  • feedback control
  • optimization
  • spinning reserve

Fingerprint

Dive into the research topics of 'Model predictive control of electric power and reserve dynamic dispatch including demand response'. Together they form a unique fingerprint.

Cite this