Valuing Demand Response Controllability via Chance Constrained Programming

Kenneth Bruninx, Yury Dvorkin, Erik Delarue, William D'haeseleer, Daniel S. Kirschen

Research output: Contribution to journalArticle

Abstract

Controllable loads can modify their electricity consumption in response to signals from a system operator, providing some of the flexibility needed to compensate for the stochasticity of electricity generation from renewable energy sources (RES) and other loads. However, unlike traditional flexibility providers, e.g. conventional generators and energy storage systems, demand response (DR) resources are not fully controlled by the system operator and their availability is limited by user-defined comfort constraints. This paper describes a deterministic unit commitment model with probabilistic reserve constraints that optimizes day-ahead power plant scheduling in the presence of stochastic RES-based electricity generation and DR resources that are only partially controllable, in this case residential electric heating systems. This model is used to evaluate the operating cost savings that can be attained with these DR resources on a model inspired by the Belgian power system.

Original languageEnglish (US)
JournalIEEE Transactions on Sustainable Energy
DOIs
StateAccepted/In press - Jun 28 2017

Fingerprint

Controllability
Electricity
Electric heating
Operating costs
Energy storage
Power plants
Scheduling
Availability

Keywords

  • Demand response
  • limited controllability
  • uncertainty
  • unit commitment

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

Valuing Demand Response Controllability via Chance Constrained Programming. / Bruninx, Kenneth; Dvorkin, Yury; Delarue, Erik; D'haeseleer, William; Kirschen, Daniel S.

In: IEEE Transactions on Sustainable Energy, 28.06.2017.

Research output: Contribution to journalArticle

Bruninx, Kenneth ; Dvorkin, Yury ; Delarue, Erik ; D'haeseleer, William ; Kirschen, Daniel S. / Valuing Demand Response Controllability via Chance Constrained Programming. In: IEEE Transactions on Sustainable Energy. 2017.
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