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Title: Robust Unit Commitment Considering Uncertain Demand Response

Journal Article · · Electric Power Systems Research
 [1];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science

Although price responsive demand response has been widely accepted as playing an important role in the reliable and economic operation of power system, the real response from demand side can be highly uncertain due to limited understanding of consumers' response to pricing signals. To model the behavior of consumers, the price elasticity of demand has been explored and utilized in both research and real practice. However, the price elasticity of demand is not precisely known and may vary greatly with operating conditions and types of customers. To accommodate the uncertainty of demand response, alternative unit commitment methods robust to the uncertainty of the demand response require investigation. In this paper, a robust unit commitment model to minimize the generalized social cost is proposed for the optimal unit commitment decision taking into account uncertainty of the price elasticity of demand. By optimizing the worst case under proper robust level, the unit commitment solution of the proposed model is robust against all possible realizations of the modeled uncertain demand response. Numerical simulations on the IEEE Reliability Test System show the e ectiveness of the method. Finally, compared to unit commitment with deterministic price elasticity of demand, the proposed robust model can reduce the average Locational Marginal Prices (LMPs) as well as the price volatility.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Electricity (OE)
Grant/Contract Number:
AC05-00OR22725; EEC-1041877; AC05-00OR 22725
OSTI ID:
1286761
Alternate ID(s):
OSTI ID: 1434800
Journal Information:
Electric Power Systems Research, Vol. 119, Issue 119; ISSN 0378-7796
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 35 works
Citation information provided by
Web of Science

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Cited By (3)

Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty journal February 2019
Large-scale unit commitment under uncertainty: an updated literature survey journal September 2018
Optimized operating rules for short-term hydropower planning in a stochastic environment journal March 2019