A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response

Siqing Sheng and Qing Gu
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Siqing Sheng: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, China
Qing Gu: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071000, China

Energies, 2019, vol. 12, issue 9, 1-26

Abstract: The uncertainty of demand response (DR) will affect the economics of power grid dispatch due to the randomness of participating users’ intentions. According to the different working mechanisms of price-based demand response (PBDR) and incentive-based demand response (IBDR), the uncertainty models of two types of DR were established, respectively. Firstly, the fuzzy variable was used to describe the load change in PBDR, and the robust optimization theory was used to establish the uncertain set of the actual interruption of the interruptible load (IL). Secondly, according to the different acting speed of the two types of DR, they were deployed in the two-stage scheduling model with other output resources; then based on the fuzzy chance constrained programming theory and multi-stage robust optimization theory, the dispatch problem was transformed and solved by the bat algorithm (BA) and the entropy weighting method. Consequently, intraday running costs decrease with increasing confidence of day-ahead, but increase with day-in reliability, and the economics of the model were verified in the improved IEEE33 node system.

Keywords: demand response; multi-time scale; uncertainty; fuzzy chance constrained programming; robust optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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