Open AccessStochastic programming model for incentive‐based demand response considering complex uncertainties of consumers
Author(s) -
Zheng Shunlin,
Sun Yi,
Li Bin,
Hu Yajie,
Qi Bing,
Shi Kun,
Li Yuanfei
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0692
Subject(s) - stochastic programming , demand response , computer science , incentive , mathematical optimization , dynamic programming , electricity , programming paradigm , stochastic modelling , electric power system , power (physics) , operations research , engineering , economics , mathematics , electrical engineering , finance , microeconomics , programming language , physics , quantum mechanics
Incentive‐based demand response (IBDR) has been recognized as a powerful tool to mitigate supply–demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncertainty and responsiveness uncertainty, have been a central challenge to implement IBDR programs. In this paper, a stochastic programming model for IBDR considering the complex uncertainties of consumers is proposed. The proposed model can effectively deals with the above two uncertainties. Besides, the model of energy storage unit (ESU) has been improved to cope with properly the deviation between total actual balancing power and required balancing power. Moreover, the model enhances the applicability of IBDR to be applicable to both curtailment IBDR programs and absorbing IBDR programs by adding dynamic parameters. The model is formulated as a bi‐level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results verify merits of the proposed model in cutting down total cost of DRA, decreasing risk cost of DRA and reducing balancing power deviation caused by uncertainty of consumers.