Power System Reliability Assessment with Quantification of Demand Response Uncertainty Based on Advanced Sigmoid Cloud Model

Demand response (DR) is usually regarded as a valuable balancing and reserve resource that contributes to maintaining power balance and integrating renewable energies. However, the price elasticity curve of the DR resources is influenced by consumers' behavioral uncertainty and therefore is difficult to predict. Consequently, additional risk may be introduced to composite power system reliability. Considering that, this paper investigates a reliability assessment of composite power system considering both the merits and potential uncertainty involved in the DR. First, the psychological behavior and consumption behavior of consumers are characterized in the DR modeling. A novel DR uncertainty index (denoted as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I^{U}$</tex>) measures uncertainty of consumer's behavior when electricity price changes. Then, an advanced Sigmoid cloud model is proposed to depict the comprehensive uncertain mapping relationships between price and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I^{U}$</tex>. Moreover, an improved demand elasticity matrix is proposed, in which price-quantity elastic coefficients are modified by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I^{U}$</tex> index. Finally, a reliability assessment framework for a composite power system is developed considering the uncertain price-based DR model and a k-means algorithm is used to accelerate the assessment process. Accuracy and effectiveness of the proposed method are investigated through the case study on RBTS, RTS79 and RTS96 reliability test systems.