Published November 2018 | Version v1
Journal article

Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties

  • 1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206 (China)
  • 2. Energy Systems Division, Argonne National Laboratory, Lemont, IL 60439 (United States)
  • 3. Department of Electrical Engineering, Texas A&M University, College Station, TX 77843 (United States)

Description

Highlights: • Capacity credit (CC) of demand response (DR) under the smart-grid is assessed. • Proposed a hybrid probabilistic-possibilistic framework for DR modeling. • Both exogenous and endogenous uncertainties associated with DR are considered. • A reliability-based algorithm combining operation optimization is developed. • The impacts of various factors on DR CC are analyzed through numerical studies. As a featured smart-grid technology, demand response (DR) provides utility companies with unprecedented flexibility to improve the reliability of electricity service in future power systems. However, due to the uncertainties arising from the demand side, the extent to which DR can be utilized for capacity support poses a major question to the utilities. To address this issue, this paper proposes a new methodological framework to assess the potential reliability value of DR in smart grids. The framework is established on the concept of capacity credit (CC), and it accommodates different types of uncertainties (i.e., probabilistic and possibilistic) accrued from physical and anthropogenic factors in DR programs. The capability of DR during operation is considered as a synthesized result of multiple facets, i.e., users' load characteristics, participation levels, and load recoveries, and different models are developed to represent each component. To characterize the stochastic nature of demand responsiveness, the fuzzy theory is introduced, and possibilistic models are proposed to describe the human-related uncertainties under incomplete information. In addition, considering that in reality, DR operation could affect the comfort of customers, the dynamics of demand-side participation have also been incorporated in our study, in which two utility-based indices are defined to quantify the effect of such interdependency. Using a probabilistic propagation technique, the different types of uncertainties involved can be normalized and systematically addressed under the same framework. Then, the relevant models can be applied to the CC evaluation procedures, wherein two dispatching schemes (i.e., reliability-driven and coordinated management) are considered to study the effect of DR operation on its CC. The proposed methodology is tested on a modified RTS system, and the obtained results confirm its effectiveness.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.apenergy.2018.07.111

Additional details

Identifiers

DOI
10.1016/j.apenergy.2018.07.111;
PII
S0306261918311462;

Publishing Information

Journal Title
Applied Energy
Journal Volume
229
Journal Page Range
p. 186-200
ISSN
0306-2619
CODEN
APENDX

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
52114387
Subject category
S24: POWER TRANSMISSION AND DISTRIBUTION;
Descriptors DEI
ALGORITHMS; DEMAND; ELECTRICITY; FUZZY LOGIC; LOAD ANALYSIS; NUMERICAL ANALYSIS; OPTIMIZATION; PROBABILISTIC ESTIMATION; RELIABILITY; SIMULATION; SMART GRIDS; SOCIO-ECONOMIC FACTORS
Descriptors DEC
CALCULATION METHODS; ENERGY SYSTEMS; INSTITUTIONAL FACTORS; MATHEMATICAL LOGIC; MATHEMATICS; POWER SYSTEMS

Optional Information

Copyright
Copyright (c) 2018 Elsevier Ltd. All rights reserved.