Published February 2019 | Version v1
Journal article

Mapping between transmission constraint penalty factor and OPF solution in electricity markets: analysis and fast calculation

  • 1. Dept. of Electrical Engineering, Chongqing University, Chongqing, 400000 (China)
  • 2. Dept. of Electrical Engineering, Tsinghua University, Beijing, 100084 (China)
  • 3. Transmission Analytics Company, Austin, TX 78705 (United States)
  • 4. The Midcontinent Independent System Operator, Inc., Carmel, In 46032 (United States)
  • 5. The Power Dispatch and Control Center, Guangdong Power Grid, Guangdong, 510600 (China)

Description

Highlights: • Influence of penalty factors on DC OPF solution is quantitatively studied. • Parametric programming for the market model is performed without re-optimization. • Proposed method for calculating the mapping is accelerated by more than 100 times. -- Abstract: -- In electricity markets, transmission constraint relaxation is frequently used to ensure the feasibility of the optimal power flow (OPF) problem and avoid the expensive yet ineffective congestion adjustment. However, transmission constraint penalty factors have a considerable impact on the market scheduling and prices. There is still no consensus on how to properly set the penalty factors. In this paper, the impact of transmission constraint penalty factors on the market solution is quantitatively investigated via the sensitivity analysis of the DC OPF model. Parametric programming is performed to determine the change of the market scheduling and prices with respect to the change of penalty factors. In particular, we prove for the very first time that when the objective function of the market model is quadratic, the parametric programming can be conveniently performed without the need of re-optimizing the DC OPF model in most cases. On this basis, an efficient method is proposed for exactly characterizing the mapping between the penalty factors and the market solution. Case studies show that the proposed method could be up to 100 times quicker than re-optimizing the DC OPF model as in the traditional parametric programming process.

Additional details

Identifiers

DOI
10.1016/j.energy.2018.11.048;
PII
S0360544218322527;

Publishing Information

Journal Title
Energy (Oxford)
Journal Volume
168
Journal Page Range
p. 1181-1191
ISSN
0360-5442
CODEN
ENEYDS

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
55018114
Subject category
S29: ENERGY PLANNING, POLICY AND ECONOMY;
Descriptors DEI
ELECTRICITY; MAPPING; MARKET; OPTIMIZATION; PRICES; PROGRAMMING; SENSITIVITY ANALYSIS

Optional Information

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