Open AccessMulti‐objective decision‐making framework for an electricity retailer in energy markets using lexicographic optimization and augmented epsilon‐constraint
Author(s) -
Esmaeel Nezhad Ali,
Ahmadi Abdollah,
Javadi Mohammad Sadegh,
Janghorbani Mohammadreza
Publication year - 2015
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2059
Subject(s) - mathematical optimization , computer science , electricity , stochastic programming , procurement , profit (economics) , lexicographical order , operations research , economics , microeconomics , mathematics , management , electrical engineering , combinatorics , engineering
Summary The objective of the retailer in medium‐term planning is managing the portfolio of contracts from different sources as well as determining the optimal selling price offered to its customers. When supplying the electricity sold to the costumers, two main challenges are faced by retailers. The first problem occurs during the electricity procurement procedure. In this stage, the retailer must deal with the uncertainty due to the pool price that propels the retailers to move towards agreeing to forward contracts signed at higher average prices. Besides, when the retailer decides on selling the electricity, another problem is to face the uncertainty caused by the demand while taking into consideration the possibility of reducing its clients in the case of high selling price. In this regard, this paper proposes a stochastic multi‐objective framework for the retailer with profit maximization and risk minimization as two objective functions. The risk, due to the market price uncertainty, is modeled, employing the expected downside risk. The problem is formulated as mixed‐integer programming while the stochastic optimization problem is characterized using the roulette wheel mechanism and lattice Monte Carlo simulation. Furthermore, lexicographic optimization and augmented epsilon‐constraint method are used to solve the proposed multi‐objective problem, and the best compromise solution is determined employing a fuzzy satisfying method. The presented model has been implemented using a realistic case study to verify the effectiveness of the method used in this paper. Copyright © 2015 John Wiley & Sons, Ltd.