Multi-Objective Optimization and Multi-Criteria Analysis Models and Methods for Problems in the Energy Sector

Abstract

The energy sector has been a fertile ground for the application of operational research (OR) models and methods (Antunes and Martins, OR Models for Energy Policy, Planning and Management, Annals of Operational Research, vols. 120/121, 2003). Even though different concerns have been present in OR models to assess the merit of potential solutions for a broad range of problems arising in the energy sector, the use of multi-objective optimization (MOO) and multi-criteria analysis (MCA) approaches is more recent, dating back from mid-late 1970s. The need to consider explicitly multiple uses of water resource systems or environmental aspects in energy planning provided the main motivation for the use of MOO and MCA models and methods with a special evidence in scientific literature since the 1980s. The increasing need to account for sustainability issues, which is inherently a multi-criteria concept, in planning and operational decisions, the changes in the organization of energy markets, the conflicting views of several stakeholders, the prevalent uncertainty associated with energy models, have made MOO and MCA approaches indispensable to deal with complex and challenging problems in the energy sector. This paper aims at providing an overview of MOO and MCA models and methods in a vast range of energy problems, namely in the electricity sector, which updates and extends the one in Diakoulaki etal. (InJ.Figueira, S. Greco, M. Ehrgott (Eds.). Multiple Criteria Decision Analysis – State of the Art Surveys. International Series in Operations Research and Management Science, vol. 78, pp. 859–897, Springer, New York, 2005). Broadly, models and methods dealing with multi-objective mathematical programming and a priori explicitly known discrete alternatives are distinguished and some of the main types of problems are stated. The main conclusion is that MOO and MCA approaches are essential for a thorough analysis of energy problems at different decision levels, from strategic to operational, and with different timeframes.