Keywords: decision-making, electricity, modelling, planning, Switzerland
Strategic electricity sector assessment methodology under sustainability conditions: a Swiss case study on the costs of CO2 emissions reductions
Growing concerns about social and environmental sustainability have led to increased interest in planning for the electricity utility sector because of its large resource requirements and production of emissions. A number of conflicting trends combine to make the electricity sector a major concern, even though a clear definition of how to measure progress toward sustainability is lacking. These trends include imminent competition in the electricity industry, global climate change, expected long-term growth in population and pressure to balance living standards (including per capital energy consumption). In order to approach this global problem on a regional level, a project has been established to develop planning methods for electrical power systems related to sustainability called SESAMS (Strategic Electricity Sector Assessment Methodology under Sustainability Conditions), under the Alliance for Global Sustainability formed by the Massachusetts Institute of Technology (MIT), the Swiss Federal Institutes of Technology (ETHZ and EPFL), and the University of Tokyo (UT). SESAMS 97 has brought together multi-attribute, multi-scenario electricity system planning, life-cycle assessment, and multi-criteria decision analysis to create an integrated methodology that has been demonstrated using a case study based on the Swiss electricity system. This case study has simulated system dispatch of the Swiss electricity system for 1296 scenarios over a study period from 1996 to 2025. The results for these scenarios include a wide range of direct and indirect sustainability measures, with conclusions that have focused primarily on cost and CO2 emissions. The pairwise scenario trade-off analysis facilitates searching the strategy option space by identifying the best and most robust options. Decision-makers benefit by being able to focus trade-off discussions on the dominant set of best choices for each trade-off pair, rather than covering the entire decision space.