IIASA International Institute for Applied Systems Analysis (IIASA) RAINS-Asia: A Tool for Optimization Analysis of the Acidification Problem in Asia while Taking into Account the Potential for Use of Renewable Energy M. Amann, J. Cofala, F. Gyarfas, W. Schöpp (IIASA) C. Boudri, L. Hordijk, C. Kroeze (Wageningen University, NL) Li Junfen, Dai Lin (Energy Research Institute, Beijing) L. Srivastava, T.S. Panwar (Tata Energy Research Institute, Delhi)

IIASA The model: RAINS developed by IIASA Energy/agriculture projections Emissions Emission control options Atmospheric dispersion Environmental impacts Environmental targets Costs

IIASA Optimization based upon... Some sources are more strongly linked than others via the atmosphere to sensitive receptors (as indicated by the source-receptor relationships) Some sources are cheaper to control than others (as indicated by the cost curves)

IIASA Optimization in RAINS Energy/agriculture projections Emissions Emission control options Atmospheric dispersion Environmental impacts Environmental targets Costs OPTIMIZATION

IIASA Emission abatement cost curves Estimate marginal costs for all available emission control options Rank available options according to their marginal cost Select an energy projection, calculate uncontrolled (or current legislation) emissions List emission reduction potentials and costs, starting from the ‘uncontrolled’ case

IIASA An example cost curve for SO 2

IIASA Optimization in RAINS: A systematic search for cost-effective solutions Goal (objective) of the optimization: For a given set of environmental targets (e.g., maximum S deposition) find the least-cost set of measures Result (solution): Least-cost set of emission controls/emission ceilings by –region/province –economic sector –LPS/area source

IIASA Objective function: Minimize total SO 2 control costs in the whole region/country subject to: so that user-specified constraints/limits on sulfur exposure/ deposition are met in the whole region Decision variables/outcome: the SO 2 emission controls for each source (province/LPS) within the bounds given by the cost curves or imposed by the user Optimization in RAINS: A mathematical optimization problem

IIASA Optimization in RAINS: The mathematical formulation  i c i  min subject to c i = f i (e i )  i (e i.t ij )+bg j  d j c …... control costs i …...source region j …...receptor point e i …...SO 2 emissions t ij …...atmospheric dispersion coefficient bg j ….background deposition d j …...deposition target

IIASA Deposition targets d ij Deposition targets ‘drive’ the optimization ‘policy’ choice of the user can be specified for each grid cell

IIASA Example targets for the optimization In the year 2020 Case A: limit total emissions in each region to the levels of 2000/1995/1990 Case B: limit sulfur deposition in each grid cell to the levels experienced in 2000/1995/1990 Case C: limit (harmful) excess sulfur deposition in each grid cell to the levels experienced in 2000/1995/1990

IIASA Cost-savings through targeted (optimized) emission controls

IIASA Cost savings through renewable energy (for Case B)

IIASA Conclusions RAINS-Asia optimization tool now available Enables systematic search for cost-effective emission controls to achieve user-defined environmental targets Optimized solutions can cut costs by 50% while maintaining same environmental benefits Renewable energy offers additional cost-saving potential