An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty

In this study, an interval-valued minimax regret analysis (IMRA) method is proposed for planning greenhouse gas (GHG) abatement under uncertainty. The IMRA method is a hybrid of interval-parameter programming (IPP) and minimax regret analysis (MMR) techniques. The developed method is applied to support long-term planning of GHG mitigation in an energy system under uncertainty. Mixed integer linear programming (MILP) technique with fixed-charge cost function is introduced into the IMRA framework to facilitate dynamic analysis for decisions of timing, sizing and siting in planning capacity expansions for power-generation facilities. The results obtained indicate that replacing fossil fuels with renewable energy sources (i.e. hydro, wind and solar power) can effectively facilitate reducing the GHG emissions. They can help decision makers identify an optimal strategy that can facilitate reducing the worst regret level incurred under any outcome of the uncertain GHG-abatement target.

We develop an interval-valued minimax regret analysis method for planning energy systems. Uncertainties existing in system components are reflected. Capacity expansion plans for power generation facilities can be obtained. Results can identify an optimal strategy for greenhouse-gas-abatement under uncertainty.