A sampling-based solution approach for electricity capacity expansion planning with chance constraint

In order to achieve carbon neutrality, several countries and regions have set specific targets for the reduction of carbon emissions. Although many policy makers agree on the uncertainty of the several factors which are involved in the planning of future energy systems, they have yet to consider the risks that their policies will incur, should they fail to meet current carbon emission targets. In this study, we propose a stochastic programming-based energy system model which takes into account various economic and technical uncertainty factors in addition to a chance-constraint, in order to assess the feasibility of meeting carbon emission targets. To determine the chance-constrained programming model efficiently, we propose a solution-based approach which adopts a sample average approximation approach. In addition, we combine the sampling-and-discarding approach with a newly devised heuristic algorithm that is suitable for our proposed model. Furthermore, as it is a numerical study, we apply our model and solution approach to evaluate the long-term capacity expansion plan under the carbon emission target within the Korean electricity sector. Through this numerical study, we verify both the solution quality and computational time of our solution approach, as well as the usability of our model for the trade-off analysis between costs and risks.