Modeling for planning municipal electric power systems associated with air pollution control – A case study of Beijing

• An innovative optimization method is developed for tackling uncertainties.
• It can reflect the risk of violating system constraints under joint probability.
• The developed method is applied to planning electric power systems of Beijing.
• Solutions related to electricity supply and capacity expansion are obtained.
• Results can generate the city's energy policy to satisfy different objectives.

In this study, an IFJMP (interval-parameter full-infinite joint-probabilistic mixed-integer programming) method is developed for supporting EPS (electric power systems) management. The IFJMP-EPS model cannot only deal with uncertainties expressed as joint probabilities, crisp interval values and functional intervals, but also examine the risk of violating joint-probabilistic constraints. The developed IFJMP-EPS model is then applied to a case study for planning EPS of Beijing within a multi-energy resource, multi-electric power plant and multi-period context, where MILP (mixed integer linear programming technique is employed to facilitate dynamic analysis for decisions of facility-capacity expansion. With the aid of IFJMP, tradeoffs among system costs, electricity-supply security, and air-pollution control can be obtained under joint probabilities. The results can be used to help managers to identify desired system designs and to determine which of these designs can most efficiently accomplish optimizing the system objective under uncertainty. The results can also address the challenges generated in the processes of electric-power production (such as imbalance between electricity supply and demand, the contradiction between air pollution emission and environmental protection); this allows an increased robustness in controlling electricity-generation and -supply risk for Beijing's EPS under various complexities and uncertainties.