Regional planning of new-energy systems within multi-period and multi-option contexts: A case study of Fengtai, Beijing, China

In this study, an IROM-ES (inexact regional optimization model for energy systems) is developed with considering NESs (new energy sources) under uncertainty. The IROM-ES integrates techniques of ILP (interval linear programming), MLP (mixed-integer linear programming) and CCP (chance-constrained programming). In the IROM-ES, multiple uncertainties expressed as intervals and stochastic numbers are allowed to be incorporated into the framework within the multi-period and multi-option contexts. The IROM-ES is then applied to a case study of Fengtai District, Beijing, China, in which the objective is to minimize system cost under a series of constraints with considering social, economic and environmental concerns. Solutions regarding energy-supply schemes, NESs utilization, capacity-expansion plans and air pollutants control in a cost-effective manner have been generated to satisfy the urban energy and atmosphere quality demands under different violation risks. Results reveal that the changes in violation risks would lead to different optimal planning schemes. A higher violation risk would correspond to a higher probability of system-constraint violation and a lower system cost; conversely, a lower violation risk would lead to an increased strictness of the constraints or a narrower decision space, then to a higher system cost. Moreover, results indicate that the urban electricity supply from NESs would be relatively high (suggested more than 14.40%). Furthermore, to improve the reliability of decisions, post-optimization analyses (including modeling comparisons, alternatives setting as well as system triangulation analysis) are preformed to examine which scheme could be more appropriate, which can facilitate providing strategy in different scenarios for the decision makers.