Electric power system planning with renewable energy accommodation for supporting the sustainable development of Tangshan City, China

Abstract

In this study, considering break the original energy structure dominated by coal and make the most use of renewable energy in the future, an inexact stochastic robust mixed-integer programming (ITSRMP) method was developed for supporting regional electric power system management in Tangshan City, China. The developed method incorporated interval-parameter programming (IPP), stochastic robust optimization (SRO), two-stage stochastic programming (TSP), and mixed integer programming (MIP) within a general optimization framework to reflect uncertainties expressed as interval values and probability distributions in the regional electric power system. Three scenarios corresponding to different subsidy price levels and three cases associated with different pollutants emission reduction levels were designed. The electricity generation schemes, facility-expansion, pollutant emission, and system cost considering the subsidy policy and air pollution mitigation control had been obtained. The results indicated that subsidy policy would exert an important influence on the development of Tangshan's electric power system, which can reduce the cost advantage of conventional power generation and enhance the development enthusiasm of renewable power generation to power enterprises. In detail, the electricity generation amount of renewable energy would increase with the improvement of subsidy price level. Moreover, decision makers could identify the possible policy implementations and enforcements under considering the trade-off among system economy, security and environmental objectives. The modeling results were valuable for promoting new energy accommodation and supporting the sustainable development of social economy.