Planning a sustainable urban electric power system with considering effects of new energy resources and clean production levels under uncertainty: A case study of Tianjin, China

This study develops a risk-aversion optimization model for an urban electric power system (RAOMUEPS), taking into account stochastic uncertainties. The RAOM-UEPS can manage stochastic uncertainties and capture associated risks from the stochastic information. This enables managers to analyze the trade-off between system cost and system risk in detail. Then, as a case study, the RAOM-UEPS is applied to the planning of an urban electric power system in Tianjin. Here, three scenarios are considered, each with different proportions of new energy resources and clean production levels (i.e., energy conversion efficiencies). This study aims to develop an urban electric power system (UEPS) optimization model that support the city's transformation from a coal-fired dominated to a low-carbon electric power mix, as well as to promote the sustainable development of society as a whole. The proposed model can facilitate a sophisticated system analysis of energy supply, electric power conversion, capacity expansion, and environment management over multiple periods. The results suggest that coal is dominant in Tianjin's electric power system, which was the primary air-pollutants and CO2 contributor in electric power system. Improving clean production levels and the proportion of new energy resources could effectively save energy resources and mitigate air pollutants and CO2 emissions. These findings can provide a scientific basis for the sustainable development of regional electric power systems, as well as for transformation from coal-dominated to low-carbon electric power cities. (C) 2017 Elsevier Ltd. All rights reserved.