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This paper is aimed at proposing a short-term hybrid energy system robust optimization model for regional energy system planning and air pollution mitigation based on the inexact multi-stage stochastic integer programming and conditional value-at-risk method through a case study in Shandong Province, China. Six power conversion technologies (i.e., coal-fired power, hydropower, photovoltaic power, wind power, biomass power, and nuclear power) and power demand sectors (agriculture, industry, building industry, transportation, business, and residential department) were considered in the proposed model. The optimized electricity generation, capacity expansion schemes, and economic risks were selected to analyze nine defined scenarios. Results revealed that electricity generations of clean and new power had obvious increasing risks and were key considerations of establishing additional capacities to meet the rising social demands. Moreover, the levels of pollutants mitigation and risk-aversion had a significant influence on different power generation schemes and on the total system cost. In addition, the optimization method developed in this paper could effectively address uncertainties expressed as probability distributions and interval values, and could avoid the system risk in energy system planning problems. The proposed optimization model could be valuable for supporting the adjustment or justification of air pollution mitigation management and electric power planning schemes in Shandong, as well as in other regions of China.

This paper aims to analyze the associated environment and climate benefits of electrification by comparing the air pollutant and CO2 emissions from the fuel cycle of battery electric commercial vehicles (BECVs) and internal combustion engine commercial vehicles (ICECVs) through a case study in Guangzhou Province. Five types of vehicles (i.e., electric buses, coaches, light-duty trucks, dump trucks, and waste haulers) used in the public service sector were selected for analysis, taking into account six development scenarios based on the prevalent ownership trends of electric vehicles and the energy system optimization process. The results reveal that an increase in commercial vehicle electrification in the public service sector will cause reductions of 19.3 × 103 tons, 0.5 × 103 tons, 9.5 × 103 tons, and 8.5 × 106 tons for NOx, PM2.5, VOCs, and CO2, respectively, from the base 2030 case (CS_II, the electrification rates of buses, coaches, light-duty trucks, dump trucks, and waste haulers will reach 100%, 26.5%, 15.4%, 24.0%, and 33.1%, and their power needs will be met by 24% coal, 18.4% gas, and 13.2% renewable power), but with a slight increase in SO2 emissions. With the further penetration of BECVs into the market, the emission reduction benefits for NOx, PM2.5, VOCs, and CO2 could be even more remarkable. Moreover, the benefit obtained from the optimization of the share of renewable energy is more noticeable for CO2 reduction than for air pollutant reduction. Prioritizing the electrification of light-duty trucks after completing bus electrification could be a potential solution for achieving ozone pollution control and lowering carbon emissions in Guangdong. In addition, these results can provide scientific support for the formulation or adjustment of advanced pollution mitigation and peaking carbon policies in Guangdong, as well as other regions of China.