Robust Planning of Energy and Environment Systems through Introducing Traffic Sector with Cost Minimization and Emissions Abatement under Multiple Uncertainties

Abstract

Motor vehicles have been identified as a growing contributor to air pollution, such that analyzing the traffic policies on energy and environment systems (EES) has become a main concern for governments. This study developed a dual robust stochastic fuzzy optimization - energy and environmental systems (DRSFO-EES) model for sustainable planning EES, while considering the traffic sector through integrating two-stage stochastic programming, robust two-stage stochastic optimization, fuzzy possibilistic programming, and robust fuzzy possibilistic programming methods into a framework, which can be used to effectively tackle fuzzy and stochastic uncertainties as well as their combinations, capture the associated risks from fuzzy and stochastic uncertainties, and thoroughly analyze the trade-offs between system costs and reliability. The proposed model can: (i) generate robust optimized solutions for energy allocation, coking processing, oil refining, heat processing, electricity generation, electricity power expansion, electricity importation, energy production, as well as emission mitigation under multiple uncertainties; (ii) explore the impacts of different vehicle policies on vehicular emission mitigation; (iii) identify the study of regional atmospheric pollution contributions of different energy activities. The proposed DRSFO-EES model was applied to the EES of the Beijing-Tianjin-Hebei (BTH) region in China. Results generated from the proposed model disclose that: (i) limitation of the number of light-duty passenger vehicles and heavy-duty trucks can effectively reduce vehicular emissions; (ii) an electric cars’ policy is enhanced by increasing the ratio of its power generated from renewable sources; and (iii) the air-pollutant emissions in the BTH region are expected to peak around 2030, because the energy mix of the study region would be transformed from one dominated by coal to one with a cleaner pattern. The DRSFO-EES model can not only provide scientific support for the sustainable managing of EES by cost-effective ways, but also analyze the desired policies for mitigating pollutant emissions impacts with a risk adverse attitude under multiple uncertainties.

Planning an Energy–Water–Environment Nexus System in Coal-Dependent Regions under Uncertainties

“…Finally, electricity demands are forecasted according to the selected ARIMA (p, d, q) model. Many parameters of EWENS show the characteristics of vagueness because of subjectivity of specialists' judgment [18,24]. T2FP can effectively cope with ambiguous information in objective and constraint functions, which is useful when the exact membership grade is difficult to determine.…”

“…The optimized electricity-generation schemes can be obtained from Equations (21) and (22). The optimized amounts of exported electricity can be obtained from constraints (23) and (24).…”

“…For example, when µ = 0.0%, the wastewater, SO 2 , NOx, and PM will be 121.19 × 10 3 -121.39 × 10 3 tons, 519.38-520. 24 Lastly, high carbon mitigation requirement could effectively optimize the electric power mix and save coal resources, leading to lower pollutants. Results indicate that a 1% increase (from 0.0% to 1.0%) in carbon mitigation level is projected to decrease 1.87 × 10 6 tons, 8.05 × 10 3 tons, 7.73 × 10 3 tons, and 1.76 × 10 3 tons of wastewater and SO 2 , NOx, and PM emissions, respectively, over the planning horizon.…”

“…Many economic parameters of are not deterministic and often shown as ambiguity, which are suitably expressed as fuzzy sets [23]. FMP can describe this fuzziness in the energy system [24]. Moradi et al [25] used an FMP method to deal with the uncertainties regarding energy prices and energy demands to determine the optimum schemes for combined heat and power capacities.…”

Hydrogen Recovery from Waste Gas Streams to Feed (High-Temperature PEM) Fuel Cells: Environmental Performance under a Life-Cycle Thinking Approach

“…This situation is more critical in undeveloped and developing countries, but even in developed countries, more than 50% of the cities cannot achieve the air quality levels declared by the World Health Organization [1]. Energy-related activities, such as industrial sectors or buildings, are significant stationary sources of air pollution, specifically for CO 2 emissions [2,3]. Energy consumption of buildings is among the largest of society, accounting for around 40% of the total energy consumptions and 36% of greenhouse gases (GHG) emissions in Europe [4].…”