An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city

Dong, Chang Qing; Huang, Guohe; Cai, Yanpeng; Liu, Y.

doi:10.1016/j.energy.2011.10.030

Cited by 82 publications

(34 citation statements)

References 55 publications

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“…The initial action is called the first-stage decision, and the corrective action is named the second-stage decision. Over the past decades, the TSP model with the expected recourse function was widely explored (Huang and Loucks, 2000;Seifi and Hipel, 2001;Ahmed, 2004;Li et al, 2006;Cai et al, 2011a, b;Shao et al, 2011;Dong et al, 2012). However, a potential limitation of the conventional TSP model is that it can only account for the expectation as the optimization criterion without any consideration on the variability of the recourse values; hence, it is a risk neutral approach.…”

Section: Introductionmentioning

confidence: 98%

Risk management for sulfur dioxide abatement under multiple uncertainties

et al. 2015

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In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.

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Section: Introductionmentioning

confidence: 98%

Risk management for sulfur dioxide abatement under multiple uncertainties

et al. 2015

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“…These uncertainties can multiply the complexities of energy systems, which could affect the related optimization processes and the generated decision schemes [30]. Therefore, the relevant decisions must be made under various uncertainties in order to select the most appropriate power conversion technology and type of fuel to meet the electricity demand and the most suitable pollution mitigation technology to satisfy the environmental constraints, according to the availability, economic, environmental and technological characteristics of different fuels and technologies [47,48]. The study time horizon is 15years (from 2014 to 2028), which is further divided into three planning periods with each period being five years (i.e., period 1: 2014e2018; period 2: 2019e2023; period 3: 2024e2028).…”

Section: Study Systemmentioning

confidence: 99%

“…In the study system, multiple energy resources (such as coal, crude oil, natural gas, diesel, gasoline and kerosene) are conversed into terminal energy carries (such as electricity and heat), which are employed for satisfying the corresponding demands on various energy productions from multiple end-users (i.e., agriculture, industrial, commercial, transportation and residential sectors) [47]. Fig.…”

Section: Study Systemmentioning

confidence: 99%

Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China

Chen

Huang

2016

Renewable Energy

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a b s t r a c tAn interval-fuzzy municipal-scale energy model (IF-MEM) is developed for energy systems planning. IF-MEM can effectively tackle uncertainties presented in terms of fuzzy sets and discrete intervals. IF-MEM is applied to a real case of energy systems planning in the City of Tianjin, which is the economic and industrial center of northern China. IF-MEM allows in-depth analyses for tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The results indicate that reasonable solutions have been generated. The results suggest that: (i) coal would be the dominant sources in city's energy systems due to the increasing energy demand; however, its consumption ratio would decrease over the planning horizon due to requirements of environment protection and sustainable development; (ii) clean (e.g., natural gas, liquefied petroleum gas) and renewable energies (e.g., wind power, solar power) would be vigorously encouraged during the planning periods; (iii) coal-fired technology (i.e., coal-fired power and heat) would be the major contributor for the city's atmosphere pollution; (iv) the pollutant emissions would be decreased over the planning horizon due to the restriction of fossil fuels and the development of renewable energy. Promoting the utilization of renewable energy sources is crucial for supporting pollutant emission reduction and solving energy shortage crisis.

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“…Some studies further incorporated probability theory into IFMP to deal with right-side uncertainties of the constraints (Li et al, 2006;He et al, 2008;Li et al, 2009;Guo and Huang, 2011;Wang et al, 2012). These mathematical programming techniques mentioned above are also applied to investigate various planning issues such as water resource planning and management (Qin et al, 2007;Zhu et al, 2009;, energy system planning and management (Zhu et al, 2012;Dong et al, 2012), and air quality management (Xu et al, , 2012, etc.…”

Section: Introductionmentioning

confidence: 99%

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

Chang

Lin

2013

Journal of the Air & Waste Management Association

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Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. A sustainable MSWM strategy should consider not only economic efficiency but also life-cycle assessment of environmental impact. This study employs the fuzzy multiobjective linear programming (FMOLP) technique to find the optimal compromise between economic optimization and pollutant emission reduction for the MSWM strategy. Taichung City in Taiwan is evaluated as a case study. The results indicate that the optimal compromise MSWM strategy can reduce significant amounts of pollutant emissions and still achieve positive net profits. Minimization of the sulfur oxide (SO x ) and nitrogen oxide (NO x ) emissions are the two major priorities in achieving this optimal compromise strategy when recyclables recovery rate is lower; however, minimization of the carbon monoxide (CO) and particulate matter (PM) emissions become priority factors when recovery rate is higher.Implications: This paper applied the multiobjective optimization model to find the optimal compromise municipal solid waste management (MSWM) strategy, which minimizes both life-cycle operating cost and air pollutant emissions, and also to analyze the correlation between recyclables recovery rates and optimal compromise strategies. It is different from past studies, which only consider economic optimization or environmental impacts of the MSWM system. The result shows that optimal compromise MSWM strategy can achieve a net profit and reduce air pollution emission. In addition, scenario investigation of recyclables recovery rates indicates that resource recycling is beneficial for both economic optimization and air pollutant emission minimization.

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An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city

Cited by 82 publications

References 55 publications

Risk management for sulfur dioxide abatement under multiple uncertainties

Risk management for sulfur dioxide abatement under multiple uncertainties

Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China

Compromising economic cost and air pollutant emissions of municipal solid waste management strategies by fuzzy multiobjective optimization model

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