An air quality index‐based multistage type‐2‐fuzzy interval‐stochastic programming model for energy and environmental systems management under multiple uncertainties

ABSTRACT In this study, a multistage type‐2‐fuzzy interval‐stochastic programming (MTIP) method is developed, which extends upon the existing multistage stochastic programming (MSP) by allowing uncertainties expressed as probabilistic distributions, interval values and type‐2 fuzzy sets to be effectively incorporated within the optimization framework. Through coupling air quality index (AQI) with MTIP, an AQI‐MTIP model is formulated for energy and environmental systems (EES) management of Tianjin. A number of scenarios based on changed AQIs are examined to analyze the impacts of environmental requirements on the city's energy system. Results indicate that (i) with the improvement of environmental requirement, utilization of clean energies (especially natural gas) is provoked markedly; (ii) PM2.5 is the primary pollutant, 64.50% of which should be reduced each period to maintain the city's air quality at a health‐safe level. These findings can help decision makers adjust energy structure, make effective mitigation strategy, and gain deep insight into the relationship between energy consumption and environmental requirement. Graphical abstract Impacts of different AQIs on capacity expansion of Tianjin's energy system Figure. No Caption available. HighlightsA multistage type‐2‐fuzzy interval‐stochastic programming (MTIP) method is developed.Air quality index (AQI) is introduced to manage energy and environmental systems.AQI‐MTIP model is formulated and results for Tianjin's EES are generated.Impacts of environmental requirements on energy systems is analyzed.Results can provide support for making effective pollutant mitigation policy.

[1]  Jonathan I Levy,et al.  Modeling variability in air pollution‐related health damages from individual airport emissions , 2017, Environmental research.

[2]  R. Kelishadi,et al.  The relationship of exposure to air pollutants in pregnancy with surrogate markers of endothelial dysfunction in umbilical cord. , 2016, Environmental research.

[3]  J. A. Sonibare,et al.  Evaluation of cooking energy cost, efficiency, impact on air pollution and policy in Nigeria , 2007 .

[4]  Christopher Juhlin,et al.  Energy, Resources and the Environment: Meeting the challenges of the future , 2017 .

[5]  R. Kelishadi,et al.  Is air quality index associated with cardiometabolic risk factors in adolescents? The CASPIAN-III Study. , 2014, Environmental research.

[6]  Manoranjan Maiti,et al.  Fixed charge transportation problem with type-2 fuzzy variables , 2014, Inf. Sci..

[7]  Ammar Ben Brahim,et al.  Methodology for developing an air quality index (AQI) for Tunisia , 2013 .

[8]  A. M. Jinturkar,et al.  A fuzzy mixed integer goal programming approach for cooking and heating energy planning in rural India , 2011, Expert Syst. Appl..

[9]  J. McDonald,et al.  Exposure to traffic‐generated air pollutants mediates alterations in brain microvascular integrity in wildtype mice on a high‐fat diet , 2018, Environmental research.

[10]  E. Flouri,et al.  Outdoor and indoor air quality and cognitive ability in young children , 2018, Environmental research.

[11]  Guohe Huang,et al.  Interval-Based Air Quality Index Optimization Model for Regional Environmental Management Under Uncertainty , 2009 .

[12]  Stefano Alessandrini,et al.  Impact of the electric vehicles on the air pollution from a highway , 2016 .

[13]  Michel Bierlaire,et al.  Strategic Energy Planning under Uncertainty: a Mixed-Integer Linear Programming Modeling Framework for Large-Scale Energy Systems , 2016 .

[14]  Mohammad Hassan Moradi,et al.  An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming , 2013 .

[15]  Hedayat Saboori,et al.  Multistage generation expansion planning incorporating large scale energy storage systems and environmental pollution , 2016 .

[16]  Guohe Huang,et al.  AN INEXACT TWO-STAGE STOCHASTIC PROGRAMMING MODEL FOR WATER RESOURCES MANAGEMENT UNDER UNCERTAINTY , 2000 .

[17]  Mei-Ling Shyu,et al.  Analysis of cardinal grey relational grade and grey entropy on achievement of air pollution reduction by evaluating air quality trend in Japan , 2017 .

[18]  Renfei Feng,et al.  Identifying optimal clean-production pattern for energy systems under uncertainty through introducing carbon emission trading and green certificate schemes , 2017 .

[19]  Oscar Castillo,et al.  Optimization of interval type-2 fuzzy systems for image edge detection , 2016, Appl. Soft Comput..

[20]  Vladik Kreinovich,et al.  Decision making under interval probabilities , 1999, Int. J. Approx. Reason..

[21]  Guohe Huang,et al.  A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning , 2017 .

[22]  Yan Zhang,et al.  Prevention and control policy analysis for energy-related regional pollution management in China , 2016 .

[23]  Y P Li,et al.  Development of an integrated model for energy systems planning and carbon dioxide mitigation under uncertainty – Tradeoffs between two‐level decision makers , 2018, Environmental research.

[24]  Ross Baldick,et al.  Multi-year stochastic generation capacity expansion planning under environmental energy policy , 2016 .

[25]  M. Lott,et al.  Quantifying the co-impacts of energy sector decarbonisation on outdoor air pollution in the United Kingdom , 2017 .

[26]  Kari Hämekoski.  The Use of a Simple Air Quality Index in the Helsinki Area, Finland , 1998, Environmental management.

[27]  Armistead G Russell,et al.  "What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health". , 2016, Environmental science & technology.

[28]  Zhang Chunxiao,et al.  The research of new daily diffuse solar radiation models modified by air quality index (AQI) in the region with heavy fog and haze , 2017 .

[29]  Peihua Cao,et al.  A novel method to construct an air quality index based on air pollution profiles. , 2018, International journal of hygiene and environmental health.

[30]  Javed Anwar.  Analysis of energy security, environmental emission and fuel import costs under energy import reduction targets: A case of Pakistan , 2016 .

[31]  Y. P. Li,et al.  A multistage fuzzy-stochastic programming model for supporting sustainable water-resources allocation and management , 2009, Environ. Model. Softw..

[32]  Jianlin Hu,et al.  Air pollution characteristics and health risks in Henan Province, China , 2017, Environmental research.

[33]  Alena Bartonova,et al.  Localized real‐time information on outdoor air quality at kindergartens in Oslo, Norway using low‐cost sensor nodes , 2017, Environmental research.

[34]  Wentao Lu,et al.  An inexact multi-objective programming model for an economy-energy-environment system under uncertainty: A case study of Urumqi, China , 2017 .

[35]  Han Li,et al.  Modelling of AQI related to building space heating energy demand based on big data analytics , 2017 .

[36]  Guohe Huang,et al.  Optimization of electric power systems with cost minimization and environmental-impact mitigation under multiple uncertainties , 2018, Applied Energy.

[37]  Gordon H. Huang,et al.  An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty , 2006 .

[38]  N. Zhong,et al.  Local pollutants go global: The impacts of intercontinental air pollution from China on air quality and morbidity in California , 2018, Environmental research.

[39]  Ignacio E. Grossmann,et al.  Models and computational strategies for multistage stochastic programming under endogenous and exogenous uncertainties , 2017, Comput. Chem. Eng..

[40]  Y. P. Li,et al.  A type-2 fuzzy interval programming approach for conjunctive use of surface water and groundwater under uncertainty , 2016, Inf. Sci..

[41]  G H Huang,et al.  Inexact multistage stochastic integer programming for water resources management under uncertainty. , 2008, Journal of environmental management.

[42]  Son-Lin Nie,et al.  A two-stage fuzzy robust integer programming approach for capacity planning of environmental management systems , 2008, Eur. J. Oper. Res..

[43]  Hazem Elzarka,et al.  A vague set fuzzy multi-attribute group decision-making model for selecting onsite renewable energy technologies for institutional owners of constructed facilities , 2017 .

[44]  M. Leili,et al.  Health impacts quantification of ambient air pollutants using AirQ model approach in Hamadan, Iran , 2018, Environmental research.

[45]  J. Kyburg.  Higher order probability and intervals , 1988 .