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

Journal Article · Thu Nov 15 00:00:00 EST 2018 · Environmental Research

DOI:https://doi.org/10.1016/J.ENVRES.2018.07.001· OSTI ID:23107916

Suo, C. ^[1]; Sun, J. ^[1]; Yin, S. ^[2]

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1. Sino-Canada Energy and Environmental Research Center, North China Electric Power University, Beijing 102206 (China)
2. State Grid Henan Economic Research Institute, No. 87 South Songshan Road, Zhengzhou 450052 (China)

Highlights: • A 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. 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) PM{sub 2.5} 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.

• OSTI ID: 23107916
• Journal Information: Environmental Research, Vol. 167; Other Information: Copyright (c) 2018 Elsevier Inc. All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0013-9351
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
29 ENERGY PLANNING
POLICY AND ECONOMY
AIR QUALITY
ENERGY CONSUMPTION
ENERGY POLICY
ENERGY SYSTEMS
ENVIRONMENTAL POLICY
FUZZY LOGIC
MITIGATION
NATURAL GAS
OPTIMIZATION
POLLUTANTS
URBAN AREAS
US ENERGY EXTENSION SERVICE