A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles

Abstract Excessive stress on fossil resources has deteriorated energy crisis and environmental problem, such that introducing renewable energies and electric vehicles (EVs) has become a main concern for government. In this study, a flexible-possibilistic stochastic programming (FPSP) method is developed for planning municipal-scale energy system (MES) with cost minimization and emission mitigation. FPSP cannot only deal with multiple uncertainties employed to the soft constraints and objective function, but also analyze the individual and interactive effects of uncertain parameters on system cost. The FPSP method is then applied to planning MES of Beijing under considering the impacts of renewable energies and EVs. Solutions in association with different constraint-violation levels, satisfaction degrees and confidence levels have been obtained. Results disclose that introducing EVs to the study MES can effectively mitigate pollutant emissions, and the emissions of sulphur dioxide (SO2), nitrogen oxide (NOx) and inhalable particles (PM10) can be reduced 7.9%, 10.8% and 9.1%, respectively. Results also imply that the city's MES can be adjusted towards a cleaner pattern through developing renewable energies and EVs. Findings can provide support for planning energy system through introducing EVs to high-traffic city and offer scientific information to decision makers for mitigating pollutant emissions under multiple uncertainties.

[1]  Walid G. Morsi,et al.  Power Quality Assessment in Distribution Systems Embedded With Plug-In Hybrid and Battery Electric Vehicles , 2015, IEEE Transactions on Power Systems.

[2]  K. Ponnambalam,et al.  Risk-averse stochastic programming approach for microgrid planning under uncertainty , 2017 .

[3]  Robert C. Green,et al.  The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook , 2010, IEEE PES General Meeting.

[4]  Mir Saman Pishvaee,et al.  Robust possibilistic programming for socially responsible supply chain network design: A new approach , 2012, Fuzzy Sets Syst..

[5]  Ramachandra Kota,et al.  An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles , 2013, IEEE Transactions on Smart Grid.

[6]  Zhile Yang,et al.  A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles , 2017 .

[7]  Javad Nematian,et al.  An Extended Two-stage Stochastic Programming Approach for Water Resources Management under Uncertainty , 2015 .

[8]  Yurui Fan,et al.  Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty , 2017 .

[9]  Yurui Fan,et al.  Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China , 2018 .

[10]  N. Hartmann,et al.  Impact of different utilization scenarios of electric vehicles on the German grid in 2030 , 2011 .

[11]  Behnam Mohammadi-Ivatloo,et al.  Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program , 2018, Journal of Cleaner Production.

[12]  Ronald R. Yager,et al.  A procedure for ordering fuzzy subsets of the unit interval , 1981, Inf. Sci..

[13]  G. Dantzig,et al.  A generalization of the linear complementarity problem , 1970 .

[14]  Goran Andersson,et al.  The role of electric vehicles in smart grids , 2013, Advances in Energy Systems.

[15]  Steven W. Su,et al.  Controlling the renewable microgrid using semidefinite programming technique , 2017 .

[16]  Raul Poler Escoto,et al.  Fuzzy optimization for supply chain planning under supply, demand and process uncertainties , 2009, Fuzzy Sets Syst..

[17]  Guohe Huang,et al.  Air pollutant and CO2 emissions mitigation in urban energy systems through a fuzzy possibilistic programming method under uncertainty , 2018 .

[18]  Cássia Maria Lie Ugaya,et al.  Environmental impact assessment of increasing electric vehicles in the Brazilian fleet , 2017 .

[19]  Troy R. Hawkins,et al.  Comparative Environmental Life Cycle Assessment of Conventional and Electric Vehicles , 2013 .

[20]  Freyr Sverrisson,et al.  Renewables 2014 : global status report , 2014 .

[21]  Y. P. Li,et al.  A type-2 fuzzy chance-constrained programming method for planning Shanghai’s energy system , 2017 .

[22]  Mir Saman Pishvaee,et al.  Novel robust fuzzy mathematical programming methods , 2016 .

[23]  Xingwei Wang,et al.  Hybrid Inexact Optimization Approach with Data Envelopment Analysis for Environment Management and Planning in the City of Beijing, China , 2012 .

[24]  Y. P. Li,et al.  Planning carbon dioxide mitigation of Qingdao's electric power systems under dual uncertainties , 2016 .

[25]  Guohe Huang,et al.  Planning regional energy system in association with greenhouse gas mitigation under uncertainty , 2011 .

[26]  Mir Saman Pishvaee,et al.  A possibilistic programming approach for closed-loop supply chain network design under uncertainty , 2010, Fuzzy Sets Syst..

[27]  Guohe Huang,et al.  Planning carbon emission trading for Beijing's electric power systems under dual uncertainties , 2013 .

[28]  Metin Türkay,et al.  A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions , 2017, Comput. Chem. Eng..

[29]  Vladimir Simic,et al.  Interval-parameter chance-constrained programming model for uncertainty-based decision making in tire retreading industry , 2017 .

[30]  George B. Dantzig,et al.  Reminiscences about the origins of linear programming , 1982, Oper. Res. Lett..

[31]  Hans-Otto Günther,et al.  The role of electric vehicles for supply chain sustainability in the automotive industry , 2015 .

[32]  Pierpaolo Girardi,et al.  A comparative LCA of an electric vehicle and an internal combustion engine vehicle using the appropriate power mix: the Italian case study , 2015, The International Journal of Life Cycle Assessment.

[33]  Boqiang Lin,et al.  Changes in urban air quality during urbanization in China , 2018, Journal of Cleaner Production.

[34]  J. Lv,et al.  Monte Carlo simulation based interval chance-constrained programming for regional ecosystem management – A case study of Zhuhai, China , 2018 .

[35]  Y.P. Li,et al.  A scenario-based interval-stochastic basic-possibilistic programming method for planning sustainable energy system under uncertainty: A case study of Beijing, China , 2018, Journal of Cleaner Production.

[36]  Marcin Szega.  An improvement of measurements reliability in thermal processes by application of the advanced data reconciliation method with the use of fuzzy uncertainties of measurements , 2017 .

[37]  Hedayat Saboori,et al.  Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources , 2017 .

[38]  Jen‐Ping Chen,et al.  Potential impacts of electric vehicles on air quality in Taiwan. , 2016, The Science of the total environment.

[39]  Guohe Huang,et al.  A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models , 2015 .

[40]  David B. Richardson,et al.  Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration , 2013 .

[41]  S. Ali Torabi,et al.  A robust possibilistic programming approach for pharmaceutical supply chain network design , 2015, Comput. Chem. Eng..

[42]  Mir Saman Pishvaee,et al.  A robust fuzzy possibilistic programming for a new network GP-DEA model to evaluate sustainable supply chains , 2017 .

[43]  Amelia Bilbao-Terol,et al.  Linear programming with fuzzy parameters: An interactive method resolution , 2007, Eur. J. Oper. Res..

[44]  Guohe Huang,et al.  Planning renewable energy in electric power system for sustainable development under uncertainty – A case study of Beijing , 2016 .

[45]  M. Abu-Madi,et al.  Estimation of main greenhouse gases emission from household energy consumption in the West Bank, Palestine. , 2013, Environmental pollution.

[46]  José L. Verdegay,et al.  Using fuzzy numbers in linear programming , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[47]  N. Essounbouli,et al.  An Interval Fuzzy Optimization-Based Technique to Optimal Generation Scheduling with Load Uncertainty , 2016 .

[48]  John Miller,et al.  CO2 emissions associated with electric vehicle charging: The impact of electricity generation mix, charging infrastructure availability and vehicle type , 2016 .

[49]  George B. Dantzig,et al.  Linear Programming Under Uncertainty , 2004, Manag. Sci..

[50]  Michel Bierlaire,et al.  Characterization of input uncertainties in strategic energy planning models , 2017 .

[51]  Tim Schulze,et al.  The value of stochastic programming in day-ahead and intra-day generation unit commitment , 2016 .

[52]  Manuel A. Matos,et al.  Economic and technical management of an aggregation agent for electric vehicles: a literature survey , 2012 .

[53]  Bor-Sen Chen,et al.  Natural Resource Management for Nonlinear Stochastic Biotic-Abiotic Ecosystems: Robust Reference Tracking Control Strategy Using Limited Set of Controllers , 2016 .

[54]  A. H. Shahirinia,et al.  Probability distributions of outputs of stochastic economic dispatch , 2016 .

[55]  Julian D Marshall,et al.  Life cycle air quality impacts of conventional and alternative light-duty transportation in the United States , 2014, Proceedings of the National Academy of Sciences.

[56]  Nina Juul,et al.  Effects of electric vehicles on power systems in Northern Europe , 2012 .

[57]  Pierluigi Siano,et al.  A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company , 2016 .

[58]  M. Brans,et al.  Analysis of factors affecting a shift in a local energy system towards 100% renewable energy community , 2017 .

[59]  Guohe Huang,et al.  An interval-possibilistic basic-flexible programming method for air quality management of municipal energy system through introducing electric vehicles. , 2017, The Science of the total environment.

[60]  Guohe Huang,et al.  ITCLP: An inexact two-stage chance-constrained program for planning waste management systems , 2007 .

[61]  Edward A. McBean,et al.  An Integrated Risk Analysis Method for Planning Water Resource Systems to Support Sustainable Development of An Arid Region , 2017 .

[62]  Syed Muhammad Anwar,et al.  A survey on electric vehicle transportation within smart grid system , 2018 .