Planning a sustainable urban electric power system with considering effects of new energy resources and clean production levels under uncertainty: A case study of Tianjin, China

This study develops a risk-aversion optimization model for an urban electric power system (RAOM-UEPS), taking into account stochastic uncertainties. The RAOM-UEPS can manage stochastic uncertainties and capture associated risks from the stochastic information. This enables managers to analyze the trade-off between system cost and system risk in detail. Then, as a case study, the RAOM-UEPS is applied to the planning of an urban electric power system in Tianjin. Here, three scenarios are considered, each with different proportions of new energy resources and clean production levels (i.e., energy conversion efficiencies). This study aims to develop an urban electric power system (UEPS) optimization model that support the city's transformation from a coal-fired dominated to a low-carbon electric power mix, as well as to promote the sustainable development of society as a whole. The proposed model can facilitate a sophisticated system analysis of energy supply, electric power conversion, capacity expansion, and environment management over multiple periods. The results suggest that coal is dominant in Tianjin's electric power system, which was the primary air-pollutants and CO2 contributor in electric power system. Improving clean production levels and the proportion of new energy resources could effectively save energy resources and mitigate air pollutants and CO2 emissions. These findings can provide a scientific basis for the sustainable development of regional electric power systems, as well as for transformation from coal-dominated to low-carbon electric power cities.

[1]  Guohe Huang,et al.  Modeling Water Trading under Uncertainty for Supporting Water Resources Management in an Arid Region , 2016 .

[2]  Yang Zhou,et al.  A robust approach for planning electric power systems associated with environmental policy analysis , 2013 .

[3]  H. Long,et al.  Effects of land use transitions due to rapid urbanization on ecosystem services: Implications for urban planning in the new developing area of China , 2014 .

[4]  M. Dicorato,et al.  A regional energy planning methodology including renewable energy sources and environmental constraints , 2003 .

[5]  Muhammad Shahbaz,et al.  Energy conservation policies, growth and trade performance: Evidence of feedback hypothesis in Pakistan , 2015 .

[6]  Y. P. Li,et al.  Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China , 2016 .

[7]  Xiaoling Zhang,et al.  Delivering a low-carbon community in China: Technology vs. strategy? , 2013 .

[8]  Ignacio J. Pérez-Arriaga,et al.  Renewables vs. energy efficiency: The cost of carbon emissions reduction in Spain , 2012 .

[9]  José Luis Míguez,et al.  The use of grey-based methods in multi-criteria decision analysis for the evaluation of sustainable energy systems: A review , 2015 .

[10]  S. Wallace,et al.  Stochastic Programming Models in Energy , 2003 .

[11]  Xiaoling Zhang,et al.  Sustainable urbanization: a bi-dimensional matrix model , 2016 .

[12]  Sunil Luthra,et al.  Sustainable assessment in energy planning and management in Indian perspective , 2015 .

[13]  A. Elkamel,et al.  Optimization Model for Energy Planning with CO2 Emission Considerations , 2005 .

[14]  N. Floros,et al.  Energy demand and energy-related CO2 emissions in Greek manufacturing: Assessing the impact of a carbon tax , 2005 .

[15]  Miguel J. Bagajewicz,et al.  Financial Risk Management in the Planning of Energy Recovery in the Total Site , 2003 .

[16]  Guohe Huang,et al.  Risk analysis for Shanghai's electric power system under multiple uncertainties , 2015 .

[17]  Marco Taisch,et al.  Energy management in production: A novel method to develop key performance indicators for improving energy efficiency , 2015 .

[18]  Mohammad Modarres,et al.  Aggregate production planning by focusing on energy saving: A robust optimization approach , 2016 .

[19]  Yong Geng,et al.  Features, trajectories and driving forces for energy-related GHG emissions from Chinese mega cites: The case of Beijing, Tianjin, Shanghai and Chongqing , 2012 .

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

[21]  Carlos Henggeler Antunes,et al.  Economic-energy-environment analysis of prospective sugarcane bioethanol production in Brazil , 2016 .

[22]  Deendarlianto,et al.  Multi-objective optimization model for sustainable Indonesian electricity system: Analysis of economic, environment, and adequacy of energy sources , 2015 .

[23]  Li He,et al.  Regional planning of new-energy systems within multi-period and multi-option contexts: A case study of Fengtai, Beijing, China , 2016 .

[24]  Guohe Huang,et al.  An inexact robust optimization method for supporting carbon dioxide emissions management in regional electric-power systems , 2013 .

[25]  Zainuddin Abdul Manan,et al.  Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target , 2010 .

[26]  Shabbir Ahmed,et al.  Robust Process Planning under Uncertainty , 1998 .

[27]  Maged M. Dessouky,et al.  A Stochastic Mixed-Integer Programming approach to the energy-technology management problem , 2012, Comput. Ind. Eng..

[28]  Xiaosheng Qin,et al.  SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty , 2009 .

[29]  中華人民共和国国家統計局.  China statistical yearbook , 1988 .

[30]  G H Huang,et al.  An interval-parameter stochastic robust optimization model for supporting municipal solid waste management under uncertainty. , 2010, Waste management.

[31]  H Mirzaesmaeeli,et al.  A multi-period optimization model for energy planning with CO(2) emission consideration. , 2010, Journal of environmental management.

[32]  Hans Schnitzer,et al.  Catalyzing sustainable urban transformations towards smarter, healthier cities through urban ecological infrastructure, regenerative development, eco towns and regional prosperity , 2016 .

[33]  Rahmat-Allah Hooshmand,et al.  An efficient scenario-based stochastic programming for optimal planning of combined heat, power, and hydrogen production of molten carbonate fuel cell power plants , 2015 .

[34]  Guohe Huang,et al.  A two-stage inexact-stochastic programming model for planning carbon dioxide emission trading under uncertainty , 2010 .

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

[36]  S. W. Jin,et al.  Development of an integrated optimization method for analyzing effect of energy conversion efficiency under uncertainty – A case study of Bayingolin Mongol Autonomous Prefecture, China , 2015 .

[37]  Robert J. Vanderbei,et al.  Robust Optimization of Large-Scale Systems , 1995, Oper. Res..

[38]  Guohe Huang,et al.  Planning sustainable electric-power system with carbon emission abatement through CDM under uncertainty , 2015 .

[39]  Guohe Huang,et al.  Inexact two-stage stochastic robust optimization model for water resources management under uncertainty. , 2009 .

[40]  Xi Chen,et al.  Two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) for planning water resources management , 2014, Eng. Appl. Artif. Intell..

[41]  J. Mulvey,et al.  Making a case for robust optimization models , 1997 .

[42]  Guohe Huang,et al.  An inexact robust nonlinear optimization method for energy systems planning under uncertainty , 2012 .

[43]  G H Huang,et al.  Development of a fuzzy-stochastic programming with Green Z-score criterion method for planning water resources systems with a trading mechanism , 2016, Environmental Science and Pollution Research.

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

[45]  Giuseppe Forte,et al.  Environmental-constrained energy planning using energy-efficiency and distributed-generation facilities , 2008 .