A goal programming based model system for community energy plan

Community energy system optimization model has great contribution to formulate community energy planning indexes. But an inappropriate response of uncertainty always makes such “optimal plan” work ended in nothing. It is still a herculean task to solve a hybrid programming model which contains stochastic and fuzzy parameters. In order to acquire more flexible and reliable energy planning indicators in a convenient way, a goal programming based model system (GPMS) is proposed to conduct dynamic variation analysis of community energy flow. GPMS contains general linear programming model, goal programming model and grey relational degree model for results analysis. General linear programming model is used to calculate optimal community energy flow on baseline situation. Deviational variables associated with each independent parameter and total fossil energy consumption (TFEC) are introduced in goal programming model. Many kinds of optimum community secondary energy flow maps can be acquired by adjusting the weight which has been given to TFEC’s deviation variables. The grey correlation degree, a measure of relevancy between two data series, is used to evaluate these optimum community energy flow results. At last, this GPMS for community energy plan is introduced, as well as a case study in Tianjin.

[1]  Massimiliano Manfren,et al.  Paradigm shift in urban energy systems through distributed generation: Methods and models , 2011 .

[2]  C. Bale,et al.  Scaling up local energy infrastructure; An agent-based model of the emergence of district heating networks , 2017 .

[3]  Ravita D. Prasad,et al.  Multi-faceted energy planning: A review , 2014 .

[4]  K. F. Fong,et al.  Energy modelling of district cooling system for new urban development , 2004 .

[5]  Xiangdong Xu,et al.  Goal programming approach to solving network design problem with multiple objectives and demand uncertainty , 2012, Expert Syst. Appl..

[6]  Hang Yu,et al.  Approach for integrated optimization of community heating system at urban detailed planning stage , 2014 .

[7]  M. M. Ardehali,et al.  General procedure for long-term energy-environmental planning for transportation sector of developing countries with limited data based on LEAP (long-range energy alternative planning) and EnergyPLAN , 2014 .

[8]  Albana Kona,et al.  EXERGETIC AND THERMOECONOMIC APPROACH FOR OPTIMAL PLANNING OF DISTRICT ENERGY SYSTEMS , 2013 .

[9]  Michihisa Koyama,et al.  A scenario analysis of future energy systems based on an energy flow model represented as functionals of technology options , 2014 .

[10]  Mei Zhao,et al.  Methods and tools for community energy planning: A review , 2015 .

[11]  Sara Verones,et al.  Energy and Urban Planning: towards an Integration of Urban Policies , 2013 .

[12]  Mark Jennings,et al.  A review of urban energy system models: Approaches, challenges and opportunities , 2012 .

[13]  Zhiyuan Liu,et al.  Two-stage Optimization Model Used for Community Energy Planning , 2015 .

[14]  K. Sperling,et al.  Centralisation and decentralisation in strategic municipal energy planning in Denmark , 2011 .

[15]  Lazaros G. Papageorgiou,et al.  A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level , 2012 .

[16]  Hooman Farzaneh,et al.  An integrated supply-demand model for the optimization of energy flow in the urban system , 2016 .

[17]  Mehrdad Tamiz,et al.  Practical Goal Programming , 2010 .

[18]  M. Parsa Moghaddam,et al.  Optimal planning of hybrid renewable energy systems using HOMER: A review , 2016 .

[19]  Rahul B. Hiremath,et al.  Decentralized energy planning; modeling and application—a review , 2007 .

[20]  Guohe Huang,et al.  Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual uncertainties , 2011 .

[21]  Marc J. Schniederjans,et al.  Goal Programming: Methodology and Applications , 2010 .

[22]  Brian Vad Mathiesen,et al.  4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems , 2014 .

[23]  Christian N. Madu,et al.  Urban sustainability management: A deep learning perspective , 2017 .

[24]  J. Deng,et al.  Introduction to Grey system theory , 1989 .

[25]  Carlos Henggeler Antunes,et al.  A model for optimal energy planning of a commercial building integrating solar and cogeneration systems , 2013 .

[26]  Mohamed Elsholkami,et al.  Financial risk management for new technology integration in energy planning under uncertainty , 2014 .

[27]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

[28]  T. R. Tooke,et al.  A review of remote sensing for urban energy system management and planning , 2013, Joint Urban Remote Sensing Event 2013.

[29]  Michelangelo Scorpio,et al.  Dynamic performance assessment of a residential building-integrated cogeneration system under different boundary conditions. Part II: Environmental and economic analyses , 2014 .

[30]  Roland De Guio,et al.  Integrated energy planning in cities and territories: A review of methods and tools , 2013 .

[31]  Christos S. Ioakimidis,et al.  On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools , 2011 .