Towards Sustainable Urban Energy Systems: High Resolution Modelling of Electricity and Heat Demand Profiles

被引:0
|
作者
Wang, Han [1 ]
Mancarella, Pierluigi [1 ]
机构
[1] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England
关键词
energy demand modelling; smart city; multi-energy systems; urban energy systems; sustainable energy;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Urban energy systems are attracting more and more attention owing to the challenges as well as potential to make them more sustainable. In particular, decarbonisation may be achieved by deploying a portfolio of multi-energy technologies (electricity, heat, cooling, gas, transport), for both distributed (e.g., PV, heat pumps, electric vehicles, thermal and electrical storage, etc.) and centralised (e.g., community-level or city-level energy systems supplied by cogeneration, trigeneration, etc.) applications. This calls for high resolution modelling from both temporal and spatial perspectives, suitable to capture infrastructure impact and requirements as well as intertemporal characteristics of new technologies (especially for storage). This paper presents an initial investigation into a modelling approach which provides high spatial and temporal electricity and heat demand profiles taking proper account of various consumption characteristics of different customer sectors, with application to the Greater Manchester's metropolitan area. A general framework for evaluating the different performances and requirements of city-level multi-energy system is also presented.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Statistical Data-Driven Regression Method for Urban Electricity Demand Modelling
    Voulis, Nina
    Warnier, Martijn
    Brazier, Frances M. T.
    2018 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2018 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), 2018,
  • [32] Stochastic modelling techniques for generating synthetic energy demand profiles
    Patidar, Sandhya
    Jenkins, David P.
    Simpson, Sophie A.
    INTERNATIONAL JOURNAL OF ENERGY AND STATISTICS, 2016, 4 (03)
  • [33] Energy Networks in Sustainable Cities: towards a full integration of renewable systems in urban area
    Bellosio, Barbara
    Giaccone, Luca
    Guerrisi, Alessandra
    Lazzeroni, Paolo
    Martino, Mariapia
    Tartaglia, Michele
    IECON 2011: 37TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2011,
  • [34] Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems
    Pedersen, Linda
    Stang, Jacob
    Ulseth, Rolf
    ENERGY AND BUILDINGS, 2008, 40 (07) : 1124 - 1134
  • [35] Technological learning modelling towards sustainable energy planning
    Neshat, Najmeh
    Hadian, Hengameh
    Alangi, Somayeh Rahimi
    JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY, 2020, 18 (01) : 84 - 101
  • [36] Towards Modelling Macro Influencing Factors to Address South African Energy Challenges: A Focus on Electricity Demand and Climate Change
    Engelbrecht, Clemens
    Brent, Alan C.
    2008 PORTLAND INTERNATIONAL CONFERENCE ON MANAGEMENT OF ENGINEERING & TECHNOLOGY, VOLS 1-5, 2008, : 188 - 196
  • [37] Agent-based Modeling of High-resolution Household Electricity Demand Profiles: A Novel Tool for Policy Evaluating
    Wang, Yu
    Lin, Haiyang
    Liu, Yiling
    Wennersten, Ronald
    Sun, Qie
    2017 IEEE CONFERENCE ON ENERGY INTERNET AND ENERGY SYSTEM INTEGRATION (EI2), 2017,
  • [38] Towards modelling diffusion mechanisms for sustainable off-grid electricity planning
    Riva, Fabio
    Colombo, Emanuela
    Piccardi, Carlo
    ENERGY FOR SUSTAINABLE DEVELOPMENT, 2019, 52 : 11 - 25
  • [39] Modelling to Support the Planning of Sustainable Urban Water Systems
    Deletic, Ana
    Zhang, Kefeng
    Jamali, Behzad
    Charette-Castonguay, Adam
    Kuller, Martijn
    Prodanovic, Veljko
    Bach, Peter M.
    NEW TRENDS IN URBAN DRAINAGE MODELLING, UDM 2018, 2019, : 10 - 19
  • [40] Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts
    Heidenthaler, Daniel
    Deng, Yingwen
    Leeb, Markus
    Grobbauer, Michael
    Kranzl, Lukas
    Seiwald, Lena
    Mascherbauer, Philipp
    Reindl, Patricia
    Bednar, Thomas
    ENERGY, 2023, 278