Abstract
This chapter provides a discussion of current multi-scale energy systems expressed by a multitude of data and simulation models, and how these modelling approaches can be (re)designed or combined to improve the representation of such system. It aims to address the knowledge gap in energy system modelling in order to better understand its existing and future challenges. The frontiers between operational algorithms embedded in hardware and modelling control strategies are becoming fuzzier: therefore the paradigm of modelling intelligent urban energy systems for the future has to be constantly evolving. The chapter concludes on the need to build a holistic, multi-dimensional and multi-scale framework in order to address tomorrow's urban energy challenges. Advances in multi-scale methods applied to material science, chemistry, fluid dynamics, and biology have not been transferred to the full extend to power system engineering. New tools are therefore necessary to describe dynamics of coupled energy systems with optimal control.
TopBackground
Many countries around the world still rely heavily on conventional fossil and nuclear energy. Targets are now being set around the world to reduce greenhouse gas emissions (European Commission, 2014). Most carbon reduction scenarios rely on a combination of decarbonised supply and demand reduction to achieve the medium to long-term targets (GEA, 2012). In recent years, significant efforts worldwide at multiple layers, from individual energy systems to whole energy systems, and at different levels, from research to policy analysis, have been undertaken to analyse the integration of renewables into the system. Furthermore, inter-governmental and non-governmental institutions have proposed various scenarios to support energy policy makers. Table 1 provides a list with worldwide scenario studies carried out by a number of institutions covering a broad range of stakeholder groups and published recently in 2012-2013. Each scenario has diverse results in terms of energy use, technological efficiency and the energy resources mix, all having as a main goal to succeed global energy access, especially for developing countries, to reduce air pollution and tackle climate change while improving energy security worldwide.
Table 1. Selected reports on worldwide energy scenarios by inter-governmental and non-governmental institutions as well as industry
| Study | Link | Scenarios | Projection Time |
| Inter-Governmental Institutions |
| Global Energy Assessment, by IIASA 2012 | http://www.iiasa.ac.at/web/home/research/researchPrograms/Energy/Global-Energy-Assessment-Database.en.htm | Supply, Efficiency, Mix | 2050 |
| Energy Technology Perspectives, by IEA 2012 | - | 6DS, 2DS, 4DS | 2050 |
| Industry |
| New Lens Scenarios, by Shell 2013 | http://www.bp.com/liveassets/bp_internet/globalbp/STAGING/global_assets/downloads/O/2012_2030_energy_outlook_booklet.pdf | Mountains, Oceans | 2060 |
| BP Energy Outlook, by BP 2012 | http://www.bp.com/liveassets/bp_internet/globalbp/STAGING/global_assets/downloads/O/2012_2030_energy_outlook_booklet.pdf | - | 2030 |
| The Outlook For Energy: a view to 2040, by ExxonMobil 2013 | http://www.exxonmobil.co.uk/Corporate/files/news_pub_eo.pdf | - | 2040 |
Energy
Perspectives, by Statoil 2012 | http://www.statoil.com/en/NewsAndMedia/News/Downloads/Energy%20Perspectives%202012.pdf | Base Case
(2 alternatives: Globalised expansion, Regionalised stagnation) | |
| Global Renewable Energy Market Outlook, by Bloomberg New Energy Finance 2013 | http://about.bnef.com/files/2013/04/Global-Renewable-Energy-Market-Outlook-2013.pdf | Traditional Territory, New Normal, Barrier Bursting | 2030 |
| Non-Governmental Organisations |
Energy
[R]evolution by Greenpeace, & EREC 2013 | http://www.greenpeace.nl/Global/nederland/report/2013/klimaat%20en%20energie/energy-revolution-scenario.pdf | Reference, Energy Revolution | 2050 |
| 2050 Global Energy Scenarios, by WEC 2013 | - | Jazz, Symphony | 2050 |
Key Terms in this Chapter
System of Systems: A system of systems is an organisational structure in which individual decentral or distributed systems are coupled together and interact across the individual systems’ boundaries.
Complex Systems: A system made up of a number of connected objects, which shows a number of properties which include nonlinearity, emergence and interactions – often these systems are referred to as complex adaptive systems.
Integrated Planning: Integrated planning (as opposed to sectorial planning) is a process involving the drawing together of level and sector specific planning efforts which permits strategic decision-making and provides a synoptic view of resources and commitments. Integrated planning acts as a focal point for institutional initiatives and resource allocation. In the context of integrated (or comprehensive) planning, economic, social, ecological and cultural factors are jointly used ( CEMAT, 2006 ).
Multi-Scale Modelling: Multi-scale modelling describes an approach to representing systems that have relevant features or interactions at different scales. Mostly temporal or spatial scales are considered.
System Dynamics: System Dynamics is a dynamic modelling approach at system level which is primarily used to understand interconnected systems and their evolution over time. Basic elements to represent the systems are internal feedback loops as well as stocks and flows.
System: A system is usually understood as a number of related individual elements, which can be understood as a whole when regarded in the context of a specific purpose.