ICT-Based Solutions Supporting Energy Systems for Smart Cities

ICT-Based Solutions Supporting Energy Systems for Smart Cities

Wolfgang Loibl (AIT Austrian Institute of Technology, Austria), Brigitte Bach (AIT Austrian Institute of Technology, Austria), Gerhard Zucker (AIT Austrian Institute of Technology, Austria), Giorgio Agugiaro (AIT Austrian Institute of Technology, Austria), Peter Palensky (Delft University of Technology, Netherlands), Ralf-Roman Schmidt (AIT Austrian Institute of Technology, Austria), Daniele Basciotti (AIT Austrian Institute of Technology, Austria) and Helfried Brunner (AIT Austrian Institute of Technology, Austria)
DOI: 10.4018/978-1-4666-8282-5.ch008
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Abstract

This chapter describes ICT solutions for planning, maintaining and assessing urban energy systems. There is no single urban energy system, but – like the city itself – a system of sub-systems with different scales, spatially ranging from buildings to blocks, districts and to the city, temporally ranging from real time data to hourly, daily, monthly and finally annual totals. ICT support must consider these different sub-systems which makes necessary dividing the chapter into different sections. The chapter starts with framework conditions and general requirements for ICT solutions, and continues discussing urban development simulating models. Then decision support tools are described for energy supply and demand as well as for energy efficiency improvement assessment. Later further instruments for Smart Grid-, district heating- and cooling-planning, as well as demand side management are addressed. In the final section tools are discussed for building automation systems as smallest physical entity within the urban energy system.
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Introduction

With respect to urban energy planning, ICT systems and solutions address all Information- and Communication Technology-based instruments and features which (i) simulate the urban system as a spatial framework and the (urban) energy system behaviour for ex ante assessment of applying energy strategies and measures, (ii) monitor energy supply and consumption as well as the state of the energy generation and transmission system, and (iii) manage – which is control and adaption of the energy supply and – if committed – also the demand side, to improve the future energy system performance: to enhance energy efficiency, mitigate environmental impacts, reduce supply and transmission costs and finally strengthen energy supply security.

Integrated city planning and management are crucial to initiate transformations of urban development, urban governance and infrastructure required to become a Smart City. There exists a wide range of ICT solutions for different purposes, audience and scales – spatial as well as temporal – to support these urban transformation processes. One urban planning approach involves supporting a holistic view by integrated modelling – i.e. modelling the city as a system of systems considering all important interdependencies. A different approach involves supporting sectorial planning, applying solutions which are tailored for experts in the sector to provide answers to technical questions, as well as assessing the related impact. Both approaches support decision makers in evaluating different options and effects of energy supply technologies and changes in demand. Thus decision support tools play a crucial role for performance assessment, benchmarking and easy-to-understand visualisation of different transformation scenarios and their economic, environmental and social impacts (Tommis & Decorme, 2013).

Going into detail would require a complete book instead of a single chapter. Taking into account the wide range of available and suggested ICT solutions and the space available in this chapter to debate the most relevant topics, we have divided the chapter into several sections to give an overview. Keirstaed (2011) has carried out a classification of models related to urban systems and energy systems, which gives some orientation for structuring the chapter:

  • Urban development models – including urban growth, land use change and transportation models. These models are the key to understanding urban energy topics as they typically model structure and activities in a city, finally used to estimate the energy demand for these activities.

  • Policy assessment models examine the city and try to assess long-range policy goals, e.g. to identify which measures and technologies might meet a given carbon target most cost-effectively.

  • Technology design models target the energy supply and demand side, dealing with optimisation of energy supply technology, supply mix and costs and finally improvements to consumption shapes to better balance supply and demand.

  • Building design (and automation) models look at the performance of buildings.

Following Keirstaed’s classification, this chapter is divided into the following sections:

  • Background and requirements for ICT solutions related to energy and Smart Cities

  • General ICT solutions for urban development, as a framework for energy planning

  • ICT solutions for energy system planning enabling smart urban development

  • ICT for energy supply solutions: Smart Grids, district heating

  • ICT for demand-side energy management

  • ICT for building automation

  • Future research directions

  • Conclusions and outlook.

Key Terms in this Chapter

Building Automation: ICT-based control of the energy performance of buildings with respect to security, fire and flood safety, lighting, shading, heating, cooling, humidity control and ventilation.

Urban Energy Planning Tool: ICT-based tool which allows spatially-explicit assessment of energy efficiency improvement potentials and energy demand and supply modeling through activities triggering energy consumption (heating, cooling, lighting, mobility) and energy generation. The spatial explicitness refers to different spatial scales ranging from building to block, neighborhood and city level and to the visualisation of model results in maps and 3D renderings.

Energy Supply Management: Monitoring, assessment and control of the energy supply, currently considering the integration of distributed (renewable) energy sources.

Energy Policy Assessment: Assessment of the effectiveness of energy policy decisions with respect to effects on (fossil) energy demand reduction, greenhouse gas emission mitigation, increases in renewable energy usage and further (positive or negative) socio-economic and environmental development.

Demand-Side Management: Control of the individual energy demand and observation of the individual and aggregated energy demand.

District Heating and Cooling: This is a technological concept comprising infrastructure for delivering heating and cooling services to distributed customers. It is mainly based on a wide range of local (fossil but renewable) energy sources that under normal circumstances would be difficult to use or remain unused (e.g. CHP (combined heat and power plants), geothermal, large-scale solar thermal or ambient energy (via heat pumps) and industrial waste heat). The energy source may change over time as the energy market and technologies change to favor new generation technologies or other more economic sources.

Decision Support Tool: ICT-based tool supporting decision making through simulating the implementation of probable measures and assessing the resulting effects.

GIS, Geographic Information System: Software designed to manage, manipulate, analyze and present spatial or geographical data. Spatial data represent geo-located, spatial objects (areas, points, lines, raster cell sets, recently also 3D-objects) with thematic attributes describing the objects’ properties) building together a digital map. They are stored either in file sets with files containing geometric features and a file containing attributes – linked to these geometric features, or they are stored in geodatabase systems with collections of digital maps. GIS-applications allow users to create interactive queries, to analyze spatial information, to edit geometric data, to generate new content through geometric functionalities (e.g. intersecting maps, calculating spatial averages, sums, etc., calculating new attribute content) and to present the results of all these operations.

Urban Development Simulation: Modelling of (spatial) urban development processes over time by simulating individual actors’ decisions resulting in change of land use, land use density and spatial interaction as well as changes in urban metabolism (energy consumption, emissions, waste, wastewater) through land-use related activities, some of them listed in the energy planning tool description.

Smart Grid: The extension of a power grid system from a unidirectional system of electric power generation, transmission, electricity distribution, and demand-driven control to a bidirectional system enabling distributed power generation and supply providing continuous information on the system state by means of digital processing and communication, making data flow and information management a central feature.

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