This post is part of a four-part series giving an overview of our recently published book Urban Energy Systems: An Integrated Approach. Parts I and II can be read here.
Part III: Techniques
Part III is titled Analysing Urban Energy Systems. The goal here is to introduce specific modelling and analytical techniques for urban energy systems, with an emphasis on optimization models which are helpful for planning new system designs.
Modelling urban energy systems
Nilay Shah opens the section with a brief chapter on the variety of modelling approaches that might be used to analyse urban energy use. One could focus on energy supply or demand, the environmental impacts, or narrow cost objectives. However a recent review shows that there is a growing interest in integrated modelling approaches that combine the strengths of different techniques in order to give a broader view of urban energy systems at all stages of development. To this end, we developed the SynCity hierarchical modelling framework which is illustrated below. It incorporates four models that respectively cover the design of energy efficient land-use plans, the simulation of citizen activities within an existing city, the design of efficient integrated energy systems to meet carbon reduction goals, and the operation of energy systems to maximize the benefits for system operators or households. The remaining chapters then explore some of these models in detail.
Overview of SynCity modelling platform
Optimization and systems integration
Nouri Samsatli and Mark Jennings discuss the role of optimization models in planning urban energy systems. Such models are usually described as being ‘normative’, i.e. they show the world the way it should be and not necessarily the way it is. However when designing a new urban energy system, this is usually a good thing as you want to design a system, for example, that meets a given level of performance but for a minimum cost.
The chapter gives the detailed formulation of a mixed integer linear programming model called TURN (Technologies and Urban Resource Networks). The model allows multiple energy system technologies and fuels to be evaluated simultaneously, choosing the number of technologies, their locations, and operating rates, and other parameters so that a given pattern of energy service demands are met.
An example TURN for an urban energy system using biomass. Boxes represent conversion processes, circles resources.
Two example applications of the modelling framework are shown. The first investigates the optimal use of bioenergy resources within a proposed eco-town development, selecting a wood-chip based district heating solution with storage that illustrates the benefits of an integrated assessment framework. The second case study considers the optimal timing of retrofit investment decisions over a ten-year period and takes into account the different preferences of owner-occupiers versus private or public landlords.
Ecologically-inspired UES models
The objective of an optimization model like the TURN model is typically to minimize the total system cost subject to some constraints. However an alternative perspective would be to minimize environmental impact. In her chapter, Nicole Papaioannou develops such a model using a range of environmental impact measures. The case study she presents looks at the energy requirements of a typical urban area and investigates how the optimal energy system configuration will vary depending on whether one’s priority is to minimize global warming potential, local air pollution, or resource consumption. A second analysis uses scenarios instead of an optimization model to describe the energy system options and then applies three different environmental impact assessment methods: life cycle analysis, material flow analysis, and ecological footprinting.
Energy demands are described by economists as a ‘derived demand’; in other words, people don’t consume energy for its own sake but only do so in order to achieve other goals, like travelling from A to B or keeping one’s house warm. [link][Aruna Sivakumar] therefore presents activity-based modelling as a way of explicitly describing what people are doing within the city and using this information to then infer energy demands. It is a very powerful technique that allows complex, and more realistic, urban behaviour to be captured. The figure below, for example, shows how a change in work patterns might create an opportunity for a longer shopping period, leading to a ‘cold start’ of the vehicle’s engine and worse environmental performance.
If you got off work early, you might spend a longer time shopping which could have an impact on vehicle emissions.
We have been developing our own activity-based modelling platform, known as AMMUA (Agent-based Microsimulation Model of Urban Activities), but as the chapter describes, there are many difficulties in gathering the required data, understanding the behaviour of such complex model systems, and evaluating their performance as a tool for policy analysis. Nevertheless, activity-based modelling is one of the most powerful and unique tools for urban energy systems analysis.
Uncertainty and sensitivity analysis
The final chapter in this section discusses uncertainty and sensitivity analysis. These techniques are not modelling techniques /per se/, but rather general methods that could be applied to any modelling framework in order to assess the impact of uncertainty on the model results. Having formal methods for evaluating uncertainty is important for urban energy systems because of the complexity of such systems: even if we manage to make a nice neat quantitative model of the system, there will still be questions about the choice of values for specific model parameters.
Penetration of domestic energy efficiency measures as part of Newcastle’s overall energy strategy
The chapter illustrates how uncertainty analysis — the description of model output variability given uncertain inputs — can be performed using the R package and Monte Carlo simulation. I also discuss the use of sensitivity analysis — the attribution of model output variability to specific uncertain inputs — as a way of improving the quality of model results and promoting more robust policy conclusions. The methods are applied to a case study from Newcastle-upon-Tyne where we worked with the local council to develop an energy systems strategy to 2050 that clearly illustrated the benefits of prioritising energy efficiency interventions.
These chapters show that no one modelling technique can answer all questions about urban energy systems. Optimization models work well for technical design problems and long-term planning, but simulation models based on citizen activities are valuable for capturing the complexity of urban life and its impact on energy consumption. But even more to the point, computational models are only part of an analyst’s toolkit. Part IV therefore looks at some qualitative lessons that be drawn from experience in urban energy systems around the world.
Get the book
Available now from Amazon and other bookshops.