How can Newcastle achieve their 2050 carbon target?

By James Keirstead | Published: May 14, 2012

Another new paper out, this time looking at urban carbon reduction strategies. Working with Carlos Calderon at Newcastle University, we investigated how a goal-oriented optimization model could inform urban energy and carbon reduction strategies in contrast to current practice, which relies mainly on ad hoc bottom-up models. In particular we focused on the need for models that could capture spatial dependencies in both energy supply and demand, as well as parameter uncertainty.

The results allow us to offer robust policy advice, such as installing loft insulation and cavity wall insulation over the next ten years, because it is cost- and carbon-effective in almost all future scenarios. Here’s the key figure, with the maximum rates of penetration indicated by the dashed line and the grey areas showing the interquartile range:

Penetration of domestic energy efficiency measures as part of Newcastle’s overall energy strategy

Penetration of domestic energy efficiency measures as part of Newcastle’s overall energy strategy

The abstract:

Local authorities often rely upon urban energy and carbon modelling tools to develop mitigation policies and strategies that will deliver reductions in greenhouse gas emissions. In this paper the UK example of Newcastle-upon-Tyne is used to critique current practice, noting that important features of urban energy systems are often omitted by bottom-up tools including interactions between technologies, spatial disaggregation of demand, and the ability to pursue over-arching policy goals like cost minimization. An alternative optimization-based approach is then described and applied to the Newcastle case, at the scale of both the whole city and the South Heaton district, and using Monte Carlo techniques to address policy uncertainty. The results show that this new method can help policy makers draw more robust policy conclusions, sensitive to spatial variations in energy demand and capturing the interactions between developments in the national energy system and local policy options. Further work should focus on improving our understanding of local building stocks and energy demands so as to better assess the potential of new technologies and policies.

ResearchBlogging.orgKeirstead, J., & Calderon, C. (2012). Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study Energy Policy DOI: 10.1016/j.enpol.2012.03.058

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A brief history of urban energy systems

By James Keirstead | Published: May 8, 2012

Paul Rutter, a Visiting Professor in Chemical Engineering at Imperial, has been investigating the history of energy systems from the earliest hunter-gatherers through to the rise of modern energy services in London. I’ve been working with him recently trying to interpret this material from an urban perspective, and it’s a fascinating story of changing consumption patterns, interactions between government and private actors, and unexpected innovations, and one that hopefully can tell us something about our chances of achieving a new transition to low carbon cities.

If you’ve ever read the work of Vaclav Smil, Roger Fouquet and Peter Pearson or others, you’ll know that this is a huge field and it really deserves a book length treatment. But, for the moment, we’ve written a paper that tries to emphasize the main themes and sets the stage for further work.

The abstract:

Modern cities depend on energy systems to deliver a range of services such as heating, cooling, lighting, mobility, communications, and so on. This article examines how these urban energy systems came to be, tracing the major transitions from the earliest settlements through to today’s fossil-fuelled cities. The underlying theme is “increasing efficiency under constraints” with each transition marked by increasing energy efficiency in service provision, increasing per capita energy use, increasing complexity in the energy system’s structure, with innovations driven by a strategic view of the overall system, and accompanied by wider changes in technology and society. In developed countries, the future of urban energy systems is likely to continue many of these trends, with increased efficiency being driven by the constraints of climate change and rising fuel prices. Both supply and demand side technologies are discussed as potential solutions to these issues, with different impacts on the urban environment and its citizens. However in developing countries, rising urban populations and access to basic energy services will drive the next transition.

ResearchBlogging.orgRutter, P., & Keirstead, J. (2012). A brief history and the possible future of urban energy systems Energy Policy DOI: 10.1016/j.enpol.2012.03.072

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A review of urban energy system models

By James Keirstead | Published: April 30, 2012

Our review paper on urban energy modelling has just been published in Renewable and Sustainable Energy Reviews. This is a paper that should have been written a few years ago when our urban energy systems work started out, but the extra experience has been helpful in understanding how all the different types of models people use fit together. The paper’s divided into three sections: a review of current practice, an analysis of common problems and challenges, and a discussion of future opportunities for improved practice. I’ve been touring this paper for a few months now, and the response has been encouraging.

The abstract:

Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in a number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified – technology design, building design, urban climate, systems design, and policy assessment – each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date. Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

ResearchBlogging.orgKeirstead, J., Jennings, M., & Sivakumar, A. (2012). A review of urban energy system models: Approaches, challenges and opportunities Renewable and Sustainable Energy Reviews, 16 (6), 3847-3866 DOI: 10.1016/j.rser.2012.02.047

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Ken’s London Energy Cooperative

By James Keirstead | Published: April 23, 2012

Forget the weepy campaign video and the tongue-lashing in the lift. The most interesting part of London’s mayoral campaign is actually an innovative energy policy: Ken Livingstone’s manifesto pledge to “establish the first ever London-wide Energy Co-operative.”

In 2011 the average London household spent £1140 on their combined electricity and gas bills when paying by direct debit, up 8.6% on the year before. Since high energy bills are a sure-fire way to get bad headlines, it’s no wonder that politicians are looking for ways to bring down costs. But given the structure of the UK’s energy markets and urban governance, there’s usually very little that local authorities can do besides encouraging their residents to shop around.

This is why Ken’s big idea, if it works, would be a significant change in the way UK cities approach their energy systems. By using a Transport for London energy supply contract to buy in cheaper energy from the wholesale market, the Livingstone campaign claims that London households could switch to the new London Energy Co-operative (LEC) and save around £130 per year. Per unit gas and electricity costs for the median industrial consumer are about half of what domestic customers pay so this certainly sounds feasible.

We can double-check the maths with the help of this factsheet (pdf) from the energy regulator Ofgem, which gives a breakdown of domestic gas and electricity bills. For both fuel types, about 35% of the price is fixed and covers metering provision, transmission and distribution charges, taxes and the cost of energy saving and climate change policies; the LEC would still have to pass on these charges to consumers.

The remaining 65% is made up of the actual fuel cost, the cost of running the business, and the profit margin. Recent Ofgem analysis indicates that operating costs account for about 11% of the final bill, and average net profit margins are around 4.5%. Assuming similar operating costs for the LEC, having access to cheaper fuels and operating as a non-profit would enable a potential annual savings of around £220 per household.1 Naturally there will be set-up costs and the operations may not be as efficient as existing suppliers, but expected savings of £130 are not unreasonable.

There are many obstacles before Ken Livingstone makes this experiment a reality, not least of which is getting elected. Furthermore a number of firms have expressed their reluctance to offer wholesale contracts to these types of bulk purchase schemes. The matter may eventually need to be settled in court.

But if it were to happen, this model could provide a significant disruption to the UK’s energy supply market. Not all UK cities currently have access to wholesale supply contracts via an entity like TfL (London’s largest single electricity consumer), but with the template in place, it could encourage them to think about how they might promote more affordable urban energy provision. And once started, these urban energy co-ops could expand to offer a range of improvements in energy efficiency, renewable energy supply, and customer experience. Elected mayors, if granted appropriate powers in this area, could be at the forefront of a renaissance of urban energy innovation.

Over a hundred years ago, modern energy services like gas and electricity began to emerge in cities worldwide, led by local firms and local governments. As the networks grew, the pendulum swung towards the centralized national grids we have today. Perhaps now’s the time for the pendulum to swing back the other way.

1 There are a lot of assumptions in this calculation. I’m only guessing at TfL’s costs for gas and electricity based on DECC’s commercial price survey and it’s hard to work out the exact cost of fuel within these prices, as Ofgem doesn’t provide a breakdown for commercial bills in the same way that it does for the domestic sector.

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Hate to say we told you so…

By James Keirstead | Published: April 20, 2012

Back in 2007, Graham Sinden and I decided to respond to the government’s consultation on nuclear power. We touched on a number of issues in our submission including the back-up requirements of nuclear, the future role of nuclear generation, and waste and decommissioning, basically saying that, with a bit of careful thought and planning, one could design a system in which nuclear power contributed to the overall electricity generation mix in a fair way.

But when I saw the Guardian’s headlines this week about increasing commercial hesitation and plans for a sneaky subsidy, I thought back to our final point on the “limitations of facilitative action”. It’s a bit long, but I’ll post it in full below since I think our analysis has stood up pretty well. By threatening to pull out, nuclear companies have the government over a barrel and will use this position to squeeze for as many financial concessions as they can reasonably get.

Q16: The public interest and new nuclear power stations: the limitations of facilitative action

The consultation document makes it clear that the government’s role in this matter is only to decide whether or not the private sector should be “allowed” to construct nuclear power stations and if necessary, to initiate “facilitative action”. As this position is broadly consistent with the belief that competitive markets can best fulfill the UK’s energy needs, it begs the question: what happens if the private sector decides not to invest?

Our concern is as follows. The Energy White Paper stresses the importance of new nuclear power investment, warning that without it “we would be in danger of not meeting our policy goals” on energy security and reducing carbon emissions. This dependence, combined with nuclear power’s long-lead times, creates a risk that should private sector investment in nuclear power not be immediately forthcoming, the government might feel compelled to offer further incentives beyond the proposed “facilitative action”. There are two possible drivers for such a scenario. First, investors may be reluctant to commit significant resources to nuclear power without a guarantee on the price of carbon; E.ON, for example, recently stated that this would be a prerequisite for new nuclear development. However the consultation document’s assumption of a future carbon price of €36/tonne CO2 cannot be guaranteed under the quantity-based cap-and-trade EU ETS, as shown by the work of Profs Michael Grubb and David Newbery. The second concern can be found in Shimon Awerbuch’s work on the impact of nuclear power on financial risk in the electricity sector. Of course if further measures were needed to encourage nuclear power investment, a public consultation would be required to justify additional market intervention and this would create further delays in meeting our energy policy goals.

We recognize that these concerns represent a somewhat speculative, but not unimaginable, ‘what if’ scenario; nonetheless it does highlight two important issues which should be considered before proceeding on nuclear power. First, it demonstrates the tight timescales implied by our energy policy goals, particularly in the area of reducing carbon emissions. The government must be clear about the critical paths necessary to meet these targets and act quickly if progress is not being made. Secondly, it serves as a reminder that the UK’s energy policy challenges can be met in many different ways and that all alternatives should be fully considered before committing to a particular course of action. Box 10.1 of the white paper is an excellent demonstration of this, showing that there are at least 16 other policy measures that are likely to be more cost-effective, with shorter lead-times, and greater carbon mitigation potentials that nuclear power.

Our conclusion therefore is that it is right for the private sector to have the option of investing in new nuclear power stations, provided that they bear the full costs. However any further market intervention by the government would require a full and transparent comparison with other policy options.

Low EU ETS price? Check. New consultation for improved support? Check.

Somehow this video seems appropriate:

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Gaussian process regression with R

By James Keirstead | Published: April 5, 2012

I’m currently working my way through Rasmussen and Williams’s book on Gaussian processes. It’s another one of those topics that seems to crop up a lot these days, particularly around control strategies for energy systems, and thought I should be able to at least perform basic analyses with this method.

While the book is sensibly laid-out and pretty comprehensive in its choice of topics, it is also a very hard read. My linear algebra may be rusty but I’ve heard some mathematicians describe the conventions used in the book as “an affront to notation”. So just be aware that if you try to work through the book, you will need to be patient. It’s not a cookbook that clearly spells out how to do everything step-by-step.

That said, I have now worked through the basics of Gaussian process regression as described in Chapter 2 and I want to share my code with you here. As always, I’m doing this in R and if you search CRAN, you will find a specific package for Gaussian process regression: gptk. The implementation shown below is much slower than the gptk functions, but by doing things manually I hope you will find it easier to understand what’s actually going on. The full code is given below and is available Github. (PS anyone know how to embed only a few lines from a gist?)

Step 1: Generating functions

With a standard univariate statistical distribution, we draw single values. By contrast, a Gaussian process can be thought of as a distribution of functions. So the first thing we need to do is set up some code that enables us to generate these functions. The code at the bottom shows how to do this and hopefully it is pretty self-explanatory. The result is basically the same as Figure 2.2(a) in Rasmussen and Williams, although with a different random seed and plotting settings.

Example of functions from a Gaussian process

Example of functions from a Gaussian process

Step 2: Fitting the process to noise-free data

Now let’s assume that we have a number of fixed data points. In other words, our Gaussian process is again generating lots of different functions but we know that each draw must pass through some given points. For now, we will assume that these points are perfectly known. In the code, I’ve tried to use variable names that match the notation in the book. In the resulting plot, which corresponds to Figure 2.2(b) in Rasmussen and Williams, we can see the explicit samples from the process, along with the mean function in red, and the constraining data points.

Example of Gaussian process trained on noise-free data

Example of Gaussian process trained on noise-free data

Step 3: Fitting the process to noisy data

The next extension is to assume that the constraining data points are not perfectly known. Instead we assume that they have some amount of normally-distributed noise associated with them. This case is discussed on page 16 of the book, although an explicit plot isn’t shown. The code and resulting plot is shown below; again, we include the individual sampled functions, the mean function, and the data points (this time with error bars to signify 95% confidence intervals).

Example of Gaussian process trained on noisy data

Example of Gaussian process trained on noisy data

Next steps

Hopefully that will give you a starting point for implementating Gaussian process regression in R. There are several further steps that could be taken now including:

  • Changing the squared exponential covariance function to include the signal and noise variance parameters, in addition to the length scale shown here.
  • Speed up the code by using the Cholesky decomposition, as described in Algorithm 2.1 on page 19.
  • Try to implement the same regression using the gptk package. Sadly the documentation is also quite sparse here, but if you look in the source files at the various demo* files, you should be able to figure out what’s going on.


The code

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