Tag Archives: energy

New paper: Activity-based modelling of urban energy demands

I’m not sure how this slipped between the cracks, but we had a new paper published recently in the Journal of Industrial Ecology. The main idea is that you can use detailed patterns of individual activities (e.g. working, shopping, resting at home) as produced by transport models to estimate the stationary energy demands for heat and power at high spatial and temporal resolutions. The paper presents a basic proof of concept but there is lots of room for improvement here.

Urban metabolism is an important technique for understanding the relationship between cities and the wider environment. Such analyses are typically performed at the scale of the whole city using annual average data, a feature that is driven largely by restrictions in data availability. However, in order to assess the resource implications of policy interventions and to design and operate efficient urban infrastructures such as energy systems, greater spatial and temporal resolutions are required in the underlying resource demand data. As this information is rarely available, we propose that these demand profiles might be simulated using activity-based modeling. This is a microsimulation approach that calculates the activity schedules of individuals within the city and then converts this information into resource demands. The method is demonstrated by simulating electricity and natural gas demands in London and by examining how these nontransport energy demands might change in response to a shift in commuting patterns, for example, in response to a congestion charge or similar policy. The article concludes by discussing the strengths and weaknesses of the approach, as well as highlighting future research directions. Key challenges include the simulation of in-home activities, assessing the transferability of the complex data sets and models supporting such analyses, and determining which aspects of urban metabolism would benefit most from this technique.

Keirstead, J., & Sivakumar, A. (2012). Using Activity-Based Modeling to Simulate Urban Resource Demands at High Spatial and Temporal Resolutions Journal of Industrial Ecology, 16 (6), 889-900 DOI: 10.1111/j.1530-9290.2012.00486.x

How can Newcastle achieve their 2050 carbon target?

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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 Newcastles 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.

Keirstead, 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

Positive coefficient regression in R

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I’m currently working on a paper about simulating urban demands for electricity and gas at 5 minute resolution. To do this, I have a simple regression model that tries to explain observed consumption based on local population figures and simulated levels of activity demands (e.g. minutes spent at work, leisure, etc). The data set looks like this, where each of the letters is an activity code:

> head(data)
  zone  pop     elec       gas  A    B   I    J  L M     O   R    S     W
1    0 7412 46221768 124613714  0    0   0    0  0 0     0   0    0     0
2    4 7428 37345875 100944002 60 3060 120 1020  0 0  4900 390  510 28635
3    7 7464 20914281  64109628  0 1155 510  255  0 0 11000 225 2475 29580
4   10 7412 46221768 124613714  0  680   0  390  0 0  5145   0    0  9300
5   14 7128 69233086  36611811  0 1335 105  210 60 0  5970   0 2520 14910
6   17 7608 40783190  59343776  0  150   0  150  0 0  1500   0    0   555

Created by Pretty R at inside-R.org

I then performed a basic regression using lm, removing the intercept (the “- 1″) as I want the population coefficient to serve a similar purpose for this analysis:

lm.elec <- lm(elec/365 ~ pop + A + B + I + J + L + M + O + R + S + W - 1, data)

Created by Pretty R at inside-R.org

Using Andrew Gelman’s helpful arm package, I can get a quick overview of the result as shown below. It doesn’t look too bad at first, but then I noticed all sorts of negative coefficients for the activity levels. This makes no sense: when individuals perform activities such as going to work, going to school, or shopping, we expect that their demand for electricity should go up, not down.

> display(lm.elec)
lm(formula = elec/365 ~ pop + A + B + I + J + L + M + O + R + 
    S + W - 1, data = data)
    coef.est coef.se 
pop    13.99     1.35
A     177.07    83.87
B     -63.90    27.80
I      -0.80    21.05
J      91.27    34.19
L   -1075.76   391.53
M      -5.57    73.50
O      55.47     9.90
R    -336.14   178.98
S      28.12     3.84
W      -8.19     3.16
---
n = 391, k = 11
residual sd = 159994.17, R-Squared = 0.65

Created by Pretty R at inside-R.org

Since a linear regression is essentially an optimization problem, my immediate thought was: can I just constrain the coefficient values so that they are all positive? This would mean that some activities might have no significant effect on consumption, but at least they couldn’t have a negative impact. And it turns out, yes, you can do this using the nnls package and function.

The nnls function is not quite as user-friendly as lm so the first thing you have to do is manually define your input variable matrix and output vector. For example:

A <- as.matrix(data[,c("pop","A","B","I","J","L","M","O","R","S","W")])
b.elec <- data$elec/365

The analysis can then be run as:

nnls.elec <- nnls(A,b.elec)

Similarly, we can’t use predict to generate our results and have to manually perform the matrix multiplication. This can be done as shown below.

coef.elec <- coef(nnls.elec)
pred.elec.nnls <- as.vector(coef.elec%*%t(A))

This method also doesn’t give you an r² value per se, but you can estimate it with the following dummy regression:

> lm.elec.dummy <- lm(b.elec~pred.elec.nnls - 1)
> display(lm.elec.dummy)
lm(formula = b.elec ~ pred.elec.nnls - 1)
               coef.est coef.se
pred.elec.nnls 1.00     0.04   
---
n = 391, k = 1
residual sd = 164861.13, R-Squared = 0.62

As you can see, the r² value is slightly lower in this case than the standard lm model but in terms of interpreting the coefficients the result makes much more sense. This can be clearly seen in the following graph, where demands for electricity and gas rise during the day as expected, rather than sinking in the basic case.

Simulated profiles for electricity and gas, comparing lm and nnls regressions

So there you go. If you ever need to run a regression and ensure that all the coefficients are greater than or equal to zero, nnls is your friend.

CHP planning restrictions and the efficiency of urban energy systems

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We have a new paper out in Energy:

Cities account for approximately two-thirds of global primary energy consumption and have large heat and power demands. CHP (combined heat and power) systems offer significant primary energy-efficiency gains and emissions reductions, but they can have high upfront investment costs and create nuisance pollution within the urban environment. Urban planners therefore need to understand the tradeoffs between limitations on CHP plant size and the performance of the overall energy system. This paper uses a mixed-integer linear programming model to evaluate urban energy system designs for a range of city sizes and technology scenarios. The results suggest that the most cost-effective and energy-efficient scenarios require a mix of technology scales including CHP plants of appropriate size for the total urban demand. For the cities studied here (less than 200,000 people), planning restrictions that prevent the use of CHP technologies could lead to total system cost penalties of 2% (but with significantly different cost structures) and energy-efficiency penalties of up to 24% when measured against a boiler-only business-as-usual case.

The performance of this particular model could be better, as several of the scenario results had large solution gaps which makes it difficult to see the underlying trends. However I think the method we used, applying an integer programming model to design multiple energy systems for generic cities, is innovative and will be of interest to others.

Keirstead, J., Samsatli, N., Shah, N., & Weber, C. (2011). The impact of CHP (combined heat and power) planning restrictions on the efficiency of urban energy systems Energy DOI: 10.1016/j.energy.2011.06.011

Britain’s Low Carbon Rush Hour

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Do you cycle to work? How about drive the kids to school? Or maybe hop a bus downtown for an early morning coffee with a friend? Whatever it is you’re doing, these trips all contribute to the carbon footprint of Britain’s rush hour.

I recently analysed these emissions as part of a research project for EDF Energy. The idea was to assess the carbon footprint of rush hour, looking at regional variations in distance travelled, transport mode, and total emissions. If you’re interested in the official results, the study was covered in the Evening Standard, Independent, Business Green and a few blogs, and here’s the official press release. But I wanted to add a few supplementary results here.^*

To calculate the carbon emissions of rush hour, I used data from the 2008 UK National Travel Survey and tallied up all the trips between 700–1000 and 1600–1900. Each trip can be characterized by its distance, purpose, the transport mode used, the location of the trip, and many other attributes. This was the first time I’d used this data set and I was surprised to see just how much information is collected.

For me, the most interesting result was to see how distinct Greater London is from the other British regions. Look at the figures below: loads of public transport, lots of commuting travel, and surprisingly high ownership of gas-guzzlers (band J and above).

Modal share of rush-hour trips. Private transport in red, public transport in blue.

Purpose of rush-hour trips

Vehicle ownership by area type

Another useful plot is the distribution of activities by time of day. You can see here that the evening rush “hour” is more dispersed than the morning rush, with the afternoon school-run starting around 3 pm and heavy travel continuing until about 7 o’clock.

Distribution of daily trips, shaded by purpose

It’s a rich data source and we’ve only scratched the surface. Hopefully we can do some follow-up research to look in detail at variety of trips and their suitability to different low carbon transport options.

^* Of course, these additional comments are my own opinions and have nothing to do with EDF.

What can cities do about climate change?

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About a year ago, I wrote a paper with Niels Schulz that asked what is unique about urban energy policy. We looked at London specifically and found that, although the Mayor’s office and local boroughs were keen to act on climate change and energy issues, they had very limited means at their disposal. Policy making by nation states or regional authorities, for example, might involve heavy-hitting economic policies such as carbon trading, renewable portfolio standards, or minimum product standards. But as open systems located within these other domains, a city like London can really only encourage citizen awareness of the issues, improve local transport, and try to attract low-carbon investment.

For a number of years now, the C40 Cities network has brought together concerned cities to try and overcome these obstacles by providing a forum for sharing best practice and establishing a visible constituency that can engage with other policy process; witness for example the C40′s presence at the Copenhagen climate conference in late 2009.

But one of the most valuable lessons from the C40 is that every city is different. There is no such thing as a one-size-fits-all urban energy and climate policy and a recent report by Arup demonstrates this nicely. Arup distributed a questionnaire to all C40 cities in March and April 2011, asking in particular about mayoral powers in different sectors such as transport, energy supply, water and waste. As shown in the figures below, most cities own and operate their water networks but their control over waste, transport, and energy use in buildings is highly variable.

Results of C40 survey on mayoral powers over water

Results of C40 survey on mayoral powers over waste

Results of C40 survey on mayoral powers over transportation

Results of C40 survey on mayoral powers over energy use in buildings

Perhaps the most significant finding is that many C40 cities have limited control over their energy supply systems and instead must rely upon “setting visions” in hopes that this will influence the relevant policy makers:

“The main sources of greenhouse gas emissions from urban areas are generated by the consumption of fossil fuels. In general, C40 cities did not register strong powers in the energy supply sector, reflecting the fact that most energy supply infrastructure is controlled by state, regional, or central governments. The strongest powers cities reported in this sector are related to the ability to set vision, which can be used to unofficially influence higher levels of government who hold most of these powers. Nevertheless, C40 cities have implemented 268 actions to create low carbon energy supply.”

The ability of cities to control their own finances is also constrained, with most relying upon government transfers or property taxes.

Results of C40 survey on mayoral powers over finances

These results point to a single, perhaps slightly boring, answer: that local context greatly affects what cities are capable of achieving in terms of energy and climate policy. And the corollary of that is that they will need to establish networks with other levels of government, private sector partners, and civil society groups if their ambitious goals are to be achieved.

Keirstead, J., & Schulz, N. (2010). London and beyond: Taking a closer look at urban energy policy Energy Policy, 38 (9), 4870-4879 DOI: 10.1016/j.enpol.2009.07.025