- The other key exogenous factor is human behaviour, as reflected in patterns of usage.
- We have already explained how we generate estimates for hourly gas demand. To convert this into heat demand, we need to adjust for the other heat sources. Not all heat sources are used equally for the different uses of heat (e.g. space heating, hot water, cooking and industrial uses). Gas, as the predominant form of space heating in the UK, skews more heavily to space heating than some of the other heat sources. We cannot simply therefore apply the gas figures pro rata to the other technologies.
- One of those heat sources (oil) appears from national statistics to have a similar seasonal profile to gas, and we treat accordingly.
- Another heat source (wood fires and stoves) is used almost exclusively for space heating. The Domestic Wood-Use Survey of 2015 identified that (a) very few fires and stoves had back boilers (i.e. they were limited to space heating) and (b) around 40% of their heat was produced in summer, even though the respondents defined the summer period as the months when they did not burn wood.[1] These two “facts” are irreconcilably conflicting. We choose to believe the credible one: that most appliances do not have back boilers and therefore supply heat according to the profile for space-heating demand that can be extracted from the gas figures, not 40% in summer. This is actually an important factor in reducing the stresses of balancing the system, because wood burning is playing the role in this model that it plays in real life – helping to supplement the primary heat source during the coldest periods, when those primary heat sources would otherwise be under greater pressure.
- The other technologies show much less seasonality in the national statistics. There is not great variation between their quarterly splits, and we treat them homogenously. We estimate the residual hourly heat after subtracting gas, oil and wood’s shares on the above basis, and then divide it for each technology and hour pro rata to that technology’s share of the total and the balance between space heating (highly seasonal) and other heating (much flatter) within those residual figures. This is a broad assumption, but as reasonable a basis to allocate figures that are not available hourly as any other way that we could conceive.
- We apply conversion factors to each heating technology to estimate their fuel-use. This is an important difference from the conventional way of dealing with heat in the national statistics. Heat is traditionally treated as synonymous with the fuel used to produce it, in contrast to electricity, which is measured as the output of the conversion process after losses. The difficulty with treating heat this way is that differences in efficiency between some technologies are significant (e.g. heat pumps at one extreme and wood fires at the other), so one cannot simply switch their fuel consumption from one to the other. But it is an important part of the model to test different contributions from various heating technologies. We therefore reverse-engineer figures for heat outputs, i.e. the heat actually used, not the fuel used to produce it, applying reasonable conversion efficiencies for each technology and the hourly shares described above. The default heat demand figures therefore look smaller than in the national statistics, because they are net of conversion losses. When the model is run, it re-applies the conversion efficiencies to the hourly figures calculated from the seed data and the modeller’s choices, to calculate the usage of each fuel.
- For the electricity technologies (direct heating, air-source and ground-source heat pumps) this fuel usage under the default assumptions is deducted from the total demand figures to estimate conventional demand net of heat (and cooling, see below) in the seed data, so that changes to the use of electric heating can be reflected separately in the total electricity figures.
- Electricity demand is based on Elexon’s figures. Their out-turn figures (INDO and ITSDO) are the longest half-hourly series available. They are imperfect representations of demand, but the best we have for demand upstream and downstream of the transmission network.
- Interconnector flows are also based on Elexon’s data. This is a difficult area because the flows are determined not only by the UK’s needs but also by those of our counterparts at the other ends. Flows may be into the UK because we need the electricity and/or because a neighbour (e.g. France) needs to dump its excess. The best we can do is treat the historical figures as an indication of the elasticity (e.g. high export means either the UK really needed to shed load or the neighbour really needed the imports). We marry this to our model’s generated balance taking into account all the other variables, and assume that flows will reflect a balance of the factors. For example:
- If we were historically exporting strongly in a period but our model predicts under different conditions that we would have a high need to import in that period, we assume that the UK would not choose to export, but the neighbour would have its own requirements that prevented substantial export to us, and treat it as a wash.
- If historical flows were not large up or down in a period, and the model calculates that the UK needs to import or export heavily in that period under different conditions, we assume that the neighbour was not stressed and would be able to accommodate the UK’s requirements.
- If the model predicts that the UK will not be under significant stress in a period, but a neighbour (most often Ireland) was relying heavily on us historically in that period, the model assumes that we will continue to accommodate that.
- Another difficulty of interconnectors is predicting the flows where they are planned with other neighbours for whom we have no historic data. The model accepts as an input the assumed capacity of each of the five existing routes under the conditions being modelled (e.g. the modeller can assume that each interconnector has been expanded or closed). It does not (yet) offer a means to add another interconnector with a different profile because there is no obvious way to generate that profile. This will obviously not mirror the real world when these new interconnectors arrive. It is just one more example of the limitations of modelling. Other models may use assumptions to address this problem, but their output will then be significantly conditioned by their assumptions rather than by the data and the model. Garbage In, Garbage Out.
- We have already covered most of the inflexible generation technologies: onshore and offshore wind and solar. We treat two other technologies as inflexible, i.e. their output is determined by their operation more than by demand.
- Nuclear is the key one. Although it can be varied, its economics mean that it rarely is. It does, however, occasionally experience step changes when one of the units has to shutdown for maintenance. Each unit is so large that these steps are material. We reflect this by using Elexon’s figures for nuclear output to determine nuclear’s output profile in the model.
- Biogas (e.g. anaerobic digestion, sewage gas and landfill gas) also tends to run relatively flat, not because it also couldn’t be varied (storage for a few hours of gas would not be too expensive or technically complex), but because of the incentives created by the subsidy regimes make it uneconomic to do anything other than export as the power is produced. We therefore use Elexon’s figures for this technology in the same way as for nuclear.
To date, it is so rare for the output of inflexibles to exceed total demand that there is not a great issue of contention. But it has started to occur, and increasing capacity of some of these technologies means that it is likely to become a significant issue. The model therefore needs a method to decide how the output of inflexibles will vary where there is insufficient demand. Our merit order, based on the economics and engineering issues of de-rating and up-rating the technologies and the history of how this has been handled in the relatively rare cases to date is: nuclear, biogas, solar, onshore wind, offshore wind. This is another case where the assumption is highly imperfect (for instance, in reality, it will vary within technology depending whether the projects are embedded or grid-connected) but some method must be chosen, and no other seems superior.
- The other generation technologies are treated as dispatchable, even though some (e.g. solid biomass) have been in a halfway house to date, created by the tension between their incentives (produce baseload to maximise subsidy) and the network requirements (marginal costs are higher than the inflexibles, so when the latter’s output approaches total demand, biomass has to de-rate). Our cost data (covered below) differentiates between capital, flat-operating (£/period) and variable-operating (£/MWh) costs, and the model can therefore estimate marginal costs for the generating technologies treated as dispatchable (gas, oil, coal, solid biomass and hydro). We do not use seed data for these technologies, as their output has to be treated as one of the key ways to balance the inflexible elements of supply and demand.
- Large volumes of intermittents mean increasing periods where some output has to be constrained. The subtly-different concepts of “load factor” and “capacity factor” both refer to outcomes, not potential. In order to differentiate between what technologies would deliver unconstrained and what they actually deliver having been constrained, the model uses a concept we have termed “availability factor”, meaning the load factor that they would achieve if they were not constrained. We use this to estimate what each technology would like to supply if not constrained. We then apply constraints according to our assumed merit order. Over a year, the load factor is the constrained availability factor. To allow for technical improvements, we differentiate between the historic availability factor for existing capacity and the availability factor for new capacity. The default availability factors for new capacity of most technologies is materially higher than the historic. They can be varied by the modeller.
- There is limited storage in our energy systems at present, but this will be one of the key factors in balancing our future energy systems. The model allows the modeller to specify MW, MWh and round-trip efficiency for each of three technologies: pumped-hydro, batteries and compressed-air storage. But no seed data are used beyond reasonable defaults for these values, as these technologies by definition will be used to respond to the balance, which is a key output of the model.
- Transmission and distribution losses are treated as a function, not as an input. Comparison of Elexon’s INDO and ITSDO data reveals that losses are greatest as a proportion when demand is lowest. Losses are calculated by spreading an assumed annual average loss factor (combined for transmission and distribution) across each hourly figure according to a simple formula that reflects this historic behaviour. The loss factor can be varied by the modeller.
- Heat demand is not only a function of the weather, but also of investments that may be made in future, whether in energy-conservation or the construction of more heat uses (e.g. homes or businesses). There has been a tendency in some models in the past to treat improvements in this factor as a way of magically resolving some of the tensions, by treating demand components as inputs for the user to specify.
- We wanted to ensure a more realistic approach, as this is an important and often-abused factor. The model therefore treats the demand from various uses not as an input, but as an output determined by inputs such as:
- the proportion of the existing building stock that has been improved to modern standards with regard to loft, cavity-wall and solid-wall insulation, and glazing, with an indication of the current proportions that are considered easy or difficult to improve, and
- the number of new houses and flats that have been built and the standards to which they have been built.
- the number of existing buildings demolished can also be accommodated by reducing the total figures under the section for existing buildings. The model relies on the modeller to choose realistic figures for the combined number of existing, demolished and new-build buildings. An unrealistic modeller could skew outcomes by assuming that we shall all live five to a flat in future. But they will then have to explain how that is desirable and deliverable in a democracy.
- These calculations rely on seed data for the cost and efficacy of building improvements and standards, drawn from government statistics, set out in a blog piece on the C4CS website.[2]
