4.4 Detailed methods for each indicator
4.4.4 Reliability
4.4.4.1 Sub-dimension 8: System adequacy
This sub-dimension focuses on ensuring that the system is adequate to meet demand with the available supply, at all times of the year. The pathways have all been designed with hour-by- hour system adequacy in mind, and all are modelled to meet the UK reliability standard of 3 hours Loss of Load Expectation (LOLE) per year (see section 5.8.1). However, there are other important factors in ensuring system adequacy, including maintaining a secure capacity margin, maintaining adequate capacity factors for dispatchable generation, and providing flexible two-way capacity options such as electricity storage and interconnection with neighbouring countries.
Indicator 8a. De-rated capacity margins Quantitative indicator
Calculates % of generating capacity which could reasonably be expected to be available at time of peak demand
Potentially applicable to other countries
De-rated capacity margin (DRCM) measures the amount of electricity generating capacity which can reasonably be expected to be available at times of peak demand, and which therefore can be ‘relied upon’ to be available for meeting system peaks, taking into account planned and unplanned outages and intermittency. Assumed de-rated capacity margins for each fuel (‘capacity credit’) are given in the National Grid 10-year statement (National Grid 2012: 30). Pathway DRCM is calculated using the following equation (RAEng 2013):
This indicator would be relevant for assessing the energy security of a wide range of pathways, including countries other than the UK. However, alternative data may be required, as the capacity credit figures used here are specific to the UK context.
Sensitivity tests are carried out to show the impact of changing assumptions regarding: - Capacity credit of imports (0%, 50% and 100%)
- Capacity credit of wind (5%, 8%, 20%, 40%) - Capacity credit of CCS (68%, 89%, 110%)
Details of the assumptions underlying these sensitivity tests are shown in Appendix E.
Indicator 8b. Capacity factors and oversupply Mixed quantitative / qualitative indicator
Uses data from the FESA model to show capacity factors for each generating technology in the pathway, in %
Qualitative analysis is then made of likely areas of risk for oversupply and for investment in dispatchable generation
Variable / limited applicability to other countries.
As well as ensuring that there is enough generation capacity available to meet demand peaks, an electricity system must also ensure that during times of low demand there is not severe oversupply of electricity. To do this, the capacity factors of each type of generation are adjusted by the FESA model which is used to generate the supply and demand mixes in the Transition Pathways. Barnacle et al (2013) and Barton et al (2013) note that a key issue for the pathways could be the high amounts of spare capacity required to back up the high
penetrations of intermittent RES in the generation mix of the pathways. A significant reduction in capacity factors could be a risk, because it risks making the initial investment in this type of generation capacity economically unviable because of the economic unattractiveness to generators of operating their plants at such low capacity factors.14 Countries other than the UK may experience different levels of risk arising from oversupply, for instance due to differences in market structure and availability of capacity sharing options such as transnational Grid systems; therefore this indicator would have variable applicability to non-UK contexts.
Indicator 8c. Electricity storage and interconnection Mixed quantitative / qualitative indicator
Calculates total electricity storage + total electricity interconnection in GW nameplate capacity
Qualitative assessment made of the level of ambition shown in the pathways Variable applicability to other countries.
14 The UK has recently introduced a Capacity Mechanism to attempt to deal with this problem (see DECC 2013c; National Grid 2014a); however, the first out-turn year is in 2018, and therefore it is too early to tell how effective or efficient this mechanism will be.
Storage and interconnection both represent forms of flexible, dispatchable power, which can in theory be called on when required (for instance, at a time of high demand), in order to help incorporate intermittent generation and to help reduce the necessary levels of spare capacity of conventional generation on the system.
The amount of electricity storage and interconnection (in GW) in the pathways is added together. The figures for interconnection are taken from the nameplate installed
interconnector capacity, and thus assume 100% imports to the UK during stress periods. The results are then compared against a potential range of interconnection and storage potential suggested in the literature. For example, does the pathway include a significant amount of electricity storage options such as distributed and Grid-level storage? Does the pathway build any new interconnectors? How ambitious are the plans for interconnectors?
This indicator would potentially be applicable to countries other than the UK, but only in certain cases, and may not be applicable to all industrialised country contexts. For some countries, interconnection may be less relevant, either because a transnational Grid system is already in place, or because they are geographically isolated enough to make interconnection unattractive. Furthermore, for some countries with abundant dispatchable low-carbon energy supplies (e.g. hydro-power), storage and interconnection may be less important for Grid balancing.
4.4.4.2 Sub-dimension 9: Shock resilience
This sub-dimension focuses on system resilience, especially resilience to sudden and unexpected changes in the supply/demand balance. No matter how secure the system, it is impossible to completely remove the risk of sudden shocks caused by unpredictable events such as geopolitical tensions, price shocks, or technical faults such as power station failures and line trips. Therefore, an important aspect of system security lies in ensuring that supply- side and demand-side aspects of the system can quickly respond to and recover from such shocks (Kiriyama and Kajikawa 2014).
Indicator 9a. Flexible supply: Frequency response capability Quantitative indicator
Calculates potential maximum and average Frequency Response capabilities of the generation mix, in MWh
Potentially applicable to other countries.
This indicator uses the capability of the generation mix to provide Frequency Response services to the System Operator as a proxy for the flexibility and responsiveness of the generation mix. Frequency Response (FR) is the ability of the system to react to short-term changes in the frequency (Hz) of supply, over timescales of less than 30 seconds. FR capabilities are calculated by extrapolating from power station data given by National Grid (available on request). From this data it is possible to calculate an average and a maximum FR capability for each type of power station; this is used alongside the recorded unit sizes to calculate FR capability per Megawatt (MW), which is then applied to the generation mix of the pathway. The results show average and maximum primary and secondary FR capabilities, in MWh.
It is important to note that DSR can provide an important source of both Frequency Response and Short-term Operating Reserve (STOR) (Ofgem 2013b). However, as explained in Indicator 9d, it is much more challenging to calculate the potential for DSR in the pathways due to lack of data. For this reason, ‘flexible supply’ and ‘flexible demand’ are assessed separately. Frequency Response and STOR capabilities are used as proxies for flexible supply only.
In the future, the requirements of the system for FR may change; this important consideration is covered in indicator 9c. This indicator would be relevant for assessing the energy security of a wide range of pathways, including countries other than the UK, although again the possibility of changing FR requirements should be taken into account. Alternative raw data would be required, as the FR availability figures used here are specific to the UK context.
Indicator 9b. STOR and black-start capability Quantitative indicator
Calculates the proportion of the nameplate generating capacity on the system which could potentially be used to provide STOR and black-start capability, in %
Potentially applicable to other countries.
FR covers the system if generation is suddenly lost. However, for the system to return to normal, reserve power then needs to come online. STOR is delivered within a maximum of 4 hours (National Grid 2011). All conventional generation can in theory provide STOR; however,
some types of plant cannot come online quickly if they are switched off at the time of the STOR request. Therefore this indicator shows results for short-term STOR (within around 45 minutes) and long-term STOR (45 minutes to 4 hours). The results show what percentage of capacity in the pathway would be capable of providing short-term and long-term STOR.
Black-start capability is used in the event of a blackout over a large geographical area. Small off-grid generators (usually liquid fuel) are used to daisy-chain power to start larger
generators, until the main plant turbines can be started. In theory, all conventional thermal generation (not including nuclear) can provide black-start power. The indicator shows the proportion of each pathway which would in theory be capable of providing black-start capability, if all compatible plants were fitted with this.
As with FR, requirements for STOR and for black-start may change in future; this is covered in indicator 9c. This indicator would be relevant for assessing the energy security of a wide range of pathways, including countries other than the UK, although again the possibility of changing STOR requirements should be taken into account.
Indicator 9c. Response and Reserve requirements
Quantitative indicator, to be used alongside indicators 9a and 9b
Calculates potential increases or decreases in FR requirements (in MWh) and STOR requirements (in %). These are compared with the capabilities calculated in 9a and 9b Potentially applicable to other countries.
The requirements for both FR and STOR may increase in a low-carbon electricity system. Requirements may increase due to decreasing system inertia (FR), increasing wind generation and wind forecast error (STOR), and increasing size of the largest generating unit on the system (FR and STOR) (National Grid 2011).
Inertia: when a turbine spins, it creates a build-up of kinetic energy. If the plant stops
generating unexpectedly, the turbine does not stop immediately, and the kinetic energy can be used to provide inertia which decreases the need for FR. A shift towards wind and solar power would decrease the amount of natural inertia on the system (Ulbig et al 2014). Increasing FR
requirements due to decreases in inertia are calculated using the proportion of generation which provides natural inertia.
Wind generation: inaccuracies in wind forecasts mean that with more wind on the system, there is increasing need for STOR to cover these inaccuracies. The increasing STOR
requirements due to wind generation are calculated using National Grid data (National Grid 2011) which shows the increases in STOR required for certain levels of wind generating capacity; this is then applied to the wind generating capacity in the pathways.
Increasing unit size: if the biggest unit on the system is of a larger size, the potential loss of power in the event of a unit trip increases, thus increasing the requirements for both FR and STOR to cover this loss. The increasing FR and STOR requirements due to larger unit sizes are calculated using National Grid modelling which assumes the connection of two 1800MW units at Hinkley C within the next decade (National Grid 2011). Sensitivity tests are carried out for the two centralised pathways, to show the potential impact of even larger units than this on the system in 2030 and 2050.
This indicator would potentially be applicable to other countries, especially as requirement levels for FR and STOR may change across different contexts, and are crucial for understanding the level of risk arising from insufficient FR and STOR capacity. However, alternative raw data would be required, as the requirements figures used here are specific to the UK context.
Indicator 9d. Flexible demand
Mixed quantitative / qualitative indicator
Calculates technically and realistically shiftable potential for 2010 (in GW); estimates realistically shiftable potential for 2030 and 2050 in % and GW
Also uses data on electric vehicles (EVs) and heat pumps as a proxy for demand flexibility. Results given in TWh/y and in % of total demand
Potentially applicable to other countries.
Flexible demand helps to improve resilience by offering increased flexibility and reducing peak load. Reducing the peak means that less generation capacity is required to meet peak demand, meaning that there is potentially less requirement for the types of shock resilience measures described above (Drysdale et al 2015). Moreover, an effective system for flexible demand can
help the system to respond to shocks, by offering an option for response or reserve from the demand-side rather than the supply-side, and thus mitigating the impact of declining response and reserve capabilities on the supply-side (Nistor et al 2015).
Data on flexible demand is not given in the pathways. Current data from the literature (AECOM 2011; Dudeney et al 2014; Element Energy 2012; Palmer et al 2013) is used to estimate
technically shiftable potential and realistically shiftable potential for 2010. This is used
alongside peak demand data in the pathways to estimate the reduction in peak demand which could be achieved with conservative and ambitious percentages of shiftable demand.
Pathways data on heat pumps and electric vehicles (EVs) is then used as a further proxy for levels of flexible demand in the pathways. EVs and heat pumps both represent relatively large shiftable electrical loads, especially compared to other appliances which could be used to load- shift such as fridges; therefore they can be used as a rough proxy for flexible demand as a whole.
The ‘flexible demand’ indicator would be relevant for assessing the energy security of a wide range of pathways, including for countries other than the UK. It is particularly relevant for assessing or comparing the security of low-carbon pathways or pathways undergoing major transition, as these could be particularly vulnerable from risks arising from insufficient flexibility.
Table 4-2: Overview of indicators with brief description of methods
‘Details’ key: QN: Quantitative. QL: Qualitative. M: Mixed : Indicator is potentially applicable to other countries
Dime nsion
Sub- Dimension
Indicator Overview of methods Details
A vai lab il it y Likelihood of domestic disruption to electricity availability Approval ratings of generation mix
Results from a nationally-representative public survey (Demski et al 2013) are applied to the generation mixes of the pathways, to show proportion of the mix (in GW and %) which is ‘approved’ and ‘opposed’ by the general public
QN
Land requirements (proxy for disruptive opposition)
The reasons people protest are complex (e.g. Devine-Wright et al 2009), and data is limited; therefore 3 proxies are used on the basis that increased proximity is more likely to result in opposition (Batel et al 2013; Devine-Wright 2005): land required for generation infrastructure (weighted 70-30 for onshore-offshore); additional onshore transmission infrastructure required; domestic extraction of primary fuel resources
QN
Participation in decisions Qualitative indicator, uses pathways storylines to assess the level of public participation in energy provision and in decision-making
QL Likelihood of non- domestic disruption to electricity availability
Diversity of fuel types in the electricity mix
Shannon-Wiener diversity calculation: -∑ Pi*(Ln(pi)), as used by Lehr (2009); Pfenninger and Keirstead (2015); Stirling (1998).
QN
Dependence on fuel imports
Pathways data used to show % of fuel mix from imports for coal, gas and oil Uranium estimates from current stockpile data
Biomass estimates using total indigenous biomass potential (estimate from pathways data)
QN
Diversity & stability of fuel imports
Current (2010) fuel import diversity is measured using Shannon-Wiener index
2010 fuel import stability measured by adding a stability parameter (Neumann 2007): NSW1= -∑ Pi*(Ln(pi))*b , where ‘b’ represents a stability parameter, derived from the Fragile States Index (Foreign Policy 2014)
Insufficient data in pathways for quantitative analysis; therefore qualitative statements made about possible future diversity and stability
M A ff ordab il it y Cost to the system Generation costs
Calculates LCOE using CAPEX (pre-development, construction), fixed OPEX (O&M, connection charges, insurance), variable OPEX (variable O&M, fuel, carbon price) (e.g. Pfenninger and Keirstead 2015)
Cost data from DECC (2013d) and Mott Macdonald (2010)
QN
Transmission upgrade costs
Onshore upgrade costs calculated using Electricity Networks Strategy Group estimates of upgrades required for different levels of new capacity (ENSG 2012)
Offshore upgrade costs calculated using estimated unit costs (from National Grid 2013c)
Distribution upgrade costs Distribution upgrade costs for the pathways modelled by Pudjianto et al (2013) QN
Cost to the consumer
Annual retail electricity bills
Wholesale electricity prices calculated using hourly demand data (from Transition Pathways modelling; see also Barton et al 2013) used to create Load Duration Curves; price-setting fuel defined by merit-order stacks; LCOE data used to give average yearly wholesale price; demand weighted seasonally
Wholesale prices added to a ‘consumer uplift’: 19% of bill for supplier costs and margins, 9% social and environmental policies, 20% network charges. VAT (5%) not included in estimate.
QN
Impact on fuel poverty Qualitative analysis carried out using annual bills estimates, existing literature on levels of fuel poverty in the UK (especially Hills 2012), and the pathways storylines
QL Sus ta inab il it y GHGs GHG emissions and intensity
Electricity system GHG emissions taken directly from pathways data (see also Foxon et al 2013) Life-cycle carbon intensity (in CO2e) of electricity generation types taken from High, mid and low estimates from IPCC global power station data (Moomaw et al 2011)
Total carbon intensity = Fuel-type intensity * (fuel-type generation TWh/y / Total generation TWh/y)
QN
Resources
Primary fuels depletion Qualitative method using information from the existing literature to assess depletion risk of primary fuels. Pathways assessed qualitatively for their reliance on depletable fuels.
QL Secondary materials
depletion
32 crucial materials are identified from Moss et al (2011) and Speirs et al (2014) and listed from ‘highly critical’ to ‘not critical’ according to risk of depletion
Pathways assessed qualitatively for their reliance on depletable materials.
QL
Water Water consumption & withdrawals
Data on water withdrawals and water consumption of different types of power generation from Davies et al (2013). Projections on types of cooling to be employed in UK thermal powergen in future from Kyle et al (2013). These are applied to the generation mix to show water consumption and withdrawals in m3/y
Baseline results weighted 70-30 to show greater environmental impact of freshwater vs seawater. Water usage for biomass feedstock production not possible to calculate because of lack of available data QN R el iab il
ity adequacy System De-rated Capacity Margins
Indicative fuel-type margins from National Grid (2012: 30) are applied to the generation mix. Fuel type margin is weighted according to generation mix, and subtracted from peak demand
Capacity margin (%) = ((total available capacity-peak demand) / peak demand) * 100 (RAEng 2013)
Capacity factors & oversupply
Capacity factors (from the Transition Pathways data) and capacity margins (see above) are used to highlight areas of oversupply
M
Electricity storage & interconnection
Electricity storage and interconnection nameplate capacities summed together; also compared to plausible storage and interconnection developments
M Resilience to sudden and unexpected changes in the supply- demand balance Flexible supply: Frequency Response capability
Power station data from National Grid (available on request) is used to calculate average FR capability of different generation types; this is applied to the fuel mix in the pathways. Maximum and mean FR capability shown for primary FR (<30 seconds) and secondary FR (30 seconds to 30 minutes)
QN
Flexible supply: Short-term Operating Reserve & black-start
capability
Calculates percentage of generation mix which would be capable of providing STOR and black- start capability (see National Grid 2011).
STOR results shown for short-term STOR (<45 minutes) and long-term STOR (45 minutes to 4 hours)
QN
Response & Reserve requirements
Increasing requirements for FR and STOR are calculated on the basis of decreasing system inertia, increasing impact of wind forecasting error, and increased credible in-feed loss due to increase of unit size. All data from National Grid (2011)
QN
Flexible demand
Calculates technically and realistically shiftable potential for 2010 (in GW), using data from