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Balancing Supply and Demand
Balancing Supply and Demand
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1. Executive Summary The need to decarbonise is driving a transformation of the UK energy system. Growing levels of inflexible, but low carbon sources of energy generation alongside changing consumer patterns and adoption of new technologies are making the balancing of supply and demand across the energy system increasingly challenging. As such the role of energy storage and competing flexible technologies, many of which are still in development and not yet deployed, will be required to change and expand. However, the nature of this role, the services required, and the technologies which can provide them at the least-cost is still uncertain.
Choices made by decision makers from the national level to the end-user will have an impact upon how the energy system will look in 2050, alongside the availability of new technologies, services and business models. Each sector faces its own challenges to decarbonise, from managing the peak demands from electric vehicles, large half hourly swings in low grade heat demand, tackling hard-to-decarbonise industrial processes or integrating intermittent but low carbon renewable electricity supply. However, the underlying question is typically the same; with the energy system available, is there enough energy to meet a demand at the required rate within a given response period. This question is further complicated as the energy system is interconnected, with energy flowing across vectors, and from one sector to another.
In light of this challenge the Energy Technologies Institute (ETI) commissioned the development of a new model, the Storage and Flexibility Model (SFM). The SFM is the most comprehensive model of its type, that casts a spotlight on the role of storage and other means of providing flexibility in the future energy system.
This report describes why there was a need to develop the SFM; the experience gained from developing and running it; and the type of use-cases the model can be applied to. It finds that the SFM fills a space in the current energy system modelling landscape and allows valuable insights about the future role of storage and flexibility to be drawn. This is due to its capability to represent multiple vectors, network levels, geographic regions and timeframes; including sub-hourly system services. The insights gained are applicable to many use-cases including long-term capacity planning, assessing the value of specific storage technologies, and identifying the system service requirements of future energy systems. The report also discusses the structure of the SFM and how it is different but complementary to the ETI’s Energy System Modelling Environment (ESME) which provides less detail relating to storage but more expeditiously explores the rest of the energy system.
Although only two scenarios were tested (base case, no CCS), the initial SFM runs show that in both cases the need for storage and flexibility increases significantly by 2050. This is particularly so for building level heat storage used to smooth electrified heat production, and longer duration electrical storage used for peak load reduction and to balance increasing reserve requirements. The need for both heat and electrical storage increases significantly if Carbon Capture and Storage (CCS) is not present in the energy system.
The SFM runs carried out demonstrate the potential of the model to provide valuable insights, however it also highlights the areas where development is required. In particular performance optimisation to reduce solving times and periodic updates to ensure the input assumptions remain up to date. A development plan is in place to ensure the SFM is continually upgraded to allow the full value of the model to be realised.
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Contents 1. Executive Summary .................................................................................................................................................. 1
2. Introduction ................................................................................................................................................................ 3
3. Balancing Supply and Demand ........................................................................................................................... 3
4. The Storage and Flexibility Model .................................................................................................................... 11
5. Running the SFM .................................................................................................................................................... 15
6. Learnings from the SFM ....................................................................................................................................... 17
7. Conclusion ................................................................................................................................................................. 25
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2. Introduction Decarbonising the UK energy system is likely to require drastic changes to the way that we design and operate the provision of services to end users. To this point, we have almost exclusively relied upon energy resources that are both excellent energy storage mediums and carbon based. Reducing our emissions to meet the Climate Change Act of 2008, or even ambitions for net zero carbon emissions, will require changes in fossil fuel use; likely to be a significant reduction in consumption or at least a fundamental shift in their place within the energy supply chain.
Choices made by national, local and end user decision makers will have an impact upon how our energy system will look in 2050, alongside the availability of new technologies, services and business models. The decarbonisation of each sector will have its own challenges, whether that be the peak demands from electric vehicles, large half hourly swings in low grade heat demand, tackling hard-to-decarbonise industrial processes or integrating intermittent but low carbon renewable electricity supply. The underlying question will typically be: with the energy system available, is there enough energy to meet a demand at the required rate within a given response period. Combine all sectors together, many of which compete for the same resources, and problems become increasingly difficult to assess.
The complexity of the question leads to inherent uncertainty within and between sectors when trying to develop markets and reduce risk of investment. Any sector-led market will likely be vulnerable to developments and decisions made elsewhere within the wider energy system. Therefore, to develop understanding, improve confidence and reduce risk, each technology needs to be assessed relative to all other technologies from a whole energy systems perspective.
As the energy system transforms, the requirement for and role of energy storage and competing flexible technologies will change to ensure that the increasingly difficult challenge to balance demand with supply is satisfied and resilient. Recognising that this is an underdeveloped area of knowledge in the UK, The Energy Technologies Institute commissioned the development of a new model that casts a spotlight on the role of storage and flexibility in the future energy system. This report explores the challenge of balancing supply and demand before describing the capability of the new Storage and Flexibility Model (SFM) and the learnings and insights gained from running it.
3. Balancing Supply and Demand The energy system can be balanced using a combination of three tools: modifying supply, modifying demand and decoupling the two through energy storage. One method of system balancing which is a combination of these tools is to switch between energy vectors (see section 3b). Figure 1 shows that we have always primarily relied upon energy dense and easy to store fossil fuels, which are generally stored between extraction and conversion – i.e. within large storage sites before being used within power stations or gas boilers. In the case of petroleum, the bulk storage sits with refineries or independent storage sites before being distributed to the smaller storage tanks at petrol stations and within vehicle.
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Figure 1 – Annual Inland Energy Consumption by Primary Fuel in the UK [Million tonnes of oil equivalent] (DUKES, 2018)
Our current dependence upon fossil fuel storage highlights that the management of supply, demand and storage needs to alter both in terms of where (and how) it takes place and through which mediums. A further example is the supply of low-grade heat within gas-connected homes, currently located at the domestic boiler. Balancing demand for low grade heat is either achieved through a boiler sized to meet peak demand, or the combination of a lower capacity gas boiler and a hot water storage tank. To provide heat through low carbon technologies, we may need to consider varying the mechanisms used to balance (i.e. managing supply, demand, storage) or moving these mechanisms to a different part of the system (e.g. away from domestic level, or post-conversion).
Decarbonised energy systems will have different characteristics to the current UK energy system for several reasons which include but are not limited to:
• Near complete decarbonisation of the domestic heat sector will require a drastically reduced reliance
on domestic gas boilers • The decarbonisation of transport, with higher reliance on electric powertrains or fuels reliant on
electricity for their production • The deployment of low carbon electricity generation (including intermittent renewables and relatively
inflexible nuclear power plants) • The development of carbon capture, utilisation and storage technologies • Increasing availability of demand side data and the associated opportunities for demand side control • The wider use of currently small scale or new energy vectors such as heat networks and hydrogen
networks
Each of these points, and several others, will have an impact upon the requirements to balance supply and demand, as well as the most cost effective and efficient mechanisms by which to do so. To reflect the value of technologies within such a complex future energy system, it is important to recognise the need to balance across different time horizons, energy vectors, network levels and geospatial areas, whilst accounting for near-term and long-term uncertainty.
a. Time horizons
Energy balancing is required over several timescales. The most cost-effective way of balancing temporal variations is dependent on the technical parameters and costs of technologies capable of delivering it. Figures 2 to 5 show examples of the temporal challenges that need to be managed.
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The provision of low-grade heat to homes is arguably the most challenging dilemma for seasonal balancing as we move to a decarbonised energy system. Figure 2 shows the average daily gas demand placed upon the National Grid transmission network, categorised by offtake. The gas offtake for Local Distribution Zones (LDZ) is largely used for the provision of low-grade heat to buildings within their distribution network.
Figure 2 - Average Daily Gas Demand on the National Grid Transmission Network 2017
The average daily gas demand from the LDZ in winter is up to five times the value in summer. Assuming a similar future demand for low grade heat, this volume of energy will need to be provided to those buildings for space heating and hot water by some other means. Figure 3 shows another way to present this data, where the difference in energy consumption between the lowest demand 6-month continuous period and the highest demand 6-month continuous period for LDZ and power stations is 242TWh. With the right means of producing and storing this energy throughout the year only half of this energy would have to be stored (i.e. that generated during the summer). Nevertheless, that is still the equivalent of 3 fully functioning Rough storage facilities, or 13,250 Dinorwig power stations, the challenge of storing such vast amounts of energy is clear. Figure 4 demonstrates that our seasonal flexibility is provided by sizing the capacities of our gas extraction above the average yearly demand and importing gas through interconnectors. The seasonal balancing challenge lies in the fact that this flexibility is difficult to economically replace with storage from any
other point in the system, but we need to remove our dependence on gas for the majority of low-grade heat.
Figure 3 - Highest vs Lowest Continuous 6 Month Demand Period LDZs and Power
Stations
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Figure 4 – Daily Gas Supply by Category
When focussing on a higher temporal granularity, the importance of short to medium duration storage and flexibility becomes clear. Figure 5 shows recent data developed by Wilson et al1 from a period in 2018 that involved a particularly cold spell named “the beast from the east”. The data shows actual local distribution zone demand over the course of a median and maximum week. Although the volumes of energy storage required for these fluctuations are not small, the most pressing challenge is the rate of change in demand. This actual data from 2018 showed a 116GW increase in demand within a three-hour period, whilst the well-known synthesised data from Robert Sansom suggested that in 2010 (a particularly cold year), UK low grade heat demand increased by up to 265GW within 2.5 hours before plummeting by 210GW over the next 2.5 hours2.
Figure 5: GBs local gas demand and electrical system supply - median and maximum demand weeks 2018
1 Challenges for the decarbonisation of heat: local gas demand vs electricity supply Winter 2017/2018, Wilson, G., Taylor, R., Rowley, P. 2018. http://www.ukerc.ac.uk/publications/local-gas-demand-vs-electricity-supply.html 2 Decarbonising low grade heat for low carbon future, Sansom, R,. 2014. https://spiral.imperial.ac.uk/handle/10044/1/25503
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Meeting these swings with a low carbon fuel can pose several challenges to the energy system, including building new networks, reinforcing existing ones, incorporating new storage and using existing storage more effectively. In the case of electricity, the need for real time balancing means that options to build a responsive but oversized low carbon power plant (e.g. gas turbines fuelled by hydrogen) need to be compared to the cost of large scale electricity storage at some point on the energy system, which will then impact upon the need for local and national grid reinforcement.
This need for real-time balancing in the electricity sector includes a requirement for sub-second balancing to maintain the power quality on the network. As the grid penetration of intermittent renewable generation continues to rise, there is a greater variation of the power supplied in real-time leading to increased real-time balancing. This has led to a focus on sub-second balancing for the electricity sector. In 2016 National Grid launched their Enhanced Frequency Response (EFR) tender to provide frequency response within 1 second; 201MW of battery storage was awarded a total of £66mn over four years, all this storage capacity is now commissioned and operational3. Following on from the EFR, National Grid then launched auctions to provide firm frequency response for periods ranging from 10 seconds to a few hours which storage technologies can bid into4. Besides National Grid, Distribution Network Operators are beginning to provide tenders seeking flexibility services, these are again typically for relatively short time periods ranging from a few minutes to a few hours. The creation of these markets has led to a significant increase in grid-scale battery deployment which is well suited to providing storage over these short time scales, with around 0.7GW installed, and 11GW’s in the pipeline, although this is unlikely to all be built5.
b. Energy Vectors
Many models which consider the role of energy storage and flexibility focus on electricity (see Figure 7). This can be for a range of reasons including that the bulk of global decarbonisation to date has focused on the electricity sector and the rapidly growing market for electricity storage technologies. However, energy systems are multi-vectored with heat representing 45% of all UK demand, gas providing 39% of primary energy consumption6, and other vectors such as hydrogen forecast in some studies to play a significant role in the future7. Furthermore, these vectors are not
3 Enhanced Frequency Response (EFR). National Grid. 2016. https://www.nationalgrideso.com/balancing-services/frequency-response-services/enhanced-frequency-response-efr?overview 4 Firm Frequency Response (FFR). National Grid. 2019. https://www.nationalgrideso.com/balancing-services/frequency-response-services/firm-frequency-response-ffr 5 UK battery storage capacity could reach 70% growth in 2019 as business models evolve. Solar Power Portal. 2019. https://www.solarpowerportal.co.uk/blogs/uk_battery_storage_capacity_could_reach_70_growth_in_2019_as_business_model 6 Energy Consumption in the UK. BEIS. 2019. https://www.gov.uk/government/statistics/energy-consumption-in-the-uk 7 Hydrogen in a Low Carbon Economy. Committee on Climate Change. 2018. https://www.gov.uk/government/statistics/energy-consumption-in-the-uk
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discrete, isolated entities. Energy can flow from one vector to another, for example electricity can be used to provide heat (electric resistive heaters, heat pumps etc) and gas can be used to provide heat or electricity, or both in the case of Combined Heat & Power (CHP) plants. As energy systems become more decarbonised this interaction between energy vectors will likely increase due to an increase in electrification for both heat and transport, as well as the potential uptake of new vectors such as hydrogen.
Multi-vector integration of the energy system can be a powerful source of flexibility. A report commissioned by the ETI considered how greater integration between energy vectors, principally electricity, gas, heat networks and hydrogen, could lead to a more flexible and resilient energy system in the future8. It identified a short list of seven case studies where integration between multiple vectors were felt to have the greatest potential to solve energy system issues or constraints over a range of scales by 2050:
• Domestic scale heat pumps and peak gas boilers • Gas CHP and heat pumps supplying district heating and individual building heating loads • Hybrid electric vehicles switching energy demand from electricity to petrol or diesel • Power to Gas – renewable generation to H2 or CH4 • Grid electricity to H2 for a hydrogen network • Renewable generation to district heating or smart electric thermal storage • Anaerobic Digestion/Gasification to CHP or grid injection
Although there were barriers to each case study with some studies more likely to provide benefit than others, each option was found to provide advantages to the energy system under certain conditions. Full details of the case studies are provided in the ETI report.
In addition to these case studies there are also storage technologies which store energy as one vector and discharge it as another, including pumped heat electricity storage and liquid air energy storage. Both of which store thermal energy (heat and cold respectively) and through thermodynamic processes discharge electrical energy. Therefore, to ensure the SFM provides the fullest possible picture of the role of storage and flexibility it optimises against multiple vectors simultaneously (Section 4).
c. Network levels
Energy storage can be located at any level of the network from an individual household scale, through distribution networks to the transmission network. The services provided by energy storage are similar across network levels; balancing supply and demand over a range of timescales. However, the scale of storage and the emphasis on the different roles it plays varies significantly, primarily because of the different nature of generation connected to each network.
8 Multi Vector Integration Study Summary report. ETI. 2017. https://www.eti.co.uk/programmes/energy-storage-distribution/multi-vector-integration
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Energy storage on the electricity transmission network is largely used for bulk supply and demand, ensuring that generation in one area at one time, can be used to meet supply in a different area at a different time. As more large-scale intermittent renewable generation is connected to the network, and fossil-fuelled generation levels decrease then this need to balance supply and demand, as well as maintaining power quality in real-time will become more important.
The transmission network currently has just over 14GW of intermittent renewable capacity and 55GW of firm generation capacity (including 27GW of CCGT), whilst the distribution network has 20.5GW of intermittent renewable capacity and 12.5GW of firm generation capacity (including 5GW of CCGT)9. Therefore, renewable energy makes up a much greater proportion of the connected generation assets on the distribution network. This larger proportion, coupled with the decentralised nature of the generation, means there are many areas with high levels of renewable generation and comparatively low demand which contributes to creating local constraints on the network. This presents opportunities for storage to provide benefits both in terms of deferring grid upgrades, which are traditionally used to manage grid constraints, and integration of additional renewables onto the network. Compared to the transmission network, individual energy storage assets are likely to be smaller in scale but more numerous on the distribution network.
Energy storage is also deployed on a domestic scale, most often in the form of hot water tanks. Domestic electrical energy storage is also becoming more popular. In some instances, this is for security of supply reasons to allow households to run off-grid. However, usually it is to facilitate self-consumption of energy generated from household solar photovoltaics. There is potential for a future ‘prosumer’ business model which sees individual households acting as consumers and producers of energy to allow balancing on a very local scale.
The UK gas transmission and distribution network has significant levels of storage both in terms of gas in the pipeline at any one time and gas storage facilities. As we decarbonise, the future heating system is likely to become increasingly electrified. As electricity networks do not have storage inherently built into the system this will increase the need for additional storage.
d. Geospatial variation
There are a range of factors which vary with geographical location which can affect the need for, and impact of, storage and flexibility in a specific area. These factors broadly vary by region and include:
1) Feasible locations of storage sites
Several storage technologies need specific features which only exist in some regions, for example Pumped Hydro Storage (PHS) requires sizeable valleys while hydrogen storage on a large scale requires salt caverns.
9 Digest of UK Energy Statistics, BEIS. 2019. https://www.gov.uk/government/collections/digest-of-uk-energy-statistics-dukes
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2) Networks coverage
The coverage of transmission and distribution networks can change significantly from region to region, each network has different requirements for storage (section 3c).
3) Generation make-up
Regional factors can influence the siting of different types of generation. Renewable generation is sited in areas with good resource, nuclear power stations are usually situated near the coast to provide access to cooling water10, while historically coal plants were sited near coal mines. Different generation technologies have various levels of flexibility which in turn impact the need for storage, and additional flexibility within a region.
4) Rural/urban
Rural areas tend to have greater deployment of renewable generation assets due to greater land availability and resource while urban areas have larger demand. These factors impact local supply and demand impacting the requirement for storage and flexibility.
One area where the need for storage changes on a much more localised scale is voltage regulation. Voltage regulation refers to the management of voltages at the transmission level of the electricity network, it is effectively a reactive power balancing problem where reactive power demand must be met by reactive power supply. Certain energy storage technologies can provide this balancing by injecting or absorbing reactive power from the network.
e. Uncertainty
Considering the role of any technology in the future has inherent uncertainties, especially for something as complex as a multi-vectored energy system. For example, future supply and demand as well as technology development are subject to many unknowns. Yet even within the context of an energy system, energy storage and competing flexible technologies are particularly susceptible to uncertainty. Alongside the longer-term uncertainties their role can be heavily influenced by a number of short-term uncertainties. These include: temperature (and indirectly space heat demand and heat pump efficiency), wind speed, solar output, electricity plant outages, interconnector prices and forecast errors. Accounting for the impact of these short-term uncertainties can provide significant value to energy storage by allowing a range of short-term operational factors to be better understood. These include the state of charge which would be most profitable to hold and the asset utilisation of different categories of storage.
There are several ways to account for long and short-term uncertainties including the use of multiple scenarios and Monte Carlo analysis, both of which are used by the SFM and discussed in more detail in section 4c.
10 Power Plant Siting Study, Project Summary Report. ETI. 2015. https://www.eti.co.uk/programmes/nuclear/power-plant-siting-study
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4. The Storage and Flexibility Model a. SFM Purpose
The purpose of the SFM is to provide the capability to improve the understanding of the future role of energy storage and the provision of flexibility within the overall energy system. The SFM provides a techno-economic evaluation of energy storage and other sources of flexibility across multiple energy vectors, network levels, geographic regions and timeframes, see Figure 6.
Figure 6 – SFM Modelled Characteristics
This capability allows a range of research questions around the future role of storage and flexibility to be explored including:
• Taking a whole energy system approach, what is the future role of energy storage and flexibility? • What is the scale of the different future service requirements (e.g. in MW, MWh) for storage and
flexibility? • What is the value of various forms of storage and flexibility to the system? • How do the key drivers of uncertainty (both short and long-term) affect the potential role of storage
& flexible alternatives?
There are also supplementary questions, which are not modelled directly, but can be informed by the results obtained:
• What might be required (e.g. policy support) to facilitate private investment in the level of storage and flexibility suggested?
• What new services / business models might emerge to maximise the value of storage and flexibility from an investor perspective?
Potential use cases of SFM based around some of these questions will be discussed in Section 5.
b. Modelling Landscape
There are several models available which can be used to assess the role of storage and flexibility in future energy systems, they each consider a different combination of vectors, temporal resolutions,
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network levels, geographic boundaries and uncertainties. Figure 7 shows where each of the models fits into the current modelling landscape. The wide range of factors considered by these models is representative of the variety of different roles they are trying to perform, including forecasting short-term electricity markets, long-term energy planning and informing energy policy.
SFM occupies a unique space in the current modelling landscape as the only model which accounts for uncertainties and multiple vectors at local and national network levels, across both short and long-term temporal resolutions. This makes it the most comprehensive energy systems model designed to represent the role of storage and flexibility within future energy systems. This is largely due to SFM being the first model developed specifically to assess the role of energy storage and flexibility, rather than this simply being an element incorporated into a larger objective (e.g. to forecast short-term electricity markets). DynEMo and IWES are the two models which most closely resemble SFM however neither of these models represent short-term uncertainty endogenously, whilst DynEMo is a simulation rather than optimisation model.
Figure 7 – Energy Storage Modelling Landscape
c. SFM Structure
The complexity of modelling energy storage and flexibility over multiple energy vectors, network levels, geographic regions and timeframes make it difficult to represent in a single optimisation problem. Therefore, SFM is made up of two individual but hard-linked optimisation modules:
1. Long-term module (LTM):
This is based on the Energy Modelling System Environment (ESME) v4.5 model developed by the ETI and now maintained and run by the Energy Systems Catapult. ESME is a least-cost optimisation model with perfect foresight designed to explore technology options for a carbon constrained energy system, subject to additional constraints including energy security and peak energy demand. The LTM uses this functionality to make long-term decadal planning decisions across multiple vectors. The LTM models 11 geographical regions representing Scotland, Wales and the former English Government Office Regions. Several modifications were made to the LTM to improve its ability to represent energy storage and flexibility including:
• A broader range of electric, thermal and gas storage technologies.
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• Explicit representation of the gas network and storage, along with additional multi-vector conversion routes such as power to synthetic natural gas.
• An updated representation of peak capacity requirements and system services for frequency containment / replacement and reserve replacement.
• A more granular representation of electricity distribution networks and the technologies connected to these with a differentiation between rural/urban and high/low voltage connections.
2. Short-term module (STM):
This module was developed for the SFM and makes short-term dispatch decisions on an hourly timescale with perfect foresight over one day. These decisions reflect unit commitment and other constraints such as ramp rates for electricity, gas, hydrogen and heat, across five characteristic weeks. The STM is least-cost optimised for operational and resource costs over each characteristic week. Each characteristic week represents one of the four seasons, as well as a peak week which is broadly based on a “1 in 10 year” worst case (low renewable supply, high demand). The STM also includes a representation of sub-hourly system services for frequency containment/replacement and reserve replacement. The STM also provides a detailed representation of demand-side flexibility across electric vehicles, heat storage, industrial demand-side response and electrolysers. It includes the same representation of geographical regions as the LTM.
Figure 8 shows how the two modules are linked into a single model. The SFM first runs the LTM and then the STM to produce one complete iteration, it then continues to iterate until the stopping criteria are met. After each run of the LTM or STM information is passed to the other module; at a high level the LTM frames the long-term system and the STM helps it to understand what the detailed operation of the system would look like.
There are two stopping criteria which must be met before the SFM will stop solving; a maximum difference in costs between successive iterations and a maximum threshold for unmet energy demand. Both criteria are user defined and aim to ensure that a stable equilibrium is reached with minimal (if any) unmet demand. The final complete iteration is taken as the model result.
The iteration process can be run in two modes:
1) Deterministic: The STM runs a single time for each iteration and then passes the information back to the LTM. 2) Monte Carlo: The STM runs multiple simulations changing the following inputs each time:
• energy demand • wind speed • solar resource • electricity plant outages • interconnector prices.
The changes made are based on distributions calculated from historical data, the distributions can vary both temporally and spatially. A combination of the mean and worst-case data from the simulations is then passed back to the LTM as another iteration begins.
Although the Monte Carlo mode provides a better representation of the energy system by accounting for uncertainty it requires significantly longer to solve due to the additional complexity.
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Figure 8 – Modular Structure of SFM.
d. SFM vs ESME Structure
SFM and ESME are both least-cost optimised multi-vector models of the GB energy system, with many of SFM’s assumptions based on, and consistent with, those used in the base case of ESME v4.5. This provides a reasonable and internally consistent central point for all long-term assumptions, covering a wide range of current and proposed energy technologies and their likely evolution. The assumptions that are consistent with ESME v4.5 include:
• End user demand assumptions • Technology assumptions (in terms of cost and build rate and quantity constraints) • Carbon emission constraints (scaled to GB rather than UK)
Nevertheless, the SFM and ESME are two distinct models with two distinct purposes. ESME aims to provide insights into how a low carbon energy system may be met by 2050, identifying ‘no regret’ technology options for a given number of constraints. While the SFM specifically aims to explore the role of energy storage and flexibility in achieving a future low carbon energy system.
To allow for a more detailed assessment of storage and flexibility several new assumptions have been developed as part of the SFM. These are focused on those areas which have a direct impact on storage and flexibility:
• Additional storage technologies and their associated costs • Dynamic dispatch assumptions for supply technologies • Parameters for the endogenous calculation of the requirement for, and provision of, energy services • Shaping profiles to allow for the automatic conversion of coarse LTM profiles to hourly profiles used
in the STM • Assumptions on managed charging of electric vehicles (EVs), home space heat storage, and industrial
load shedding Demand Side Response (DSR). These assumptions are on an hourly basis and include constraints such as maximum charging rate for EVs and minimum price industrial users must receive to shed load.
• Local Distribution Network (LDN) reinforcement cost curves
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Alongside these assumptions there are structural differences between the SFM and ESME 4.5, described below in Figure 9, which allow SFM to provide a more detailed analysis of the role of storage and flexibility. Fundamentally this is because unlike ESME, SFM has an hourly view of the dispatch of supply technologies and a representation of the need for system services, both of which have a significant impact on the requirements for storage and flexibility (section 3). However, the computing power required to model this level of resolution has led to reductions in detail in other aspects including the decision to only allow the Monte Carlo mode to be run for the STM. These trade-offs mean SFM is best suited for cases where assessing storage and flexibility is a priority, whilst ESME can more expeditiously explore the rest of the energy system.
Figure 9 – Summary of ESME v4.5 and SFM Structures
5. Running the SFM Due to SFM’s comprehensive nature it could be applied to a wide range of uses cases depending on the requirements of the user. Based on the research questions described in section 4 five typical use cases, and the way SFM would be implemented to meet them, are discussed below:
1. Identify the role of energy storage and flexibility in a user-defined future energy system
This case investigates the implications a technological intervention may have on the role of energy storage and flexibility, for example the impact of the availability of CCS, high intermittent renewable penetration, varying levels of inertia on the system or increased EV adoption.
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First, run the SFM in its standard form (base case), a second run of the SFM should then be carried out but with the technological intervention forced into the model, for example a given capacity of CCS could be installed by a set time period. This forced capacity may be informed by other models such as ESME. For instance, ESME may show that at a given capacity of CCS, the requirement for storage changes significantly, the SFM could then provide more detail.
The two SFM runs could then be compared to investigate the impact of the technological intervention on storage and flexibility. Solving additional runs of the SFM but with varying levels of the technological intervention would allow trends to be identified. This type of use case would require a minimum of two runs of the SFM which would take several weeks to solve (exact time depends on the hardware used), additional runs would increase the time on a pro rata basis. This assumes a single computer with a single software licence is being used.
2. Explore how a storage technology’s value to the system may change if its performance and/or cost characteristics change.
This use case aims to investigate how cost and performance improvements may impact the role of a storage technology. These improvements may be related to capital cost, operational costs, or technical parameters such as minimum/maximum discharge time or round-trip efficiency.
As in use case 1 the first step is to run the SFM base case, then subsequent runs of the SFM should be carried out with the expected improvements in cost or performance included. These runs could then be compared to identify how the role of the storage technology changes. This use case would require a minimum of two runs of the SFM.
3. Explore system service requirements
The SFM represents the demand for system services and the technologies which meet them. This can be further investigated by changing elements of the energy system which are likely to impact the need for system services or adjusting the technologies which can provide them. For example, how does changing the level of wind generation impact system service requirements; or what if supply chains constraints limit the number of Lithium-ion batteries on the system? Overall run time would depend on the total number of runs carried out with two runs a minimum.
4. What is the potential business case for a storage technology providing a given set of services?
Many energy storage projects operate business models which provide a specific service(s), for example batteries operating under the EFR Tender. In the future, energy storage business cases and the services they cater to are likely to change. As with ESME, the SFM does not model markets however it can provide a view on the value a technology provides to the system through providing a given service. This assumes that if a technology provides significant value then there is scope for a business case to be created.
The SFM represents three system services (section 4d), as well as the ability to provide bulk energy supply. The system services a technology can provide could be changed before each of several SFM runs is carried out. The results from each run can then be compared to identify how valuable the technology is to the system in each case. The number of runs
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required will depend on how many combinations of services are being tested but between 3 and 4 are likely to be appropriate.
5. Assessing the impact of short-term uncertainty
The SFM can be run in Monte Carlo mode to investigate the impact of short-term uncertainties on the role of storage and flexibility. In Monte Carlo mode the SFM will run a series of simulations in the STM where it varies several parameters each time (defined in section 4c). Comparing this run with the SFM base case will identify the impact of short-term uncertainty. However, running SFM in Monte Carlo mode substantially increases the solve time. Developments to speed SFM up allowing for the Monte Carlo mode to be used more often are discussed in Section 6.
The five use cases above represent a small sample of SFM’s capability, however they highlight that whilst SFM can explore use cases relating to flexibility and energy storage that ESME cannot, the added detail which allows SFM to do this comes at the cost of run-time. Use case 1 shows that an initial run in ESME can be useful to provide a reference point to a storage related problem. Providing this starting point can inform the input values to SFM runs, resulting in less runs and a shorter analysis time overall.
6. Learnings from the SFM a. SFM vs ESME
SFM has the capability to provide valuable insights regarding the future role of storage and flexibility, Figure 10 compares the total electric and thermal storage volume modelled by the SFM base case compared to that of ESME v4.111. The SFM’s more detailed assessment of the role for storage and flexibility results in the model identifying a greater need for flexibility than is captured in ESME v4.1. To meet this, the SFM selects considerably greater volumes of storage. Many of the dominant technologies are similar in both cases however there are some notable differences, for example in SFM there is substantially more pumped heat by 2050 which is used for balancing reserve requirements and peak load reduction. This increased selection is because ESME v4.1 does not assess system services while peak is measured in less detail than in SFM. Despite generally providing less energy storage, ESME selects considerably more District Heat Network (DHN) storage (i.e. large water tanks) than the SFM. ESME is not building more heat networks but is favouring DHN to meet peak demand and provide flexibility.
11 SFM is now updated to be consistent with ESME v4.5, the initial SFM runs shown here were consistent with ESME v4.1 so are compared to that version of ESME.
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Figure 10 Total electric and thermal storage volume, 2030 & 2050 ESME v4.1 & SFM
b. Key Insights from Initial Illustrative Runs
SFM has been used to provide projections of capacities and operating profiles under two different scenarios for reducing GB CO2 emissions by 80% by 2050 (compared with 1990 levels); a base case scenario and a No CCS scenario. The base case scenario was also run in Monte Carlo mode to explore the impact of short-term uncertainty on the role of storage and flexibility. A summary of the findings from the three model runs is provided to help demonstrate the value of the SFM, for more information see the final report for the SFM project12.
SFM Base Case
SFM is a work in progress and, like all models, will benefit from further development and refinement going forward (section 6c), nevertheless it can provide self-consistent sets of results which provide insights around the future evolution of the energy system. SFM, like ESME, does not present a forecast of the future but rather explores the options which may be least-regret under a set of assumptions around how the future energy system evolves. The following results demonstrate some of the most interesting findings of the SFM base scenario:
• There is likely to be a significantly increased role for energy storage by 2050 compared to ESME v4.1 o Particularly building level heat storage, used to smooth electrified heat production and avoid
coupling the electricity sector from high intra-day heat demand variation (Figure 11) o High volumes of electrical storage, predominantly longer duration Pumped Heat, used for
peak load reduction and to balance increasing reserve requirements (Figures 12 & 13) • Flexibility can be provided by multi-vector integration, which is key to how technologies are
operated o Heat storage flexibility is used to run ASHPs in fully utilised baseload operation – reducing
the total system cost and smoothing the demand on the electricity system
12 Storage and Flexibility Model Final project report. Energy Technologies Institute. 2018. https://www.eti.co.uk/programmes/energy-storage-distribution/storage-flexibility-modelling
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o Micro CHPs provide heat as well as flexibility to the electricity sector • All forms of flexibility compete with one another under the SFM’s least cost optimisation
o Interconnectors and Micro CHP capacity in 2030 replace the need for short term electricity storage provided in the previous decade by batteries
o EVs with managed charging reduce the need for additional flexibility in the electricity sector • System service requirements are unlikely to be a driver for capacity expansion of storage or other
technologies (Figure 1413) o Frequency services decrease out to 2050 in this scenario due to inertia provided from
significant levels of nuclear generation o Reserve requirements are expected to rise due to increases in electricity demand
(electrification of heat and transport) and intermittent generation, but generation and storage capacity built predominantly to cover peak load is likely to be sufficient to cover these reserve requirements
• New gas storage capacity is unlikely to be required, as gas supply gradually reduces due to decarbonisation in heat and electricity sectors
• Existing gas storage capacity is more than sufficient to soak up potential increased variability in gas supply coming from intermittent electricity generation
No CCS Scenario
The No CCS scenario makes decarbonisation more difficult across all sectors. Using the SFM to analyse this challenging decarbonisation scenario provides the following insights:
• Energy demands are further electrified to allow decarbonisation in other sectors, without the option of CCS
o Higher electrified heating, primarily through higher load factors for similar levels of heat capacity as in the base case (ASHPs and electric resistive)
o Electrified production of gaseous fuels (Synthetic Natural Gas (SNG) and H2) o Higher electrified transport through EVs
• However, flexibility normally provided by CCGTs with CCS in the Base scenario is replaced by flexible technologies in all sectors in the No CCS scenario
o Additional 28GW of hot water storage capacity in the heat sector by 2050 (Figure 11) o Electricity storage capacities double to 150GW (Figures 12 and 13) o Existing gas network capacity is sufficient to soak up excess SNG without further expansion,
with the network showing higher utilisation compared to the base case o Higher EV penetration and associated number of vehicles with managed charging
• Provision of system services remains achievable with capacity built primarily for peak load reduction (Figure 15)
o As in the base case frequency service requirements decrease under the No CCS scenario due to significant volumes of high inertia nuclear generation
o Reserve requirements increase significantly, due to increased electricity demand and intermittent generation, however, as in the Base Case technologies built primarily as peak load capacity provide sufficient capacity to cover reserve
• Higher flexibility requirements result in shorter duration storage technologies o Although the total capacity of electricity storage technologies increase, proportionally the
greatest increase is for shorter duration Batteries
13 In Figures 14,15 and 19, time is split by hour of week, by season, and by year. Only hours 25-72 have been shown, i.e. 12am Tuesday morning to 11pm Wednesday of each characteristic week.
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Fig 11 Heat capacity, 2030 & 2050 Base Case & No CCS
Fig 12 Electricity storage capacity, 2030 & 2050 Base Case & No CCS
Fig 13 Electricity storage volume, 2030 & 2050 Base Case & No CCS
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Fig 14 System Service Requirement, 2030 & 2050 Base Case
Fig 15 System Service Requirement, 2030 & 2050 No CCS
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Monte Carlo Scenario
In addition to these two deterministic runs, the base case scenario was also run in Monte Carlo mode to explore the impact of short-term uncertainty on the role of storage and flexibility. 20 STM simulations were carried out for 2050 only with the following parameters varied in each simulation: wind and solar output, power plant outages, electricity prices in neighbouring countries and temperature.
When running the SFM in Monte Carlo mode a single ‘peak week’ is not represented, instead the model substitutes multiple differentiated stress events into each simulation. Although a low number of simulations is favourable in terms of computation time it is therefore detrimental to the representation of peak conditions. However, a prior sensitivity analysis has been performed to assess the impact of the number of simulations on the sample distribution, especially the effect on extreme cases (95th percentile). This found 20 simulations provides an acceptable representation.
A single year (2050) was chosen for computational reasons, however, to allow for comparison with the deterministic Base Case, the installed capacities prior to 2050 are pre-defined, based on the Base Case results. 2050 is the year of maximum capacity change in the Base scenario, and this is free to be re-optimised in the Monte Carlo run.
• The peak conditions are similarly stressful in the Monte Carlo model as the Base Scenario, but the Monte Carlo model assesses a greater range of possible stress events that may occur at different parts of the energy system. As a result, the capacity mix is designed to cope with a wider range of operating conditions.
• For heat, the total capacity requirements are higher in the Monte Carlo results, due to the uncertainty of weather-related temperatures directly affecting heat demand (Figure 16).
o There is a shift of energy supply from electric resistive heating and gas boilers to Micro-CHP and ground source heat pumps. The diversity of system conditions faced over the range of Monte Carlo simulations carried out favours technologies which are more versatile than “single-use” technologies which struggle with changing conditions.
• For electricity the total capacity requirements remain similar, though with a shift from Gas CCGT to Wind
o The use of multiple simulations means wind load factors are found to be on average much higher in stress periods than assumed in the Peak week of the Base scenario. This results in their increased capacity and an elimination of CCGTs with CCS as a low carbon generation technology. It also suggests that the “1 in 10 year” worst case represented by the peak week in deterministic mode is more stressful than the P95 approach used in the Monte Carlo mode.
o Flexibility is required by the system due to the removal of CCGT with CCS and the increase in intermittent generation. To meet this significant capacity of highly flexible short duration Li-ion batteries are added to the system, whilst the capacity of longer duration pumped heat decreases slightly (Figures 17 and 18). This results in the net volume of electrical storage decreasing slightly.
• Frequency service requirements are similar to the Base Case, whilst reserve requirements are noticeably lower (Figure 19).
o Whilst higher wind capacity in the Monte Carlo run compared to the Base Case increases service requirements this is more than offset by a reduction in solar capacity and electricity demand which decreases reserve requirements.
• For gas, the annual consumption drops significantly due to the shift away from CCGT with CCS as a way to decarbonise. The increased usage of micro-CHPs also makes the gas usage more efficient.
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Fig 16 Heat capacity, 2030 & 2050 Base Case & Monte Carlo
Fig 17 Electricity storage capacity, 2030 & 2050 Base Case & Monte Carlo
Fig 18 Electricity storage capacity, 2030 & 2050 Base Case & Monte Carlo
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Fig 19 System Service Requirement, 2050 Monte Carlo
c. Future Development Plans
The SFM is the most comprehensive energy systems model designed to represent the role of storage and flexibility within future energy systems that currently exists, nevertheless it is in the early stages of maturity and requires further development to improve its functionality. As such there is a future development plan in place, this includes upgrades to keep the SFM consistent with the most recent version of ESME as well as developments specific to the SFM. The current development plan includes:
• Upgrade the SFM to ESME 4.5 (complete): this is the most recent version of ESME and contains the most up-to-date input assumptions regarding the evolution of the energy system
• Investigate performance optimisation for the SFM to reduce runtime (ongoing): the complexity of the SFM results in long solving times in the order of weeks which places additional considerations around using the full functionality of the model. A study is underway to explore options for speeding the SFM up (e.g. parallelising characteristic weeks in the STM, use of web servers, additional hardware etc)
• Work with partners to apply the SFM to a variety of projects (ongoing): to maximise the value gained from the SFM and to improve the understanding and evidence base of the model’s functionality it is important that the SFM is used for a range of applications. Discussions are underway regarding the use of the SFM with several different partners.
• Upgrade the SFM to account for net-zero (planned): the UK Government’s recent change in legislation to a net-zero emissions target by 2050 will have impacts on the evolution of the whole energy system including storage and flexibility, therefore it is important the SFM represents this.
• Improved functionality (planned): there are still elements of the energy system which have potential to impact the role of storage and flexibility which the SFM does not represent. Two of these which will be added to future versions of the SFM are a representation of vehicle to grid technologies and voltage constraints (section 3d).
• Continual updating and upgrading of the SFM (planned): the energy sector is constantly evolving. As ESME is upgraded to account for this evolution the SFM will also be updated to maintain its
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consistency with ESME. Where upgrades specific to SFM are required to keep it up to date and relevant these will also be carried out.
7. Conclusion As the UK energy system decarbonises, generation sources and consumer patterns are changing resulting in an increasing challenge to balance supply and demand. This has led to a changing role for storage and competing flexibility technologies. To investigate this role the ETI commissioned the development of the Storage and Flexibility Model (SFM).
This report has highlighted the potential of the SFM, the most comprehensive model of its type, to investigate the future role of energy storage and competing flexible technologies. It has shown that the capability of the SFM to represent multiple network levels, geographic regions and timeframes; including sub-hourly system services, allows valuable insights to be drawn. These insights are applicable to many use-cases including long-term capacity planning, assessing the value of specific storage technologies, and identifying the system service requirements of future energy systems.
The nature of the findings SFM can provide is demonstrated by a summary of three initial model runs: base case, no CCS and Monte Carlo. For the scenarios shown there is a significantly increased role for energy storage by 2050 with high levels of electric storage and building level heat storage. More details regarding the results of these runs is provided in section 6b and the final report for the SFM project.
However, the model is still in the early stages of maturity with further development required. In particular, the comprehensive representation of energy storage and flexibility provided has resulted in high levels of complexity, which has in turn led to relatively long solving times. There is a development plan in place (section 6c) which will reduce solving time, as well as allow the functionality of the SFM to be further developed.
Despite these areas where further development is required, the SFM has significant potential to cast light on a range of storage and flexibility related questions. Alongside the development plan, we are working with partners to explore opportunities to apply the SFM to projects. This will allow the full value of SFM to be realised and improve the understanding and evidence base of the model’s functionality.
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© 2019 Energy Systems Catapult Published September 2019
Report funded by the Energy Technologies Institute
