Executive Summary

Renewable generation in the United Kingdom (UK) will need to increase dramatically by 2025 – from 41% to 60% of the UK’s energy supply – if the UK is to reach its climate and energy targets. The best renewable resources aren’t always located near the cities and commercial and industrial centers where we live and work, so we rely on transmission lines to move power from where it’s generated to where it’s needed most. However, at times of peak demand or generation, transmission lines become “congested,” meaning moving more power over the line would lower costs to consumers, but the line is unable to move more power due to technical reasons.

Without additional investments to support the grid, scaling renewables to the levels required to meet the UK’s climate targets could result in substantial amounts of congestion. Indeed, bottlenecks in just one region of the UK grid could lead to up to 14.8 terawatt-hours (TWh) per year of curtailed renewable energy — wasting roughly 20% of the energy at that one bottleneck.

When energy is curtailed, more expensive generators are dispatched to take the place of cheaper generators, often renewables. The costs of constraint management in the UK today exceed £1.1 billion per year and are expected to rise without intervention. When low carbon generation is curtailed, polluting generators such as natural gas are often required to ramp up to meet demand. This dynamic could lead to more than 5.5 million metric tonnes of additional greenhouse gas emissions per year by 2025. Congestion is a climate problem, not just an economic one.

Grid operators like National Grid ESO can invest in transmission infrastructure or “non-wires alternatives” such as energy storage to mitigate congestion. When the expected costs of congestion exceed the costs of investment to mitigate it, investments in grids or alternatives can lower costs to consumers. In partnership with National Grid ESO, Form Energy (Form) examined the economics of using energy storage technologies as an alternative to wires in order to mitigate congestion on the UK grid.
Form modeled the ability of energy storage to mitigate grid congestion and provide other grid services by allowing FormwareTM, Form’s cost minimizing capacity expansion, unit commitment, and economic dispatch model, to choose between either:

1. Business as usual: Continuing to rebalance the grid by curtailing and dispatching generation.
2. Using storage in addition to rebalancing: Deploying energy storage to charge during periods of congestion and discharge during periods of excess grid capacity, in addition to rebalancing when needed.

Form modeled lithium ion, hydrogen stored in tanks and geologic formations, zinc-air, and aqueous metal-air technologies. Form optimized the various technologies’ provision of ancillary services, capacity, and transmission support services at four key transmission boundaries on the UK grid in four different Future Energy Scenarios. Formware’s optimization relied on 10 years of hourly forecasts of electricity flows across the boundaries and hourly boundary capabilities (the maximum amount of electricity that could be transmitted across the boundary).

The results showed promise for energy storage: all technologies showed the potential to reduce curtailment and increase renewables utilization. At the “B7a boundary” in the UK system, a primary north-south transmission boundary and one of the most congested boundaries in

the UK, the optimal lithium ion configuration reduced curtailment by up to 2%, while the optimal aqueous metal-air configuration reduced curtailment by up to 89%. The figure below shows the increase in renewable energy consumption enabled by deploying energy storage at the B7a transmission boundary in the UK in 2029; these figures represent millions to billions of kilowatt-hours of renewable energy that, rather than being curtailed, was charged by storage and discharged during periods of excess grid capacity. Because these are the results of a least-cost optimization model in which the model could choose business-as-usual grid balancing if storage installation and operation was not more cost effective, this avoided curtailment reduces cost in addition to emissions.

Storage Can Enable Terawatt-hours of Additional Renewable Energy Consumption⁶

The technologies that delivered the most significant curtailment reduction had durations of greater than 40 hours, demonstrating the value of multi-day energy storage. These results underscore the need for innovation and demonstration – all of the technologies that delivered more than 50% reduction in curtailment are pre-commercial.

The outsized value of long duration storage was driven by the significant durations of potential congestion events. The durations of congestion events on the UK grid are expected to increase. By 2025, more than 12% of curtailment events will last for more than 48 hours, accounting for more than 60% of total curtailed energy. By 2025, nearly 20% of total curtailed energy will occur during curtailment events lasting more than 100 hours.

The potential for storage to support grid operations and renewable integration will vary from region to region and will depend on the regulatory and market context. Policy makers, grid operators, and their regulators can examine the potential for storage on their grids, and, where results show promise – as these results do for National Grid ESO’s system – examine the Incentives and market structures needed to capture storage’s value.

Introduction

Grids globally will need to deploy substantial renewable energy to keep the planet from reaching catastrophic levels of warming. The International Energy Agency recently estimated that global wind installations will need to expand 16 fold and solar 28 fold by 2050 to limit warming to 1.5 degrees celsius. In the United Kingdom, renewable generation will need to increase by 50% by 2025 if the country is to reach its climate and energy targets. Unfortunately, the best renewable resources are often located far from the cities and commercial and industrial centers. As a result, grids rely on transmission lines to move power from where it’s generated to where it’s needed most. However, transmission lines can become congested during peak demand and generation periods, preventing the grid from supplying demand with the least cost source of electricity – often renewables.

Congestion is costly to consumers: congestion means that the lowest cost generator – very often remote renewables in high renewables grids – can’t meet demand, and more expensive and often more polluting generators have to be turned on to take their place. This constraint management – ‘bidding off’ low cost generation and ‘offering on’ higher cost generation – already costs UK consumers more than £1.1 billion per year, and these costs are expected to rise without intervention. Curtailment can also be an emissions problem when low cost, zero emissions renewables are replaced by ‘dispatchable’ – that is, available on demand – generators like gas plants.

Traditionally grid operators have relied on new grid infrastructure like power lines and transformers to meet new demand and move clean energy to where it’s needed. However, grid infrastructure can be costly and require long lead times to build, so grid operators like National Grid ESO are exploring new tools to support the existing transmission grid and maximize the benefits of future transmission expansion. National Grid ESO engaged Form to assess whether energy storage technologies could support the UK transmission system under future scenarios of renewable energy buildout.

What is congestion and how does it impact consumers?

Grid congestion occurs when the grid cannot supply demand in one region with the cheapest source of generation from another region, and is the result of the laws of physics that govern how power flows over electrical networks connecting the two. Attempting to move more power over a line than the line is designed to handle can cause the line to overheat or the grid to become unstable, risking fires or other failures.

Because demand can’t be met with the cheapest source of generation, the result of congestion is that customers pay more for power than they would on an uncongested grid. Wind and solar power have no marginal costs – wind and sunshine are free -, which means that when available, they’re the least expensive resource on the grid. In the UK’s future renewables-heavy grid, congestion will often mean that demand can’t be met with renewable energy that is generated remotely and must be met with fossil fuels instead. In short, congestion is an economic problem and can be an emissions problem as well.

A brief overview of the economics of congestion

The cost of congestion can be measured by the increase in the amount consumers have to pay for energy relative to a grid with no (or less) congestion. In the UK, congestion costs show up as constraint costs in the Balancing Mechanism. When the grid has too much power in one area, National Grid ESO ‘bids off’ generators, meaning they pay the generators to produce less power; at the same time, to ensure there’s enough energy to meet demand, National Grid ESO must ‘offer on’ generators in other parts of the grid closer to electrical demand, and pay these generators to generate more power than they otherwise would in a less congested grid.

When congestion occurs frequently and in large enough volumes, its costs add up. When the current or expected costs of congestion exceed the costs of solving the congestion, grid operators like National Grid ESO can take action. Historically, taking action meant building more or larger power lines or other types of transmission infrastructure. However, with the proliferation of new options like energy storage to support efficient grid operations, companies like National Grid ESO are increasingly evaluating alternatives to traditional network infrastructure.

Energy storage as a potential solution to costly congestion

Energy storage located “upstream” of a constraint can charge with the available low cost energy in excess of the transmission capacity, avoiding bidding off generators. This same asset can discharge when the line is no longer congested, displacing more expensive generation. Energy storage located “downstream” of a constraint can charge during normal operations and discharge when the grid is congested, avoiding offering on more expensive generation.

While many market or regulatory models could be enacted to compensate storage for this grid management service, we modeled one case as indicative of the overall value opportunity. We modeled a case in which National Grid ESO pays ‘upstream’ storage to absorb power, avoiding having to ‘bid off’ generators, and pays ‘downstream’ storage to discharge, avoiding having to ‘offer on’ generators. As long as the prices paid to the storage systems to charge (upstream) or discharge (downstream) are less than the costs of ‘bidding off’ (upstream) or ‘offering on’ (downstream), National Grid ESO and UK electricity customers could save money.

Energy storage can mitigate grid congestion and increase renewable energy utilization

Form Energy used FormwareTM to identify the optimal quantity of storage, balancing the costs of building and operating storage against the value of the services that the storage systems could provide, such as congestion management, capacity, and reserves. Formware’s optimization relied on 10 years of hourly forecasts of electricity flows across the boundaries and hourly boundary capabilities (the maximum amount of electricity that could be transmitted across the boundary).

The results show that, with the right market conditions and technologies, energy storage could substantially reduce congestion on the UK grid, avoiding curtailing renewable energy and increasing the utilization of power lines.

To simplify the narrative and graphics, the main body of this text focuses on National Grid ESO’s Two Degrees (TD) scenario from its 2019 Future Energy Scenarios, but the full results can be found in an accompanying technical slide pack and a description of the alternative scenarios can be found in the Methods and Data section.

Also for simplicity, the main body of this text focuses on the B7a transmission boundary, a major boundary on National Grid ESO’s system that cuts east-west across England, just north of Leeds and Manchester. Results for all boundaries are provided in the accompanying technical slide pack and a description of all boundaries is found in the Methods and Data section.

Finally, we examined scenarios representing high value for mitigating congestion and a low value for mitigating congestion. These upper and lower bounds were vetted with National Grid ESO. In the high value case, storage systems pay the market price for energy less £59/MWh to charge during curtailment events upstream of a constraint, while, in the low value case, storage systems pay the market price for energy less £40/MWh to charge during curtailment events. This represents approximations of the costs of bidding off generation upstream of the constraint. These bid off and offer on price scenarios were designed with National Grid ESO and represent a reasonable range of potential costs in the UK energy imbalance markets. The results in the main body of this text focus on the high value case, but results for the low value case are provided in an accompanying technical slide pack.

We chose to highlight the TD scenario and B7a boundary under the high value case because it is a particularly high powerflow scenario and boundary and the value of avoiding curtailment is high. These conditions lead the total magnitudes of storage shown to be valuable to be higher than in other scenarios and boundaries. Nonetheless, the dynamics we highlight and the potential we uncover hold across scenarios, boundaries, and value sensitivities. Indeed, as the technical slide pack shows, even at less constrained boundaries, in lower powerflow scenarios, and lower value sensitivities, we still uncovered a multi-gigawatt opportunity for energy storage to support the UK transmission network.

The following sections focus on the results pertaining to the size of the opportunity for storage to support the UK transmission system. Because the results presented in this paper are generated using a least-cost optimization model that can choose business-as-usual grid balancing if storage installation and operation is not more cost effective, all storage installations shown as results in this paper would reduce the cost of power in the UK relative to business-as-usual. However, at the request of National Grid ESO, this paper does not report these cost savings, because, in practice, these cost savings will need to be compared with the relative benefits of transmission network investments and other alternatives.

Energy storage can substantially reduce curtailment

Figure 1 shows the optimal deployment of energy storage in megawatts (MW) at the B7a boundary and the TD scenario, as calculated by Formware. The large magnitudes deployed – with several technologies showing gigawatts (GW) of potential – highlight the scale of the congestion challenge that the UK could face as it decarbonizes. It also underscores that large volumes of storage could be lower cost than simply rebalancing the grid through offering on and bidding off generators.

Figure 1: Optimal Energy Storage Capacities Deployed in 2030

The large capacities of storage deployment enable substantial amounts of curtailment reduction. Figure 2 shows the fraction of curtailment avoided by the various technologies modeled, relative to the business as usual case without energy storage. Figure 2 shows that, under the right conditions and with the right technologies, the optimal deployment of energy storage can reduce the curtailment of renewable energy meaningfully.

Figure 2: Cost Effective Avoided Curtailment at the B7a Boundary in the TD Scenario in 2030

It’s important to note that these volumes are not additive across technologies (that is, the results do not indicate that the grid would benefit from 3.3 GW of geologic hydrogen and 3.5 GW of zinc-air), and should be considered indicative of the scale of the opportunity of supporting the UK transmission grid, rather than a detailed market size.

These large deployments are enabled in part by the storage technologies’ ability to provide ancillary services such as reserves (maintaining state of charge in order to respond to grid contingencies) and capacity (being available to discharge during peak demand periods). While in the higher priced transmission management scenario, more than 90% of storage revenues for all technologies but lithium ion came from transmission management (that is, avoiding bidding off generators), nearly 40% of revenues came from ancillary services and capacity in the lower priced transmission management scenario, highlighting the importance of storage technologies’ ability to provide multiple services.

Figure 3 shows the optimal duration of the storage technologies installed to reduce curtailment as shown in Figure 1. All of the technologies that substantially reduce curtailment are pre-commercial, and all have cost structures or technical factors that enable significantly longer durations than lithium ion. Tank hydrogen can achieve very long durations, but, as modeled, the costs of the tanks required to store energy make large quantities of tank-based hydrogen prohibitively expensive. As a result, the overall magnitude of tank hydrogen deployed is relatively limited (see Figure 1).

Figure 3: Optimal Storage Duration at the B7a Boundary in the TD Scenario in 2030

Figures 1, 2, and 3 also highlight the need for continued innovation in energy storage: all of the technologies that substantially reduce curtailment are pre-commercial.

Multi-day transmission constraints drive multi-day storage benefits

Multi-day curtailment and constraint events drive the multi-day storage durations shown in Figures 1, 2, and 3. Figure 4 shows the duration of constraints at the B7a boundary in the TD scenario in 2025, as well as the total magnitude of energy curtailed during events of that duration. Figure 4 was produced from constraint data provided by National Grid ESO.

Figure 4 shows that National Grid ESO’s 2019 Future Energy Scenarios predicted constraint events regularly lasting more than 48 hours, with many lasting more than 100 hours. By 2025, more than 12% of curtailment events will last for more than 48 hours, accounting for more than 60% of total curtailed energy. Nearly 20% of total curtailed energy will occur during curtailment events lasting more than 100 hours. These multi-day constraints require multi-day solutions, which helps explain the previous results.

Figure 4: Constraint Duration and Total Curtailed Energy, B7a Boundary and TD Scenario, 2025

Figure 5 shows a time series of constrained power – that is, the excess energy that would be curtailed absent intervention. As renewable energy generation rises, curtailment increases. Curtailment peaks in 2027, and subsides slightly as new transmission investments come online. Figure 5 underscores the frequency and magnitude of the potential constraints across B7a boundary, and highlights how National Grid ESO expects congestion to increase in frequency and magnitude unless action is taken.

Figure 5: Constrained Power Over Time, B7a Boundary and TD Scenario

These multi-day constraints are the primary driver of the value of the multi-day energy storage systems modeled in this project.

Energy storage increases renewable energy flows over constrained transmission

By charging up with renewable energy when transmission lines are constrained and discharging when they’re unconstrained, energy storage can increase the flow of renewable energy across constrained transmission boundaries. Figure 6 shows the additional flow across the B7a boundary, measured in GWh of renewable energy that is consumed rather than curtailed, from the optimal deployment and operations of storage, as calculated by Formware. Storage technologies are able to increase the amount of renewable energy flowing across one of the UK’s most congested transmission choke points by up to 2 terawatt-hours, substantially reducing the need for fossil generation. We see again that longer duration storage enables significantly more renewable energy flow than shorter duration storage. We also see that, while cavern hydrogen reduces curtailment more than zinc-air storage, it enables less total flow due to its lower round trip efficiency.

Figure 6: Annual Additional GWh of Renewable Energy Consumption Enabled by Storage

Figure 7 shows the hourly operations for the Aqueous Metal-Air technology for three weeks in January and July 2030. We see several instances of multi-day charging during multi-day congestion events (see, for example, the events between January 15 and 22 or between July 2 and 9). This otherwise-curtailed energy is then dispatched around the transmission constraint (represented by the solid black line) over multi-day renewable energy lulls (see, for example, the events between January 8 and 15) or for shorter bursts during peak demand periods (see, for example, the events between July 9 and 16).

Figure 7: Optimal Hourly Operations of Aqueous Metal-Air in January & July, 2030

The exact hourly operations will depend on the grid’s renewable energy mix, the market model, demand patterns, and other factors. Nonetheless, the hourly operations shed light onto the mechanics of how storage can support renewable energy integration.

Conclusions

As decreasing renewable energy costs and increasingly ambitious clean energy policy drives renewable energy adoption, grid operators globally will need to grapple with how to efficiently integrate these resources. The UK is, in many ways, leading the way in reducing emissions and, in particular, at integrating carbon free energy. Continuing to integrate significant volumes of renewable energy will require investments in supporting infrastructure, such as transmission and energy storage.

Form used Formware parameterized with power flow and transmission constraint data provided by National Grid ESO to find the optimal deployment of energy storage across four key transmission boundaries in the UK in four different Future Energy Scenarios. The results show a significant – potentially gigawatt-scale – opportunity to deploy storage to reduce renewable energy curtailment and increase renewable energy consumption.

Form found that, under the right market conditions and with sufficient technological innovation, energy storage technologies can:

• Reduce curtailment: Reduce curtailment by up to 90% across key transmission boundaries on National Grid ESO’s system.
• Provide multi-day support: Support the grid during prolonged, multi-day periods of congestion and low generation that the UK system might experience by charging and discharging over several days, in some cases more than 100 hours.
• Increase renewable energy consumption: Increase the total amount of renewable energy that flows across key transmission boundaries, and that is ultimately consumed by consumers, by more than 2 terawatt-hours.

These results show the promise of using energy storage to support the grid as it transitions to clean energy and are only possible because we simulated an environment in which a grid operator pays storage operators for the congestion mitigation service it provides. If there are no mechanisms to reward storage for providing this service, these results would not be achievable. We hope that this motivates policy makers, grid operators, and their regulators to study how energy storage can support decarbonizing grids in other geographies and market contexts.

Methods and Data

The Formware Model and Model Setup

Formware

Form used Formware, Form’s capacity expansion, unit commitment, and economic dispatch model to perform this analysis. Formware co-optimizes asset sizing and operations, solving for the least cost outcome. Formware’s inputs are common to many capacity expansion models, with the exception of the time granularity it requires. Formware optimizes assets over a full year or longer period with an hourly or more granular time profile. While Formware optimizes for a single time horizon, that horizon can be flexible and quite long. Formware can also optimize resources to meet electricity demand across diverse weather, load, and contingency scenarios by co-optimizing across these scenarios.

Formware’s inputs include required loads and capacity, renewable resource availability and cost, market conditions including electricity pricing and fuel prices, storage resources’ characteristics and costs, and system level constraints such as transmission capacity and limits on minimum and maximum generation. Formware outputs the asset mix, a set of operational decisions for each hour of the year (or multiple years when modeled), capital expenditures, and operational costs that meet all specified system constraints at lowest net present cost or highest net present value.

Model Setup

Model Basics:

In this analysis, Form modeled the desired power flow – that is, the flow data that National Grid ESO provided Form – as a demand that must be met. During periods where the transmission boundary capability exceeds the desired flow, Form modeled prices for energy as equivalent across the upstream and downstream side of the transmission boundary (note that this is consistent with how the electricity market in the UK operates). However, when the desired flow exceeds the transmission boundary capability, Form changed prices upstream and downstream of the boundary. Transmission boundary capabilities were provided by National Grid ESO and are respected as a constraint in the Formware model at all times (that is, the model never allows storage to charge or discharge in a manner that would create new congestion at the boundary).

Bid off/ offer on prices:

Specifically, Form allowed storage upstream of the constraint to charge up to the constrained amount of power at a price equal to the market price less the “bid off” price (see the main text for more details on the bid off price). This represents the value of a storage asset charging to avoid bidding off an alternative generator. Form allowed storage downstream of the constraint to discharge up to the amount of constrained power at a price equal to the market price plus the “offer on” price (see main text for more details). This represents the value of a storage asset discharging to avoid offering on an alternative generator. Form set the offer on and bid off prices equal, and used electricity market clearing prices provided by National Grid ESO.

By representing the costs of bidding off and offering on generators, Form simulated the costs of business as usual operations. Formware then selects how to size and operate the battery against these set of prices created by business as usual operations.

The sizes and operations of the storage output by Formware represent the largest possible savings in net present value terms relative to business as usual operations (that is, bidding off and offering on generators when the grid is congested). That is, the installation and operations of the energy storage systems shown in this paper would have saved UK customer money relative to business as usual under the assumptions used in this paper. As discussed in the main text, Form simulated two bid off/ offer on price scenarios: a high value, £59/MWh scenario, and a lower value £40/MWh scenario. These bid off and offer on price scenarios were designed with National Grid ESO and represent a reasonable range of potential costs in the UK energy imbalance markets.

Technology and boundary detail:

To understand the dynamics across technologies, Form modeled each storage technology individually. The results are therefore not additive across technologies at a given boundary. Additionally, Form modeled only one boundary at a time, so results across boundaries for a given technology are not additive. Form Energy performed a brief analysis of a nested transmission model, modeling both the B6 and B7a boundaries — two major north-south boundaries that are located close to each other — simultaneously. Given that the B6 and B7a boundaries run roughly parallel and are located very close to one another, these results showed that the aggregate volume of storage deployed when considering the two boundaries simultaneously was roughly equal to the volume at a single boundary. A more thorough analysis considering all boundaries simultaneously would add value to the review of the opportunity.

Additional services:

In addition to providing transmission support services, Form modeled the ability of the storage technologies to provide ancillary services and capacity. Specifically, Form modeled the ability of the storage assets to provide regulation – rapid charging and discharging to support grid stability -, reserves – holding state of charge to provide backup during contingencies -, and capacity – being available to discharge during peak periods). These services are not strictly represented in the UK market. Rather, they are intended to represent the general contours of similar services traded in the UK market. Form used the following operating and pricing assumptions for these services, which were drawn from historical data and were vetted by National Grid ESO, shown in the figure below. The accompanying graphs attempt to show the operating constraints placed on the battery. For example, the “Allowable SOC window” (SOC stands for State of Charge) figure shows that, in order to provide the regulation service, a battery would be required to maintain a state of charge in the orange zone (above the minimum SOC and below the maximum SOC). Contrast that with the Allowable SOC window for Reserves, in which the battery can maintain an SOC above the minimum and all the way to the maximum SOC.

The availability price indicates the price the asset is paid per MW of provision, while the dispatch price is the amount the asset is paid per unit of energy discharged (MWh) while providing the service. The assets could only bid in a way that was physically representative of the storage technology’s capabilities.

Transmission Network Use of System Charges (TNUoS):

Finally, Form modeled transmission use of system charges in a way that is consistent with the UK grid. These are charges that the storage assets have to pay to use the network. Specifically, Form modeled the following charges and assumptions, which were vetted by National Grid ESO:

1. Calculated assuming a 10.29% annual load factor (ALF), consistent with generic pumped
2. hydro projects in the UK. Assumes all interconnections are less than 1,320 MW (larger deployments, as shown in Figure 1, could be broken up into smaller interconnections). Adjacent zones directly above or below a boundary are averaged to create a single upstream and downstream charge for each boundary.
3. Focus on Wider Tariff: Assume the storage resources connect to the Main Interconnected Transmission System (MITS), avoiding any onshore local circuit tariffs.
4. Onshore substation: We calculate onshore substation tariffs assuming interconnection at 275kV substations at less than 1,320 MW, taking an average of redundancy and no redundancy interconnection values.
5. Model as OpEx: Include the Network Access Charge (NAC) as GBP/kW-yr charges which generators must pay (equivalent to a fixed GBP/kW-yr operational expenditure).
6. Average Annual Load Factor (ALF): Use an average of historical ALFs for pumped storage to determine the ALF in the Wider Tariff calculation.
7. Average Forecast: Use the 2021-2026 average forecasted NACs to calculate the Wider Tariff.
8. Zone Allocation: We use the zone allocation as in the table below. Where multiple zones are possible, we take an average value for the NACs.

Data

Future Energy Scenarios and Transmission Boundaries

Form generated results for the four 2019 Future Energy Scenarios, provided by National Grid ESO:

1. Two Degrees (TD)
2. Community Renewables (CR)
3. Steady Progression (SP)
4. Consumer Evolution (CE)

The TD and CR scenarios are consistent with the UK’s 2050 climate and energy targets, which SP and CE are not. The scenarios also differ in the degree to which renewable energy is “centralized” – that is, deployed in large power stations – versus “distributed” – that is, deployed in a larger number of smaller power stations, often at consumer’s homes. The SP and TD scenarios rely more heavily on centralized power plants, while the CR and CE scenarios rely more heavily on distributed power plants. To simplify the narrative and graphics, the main body of this paper focuses on the TD scenario, but the full results can be found in an accompanying technical slide pack.

For each Future Energy Scenario, Form analyzed four transmission network boundaries. Descriptions of each of these boundaries can be found in the Regional Boundaries section of National Grid ESO’s Electricity Ten Year Statement website, and are quoted below:

1. B1a: “Boundary B1a runs from the Moray coast near Macduff to the west coast near Oban, separating the north west of Scotland from the southern and eastern regions.”
2. B6: Scottish Power Transmission to National Grid Electricity Transmission, “Scotland contains significantly more installed generation capacity than demand, increasingly from wind farms. Peak power flow requirements are typically from north to south at times of high renewable generation output.”
3. B7a: “Boundary B7a bisects England south of Teesside and into the Mersey Ring area. It is used to capture network restrictions on the circuits feeding down through Liverpool, Manchester and Leeds.”
4. EC5: “The East of England region includes the counties of Norfolk and Suffolk.”

Similar to the Future Energy Scenarios, this paper focuses on the B7a boundary to simplify messaging and graphics, but all results are provided in the accompanying technical slide pack.

As discussed in the main body of the text, the TD scenario and B7a boundary had generally larger volumes of storage deployment than other scenarios and boundaries. However, the dynamics uncovered hold.

Energy Storage Technologies

Form modeled the technologies with the parameters represented in the table below. These are indicative numbers for the at scale deployment of these technologies in the 2030 timeframe. These cost assumptions are based on very large economies of scale and significant technological learning, in some cases reaching close to entitlement costs. Some of the costs and parameters of these storage technologies are aggregated from a variety of academic and industry sources. Lithium ion costs are for a system with a 25 year asset life with daily cycling, and are scaled up from a 15 year asset life accordingly.