
Breadcrumbs
Cutting the bills: UK households profit from clean power
Deploying renewables at speed brings bill savings for UK households, while also boosting the economy and increasing security.
Highlights
£300
Annual average household bill saving in 2030 compared to today, leading to UK consumers saving a total of £8.7 billion.
49 TWh
UK could export 49 TWh of electricity in 2030, compared to importing 4 TWh if renewables deployment lags behind
98%
UK could achieve 98% clean power by 2030 if commitments are delivered on time
About
In this report Ember evaluates two possible futures for the UK power system, showing that keeping renewables deployment on track brings both cost of living and energy security benefits. The study uses Ember’s open power system model with all input data publicly available.
Executive summary
A clean power system saves UK households £300 per year
Delivering the government’s own commitments would save money for UK citizens. Falling behind on renewables deployment would drive costs up and miss the opportunity for the UK to profit from power exports.
The UK is capable of producing 98% of its electricity from clean sources by 2030, if existing commitments are delivered on time. This would bring cost savings for consumers, as well as increasing energy security and helping the UK reach its net zero targets. A clean power system minimises the UK’s exposure to imported gas prices and instead turns it into a net exporter of electricity. However, the government is lagging behind and urgent action must be taken if UK climate ambition is to be met.
Energy & Climate Data Analyst, Ember
The longer the UK relies on fossil fuels, the longer households will feel the pinch. A clean power system will save households hundreds of pounds a year off bills, but lagging government action risks blocking those benefits.

UK power bills
Clean power lowers cost of living
The UK is capable of producing 98% of its electricity from clean sources by 2030 if existing commitments are delivered on time. However, the current government is lagging behind, and the UK risks missing out on opportunities to cut consumer bills and become a net exporter of electricity to the EU.
Due to these investments, both of the analysed scenarios see network charges increase compared to current prices, from around £75 per MWh in Q3 2023, to £89 per MWh in ‘Falling short’ and £100 per MWh in ‘Delivering commitments’. However, these higher network charges are more than offset by a larger fall in wholesale electricity costs. Compared to 2023, in the 2030 ‘Delivering commitments’ scenario, wholesale costs fall by £112 per MWh whilst network costs increase by only £26 per MWh.
Conclusion
Timely deployment of renewables brings benefits to UK households and the economy
The government needs to ensure its own commitments are met and barriers to renewables are removed.
The huge expansion of renewable energy and associated infrastructure will require an equally important increase in the number of green, skilled jobs. Plans must be put in place to up- and re-skill a workforce (especially considering workers who will be at risk of losing their jobs as fossil fuels are phased out), ready to undertake jobs across the clean energy sector.
Supporting Material
Methodology
Ember’s power system model
The analysis presented in the report uses Ember’s in-house power system optimization model EMBER-PyPSA, that is publicly available along with all input data under the MIT licence, allowing for all analysts to replicate our results or build their own scenarios for the UK’s future energy system. EMBER-PyPSA is based on the PyPSA framework, a Python-based ‘open source toolbox for simulating and optimising modern power systems’, used globally in research and policy applications.
The base assumptions and methodology are described in the ‘Path out of the gas crisis’ report from 2022, but several updates have been made – such as taking into account National Grid’s latest Future Energy Scenarios 2023, adding in a hydrogen demand profile, as well as combining Ember’s UK and EU models (described in ‘In it together’) to provide a comprehensive overview of the internal and external factors influencing the UK’s power system.
The model is run for each hour of the year, with 12 nodes representing the regions of the UK and 28 nodes representing all EU countries except Luxembourg, Malta and Cyprus, plus Norway, Switzerland, Turkey and Russia.
Current and planned power station locations are used (based on DUKES data, planned hydro pumped-storage units, the REPD database, among others), with some generators being decommissioned and some replaced e.g. gas turbines being replaced/equipped with carbon-capture and storage units (CCS). For countries outside of the UK, generation units and installed capacities are based on the latest national announcements and official plans, including the updated National Energy and Climate Plans or coal phase out commitments.
The model runs the capacity projections against ‘worst case scenario weather years’, following a methodology used by European grid operators in their planning. The European Network of Transmission System Operators (ENTSOE) prepares Ten Year Network Development Plans (TYNDP) and the European Resource Adequacy Assessment (ERAA) to assess the safety of the European power system, the availability of generators against the demand, grid expansion needs, etc. Both processes use the Pan-European Climate Database (PECD) to estimate the demand and variable renewables feed-in under different climatic conditions, with the TYNDP pathways checked against 1995, 2008 and 2009 (the baseline year) weather profiles as the worst case years. Using the ENTSO-E’s methodology ensures the model maintains energy security, with supply matching demand on an hourly basis, even in high stress situations (for example, dunkelflaute, or mid-winter wind lulls).
A merit-order scheme is resembled, in which the dispatching is optimised based on short-run marginal costs. Fuel and CO2 price forecasts were estimated based on latest forward contracts. The running costs of non-fossil generators were set to represent their dispatching priority in the merit order.
The model was calibrated using historical weather and demand data to ensure the cost assumptions correctly represent reality. CHP units were set to run following hourly country temperature profiles derived using the Atlite tool. A minimum load of 40% for nuclear plants was assumed based on ENTSO-E’s ERAA input data. No ramp up/down limits were introduced due to their impact on model solving times, but these can be added by the user if needed.
The outputs produced by the model give an indication of the resources needed to fulfil the power demand in the future, as well as the emissions intensity, load balancing needs across regions, storage requirements, among other metrics.
All model input files as well as the Python code are available on Github.
Calculating future bill costs
There are several different components that make up a household electricity bill and different approaches have been used to estimate how they might change in 2030. Current average household electricity bill costs are taken from OFGEM’s default tariff cap level for ‘Other payment’, single-rate metering households in Q3 2023. OFGEM’s default tariff cap was introduced to protect households by capping the amount customers on standard variable and fixed term default tariffs could pay. However, due to the energy crisis, a large majority of households are now on standard variable tariffs and therefore OFGEM’s default tariff cap is taken to represent the UK average.
Wholesale costs
The EMBER-PyPSA model outlined above was used to estimate the average price of UK power in 2030, assuming the short run marginal cost of plants acts as a proxy for day ahead prices. Ofgem includes CfD costs in this component along with direct fuel costs. It is assumed that the majority of these CfD costs will come from offshore wind, and an estimate of the interim levy rate (the cost required to cover payments for the scheme per MWh) is calculated based on the methodology described in a recent report on UK CfDs. The difference of the hourly price returned by the model and the average strike price (taken from a weighted average of existing prices, assuming future capacity secures the same price as auction round 4) is multiplied by the proportion of offshore wind generation assumed to be under a CfD (75% in the ‘Falling short’ scenario and 84% under ‘Delivering commitments’). This number is then divided by electricity demand in 2030.
Network costs
National Grid forecast expected capital investments in their transmission and distribution grids. For the ‘Delivering commitments’ scenario, this is taken from National Grid’s latest investment proposition. The ‘Falling short’ scenario assumes investment announced prior to the British Energy Security Strategy when offshore wind and solar targets were increased. The ratio of future to current National Grid investment is used to scale total grid investment (capital and operating costs) across all distribution and transmission operators from 2022/23 to 2030, using the distribution and transmission use of system charges given by Ofgem in the Q3 2023 price cap. Balancing charges are assumed to remain at similar levels to the Q3 2023 price cap given higher investment in the transmission and distribution grid should reduce curtailment costs.
Operating costs
A small decrease in operating costs due to a successful rollout of smart meters by 2025 is assumed in the ‘Delivering commitments’ scenario. Otherwise, operating costs are assumed to remain the same as the Q3 2023 Ofgem price cap.
Policy costs
Policy costs are made up of (in descending order of magnitude): Renewables Obligation, Warm Homes Discount, Energy Company Obligation, Feed in Tariff and assistance for areas with high electricity distribution costs. Although several of these schemes have ended, they have payment obligations past 2030, and it is therefore assumed the policy cost component of bills will remain the same.
Margins & VAT
Margins (EBIT and headroom) are calculated based on Ofgem Q3 2023 price cap percentages of total bills. An additional 5% is added to the total bill for VAT.
Total household savings are calculated by multiplying expected household bill savings by the projected number of households in the UK in 2030. The latter number is derived by applying the national population projection increase to ONS 2022 household data.
Fossil gas avoided cost calculations
A gas plant efficiency rate of 50% (gross calorific value/higher heating value) has been used and a conversion factor of 1 bcm = 9.7 TWh has been applied.
Acknowledgements
Ember’s power system model is based on the PyPSA framework: T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, 2018, Journal of Open Research Software, 6(1), arXiv:1707.09913, DOI:10.5334/jors.188
ReviewersThank you to Green Alliance for their contribution.
Contributors at EmberAlison Candlin, Reynaldo Dizon, Sarah Brown, Phil MacDonald, Richard Black
Cover PhotoA person opens their energy bill in London, UK in 2022.
Contributor: horst friedrichs / Alamy Stock Photo