To meet surging demand, networks need to optimise energy system modelling

The United Kingdom offers a prime example of some of the key challenges ahead for gas and electricity network operators. Its energy transition is driving a surge in the demand for new grid capacity, arising from increasing connection requests to support the expansion of renewable generation, and from the demand-side electrification of transport, heating, and industry. What’s more, UK policymakers are placing ever greater scrutiny on network operators, requiring them to increasingly evidence whole system spatial planning to promote efficiencies and reduce costs for end consumers. All this comes amid growing interdependencies between gas, electricity, and emerging energy vectors such as hydrogen and biomethane.

The demand outlook is daunting. According to the UK’s National Energy System Operator (NESO),¹ which balances energy supply and demand on the island of Great Britain, even the least ambitious pathway, one that fails to meet net zero, would still see overall installed capacity in Great Britain increase from 174 GW in 2023 to 276 GW by 2035 and 388 GW by 2050. Based on peak demand, generation, interconnectors, and storage, this least ambitious pathway would see total capacity increase by 58% and 123% respectively. NESO’s more ambitious pathways, where the UK actually achieves net zero, forecast capacity increases of 84% to 110% by 2035 and 157% to 199% by 2050. Considering the current five-year average wait time for new grid connections in Great Britain,² realising even the most pessimistic of these pathways will require an unprecedented increase in the rate at which the grid can be reinforced and new load energised.

Effective use of energy system modelling empowers network operators to meet this challenge. Optimised energy models can forecast which locations will have a grid capacity surplus that’s able to absorb new load with minimal upgrades, allowing connection requests to be fulfilled more quickly and at a lower cost. Likewise, effective system models can identify key grid constraints, maximising the value of reinforcement by directing it to where it is needed most, when it is needed most.

Figure 1: Peak demand and installed capacity in 2023, 2035, and 2050 according to the National Electricity System Operator’s Future Energy Scenarios 2024

Adapted from National Energy System Operator data

In an evolving regulatory and policy environment, UK network operators are also increasingly expected to undertake whole system spatial planning to drive efficiencies, accelerate decarbonisation, and mitigate costs for end consumers. The UK regulator, the Office of Gas and Electricity Markets (Ofgem), has proposed a consumer outcome for the upcoming RIIO-ED3 price control period, the process which will define the prices network operators can charge their customers to recover infrastructure investment costs between 2028 and 2035. Ofgem’s provision, “Networks for net zero,” specifically calls for “strategically planned network investment, providing capacity and access for users when it is needed at least cost based on whole system value for current and future users.”³

This emphasis on whole systems is further evidenced by Ofgem’s Regional Energy Strategic Plans, which will guide and scrutinise network regional planning. Ofgem states that these strategic plans are intended to be “whole system, leading to coordinated development of the system across multiple vectors.”⁴ The formation of NESO (which integrates formerly separate gas and electricity system balancing) and the National Infrastructure and Service Transformation Authority (created to drive efficiencies in infrastructure spending) additionally underscore a broader policy push for integrated infrastructure planning. These initiatives highlight the mounting pressure on network operators to plan infrastructure that accommodates multiple energy vectors, aligning with both regulatory and policy objectives for cost-efficient, integrated infrastructure development.

Effective use of energy system models can enable network operators to undertake whole system spatial planning. Traditionally, gas and electricity networks have been planned separately, optimised for their respective supply-demand patterns. Energy models can integrate these systems, considering interdependencies between methane, electricity, hydrogen, and biomethane. By also incorporating distributed energy resources, energy storage, interconnectors, and demand-side flexibility—at varying temporal and spatial resolutions—these models can identify synergies and trade-offs across the energy system. These models can enable network operators to address the challenges associated with regulatory and policy expectations, allowing them to plan their infrastructure in a manner that is efficient and cost-effective for the whole energy system.

Energy system models can also be used to anticipate demand for energy infrastructure based on regional characteristics and broader demand-supply shifts, enabling regional integrated energy plans that drive efficiencies at all levels. Gas and electricity network operators can use energy models to inform joint infrastructure planning, aligning investments in anticipation of where and when capacity will be needed. Operators can utilise modelling outputs to inform proactive engagement with energy production and demand-side developers, as well as regional governments, to develop integrated regional energy plans. Additional applications include:

• Identifying locations where gas dispatch facilities will be needed to balance intermittent renewable generation, based on regional electricity demand patterns and the anticipated distribution of wind and solar resources.
• Identifying areas suited for bioenergy plants with carbon capture and storage capabilities, by analysing regions close to biomass import lanes, and by assessing the accessibility of existing or potential CO₂ transportation and storage infrastructure.
• Identifying optimal sites for electrolysers that are close to abundant renewable energy potential, such as coastal regions with strong offshore wind resources or inland areas with high solar irradiance, while also assessing the availability of hydrogen storage and transport infrastructure.

With their ability to visualise complex production and demand scenarios across multiple geographies, energy system models pave the way for network operators to undertake integrated energy planning that unlocks efficiencies, fuels investor confidence, and accelerates the rate of decarbonisation—all while mitigating whole system costs to align with regulatory and policy expectations. By following a structured and iterative approach, operators can unlock new opportunities, reduce risks, and accelerate the deployment of cost-effective infrastructure, ensuring a more resilient, decarbonised energy system for the future.

Eight key steps for successfully leveraging energy system models in infrastructure planning

1. Put the right resources in place. Before beginning a modelling project, it is essential to assemble a team with the right skills and knowledge. This includes individuals with expertise in modelling tools, as well as stakeholders and analysts who understand the broader project context.
2. Define the goals. A well-defined objective is the foundation for a successful model. Clarify whether the model will optimise for costs, emissions, or reliability. Decide whether it will focus on grid expansion, generation planning, or fuel transitioning. Ensure the objective’s complexity is aligned with the timeline.
3. Choose the right modelling tool. For short-term asset dispatch, select a model capable of handling temporal data down to hourly timesteps, along with energy markets data. For long-term infrastructure capacity expansion, prioritise a model optimised for representative days. For power markets modelling, select a model optimised for electricity. For whole systems, prioritise a model able to handle multiple energy vectors.
4. Collect high-quality data and validate the processing approach. Use recent data from credible sources to enable forecasts across energy supply and demand, fuel prices, technology costs, and grid capacities. Validate data, data sources, and transformation approaches with key stakeholders, operating across different areas of the energy value chain.
5. Set up the right assumptions and structure. Define scenario assumptions, such as high-renewables or high-fossil fuels. Determine whether the scale will be international, national, regional, or local, and include only electricity or methane, or be multi-vector.
6. Identify uncertainties and run sensitivity analyses. Stress-test the model under less certain parameters. Adjust fuel costs, demand growth, technology learning rates, or renewable energy availability. Develop high and low cases for uncertain factors to evaluate robustness and identify potential vulnerabilities in the analysis.
7. Undertake further iterations. Modelling is an iterative process. Based on initial results, refine and re-run the model, adjusting key parameters as needed. Allow the modelling process to evolve as new insights are gained.
8. Know the limitations. Acknowledge gaps in data quality or availability, especially concerning emerging technologies like hydrogen. Be transparent with model boundaries, such as sectors or regions excluded from scope. Note any simplifications that may impact outcomes, such as assumptions of perfect foresight or exclusion of stochastic events.