Planning Tomorrow's Grid: FES, SSEP, and Beyond

FES: Future Energy Scenarios

Future Energy Scenarios is now in its 15th edition. Published annually by NESO, FES models four pathways to 2050, each representing a different combination of policy ambition, technology development, and consumer behaviour. The four pathways are not predictions — they are scenarios that bracket the range of plausible futures for the GB energy system.

FES is the single most influential data publication in GB energy planning. Every investment decision by network companies references FES scenarios. Ofgem's price control assessments use FES as the basis for evaluating network company business plans. The connections queue is assessed against FES to determine which projects align with credible demand pathways. FES is not just a publication: it is the shared reference frame that makes coordinated planning possible.

The data inputs to FES are extensive. NESO draws on historical demand data, weather patterns, technology cost projections, government policy commitments, consumer behaviour surveys, and international energy market trends. The outputs include demand forecasts, generation mix projections, storage requirements, interconnector flows, hydrogen production, and transport electrification trajectories — all at national and regional granularity, projected annually to 2050.

SSEP: Strategic Spatial Energy Plan

The Strategic Spatial Energy Plan is the first-ever GB spatial plan for energy infrastructure. Expected in Autumn 2027, the SSEP will identify where different types of energy infrastructure should be located to achieve the national energy transition at lowest cost and with the least environmental impact. This is a fundamental shift from the current approach where individual projects choose their own locations and then apply for grid connections.

The SSEP answers questions that FES cannot. FES tells you how much offshore wind GB needs by 2035. The SSEP tells you where those wind farms should be located, considering grid capacity, seabed conditions, fishing impacts, environmental designations, and proximity to demand centres. FES tells you how many heat pumps will be installed. The SSEP helps determine where the distribution network reinforcement should be concentrated to accommodate them.

The data requirements for the SSEP are immense. It requires CIM-standardised network models from all 14 DNOs and the transmission operators. It needs demand forecasts at sub-regional granularity. It requires land use data, planning constraints, environmental designations, and transport infrastructure. It must integrate with Scotland's existing energy planning framework and Wales's devolved planning powers. The SSEP is arguably the most data-intensive planning exercise ever undertaken in GB energy.

RESPs: Regional Energy Strategic Plans

Eleven Regional Energy Strategic Plans translate the national SSEP into local action: one for Scotland, one for Wales, and nine for England. RESPs identify the specific infrastructure needs, workforce requirements, and stakeholder considerations for each region. They bridge the gap between NESO's national planning and the local authorities, communities, and businesses that must deliver the transition on the ground.

RESPs are data products. Each RESP draws on FES scenarios filtered to the regional level, local authority development plans, DNO network capacity data (published in CIM format under the LTDS programme), and regional economic data. The quality of each RESP depends directly on the quality and accessibility of the underlying data. This is where the transformation programmes described in Module 12 connect to real-world planning: without FMAR, RESPs cannot account for flexibility assets; without CIM-standardised network models, they cannot assess network capacity; without DSI, they cannot discover what data exists.

CSNP and ETYS

The Centralised Strategic Network Plan determines which transmission investments should be funded. Under RIIO-3, the CSNP has a £28.1 billion budget for transmission network investment. The CSNP uses FES scenarios, SSEP spatial priorities, and network modelling to identify which reinforcements, new circuits, and substations are needed and when.

The Electricity Ten Year Statement (ETYS) provides the shorter-term complement: a ten-year forecast of transmission network needs. ETYS is published annually and provides the network planning data that informs both the CSNP and individual connection offers. Together, CSNP and ETYS create a planning pipeline from strategic vision (CSNP, 15-30 year horizon) through medium-term planning (ETYS, 10 years) to operational delivery.

On 8 December 2025, NESO announced the most significant reform to GB electricity connections in decades. The connections queue had grown to approximately 770 GW of applications — more than twelve times GB's peak demand. Many of these applications were speculative: developers submitting applications for multiple sites knowing that most would not proceed, in order to secure a place in the queue under the first-come-first-served system.

The reform replaces first-come-first-served with evidence-based assessment. Instead of ordering applications by the date they were received, the new process assesses applications against criteria including project readiness, alignment with FES scenarios, contribution to Clean Power 2030, and deliverability within a realistic timeframe. The target is to reduce the queue from 770 GW to approximately 381.5 GW by removing speculative and non-viable applications.

The data requirements of evidence-based assessment

Evidence-based assessment is inherently data-intensive. To assess whether a project is aligned with FES scenarios, you need the FES data at sufficient granularity to evaluate the project's location and technology type. To assess deliverability, you need construction timeline data, supply chain information, and planning consent status. To assess network impact, you need CIM-standardised network models that show available capacity and constraint locations.

The reform exposes a fundamental truth: you cannot make evidence-based decisions without evidence. And the evidence required for connections reform is precisely the data that the transformation programmes are designed to provide. FMAR provides flexibility asset visibility. CIM profiles provide network capacity data. FES provides demand and generation scenarios. The SSEP will provide spatial priorities. Connections reform is not just a process change — it is a data change.

Carbon Intensity API and data quality

The Carbon Intensity API demonstrates what is possible when energy data is standardised, accessible, and well-governed. The API provides 30-minute granularity carbon intensity data across 14 GB regions, with 96-hour forward forecasts. It is openly accessible, well-documented, and has been adopted by hundreds of organisations for applications ranging from data centre scheduling to EV charging optimisation.

The Carbon Intensity API works because it sits on top of clean, consistent operational data from NESO's control room systems. The generation mix data is accurate. The regional allocation methodology is transparent. The forecast models are validated against actual outcomes. This is the data quality standard that the rest of the energy data ecosystem needs to achieve.

The data quality pipeline matters as much as the data itself. Raw meter readings contain errors, gaps, and anomalies. Network models contain approximations and out-of-date information. Consumer data contains duplicates, formatting inconsistencies, and incomplete records. Every data-driven application — from connections assessment to demand forecasting to digital twins — depends on a quality pipeline that validates, cleanses, and enriches raw data before it becomes usable. The transformation programmes must deliver not just data access but data quality.

“The Strategic Spatial Energy Plan will be the most significant spatial planning exercise ever undertaken in the UK energy sector, drawing on data from across the electricity, gas, heat, and transport systems.”

DESNZ, Strategic Spatial Energy Plan: Scope and Approach (2024)

The SSEP's data requirements are unprecedented in scale: CIM-standardised network models from all 14 DNOs, demand forecasts at sub-regional granularity, land use, environmental, and planning data across England, Scotland, and Wales. Every data infrastructure gap translates directly into SSEP uncertainty.

The argument that Clean Power 2030 depends on data is not rhetorical. It is structural. Every element of the decarbonisation pathway requires data infrastructure that is either incomplete or under construction.

Connecting 50 GW of offshore wind requires connections reform, which requires evidence-based assessment, which requires CIM network models, FES scenarios, and SSEP spatial priorities. Integrating millions of heat pumps requires distribution network reinforcement, which requires DNO visibility of low-voltage network loading, which requires smart meter data flowing through MHHS. Unlocking demand-side flexibility requires FMAR for asset visibility, MHHS for accurate settlement, and the SDR for consumer data access that enables third-party flexibility services.

Each link in these chains is a data dependency. Break any link and the downstream capability is degraded. The planning ecosystem — FES, SSEP, RESPs, CSNP, ETYS — is only as good as the data it consumes. The market infrastructure — connections reform, settlement, flexibility dispatch — is only as effective as the data that informs it. The consumer services — tariff optimisation, EV smart charging, home energy management — are only as useful as the data they can access.

This is not a future problem. It is a present problem. The decisions being made today about which wind farms to connect, which networks to reinforce, and which flexibility services to procure are being made with incomplete data. Good data governance is not a bureaucratic formality — it is the prerequisite for decisions of this scale and consequence. The transformation programmes will improve this, but the critical planning decisions for Clean Power 2030 cannot wait until 2028 or 2030 for perfect data. They must be made with the best available data now, and improved iteratively as the data infrastructure matures.

The honest conclusion is that Clean Power 2030 will be achieved — or not — in part because of data infrastructure. Not solely because of it: policy, investment, supply chains, planning consent, and public acceptance all matter enormously. But data is the connective tissue that links all of these elements. Without it, planning is guesswork, markets are inefficient, and consumers are disconnected from the transition that is reshaping their energy system.