Effective communication between stakeholders is key to delivering network investment in time to meet demand and ensure optimal outcomes. So how can new network analysis tools help plot the most efficient path to net zero? By Shahab Khan, Senior Consultant, PSC Consulting
Since almost the earliest days of electrification, network planning has been a challenge, and it’s becoming steadily more complicated. Not only has generation capacity migrated to the edges of the network with the widescale rollout of rooftop solar, but there are also significant changes in demand profiles with the advent and uptake of devices like electric vehicles and air source heat pumps.
Transmission and distribution network operators deploy various analytical tools to support decision-making for network investment – but it’s vital to continue updating this toolbox to meet the challenges of a changing energy landscape.
Managing network investment
Changes to energy use and generation call for an even greater need for coordination between energy networks and the various energy network stakeholders, particularly local authorities and councils. The policies being developed by many of these stakeholders to help deliver their clean energy ambitions inevitably impact electricity and gas networks. For example, many local authorities are making long-term plans to install large numbers of heat pumps or EV chargers in specific locations. Failure to communicate this may result in a mismatch between the ambitions of the local authority and the additional load that Distribution Network Operators (DNOs) are expecting to see on their networks. Where this occurs, network reinforcement may be delayed, resulting in a knock-on delay to stakeholders’ energy plans.
National Grid Electricity Distribution, formerly Western Power Distribution (WPD), creates Distribution Future Energy Scenarios (DFES) to support its strategic goals, including net zero, economic and industrial growth, and broader societal benefits. As it stands, local authorities are consulted and invited to give their input, although their longer-term plans are currently not incorporated into DFES – arguably a missed opportunity to take a more holistic view of future investment options. Because DFES are based on national scenarios using data captured from whole licence areas over a relatively short period of time, it is not currently possible to fully incorporate local authorities’ longer-term plans for specific geographic areas.
This approach means that, for example, planning for an anticipated rollout of air source heat pumps in an area could be wholly upended where the local authority is already developing a district heating network. Putting in non-hybrid heat pumps adds to the electricity network load and might prompt investment in network reinforcement. However, in this scenario, investing in an over-engineered transmission and distribution network is entirely possible but may result in an inadequate gas network. This highlights the importance of increased coordination between the gas and electricity networks and the local authorities to ensure all three have clear visibility of each other’s plans.
To increase the effectiveness of its distribution network planning tools, National Grid set out to discover if it was possible to integrate more data from local authorities into the network planning process. In response, it launched its Energy Planning Integrated with Councils (EPIC) project. The project aimed to develop a process that considers the impacts of local authority energy planning on both the electricity and gas networks. By bringing these considerations into play, project EPIC is expected to enable better decision-making on network reinforcement investment that will, in turn, result in lower overall costs to the consumer. Ultimately, better oversight enables clean energy targets to be supported by the energy distribution networks more efficiently and cost-effectively.
Funded under the Network Innovation Allowance (NIA) by Ofgem, EPIC was a joint project funded by National Grid’s distribution business and Wales and West Utilities (WWU) with an overall budget of £540,000. Project EPIC, which concluded in December 2022, explored how DNOs and local authorities could work more effectively to create local energy plans based on the impact of fundamental ‘building blocks’ within DFES. These building blocks – including EV chargers, domestic PV systems, heat pumps, and new housing developments or industrial centres – all impact network demand profiles. The building blocks reflect local authority plans and are used to determine where and when network capacity issues may be expected.
Although EPIC was a trial to explore and develop a process to deal with different data sets from various partners and stakeholders, one of the key deliverables was the High Voltage Network Analysis Tool (HV NAT). HV NAT had to be developed from scratch because no such tool was previously available. HV NAT, which was developed by PSC as part of the EPIC project, is a Python-powered module that allows network operators to feed all the relevant data into conventional and commonly used network planning software, in this case, the Siemens PTI’s PSS SINCAL platform, which is used in over 100 countries by transmission and distribution planning engineers. The outputs from HV NAT can subsequently inform a cost-benefit investment analysis to direct knowledge-based decision-making.
The HV NAT tool that was developed considers both the top-down and bottom-up analysis of the network. The top-down approach is how the TNOs and DNOs conventionally conduct planning. It typically considers impacts as far as 33 kV and above. The novel bottom-up elements consider what goes on at the local distribution levels and then build up detail to determine the load pattern up to the HV feeder. As part of the subsequent assessment, considering the top-down and bottom-up approach, one of the goals of EPIC was to determine how closely these different analyses align. This was a unique treatment for both the sub-33 kV network and the gas network to see if it is possible to get a clear view of the most strategically important investment in the network considering all these elements.
Another use-case considered by the HV NAT tool, amongst others in project EPIC, was the timing of potential investments. One strategy is the so-called ‘fit for the future’ method, in which the maximum upgrade, such as the size of a transformer, is chosen in the first instance. In this case, the network may have excess capacity but allows for possibly realisable benefits in the future as the demand profile changes. An alternative approach is ‘just in time’ in which the next upgrade will cater only to the immediate need rather than looking 10 or 20 years ahead. In any event, HV NAT determines where and when issues emerge in the networks based on all available inputs.
Learning how to learn
Overall, the objectives of EPIC were met, and the project successfully explored the idea that network operators could work closely with councils, access the relevant data, and combine it with grid information to perform more precise network analysis. The outcomes reveal a good correspondence between what the HV NAT model showed and both the bottom-up and top-down analysis.
However, this was an innovation project, and the lessons learned are essential. For example, one of the challenges of the EPIC project concerned the accuracy of the various demand profiles associated with the DFES blocks, such as the effective modelling of the heat networks and solar PV volume. This information came partly from National Grid and partly from the various project partners and is critical to represent the load on the network. There will be an improvement over time, but some assumptions must be made to create a profile. The results of any analysis will only be as good as the quality of the data used. There were differences in specific results, as expected. Still, the data itself needs to improve for more meaningful conclusions to emerge, and some sort of data sense checking would be beneficial. For example, assumptions regarding PV panel performance could be better informed with orientation information, such as if they are facing southwest or not. That is part of the challenge.
Nonetheless, EPIC and HV NAT improve the visibility of how to best deal with different data sets from various partners and how to make that information work together to deliver actionable outcomes. The analysis tool allows network operators to develop reasoned decision-making based on specific primary inputs. It reveals whether it will be beneficial to execute upgrades and what the most favourable outcomes will be if adopting the ‘fit-for-future’ or ‘just-in-time’ approach.
The key conclusion is that TNOs and DNOs must work with local authorities to get better quality data to achieve the most cost-effective and advantageous network investments. Ultimately, that leads to optimal network development that can allow us all to meet our clean energy ambitions.