How to use Heat Network Efficiency Scheme funding to improve your existing heat networks

The Heat Network Efficiency Scheme (HNES) is a £32m grant funding scheme to improve the performance of existing heat networks.

Guru Systems supported six projects as part of the previous HNES Demonstrator, and in this article we share case studies from two of those projects as well as covering the main details of the current Scheme.

In this article we cover:

• What is the Heat Network Efficiency Scheme (HNES)?
• Guru Systems and the HNES Demonstrator – including case studies
• How Guru Systems technology can support your HNES reporting requirements
• What kind of heat networks will qualify for HNES funding?
• What can HNES funding be used for?
• Acceptance testing and HNES
• Deadlines

HNES is a £32m grant funding scheme to improve the performance of existing heat networks. 

Funding to deliver targeted and cost-effective performance improvements, or complete optimisation studies to identify costed and cost-effective performance improvement measures is available to local authorities, housing associations, private developers, public sector organisations and charities in England and Wales from February 2023.

This two year scheme (2023-2025) builds on the HNES Demonstrator that ran in 2021/22.

Guru Systems technology was installed on six sites as part of the Demonstrator. In this article we share our learnings so far, as well as a summary of how to use HNES to improve your existing heat networks.

Guru Systems captures performance data from over 250 heat networks in the UK, and over the past ten years we’ve worked with a wide range of local authorities, housing associations and private developers to support them as they improve the performance of their heat networks, and help to reduce costs for residents.

We drew on this experience to support six projects as part of the HNES Demonstrator. 

HNES capital funding was used to install either Guru Hub 2 or Guru Hub 3 to capture performance data, and all projects then used Guru Pinpoint to identify problems and inefficiencies. Data-led interventions were made, with results visible in the data.

Here are two examples of the impact of data-led improvements on our HNES funded projects.

Site 1: Before

This graph from Guru Pinpoint shows one dwelling before interventions and performance is very bad. It shows flow temperature (dark blue), return temperature (green) and flow rate (light blue) across one week. Here we can see that flow temperature and return temperature are almost identical, and base flow rate (where the grey line is shown) is 0.045m3/hr (45 litres), rising to nearly ten times that rate at different points across the week. A well performing dwelling might see around 4 litres of water per hour flowing through when in standby mode, but this dwelling is returning huge amounts of hot water to the plant room. Even if this dwelling was the only misbehaving dwelling (which sadly it wasn’t) then the cost to run this network and the subsequent tariffs charged to residents is likely to be much higher than it needed to be. 

Site 1: After

Guru Pinpoint also shows a dwelling from the network where interventions had already taken place. Here we see much better performance. When the HIU is in standby mode (power at 0 kW) flow temperature (dark blue) and return temperature (green) is low, but when heat is demanded, flow temperature jumps to around 65°C and return temperature drops right down to near 20°C showing that lots of heat energy has been extracted, and cool water is flowing back to the plant room. This is exactly what you want to see.

Site 2: Before

The graph above shows another dwelling before interventions are made and again performance is very poor. Guru Pinpoint shows flow rate (light blue), flow temperature (dark blue) and return temperature (green) across one day. The values for flow temperature and return temperature are so close together that they almost appear as one line. By showing delta temperature (the difference between flow and return temperature) you can see that the difference is less than 0.5°C. This means return temperatures are far higher than they should be, leading to higher heat losses from the network, higher cost of heat to customers and higher carbon emissions from the plant room.

Site 2: After

After interventions were made, a similar trend is seen to Site 1. When heat is demanded (power in kW, shown in yellow), then flow temperatures rise to around 70°C but return temperatures are way down, around 25°C – exactly what you expect to see from a dwelling that’s performing well

One of the important differences between the Demonstrator and the main Scheme when it opens in February is the performance reporting requirements.

Successful HNES projects are subject to a variety of minimum reporting requirements – including reporting performance related KPIs for 24 months after all funding measures have been installed and commissioned.

Section 1.2 in the Scheme guidance details what kind of performance related KPIs should be included:

“Suppliers will propose and quantify suitable metrics, targets and KPI’s for measuring Project operational performance, based upon previous experience of delivering similar work. The baselining must cover assessment of the whole network architecture, including plant room/energy centre, primary / secondary /tertiary network and customer interfaces.”

“KPI’s will be Project-specific, but the Supplier should aim to report against as many as possible of the following:

• Annual network carbon emissions (kg.CO2e)
• Carbon content of delivered heat (kg.CO2e/kWh)
• Annual fuel use (gas, electricity, other) (kWh)
• Overall network efficiency (gas in / heat out) (%)
• Network distribution efficiency (heat leaving energy centre / heat delivered) (%)
• Network distribution losses (broken down by primary, secondary and tertiary where possible) (kWh)
• Network heat losses (W/dwelling)
• Network flow and return temperatures (deg)
• Cost to operator of delivering heat to customer interfaces (p/kWh)
• Heat tariff paid by network customers (p/kWh [variable], £/day [fixed])
• Overheating (description / annual number of hours reduction)
• Service outages/interruptions, planned and unplanned (# in recent 12-month period)
• Service outages/interruptions, planned and unplanned (total no. hours in recent 12 month period)
• Other appropriate/relevant KPI’s or metrics as proposed by the Client to support assessment of performance of a heat network.”

Guru Systems can provide data to support these performance reporting requirements.