Project Summary
Meeting the SIF challenge: This project meets all 4 of the aims of improving energy system resilience and robustness category by:
- By accurately mapping our network, DNIM will enable future challenges and risks to be identified in a quick and cost effective manner. This will help create a more resilient network that can adapt to the energy transition.
- Technology developed under the DNIM platform includes Artificial Intelligence and Machine Learning as well as robotic automation hardware. These technologies offer significant opportunities to facilitate hydrogen and heat energy system configurations for example.
- Improving our operational activities as we transition to net-zero whilst reducing impact to customers. With DNIM surveying the network autonomously without excavations, the system offers the gas networks a chance to improve resilience and robustness for a future green gas scenario in a sustainable and relatively clean manner.
- Overall strengthens our operation activities by having a better understanding of our assets and their precise locations. With this knowledge and the data collected, condition of the energy system and how those changes with future energy system configurations will be able to be analysed and evaluated.
SGN: SGN is one of the largest utility companies, distributing natural and green gas safely and reliably through our 74,000km of pipes to 5.9 million homes and businesses across Scotland and southern England. We are committed to exceeding the expectations of our stakeholders by delivering value for money and exceptional customer service as well as providing a safe, secure and sustainable future for our network.
SGN are the lead participant in this project and will provide clear direction and insight to the project partners. SGN will also provide insight and expertise from a gas distribution network perspective for the project, ensuring alignment to the challenge area and realisation of benefits to be captured.
ULC Technologies: ULC Technologies has over 20 years of experience developing robotic solutions and deploying them as services using their field teams. ULC's team includes engineers (mechanical, electrical, software, robotics), research scientists, and technicians. This enables ULC to tackle highly complex and multi-functional problems with innovative solutions. These solutions may then be driven deployed in-house using ULC's extensive field teams which has had success deploying robots in the UK and the US for over two decades.
Innovation Justification
In August of 2021, the UK unveiled an aggressive strategic energy policy to transition to a world-leading hydrogen-based economy. These policies set out to reach Net Zero by 2050. For gas utilities, this policy represented a powerful shift from methane to hydrogen-based energy ecosystems that have introduced a variety of questions related to cost-effectiveness, safety, and risk mitigation.
Prior to the transition, gas utilities will be tasked with demonstrating the economic benefits of utilising existing network infrastructure with hydrogen compared to laying new, electrical-based heating infrastructure. Conversely, safety questions may arise in large part due to the heightened dangers of gaseous hydrogen compared to that of methane.
To support this transition, every portion of the gas network supply chain will require assessment and potential modification, ranging from network infrastructure to consumer appliances. To safely introduce hydrogen into the UK's gas networks, network operators will require extensive locating and mapping operations to mitigate risk to consumers. This analysis will define as-built network infrastructure prior to injection of hydrogen and allow gas utilities to ensure consumers are safely equipped to support hydrogen in their homes. In certain cases, consumers may be stealing gas which may introduce high-risk uncertainties to gas utility operators. As a result, mapping these networks has become a crucial step towards the transition.
Previously, ULC & SGN had completed an early-stage R&D project where a conceptual pipe mapping method was investigated which has been defined as DNIM. DNIM is a proposed tetherless robotic system to perform the network mapping from inside the pipe. When combining advanced mapping techniques in conjunction with machine learning-based feature detection, gas utilities may identify and quantify the cost of repair and/or remediation of high-risk network features (joints, plugs, etc.) in their network. In addition, these outputs have the potential to enable digitisation of networks through Building Information Modelling style models.
DNIM will be the first tetherless robot of its kind and as a result, introduces many new challenges and benefits. Due to the innovative approach, this style project would normally have a risk profile that is too high for BAU or other funding methods. In addition, if the project were funded under BAU or other methods, it would take significantly longer, and the solutions would arrive too late to enable effective transition to net zero. DNIM's aligns with the transformational challenge areas set forth by SIF whilst aligning with regulatory goals for Net Zero.
Project Benefits
DNIM will deliver value directly to gas networks customers by reducing transporter accountable theft of gas as well as decreasing the costs for customer connections. Furthermore, DNIM will offer future savings to the consumers by enabling superior energy models. This would reduce needless spend to upgrade the network for future energy scenarios.
Having a fully digitalised record of the network would allow customer enquiries for new connections or alterations to be delivered through the use of smart engineering and commercial models, reducing SGN's cost to serve, creating savings for the customer. In this way customers can quickly assess options for siting their plant and equipment where the cost of connection to the gas network may be an important commercial consideration. From a GDN's perspective the solution would encourage best use of available capacity and minimise or avert potentially expensive reinforcement costs that are borne by all users of the system.
Furthermore, theft of gas which is absorbed by the consumer would be minimised resulting in 26.72 GWh less energy stolen.
This cost evaluation will be validated in more detail as the project technology development evolves and we fully understand the capabilities of such a system. This evaluation may include but not be limited to a desktop cost and timeline comparison to map a regional network location using multiple traditional techniques.
As the projects develop through the phases, metrics shall be put in place to continue to assess the DNIM solutions effectiveness and benefits so that it can be continuously improved if required.
