Cyber-physical Advances

Why are cyber-physical advances important for Smart Local Energy Systems?

Smart Local Energy Systems (SLES) are becoming more complex. Renewable energy sources and storage options are rapidly multiplying, at the same time as connectivity between multiple generation and distribution sub-systems vary over time and space. Stakeholders, users and beneficiaries involved in the energy sector need to overcome SLES constraints and develop systems that are demonstrably futureproof.

To enable this transition to happen, SLES call for more, and better, use of data through advanced modelling using Artificial Intelligence (AI) techniques and improved control. Increased use of data introduces concerns around cyber security infrastructure, meaning that very careful consideration must be given to how securely the data is stored and the need for protocols to ensure this security. The system, and the data within it, must be “wrapped” in a cyber-security shell that is appropriate to the context, including the type of users and the processes involved in data transfer.

How did EnergyREV explore these issues?

We aimed to inform Prospering From an Energy Revolution (PFER), the Demonstrators and the wider energy sector by enabling them to build data architectures and data pipelines that can support SLES deployments and extensions.

To assist this we developed:

Digital testing and monitoring testbeds for the PFER demonstrator projects to showcase forecasting; markets; control and AI capabilities; data pipelines, measurements, and architectures.
Plug-In Modules for forecasting; energy network topology identification; fault-detection; agent negotiation; distributed control.
Cyber-security wrappers designed around digital components to ensure user privacy and system robustness.
Reviews of advances in cyber-physical infrastructure supporting SLES and their opportunities.

We also contributed to specifications for plug and play monitoring, control, and operation of SLES.

Our work will allow SLES to transition smoothly from minimum viable services to fully featured systems and use-cases through the development of agile and future-ready architectures.

Our advances can translate from a research environment into real-life contexts and prototypes and our technical innovations aim to support the deployment of increasing levels of AI and intelligence.

By carrying out reviews, and producing briefing papers and white papers, we informed stakeholder groups on the current state of the art in data pipelines, measurements and architectures; AI and agent-based systems; distributed control systems and cyber-security.

What did Energy Rev Learn?

Digital Flexibility: There is an implementation gap between research-level digital flexibility enhancing control strategies and the legacy control systems routinely deployed in SLES.
Increased focus on operational data produced within the SLES, rather than geographical, historic and other output data, is required. Operational data is a key enabler of smart and flexible features.
AI and Machine Learning have been shown to provide real value to energy systems, but current systems still lack the capability to cost-effectively integrate them.
A cyber-security backbone is instrumental for reliable and robust interoperability and to boost confidence in SLES.
Low-cost sensing systems and measurement best practice can be better utilised to deliver increased performance and co-benefits from SLES.