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.

What are the EnergyREV team doing in this area?

Our goal is 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 have work streams developing:

  • 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.
How is EnergyREV exploring these issues?

We are contributing to specifications for the 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 can inform 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 are the emerging insights?
  • 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.
Meet the team

Theme Lead: Elena Gaura

Co-Investigator’s: George Konstantopoulos; Jianzhong Wu; Stephen McArthur; and Zhong Fan

Researchers: Alison HalfordAndrei Braitor; Colin StephenEuan Morris; Lakshmi Srinivas Vedantham; ; Pablo Baldivieso-Monasterios; Siyuan Dong; and Yue Zhou

Outputs

2021

Report: A plug and play artificial intelligent architecture for smart local energy systems integration (October 2021)

Journal Paper: Control design and small-Signal stability analysis of inverter-Based microgrids with inherent current limitation under extreme load conditions (April 2021)

Journal Paper: Enhanced Current-Limiting Droop Controller for Grid-Connected Inverters to Guarantee Stability and Maximize Power Injection Under Grid Faults (March 2021)

Briefing: ICT Infrastructure Supporting Smart Local Energy Systems (January 2021)

Journal Paper: Current-Limiting Droop Control Design and Stability Analysis for Paralleled Boost Converters in DC Microgrids (January 2021)

2020

Journal Paper: Grid-Supporting Three-Phase Inverters with Inherent RMS Current Limitation Under Balanced Grid Voltage Sags (November 2020)

Journal Paper: Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community (November 2020)

Journal Paper: Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model (September 2020)

Conference Paper: Flexible Fog Computing Architecture for Smart Microgrids (September 2020)

Report: The Energy Revolution: Cyber Physical Advances and Opportunities for Smart Local Energy Systems (June 2020)

Journal Paper: Stability analysis and nonlinear current-limiting control design for DC micro-grids with CPLs (May 2020)

2019

Conference Paper: Online pricing via stackelberg and incentive games in a micro-grid (June 2019)