Digital twins (DTs) are virtual representations used for understanding, monitoring, diagnosing, simulating and forecasting installations, processes and, ultimately, an entire system such as the European electricity or energy system. DTs are emerging as future tools for improving performance, for example, through outage anticipation and increasing resilience through remote automatic control and near real-time decision-making support. Their aim is both to enable operations to be represented as closely as possible to reality and to improve the lifecycle of structures, by making use of digitally mapped descriptive data. DTs include four core technologies: the Internet of Things (IoT), simulations (using 3D modelling where appropriate), artificial intelligence (AI), and cloud.
Highlights
According to Fortune Business Insights, the world digital twin market size was valued at $8.60 billion in 2022 and is projected to grow from $11.51 billion in 2023 to $137.67 billion by 2030. In December 2022, the DSO Entity and ENTSO-E have signed a Declaration of Intent (DoI) to support the development of a Digital Twin of the European Electricity Grid in the presence of the European Commissioner Kadri Simson. The DoI defines the cooperation framework to achieve this result, in alignment with the five areas defined in the Digitalising the energy system – EU action plan: (1) observability and controllability; (2) efficient infrastructure and network planning; (3) operations and simulations for a more resilient grid; (4) active system management and forecasting to support flexibility and demand response; and (5) data exchange between TSOs and DSOs.
Challenges and opportunities for DSOs
- The acquisition of more comprehensive, reliable, and enhanced asset data presents both a challenge and an opportunity for DSOs. The challenge lies in the collection and management of such vast amounts of data, while the opportunity lies in the potential insights and improvements this data can bring to operations.
- Using DTs at different geographical scales could help to determine with greater precision the real capacity for integrating renewable energy sources into the grid, and the rate at which they can be incorporated.
- The DTs should enable DSOs to work better with their partners: the ability to integrate into third-party ecosystems, extended businesses for DSO activities, market platforms, smart city platforms, etc.
- The development of DSO use cases (including augmented reality and virtual reality) in various businesses: engineering, operations (predictive maintenance, training, safety), driving, and external relations (service providers, cities, customers/suppliers), offers a multitude of opportunities for innovation and improved service delivery. However, it also presents challenges in terms of technological development and implementation.
- Relying on high-performance, scalable infrastructures to exploit an ever-increasing amount and variety of internal and external data (Lidar point clouds, 3D, new sensors, etc.) is a significant challenge due to the technical requirements and potential costs involved. However, it also presents an opportunity for DSOs to leverage this data for improved decision-making and operational efficiency.
EDSO Considerations
- DSOs must follow technological and regulatory developments throughout the different sectors related to DTs to make the most out of these advances and improve their operational performance.
- DSOs must define the functional requirements of DTs for their needs (physical asset, digital model of the asset, data flow linking the two, continuous mirroring of the asset in the digital model, feedback loop, continuous operation, etc.).
- Before embarking on the previously described process, DSOs will have to ensure that the development of the DT of the European electricity grid promoted by the European Commission is consistent with the industrial realities of network management. The organisation of data sharing between DSOs, TSOs and market players, as well as the preservation of privacy, will be a crucial aspects of this project.
- It is necessary for DSOs to reach a consensus on the definition and position of the DT concept, and to facilitate an exchange of best practices. On this note, a stronger push and funding opportunities for DT projects should be foreseen, to not only allow an in-depth exploitation of the different potential use cases of the DT, but also to allow higher scalability and replicability of the DT solutions.
- DT solutions should be interoperable and should use open-source components when they are valuable.
Potential use cases
- Grid Optimisation. DTs can simulate the entire value chain of power grids, encompassing electricity generation, transmission, distribution, and consumption, with the aim of enhancing grid performance.
- Active System Management. DTs can be utilised to forecast, simulate, and optimise power flows. With the aid of simulation tools and improved data exchange among energy stakeholders, the assessment of flexibility needs can be enhanced.
- Asset management. DTs offer virtual representations of electricity asset models, which can be employed to monitor the health status of the asset and predict when maintenance is required.
- Cyber-physical grid resilience and security. DTs provide essential tools to simulate and analyse potential vulnerabilities and cyber-attacks in a controlled environment, thereby bolstering grid resilience.
- Network planning. DTs could serve as a planning tool for DSOs as they provide modelling and simulation tools to calculate and visualise the grid hosting capacity. They can test different scenarios to determine the impact of adding additional loads on the grid, thereby helping to draw conclusions on the best alternative for the future grid, which could include network upgrades, the use of flexibility, and non-firm connections, amongst others.
- TSO-DSO coordination. DTs can facilitate data exchange between SOs in order to enhance grid management, allowing optimal power flow calculation which improves the system security.
- Training and Education. DTs can be used to create realistic training scenarios for operators, helping them understand how the system reacts under different conditions.
- Disaster Response. In the event of a natural disaster or major system failure, DTs can be used to simulate and plan the most effective response strategies.
- Energy Efficiency. By simulating energy usage patterns, DTs can help identify areas where energy efficiency can be improved, leading to cost savings and reduced environmental impact.
Ongoing projects
- The TwinEU project aims to enable the utilisation of new technologies to foster an advanced concept of DT, while determining the conditions for interoperability, data and model exchanges through standard interfaces and open application programming interface (APIs) to external actors.
- E.ON’s Intelligent Grid Platform is a smart grid technology platform that unites grid data and allows for the digitalisation and automation of the processes related to grid impact studies, short- and long-term grid planning, and grid monitoring. The platform provides digital and automated handling of all processes related to the integration of new distributed generators, battery storage and consumers into the power grid. Moreover, the platform allows providing full transparency on the possibility to integrate additional distributed energy resources (DERs) into the grid and effectively manage connections requests.
- E-REDES is carrying out several initiatives in the field of DTs:
- DPlan (network power flow program), consists of optimisation models for network structure and reactive compensation. It allows anticipating contingencies in the high voltage (HV) and medium voltage (MV) network and solving them before they occur. Furthermore, it has the capacity to assess the failure risk of HV and MV assets in accordance with the Common Network Asset Indices Methodology.
- Analytics4Assets is an AI initiative based on advanced analytics models to forecast the behaviour of technical assets over time. It covers 3 types of assets: HV/MV power transformers; HV circuit breakers and HV lines. Analytics4Assets allows anticipating failures and optimising investment and maintenance plans, contributing to better asset management, quality of service and maximization of asset lifetime.
- LiveGrid is an interactive system that guarantees the real-time monitoring of occurrences, thus allowing a clear and detailed visibility of the real-time situation of the distribution grid. The system includes dashboards for the visualisation of important indicators and metrics for the operational management of the system and maps to visualise the networks affected by outages. LiveGrid enables the visualisation of layers from official/external sources, thus contributing to a proactive analysis of scheduled work and potential constraints on the network.
- GridView is a solution providing schematics and recorded data of the electrical network, its geographical context on the ground, as well as online real-time information on the operational status of the network.
- VEGA predicts vegetation distance anomalies through correlation of multiple sources and predictive and prescriptive analytics models. It is used to plan and prioritise all tasks and generate vegetation cutting orders automatically. The solutions includes customised dashboards with financial, vegetation risk, and operational information, supporting prompt decision making.
- PREDIS is an AI initiative based on advanced analytics models to carry out short-term forecasts and estimate load and generation at all the various points of the HV and MV network. It includes models to estimate the active and reactive power of load, load with photovoltaic (PV) generation, wind generation, hydro generation, thermal generation and cogeneration. PREDIS allows estimating the behavior of over a hundred thousand customers for in the near future (several days).
- ORES has developed the following DT applications:
- The PSI Grid Connect DT (Solormax project) provides a dynamic (near to real-time) digital representation of a low voltage (LV) circuit by using sensors placed on smart meters. It is tested in a pilot project to manage over-voltages caused by PV panels.
- ORES ADMS (Advanced Distribution Management System) combines several applications, such as monitoring, control or outage management. This system will be operational for ORES from 2025.
- Neplan is a software tool used to analyse, plan, optimise and simulate electrical networks. The tool is already used in the ORES business-as-usual.
- Lastly, Enedis has developed the following applications:
- Asset Management Digital Twin. Enedis’ GIS network database allows for probabilistic simulations used for network development planning and maintenance optimisation. The database includes AI technologies (e.g., photo library used for automatized overhead line components diagnostics.
- Digital Twin for operational planning and Network Control. Enedis’s DT for operational planning and network control includes functionalities for supervisory control and data acquisition (SCADA), fault location, isolation and service restoration (FLISR) and distributed energy resources management system (DERMS) (i.e, production and demand forecast, network congestions identification and management, flexibility management).
Last update: 4 October 2023