Smart Grids
Real-Time Thermal Monitoring at Anell's Network
At OPENTUNITY, we are committed to unlocking the full potential of the grid, making it smarter, more flexible, and ready for the demands of a high-renewable future. This summer, our partner Anell has taken a significant step toward that goal.
Where?
In a medium-voltage line within Anell’s distribution network in Catalonia (Spain).
What Happened?
Two temperature sensors were installed directly on the conductor of a medium-voltage line as part of OPENTUNITY’s Real-Time Thermal Rating (RTTR) use case.
Why does this matter?
Traditionally, distribution system operators use static and conservative estimates to define the maximum current capacity of their lines, often based on worst-case weather conditions. In reality, line capacity varies depending on the actual conductor temperature, which fluctuates with current and ambient conditions. Under favorable conditions, the line can safely carry more energy than these conservative limits suggest.
That’s where OPENTUNITY comes in.
Smarter Algorithms for a Smarter Grid
Instead of deploying expensive sensors across the entire grid, OPENTUNITY is developing a data-driven algorithm to estimate conductor temperature in real time, using only ambient temperature and electrical current measurements.
To validate this model, Anell installed two temperature sensors on a live grid segment, enabling a comparison between measured and estimated conductor temperatures. These insights will support further development of the algorithm and its future deployment at scale.
“If we have better information on the capacity of our grid in real time, we can take better decisions when operating it at critical moments. Real-time thermal rating provides us just that.” – Anell team
Why Sentrisense?
As part of the validation, Anell chose to work with Sentrisense, a startup offering a lightweight and cost-effective temperature sensor design that differs from most commercial options. OPENTUNITY partners are now receiving data from the devices to refine their tools and algorithms.
OPENTUNITY's Network Planning Tool
Strategic planning of the distribution network is essential to efficiently integrate Renewable Energy Sources (RES), reduce costs, and ensure a secure grid operation. Traditionally, grid planning is based on expensive infrastructure expansion, which does not adapt to rapidly evolving flexibility markets or available flexibility resources.
New OPENTUNITY Network Planning Tool
Within the OPENTUNITY project, ICCS is developing an advanced Network Planning Tool that integrates best planning practices with novel research methodologies and very fast algorithms to address the challenges of modern distribution grids. The tool supports Distribution System Operators (DSOs) in making strategic and informed decisions by integrating sophisticated programming techniques such as Mixed-Integer Linear Programming (MILP) and Mixed-Integer Second-Order Cone Programming (MISOCP) for performing network planning, a fast power flow algorithm for critical scenario identification and timeseries clustering for scenarios extraction. Different objectives can be set, such as cost optimization, investments deferral, or maximizing RES capacity, allowing DSOs to quickly design and compare alternative investment options and operation scenarios.
A user interface provides graphical representations of key results. For example, in a cost optimization scenario (figure on the right side), the planning tool graphically represents the impact of strategic decisions, such as the optimal time to invest in infrastructure and its corresponding impact on power loss costs. The interface can alternatively illustrate scenarios with a focus on delaying expenditures, highlighting how strategic use of flexibility resources can defer costly investments (figure on the left side).
Power flow results can also be presented in all scenarios, e.g. for RES capacity maximization (Figure 3), identifying optimal locations and capacities of new RES power plants for given budgetary constraints. The tool ensures that RES capacity expansion is closely aligned with existing grid constraints. The computed RES capacity at each substation in this case is represented in map-based visualizations.
OPENTUNITY at the BRIDGE General Assembly 2025
OPENTUNITY is proud to participate in the BRIDGE General Assembly 2025, an essential gathering that brings together key players from EU-funded projects in the fields of smart grids, energy storage, islands, and digitalization. This annual event serves as a platform for knowledge exchange, collaboration, and strategic alignment to accelerate the European energy transition.
What is BRIDGE?
BRIDGE is an initiative by the European Commission that unites Horizon 2020 and Horizon Europe projects working on smart grids, demand response, energy communities, and digitalization. By fostering collaboration between projects, BRIDGE helps create a coherent regulatory, technical, and market framework that supports innovation and integration across the European energy landscape.
Through dedicated working groups and task forces, BRIDGE facilitates discussion on key topics such as:
- Regulatory framework for new market models.
- Data management and interoperability in digitalized energy systems.
- Business models for flexibility and storage services.
- Consumer and citizen engagement strategies.
The BRIDGE General Assembly: Objectives and Key Discussions
The BRIDGE General Assembly is a key event that brings together all participating projects to:
- Share project results and best practices.
- Discuss policy and regulatory developments impacting the energy transition.
- Align efforts across projects to maximize impact.
- Identify synergies between different initiatives.
This year’s assembly focused on scaling up innovative solutions and reinforcing the role of digitalization and flexibility markets in achieving Europe’s clean energy goals.
Why OPENTUNITY’s Participation Matters
OPENTUNITY is committed to contributing to BRIDGE by sharing insights from our work on flexibility markets, data-driven grid management, and citizen engagement. Our participation allows us to:
- Showcase how OPENTUNITY innovations support DSO-TSO coordination and local flexibility markets.
- Exchange expertise on interoperability, data spaces, and digital energy platforms.
- Strengthen partnerships with other EU-funded projects and stakeholders.
- Influence the policy and regulatory discussions shaping the future of the European energy system.
Looking ahead
Engagement in BRIDGE activities is crucial for ensuring that the solutions developed in OPENTUNITY align with broader European objectives and remain at the forefront of energy system transformation.
Advanced State Estimation for Distribution Grids Based on Deep Learning
State estimation techniques establish a link between the measuring devices of an electrical network and the control centers to present and monitor all the electrical magnitudes of the network itself. State estimation approaches have been applied in transmission networks for many years, but these techniques are not directly applicable in distribution networks.
In distribution systems, State estimation algorithms aim to obtain the state of the system, i.e. the voltages at all buses and connection points. It is a necessary process before calculating the power flow and safety analysis. In addition, it should be added that traditional state estimation only takes into account statistical distributions to model uncertainties in measurements (e.g., power distribution among single-bus feeders).
Development of Deep Learning Algorithms
The development is based on a multipurpose prediction software module. This module includes a time series decomposition model with three main components: trends, seasonality (in different periods, varying from daily period to an annual period), and exogenous variables (holidays, weather, etc.). The modeling of these components can be summarized as follows:
- Trends can be modeled using two internal models: A saturated nonlinear growth model and a trend model with change points. The saturated nonlinear growth model captures the initial acceleration and subsequent deceleration as a limit is reached. The trend model with change points identifies specific moments where the trend changes direction or speed, allowing a better adaptation to market dynamics
- Seasonality in energy consumption, such as increased demand in summer due to the use of air conditioning, can be modeled using Fourier series. By breaking down demand into sinusoidal components, seasonal patterns can be identified, and deep learning models can be adjusted to forecast these periodic variations. This improves the accuracy of the state estimation by taking into account regular fluctuations in energy demand.
- Exogenous variables are added to the overall model as additive linear regressions. These variables, which are factors external to the system (but influence its behavior) are incorporated into the model to improve the accuracy of the predictions. In the context of state estimation, exogenous variables can include factors such as temperature, time of day, day of the week, special events, or even energy prices. By adding these variables using additive linear regressions, their direct and linear impact on the variable of interest can be captured, allowing the model to adjust its predictions more accurately and better reflect the reality of the electricity system. These results in a more robust and reliable.
For the generation of the models, historical data by the project Pilot Sites have been used.
Integration into the ETRA I+D ÉTER software tool
ETRA I+D has its own tool called ÉTER, developed and validated in real conditions in the different European projects in which it has participated. The purpose of ÉTER is to control, manage and monitor an electricity distribution network, improving its flexibility, stability and security; being particularly relevant in scenarios with a high penetration of renewable energies and that require better demand management.
ÉTER currently integrates a State estimation module, but based mainly on probabilistic methods. The ultimate goal of ETRA is to integrate this new State estimation module based on Deep Learning into ÉTER once it has been validated in the OPENTUNITY pilots.
The following image shows how the power flows calculated with the current State estimation module are displayed in ÉTER. The visualization once the new module is integrated will be similar, but the calculations are expected to be more accurate and reliable.
