Periodic Reporting for period 1 - Smart4RES (Next Generation Modelling and Forecasting of Variable Renewable Generation for Large-scale Integration in Energy Systems and Markets)
Reporting period: 2019-11-01 to 2021-04-30
This is why the overarching objective of Smart4RES is to develop and test next generation tools that enable an increase of at least 15 % in RES forecasting performance and enhanced value in applications by considering the entire modelling and value chain of RES forecasting.
A first objective of the project consists in defining requirements for forecasting technologies to enable near 100% RES penetration by 2030 and beyond. Based on these requirements, Smart4RES proposes RES-dedicated weather forecasting and new RES production forecasting tools, using various sources of data and developing high-resolution approaches.
Streamlining the optimal extraction of value from RES data and forecasts is crucial to enhance the value of RES forecasting and associated services. Smart4RES proposes new forecasting products, data markets and new business models in order to remunerate agents who contribute to an increase in RES forecasting quality and value in applications.
Finally, Smart4RES addresses the value of RES forecasting in power system applications such as trading in electricity markets and provision of system services to TSOs and DSOs. Data-driven solutions aim at simplifying the model chain from data to decision. Decision-aid tools are designed to enable a large penetration of RES production, combined with storage. Models will be validated in living labs and the value of forecasting will be assessed in cost benefit analysis.
The collection of datasets presented in Deliverables D1.3 and D1.4 supports Smart4RES models development and validation.
The development of new forecasting solutions has started on weather forecasting and RES forecasting Operational Numerical Weather Predictions have been produced at their highest spatial resolution with high-frequency outputs. High-temporal resolution approaches are in progress for both weather and RES power forecasting. The integration of advanced weather forecasting produced in WP2 into RES forecasting models shows promising results.
Methods have been defined for the optimal extraction of value from data and forecasts and for the decision-aid tools for large-scale penetration of renewable energy in power systems and electricity markets. Algorithmic solutions have been obtained for collaborative forecasting and data markets, respecting privacy constraints. Data-driven approaches are proposed to reduce the modelling effort and increase application value in predictive grid management and renewable energy trading.
In this first period, the focus of dissemination activities was placed on the visibility of Smart4RES thanks to the webinar series and participation to conferences.
Publications in high-quality journals have started and will intensify in the next period, along with the start of the exploitation of project results.
- Pseudo-deterministic forecasts were constructed to exploit the high-resolution ensemble weather predictions and better predict infra-hour variability of wind speed in a user-friendly way for RES applications.
- First promising outputs of Large-Eddy Simulations (LES) on case studies with complex terrain.
- High-resolution regional forecasts of solar irradiance were derived from an array of all-sky imagers covering a large region in northern Germany.
RES production forecasting innovations:
- Integration of advanced weather forecasts into RES power forecasting models,
- RES production forecasting at high temporal resolution via machine learning solutions,
- seamless approach for RES forecasting.
In the next period, weather forecasting and RES production forecasting models developed will (1) integrate all the data available and address both Photovoltaic and Wind case studies, and (2) will be further improved and compared to state-of-the art approaches on the collected datasets.
Innovations in optimal extraction of value from data:
- Development of a cutting-edge method for privacy-preserving collaborative forecasting based on distributed optimization and peer-to-peer information exchange
- Development of a first algorithmic solution for a data marketplace of RES applications, which remunerates agents who share information useful for RES forecasting
Innovations in decision-aid tools for large-scale penetration of renewable energy in power systems and electricity markets:
- Machine-learning based approach for fast security assessment in isolated power systems, reducing the modelling effort.
- Data-driven predictive management of distribution grids integrating prediction of the network sensitivity to constraints and hierarchical forecasting of load/RES
- Optimization of multiple products under joint operation of RES & storage, including the case of isolated power systems.
- Prescriptive analytics method, which simplifies the renewable trading model chain
- Formalization of the population effect of RES producers, where a large penetration of renewable energy impacts electricity prices
In the next period, models for collaborative forecasting, data markets, and decision-aid tools will be completed and evaluated on various test cases. Decision-aid tools will be validated by living labs and realistic software simulations.
The potential impacts of Smart4RES are the following:
- Recommendations in terms of market design, of new forecasting requirements and of system services from RES in grid codes
- Contribution to functional objectives of ETIP SNET R&I Roadmap (digitalisation services, energy system business, upgraded electricity networks a.o.);
- Increase of revenue for RES producers in the electricity markets;
- Decrease of grid management cost under RES uncertainty;
- Increased added value of RES-related services (e.g. forecasting, Virtual Power Plant management) which strengthens the business of SMEs present in the field
- Increased European leadership in the field of decision-making under RES uncertainty by exploiting the synergies between meteorology, power system engineering and data science.
- Green-house-gas emissions will be reduced by a larger penetration of RES production thanks to higher RES profitability, reduced grid constraints and lower activation of fossil-based ancillary services.