Research directions and results in the Smart4RES project for improving renewable energy forecasting

International audienceIn this presentation we will show the research directions and innovative solutions developed in the European Horizon 2020 project Smart4RES (http://www.smart4res.eu) for better modelling and forecasting of weather variables necessary to optimise the integration of renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES started in November 2019 and gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to reach very high RES penetrations in power grids of 2030 and beyond, through thematic objectives including:• Improvement of weather forecasting with focus on variables that are important for RES applications,• Improvement of RES power forecasting and better estimation of uncertainties, • Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models,• New data-driven optimization and decision-aid tools for market and grid management applications.• Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). Smart4RES focuses both on improving forecasting models of weather (e.g. physical models, data assimilation, Large Eddy Simulation, enabling weather forecasts seamless) and RES production (e.g. seamless models, highly resolved predictions), and on addressing applications in power grids. Developments in the project have been formalized in Use Cases that cover a large range of time frames, technologies and geographical scales. For example, use-cases on power grids refer to the provision of ancillary services to the upper-level grid (e.g., balancing power) and the local grid (e.g., voltage control and congestion management), where the accurate forecasts of variable generation are key for accurate decision-making. A grid state forecasting will quantify dynamically the flexibility potential of RES in distribution grids. Collaborative forecasting investigates the improvement associated to local data sharing between distributed RES plants. This data sharing paves the way to a data market where agents exchange measurements, predictions or other types of valuable data. Lastly, data-driven approaches will streamline decision-making by simplifying the model chain of bidding RES production, storage dispatch or predictive management electricity grids. They will also provide interpretable hindsight to decision-makers by integrating the decisions of experts (human-in-the-loop) and will be tested in realistic laboratory conditions (software-in-the-loop).In this presentation we focus on the work done for improving modelling and forecasting of weather variables with accent to wind energy; i.e. through innovative measuring set-ups; through the development of seamless numerical weather prediction (NWP) approaches to be able to couple outputs of NWP models with different resolutions; through ultra-high resolution NWPs based on Large Eddy Simulation. We present results using data from real world test cases considered in the project. Finally, we assess how the new forecasting products may bring value to the applications

Smart4RES: Next generation solutions for renewable energy forecasting and applications with focus on distribution grids

International audienceThis paper presents the solutions on renewable energy forecasting proposed by the Horizon2020 Project Smart4RES. The ambition of the project is twofold: (1) increase substantially the performance of short-term forecasting models of Renewable Energy Sources (RES) production and associated weather forecasting and (2) optimize decisions subject to RES uncertainty in power systems and electricity markets. Developments are based on latest advances in meteorology and original use of data science (combination of multiple data sources, data-driven approaches for trading and grid management). Finally, solutions such as flexibility forecast of distributed resources and data markets are oriented towards value for power system stakeholders. Although the project covers a broad scope, in this paper we focus on a selection of use cases that concern the integration of renewables in distribution grids

Highlight results of the Smart4RES project on weather modelling and forecasting dedicated to renewable energy applications

International audienceIn this presentation we detail highlight results obtained from the research work within the European Horizon 2020 project Smart4RES (http://www.smart4res.eu). The project, which started in 2019 and runs until 2023, aims at a better modelling and forecasting of weather variables necessary to optimise the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar, run-of-the-river hydro) into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market- and grid-integration. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond, through thematic objectives including:Improvement of weather and RES forecasting,Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models;New data-driven optimization and decision-aid tools for market and grid management applications;Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). In this presentation we will focus on our results on models that permit to improve forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras). Also results will be presented on the development of seamless approach able to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions. Advances on the contribution of ultra-high resolution NWPs based on Large Eddy Simulation will be presented with evaluation results on real case studies like the Rhodes island in Greece.When it comes to forecasting the power output of RES plants, mainly wind and solar, the focus is on improving predictability using multiple sources of data. The proposed modelling approaches aim to efficiently combine highly dimensionally input (various types of satellite images, numerical weather predictions, spatially distributed measurements etc.). A priority has been to propose models that permit to generate probabilistic forecasts for multiple time frames in a seamless way. Thus, the objective is not only to improve accuracy and uncertainty estimations, but also to simplify complex forecasting modelling chains for applications that use forecasts at different time frames (i.e. a virtual power plant - VPP- with or without storage that participates in multiple markets). Our results show that the proposed seamless models permit to reach these performance objectives. Results will be presented also on how these approaches can be extended to aggregations of RES plants which is relevant for forecasting VPP production.How to cite: Kariniotakis, G. and Camal, S. and the Smart4RES team: Highlight results of the Smart4RES project on weather modelling and forecasting dedicated to renewable energy applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12923, 2022

Next Generation Forecasting Solutions for Wind Energy – Results from the Smart4RES Project

International audience1. Abstract Forecasting variable renewable energy (RES) production, and namely wind power, is a quite mature technology. The majority of actors (grid operators, aggregators, traders...) use today operational forecasting tools and services to optimize their decisions related to wind power integration in power systems and electricity markets The horizons of interest are a few minutes to a few days ahead. Despite that maturity, RES forecasting is a very active field of research internationally due to the high level of uncertainty (mainly from weather conditions), the increasing number of emerging use cases in power systems that involve development of new forecasting products and the need to continously improve accuracy especially in systems with high RES penetration. In fact, wind power prediction errors may reach very high levels (i.e. above 50%) especially in situations of weather fronts, ramps etc, and these may have a potentially high impact to the grid and result in financial losses in the markets. The H2020 Smart4RES project was initiated in 2019 to develop next generation forecasting solutions with increased forecasting performance and also innovative approaches to leverage the value (technical or economic) of forecasts by considering the whole model chain from weather forecasting to end-use applications. The project which will be completed in 2023 has followed some disruptive directions of research. In this Conference we will present the final results both regarding forecasting and also the optimal use of forecasts in a broad range of applications that range from grid management to storage/RES hybrid plant management and trading to multiple markets.2. MethodThe presentation will focus on highlight results from the project like: * Ultra-high spatial/temporal resolution (i.e. 50m) forecasting of wind fields using LES (Large EddySimulations). * Seamless weather forecasting where ensemble forecasts from different models are coupled to cover multiple horizons. * Seamless probabilistic wind power forecasting where a unique model was developped able to cover all prediction horizons and available inputs. * Resilient forecasting where an approach based on robust optimisation is proposed to handle missing or corrupted data (i.e. from cyber attacks). * Data sharing solutions that permit to share data while respecting confidentiality and privacy constraints and thus enable among others spatiotemporal forecasting. * Solutions based on two completely new paradigms: value-oriented forecasting and prescriptive analytics. The later jointly considers forecasting and optimisation steps in applications.3. ResultsAlthough the project deals with RES forecasting in general this presentation will focus on the use cases that refer to wind power forecasting and will present the final results of the research project. The developed methods were validated with real-world data from various wind farms in Europe. The results on forecasting using LES are based on data from the island of Rhodes. The proposed methods like seamless RES forecasting are compared to state-of-the-art wind power forecasting methods benefits are quantified both in terms of accuracy and regarding simplification of the model chain. The value-oriented forecasting and the prescriptive analytics approaches are illustrated for the case of trading to energy markets. These later approaches are based on artificial intelligence enhanced with interpretability.4. Conclusions Smart4RES is the only ongoing European project on short-term forecasting and applications. The project has developed disruptive research supported by high-quality publications. New concepts have been introduced and validated. Seamless forecasting (a single model for all data, horizons and all RES technologies – replicable to PV and aggregations of PV/Wind) marks a corner stone in RES forecasting technology. Data sharing solutions bring benefits to wind power forecasting and are replicable to other sectors. The prescriptive analytics approach permits to link data to decisions through interpretable AIbased models. Finally, the forecasting solution proposed based on robust optimisation makes wind power forecasting resilient and becomes a paradigm on how resilience can be introduced in the intelligence layer of future power systems. 5. Learning Objectives Aggregators and wind power producers will learn innovative approaches to trade on multiple electricity markets in order to increase revenue and mitigate technical and economic risks, including the case of a joint operation of wind and storage. Regulators and system operators will see the potential benefit of new forecasting products of weather and wind production in markets and systems with high renewable penetration. This can help them when selecting or monitoring forecasting services in the near future. Aggregators, wind power producers and system operators will be able to grasp the potential value of their data streams in a collaborative framework and understand the constraints related to privacy-preservation for a reliable application of data sharing in their daily process

Next Generation Forecasting Solutions for Wind Energy – Results from the Smart4RES Project

International audience1. Abstract Forecasting variable renewable energy (RES) production, and namely wind power, is a quite mature technology. The majority of actors (grid operators, aggregators, traders...) use today operational forecasting tools and services to optimize their decisions related to wind power integration in power systems and electricity markets The horizons of interest are a few minutes to a few days ahead. Despite that maturity, RES forecasting is a very active field of research internationally due to the high level of uncertainty (mainly from weather conditions), the increasing number of emerging use cases in power systems that involve development of new forecasting products and the need to continously improve accuracy especially in systems with high RES penetration. In fact, wind power prediction errors may reach very high levels (i.e. above 50%) especially in situations of weather fronts, ramps etc, and these may have a potentially high impact to the grid and result in financial losses in the markets. The H2020 Smart4RES project was initiated in 2019 to develop next generation forecasting solutions with increased forecasting performance and also innovative approaches to leverage the value (technical or economic) of forecasts by considering the whole model chain from weather forecasting to end-use applications. The project which will be completed in 2023 has followed some disruptive directions of research. In this Conference we will present the final results both regarding forecasting and also the optimal use of forecasts in a broad range of applications that range from grid management to storage/RES hybrid plant management and trading to multiple markets.2. MethodThe presentation will focus on highlight results from the project like: * Ultra-high spatial/temporal resolution (i.e. 50m) forecasting of wind fields using LES (Large EddySimulations). * Seamless weather forecasting where ensemble forecasts from different models are coupled to cover multiple horizons. * Seamless probabilistic wind power forecasting where a unique model was developped able to cover all prediction horizons and available inputs. * Resilient forecasting where an approach based on robust optimisation is proposed to handle missing or corrupted data (i.e. from cyber attacks). * Data sharing solutions that permit to share data while respecting confidentiality and privacy constraints and thus enable among others spatiotemporal forecasting. * Solutions based on two completely new paradigms: value-oriented forecasting and prescriptive analytics. The later jointly considers forecasting and optimisation steps in applications.3. ResultsAlthough the project deals with RES forecasting in general this presentation will focus on the use cases that refer to wind power forecasting and will present the final results of the research project. The developed methods were validated with real-world data from various wind farms in Europe. The results on forecasting using LES are based on data from the island of Rhodes. The proposed methods like seamless RES forecasting are compared to state-of-the-art wind power forecasting methods benefits are quantified both in terms of accuracy and regarding simplification of the model chain. The value-oriented forecasting and the prescriptive analytics approaches are illustrated for the case of trading to energy markets. These later approaches are based on artificial intelligence enhanced with interpretability.4. Conclusions Smart4RES is the only ongoing European project on short-term forecasting and applications. The project has developed disruptive research supported by high-quality publications. New concepts have been introduced and validated. Seamless forecasting (a single model for all data, horizons and all RES technologies – replicable to PV and aggregations of PV/Wind) marks a corner stone in RES forecasting technology. Data sharing solutions bring benefits to wind power forecasting and are replicable to other sectors. The prescriptive analytics approach permits to link data to decisions through interpretable AIbased models. Finally, the forecasting solution proposed based on robust optimisation makes wind power forecasting resilient and becomes a paradigm on how resilience can be introduced in the intelligence layer of future power systems. 5. Learning Objectives Aggregators and wind power producers will learn innovative approaches to trade on multiple electricity markets in order to increase revenue and mitigate technical and economic risks, including the case of a joint operation of wind and storage. Regulators and system operators will see the potential benefit of new forecasting products of weather and wind production in markets and systems with high renewable penetration. This can help them when selecting or monitoring forecasting services in the near future. Aggregators, wind power producers and system operators will be able to grasp the potential value of their data streams in a collaborative framework and understand the constraints related to privacy-preservation for a reliable application of data sharing in their daily process

Next Generation Forecasting Solutions for Wind Energy – Results from the Smart4RES Project

International audience1. Abstract Forecasting variable renewable energy (RES) production, and namely wind power, is a quite mature technology. The majority of actors (grid operators, aggregators, traders...) use today operational forecasting tools and services to optimize their decisions related to wind power integration in power systems and electricity markets The horizons of interest are a few minutes to a few days ahead. Despite that maturity, RES forecasting is a very active field of research internationally due to the high level of uncertainty (mainly from weather conditions), the increasing number of emerging use cases in power systems that involve development of new forecasting products and the need to continously improve accuracy especially in systems with high RES penetration. In fact, wind power prediction errors may reach very high levels (i.e. above 50%) especially in situations of weather fronts, ramps etc, and these may have a potentially high impact to the grid and result in financial losses in the markets. The H2020 Smart4RES project was initiated in 2019 to develop next generation forecasting solutions with increased forecasting performance and also innovative approaches to leverage the value (technical or economic) of forecasts by considering the whole model chain from weather forecasting to end-use applications. The project which will be completed in 2023 has followed some disruptive directions of research. In this Conference we will present the final results both regarding forecasting and also the optimal use of forecasts in a broad range of applications that range from grid management to storage/RES hybrid plant management and trading to multiple markets.2. MethodThe presentation will focus on highlight results from the project like: * Ultra-high spatial/temporal resolution (i.e. 50m) forecasting of wind fields using LES (Large EddySimulations). * Seamless weather forecasting where ensemble forecasts from different models are coupled to cover multiple horizons. * Seamless probabilistic wind power forecasting where a unique model was developped able to cover all prediction horizons and available inputs. * Resilient forecasting where an approach based on robust optimisation is proposed to handle missing or corrupted data (i.e. from cyber attacks). * Data sharing solutions that permit to share data while respecting confidentiality and privacy constraints and thus enable among others spatiotemporal forecasting. * Solutions based on two completely new paradigms: value-oriented forecasting and prescriptive analytics. The later jointly considers forecasting and optimisation steps in applications.3. ResultsAlthough the project deals with RES forecasting in general this presentation will focus on the use cases that refer to wind power forecasting and will present the final results of the research project. The developed methods were validated with real-world data from various wind farms in Europe. The results on forecasting using LES are based on data from the island of Rhodes. The proposed methods like seamless RES forecasting are compared to state-of-the-art wind power forecasting methods benefits are quantified both in terms of accuracy and regarding simplification of the model chain. The value-oriented forecasting and the prescriptive analytics approaches are illustrated for the case of trading to energy markets. These later approaches are based on artificial intelligence enhanced with interpretability.4. Conclusions Smart4RES is the only ongoing European project on short-term forecasting and applications. The project has developed disruptive research supported by high-quality publications. New concepts have been introduced and validated. Seamless forecasting (a single model for all data, horizons and all RES technologies – replicable to PV and aggregations of PV/Wind) marks a corner stone in RES forecasting technology. Data sharing solutions bring benefits to wind power forecasting and are replicable to other sectors. The prescriptive analytics approach permits to link data to decisions through interpretable AIbased models. Finally, the forecasting solution proposed based on robust optimisation makes wind power forecasting resilient and becomes a paradigm on how resilience can be introduced in the intelligence layer of future power systems. 5. Learning Objectives Aggregators and wind power producers will learn innovative approaches to trade on multiple electricity markets in order to increase revenue and mitigate technical and economic risks, including the case of a joint operation of wind and storage. Regulators and system operators will see the potential benefit of new forecasting products of weather and wind production in markets and systems with high renewable penetration. This can help them when selecting or monitoring forecasting services in the near future. Aggregators, wind power producers and system operators will be able to grasp the potential value of their data streams in a collaborative framework and understand the constraints related to privacy-preservation for a reliable application of data sharing in their daily process