Search

International audience; In the last two decades, electricity grids have faced many challenges that they were not designed to handle. These include integrating weather-dependent renewables, distributed generators, storage units and other advanced components, as well as taking into account active demand. These challenges, together with the ageing of infrastructures, make it more difficult to deliver cost-effective, reliable power. To overcome these issues requires creating new network architectures. The research project Fractal Grid proposes fractality as a core concept to model, analyze and design smart grids in their evolution up to 2030 and beyond. This paper presents the project, its methodological approach and the first results.

International audience; Several carbon materials (one carbon nanotubes, two carbon blacks and one home-made carbon aerogel) were covered by thin tin dioxide (SnO2) nanoparticles coatings to improve their resistance against oxidation under the working conditions of Proton Exchange Membrane Fuel Cells. A pretreatment for the nanotubes in acidic media was also performed to improve their dispersion in the reactive medium. The coating was done by a chemical route. Samples were analysed by Scanning Electronic Microscopy (SEM), nitrogen sorption, X-ray diffraction (XRD), Raman spectroscopy, Fourier Transformed InfraRed (FTIR) and X-ray Photoelectron Spectroscopy (XPS). Their electrical conductivities were also measured. Using a reactive medium with a pH value exceeding the point of zero charge of the carbon materials is mandatory to favour electrostatic attractions, and to obtain covering and homogeneous coatings. In this condition, it is possible to reduce the quantity of precursor and to optimise the coating. The intrinsic properties of the carbon materials also influence the characteristics of the coatings. In fact, the least organised carbon materials with high specific surface area and porosity values exhibit homogeneous and covering coatings. On the contrary, organised carbon materials, with few oxygen-containing groups, lead to a smaller quantity and heterogeneous coatings (with some areas of carbon surface uncovered). The pretreatment performed on the nanotubes improves the characteristics of the coatings, even if using significant amounts of precursor remains necessary to obtain a covering and a homogeneous coating. The transformation mechanisms of the precursor into tin dioxide, depending on the pH, value are also discussed.

International audience; The present work proposes a stochastic approach for Day-Ahead operation of Home Energy Management Systems when batteries, solar photovoltaic resources and Electric Water Heaters are considered. The optimization problem minimizes the operation costs formed by energy procurement in the wholesale market and the equivalent cycling aging cost of the batteries, and also includes the uncertainty of the PV production and the load. The complete two-stage stochastic formulation results in a Mixed-Integer Nonlinear Programming problem that is decomposed using a Competitive Swarm Optimizer to handle the calculation of the battery cycling aging cost. A Storage Disaggregation Algorithm based on Lagrangian relaxation is used to reduce the problem size and to allocate individual State of Charge for the batteries. In addition, the advantages of considering a stochastic approach are shown by means of the Value of the Stochastic Solution. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to a use case that considers an aggregator that participates in the electricity market with a portfolio of prosumers with active demand capability.

International audience; This paper analyses the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the re-dispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modelling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of DLR in supporting costeffective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyses the impact of different operational strategies, the coordination among multiple flexible technologies and installed capacity of wind generation on the benefits of DLR.

International audience; In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several countries has significantly increased thanks to policies favoring development of renewables and also to the significant cost reduction of this specific technology. The PV power production process is characterized by significant variability, as it depends on meteorological conditions, which brings new challenges to power system operators. To address these challenges it is important to be able to observe and anticipate production levels. Accurate forecasting of the power output of PV plants is recognized today as a prerequisite for large-scale PV penetration on the grid. In this paper, we propose a statistical method to address the problem of stationarity of PV production data, and develop a model to forecast PV plant power output in the very short term (0-6 hours). The proposed model uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve forecasts. The computational requirements of the method are low, making it appropriate for large-scale application and easy to use when on-line updating of the production data is possible. The improvement of the normalized root mean square error (nRMSE) can reach 20% or more in comparison with state-of-the-art forecasting techniques

International audience; This paper describes a research on the influence of wind power prediction error autocorrelation on the sizing of storage coupled with a wind farm. The stochastic nature of renewable energies resources such as wind speed or solar radiation represents a challenge for the grid integration of renewable energy plants. The imbalances between renewable power predictions and realised production are generally penalised by system operators since additional reserves are required to maintain the stability of the grid. The coupling of storage devices with renewable energy plants is one of the solutions studied to reduce those imbalances. In this work, a methodology to manage imbalances and to size storage in order to achieve a determined level of controllability is proposed. It is applied to a specific use case: the integration of a combined wind-storage plant in French Guyana. The influence of the autocorrelations of errors on the battery size is investigated in detail and a methodology for producing wind prediction errors time series is presented.

International audience; This paper presents the initial findings on a new forecast approach for ancillary services delivered by aggregated renewable power plants. The increasing penetration of distributed variable generators challenges grid reliability. Wind and photovoltaic power plants are technically able to provide ancillary services, but their stochastic behavior currently impedes their integration into reserve mechanisms. A methodology is developed to forecast the flexibility that a wind-photovoltaic aggregate can provide. A bivariate Kernel Density Estimator forecasts the probability to provide reserve. The methodology is tested on a case study where volumes of automatic Frequency Restoration Reserve (aFRR) are forecasted on a day-ahead horizon. It is found that the wind-photovoltaic aggregate can dedicate a limited share of its forecast production to aFRR. The frequency of insufficient reserve capacity is assessed, by comparing the capacities offered with the measured production.

International audience; The increasing share of weather-dependent renewable energies in power systems creates a need for energy storage technologies to reduce the impacts of variable production. The most mature technology to store energy on the grid remains Pumped Hydro Energy Storage (PHES). The potential of high-energy sites has already been assessed in Europe by the EU JRC, considering mostly dams and reservoirs from global European databases which include only massive water bodies. This paper focuses on estimating the potential for small-PHES, proven to have lower environmental impact and an positive impact on grid balance and reliability. A generic method is designed, able to evaluate a global PHES storage capacity at large scale. It considers both existing lakes and natural depressions suitable to be filled for PHES purposes. The volume of filled lakes is estimated using the surrounding topography. The method is organized so that the “heavy” calculations, i.e. sink detection, volume evaluation, constraints verification, etc. are run only once. Consequently, the actual potential estimation phase only includes fast calculations and can be integrated in a loop for carrying out a sensitivity analysis. The proposed method is then applied considering France as a test case. Suitable environmental, land-use and structural constraints are applied to eliminate irrelevant sites. The analysis leads to an estimated value of the small-PHES potential in France, which ranges from 14 GWh when only existing lakes are considered to 33 GWh when lakes and depressions are considered. These estimations represent respectively 8% and 18% of the current hydro storage capacity in France. Thanks to a global sensitivity analysis, factors like the maximum distance between lakes, the maximum altitude of the sites, and the distance to the electrical grid are shown to have the most influence on the global evaluated potential, which is further sensitized. Lastly, another application is suggested that makes it possible to select the connections to be built first within a restricted area, based on a cost-per-energy-like approach. It uses the connections between reservoirs detected at large geographical scale.

International audience; This paper presents an optimization model for energy management in smart buildings, when electrochemical and thermal storage are considered as flexibilities to achieve minimum operation costs. The optimization problem takes into account the battery’s cycling cost and the possibility of storing energy in the electric water heater. To deal with the cycling aging process, the problem is decomposed into two subproblems that are iteratively solved, in which a Particle Swarm Optimization decides the battery’s State of Charge and then a day-ahead dispatch takes place to determine the total operation cost. This approach allows us to deal with the non-linearities of battery aging in a simple an effective way. The results show that the potential presence of both storage technologies has a positive impact on the operation costs; they also show the impact on the device settings when battery’s cycling aging cost is considered. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to the use case, Flexibility and Demand Side Management in Market Participation.

International audience; Enedis (France's main electricity DSO) and Mines Paris Tech are working on a new method called MOSAIC that aims at assessing the impact of long term local development projects on the electrical grid. The MOSAIC method uses a bottom-up simulation tool able to determine the current and future consumption and production load curve of an area. The consumption and production simulators are based on a multi-level data model that makes it possible to run a simulation even if some data are missing. The simulation parameters are calibrated by comparing the simulated load curves with observations on MV feeders (for the consumption simulator) and on 100 renewable energy producers (PV and WP). Once the simulators are calibrated for the current situation, the load curve of each development scenario of the area is estimated (the evolution scenarios are proposed by the local government, based on infeed growing, building projects …). Enedis and Mines Paris Tech are now working on linking the load curve simulators with more traditional electrical study tools. From the simulated load curve, we estimate the most likely maximal power of the grid infrastructures and use these values in the load flow tool called ERABLE to evaluate the impact of the scenarios on the grid. Initial tests were successful and this method will be further developed in 2017 on ten experimentation projects in France. Each project will help to develop new functionalities such as integrating Demand Response or electrical vehicle infrastructures.

International audience; This paper presents a method for the energy management of a set of smart homes, in which batteries, thermal storage and demand response are considered as flexibilities in order to achieve minimum operation costs. To cope with the uncertainty of load forecast, a two-stage stochastic optimization process is proposed, in which the first stage decision is the committed energy to be purchased, and the second stage returns the devices’ set-points. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to the use case, Flexibility and Demand Side Management in Market Participation.

International audience; The large-scale deployment of distributed generation including intermittent renewable energy sources introduces several challenges to power systems operation and planning. Although power systems often evolve in a fairly incremental way to meet these challenges, the ambitious objectives for RES development in the next decades (2030-2050), together with the deployment of storage options and active demand, indicate that a more essential paradigm change shift may be required. This paper presents the future challenges and the state of the art of research works that study new concepts for the power systems of the future, with a particular focus on the Web-of-Cells concept, multi-microgrids, the fractal grid approach and autonomic power systems.

International audience; This chapter gives an overview over past and present attempts to predict wind power for single turbines, wind, farms or for whole regions, for a few minutes up to a few days ahead. It is based on a survey and report (Giebel et al., 2011) initiated in the frame of the European project ANEMOS, which brought together many groups from Europe involved in the field with long experience in short-term forecasting. It was then continued in the frame of the follow-up European projects SafeWind and ANEMOS.plus, which concentrated respectively on the forecasting of extreme events and the best possible integration of the forecasts in the work flow of end users.

International audience; This chapter shows some test cases from the EU project SAFEWIND on different ways that the planning phase of wind farms can benefit from early knowledge of wind power predictability. The interest is focused on day-ahead short-term forecasting, as the most relevant range for TSOs and wind energy traders. We shall use the term predictability assessment when we quantify wind predictability during the planning phase of a wind farm. This can be associated to the prediction of the hub-height wind speed (the meteorological wind resource), the gross wind power from a specified turbine power-curve model, or the net wind power output, which includes wind farm losses due to wake effects and other environmental aspects that are site and wind farm layout specific. Within the SAFEWIND project, wind power predictability is evaluated to suggest potential benefits during the planning phase of wind farm deployment. Predictability assessment can be useful, for instance, to anticipate operational costs due to market imbalance for energy traders, costs related to prediction of offshore weather windows for turbine installation or maintenance, or to benefit from portfolio effects when aggregating wind farms that operate in uncorrelated wind climates. While the weight of predictability for a wind farm developer is low in the decision-making process compared to the capacity factor, it remains a useful indicator of the quality of wind for grid integration purposes. A series of case studies are presented in various European regions to present tools for wind power predictability assessment at different scales.

National audience

International audience

International audience; The high variability and uncertainty introduced into modern electrical distribution systems due to decentralized renewable energy generators requires new solutions for grid management and power quality assurance. One of these possible solutions includes grid integrated energy storage. The appropriate size and placement of decentralized storage is highly dependent on purpose of the battery system and expected operational strategy. However, battery operational strategies are difficult to simulate simultaneously during a sizing and placement planning calculation. The motivation of this paper is to propose an algorithm that is capable of integrating sizing, placement and operational strategies of batteries into an Optimal Power Flow (OPF) distribution grid planning tool. The choice of the OPF approach permits to account for grid constraints which is more adapted for grid-connected storage devices compared to other approaches in the state of the art that are based only on an email balance analysis. This paper presents an alternating current (AC) multi-temporal OPF algorithm that uses a convex relaxation of the power flow equations to guarantee exact and optimal solutions with high algorithmic performance. The algorithm is unique and innovative due to the fact that it combines the simultaneous optimization of placement and sizing of storage devices taking into account load curves, photovoltaic (PV) production profiles, and distribution grid power quality constraints. The choice to invest in battery capacity is highly sensitive to the price of battery systems. The investment in battery systems solely for reducing losses an operational costs was proven not to be cost effective, however when battery systems are allowed to buy and sell electricity based on variable market prices they become cost effective. The assumptions used for this study shows that current battery system prices are too high to be cost effective even when allowing battery system market participation.

International audience; Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast’s properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices

International audience; Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects.

International audience; Dr. Georges Kariniotakis, Responsable du groupe des smartgrids et des énergies renouvelables du Centre PERSEE aux MINES ParisTech, a traité du rôle de la prédictibilité en lien avec les défis de l’intégration des énergies renouvelables dans les systèmes de production électrique et des marchés de l’électricité.

International audience; Nowadays, solar power (PV) capacity is undergoing a fast growth. The development of network management systems facilitating its penetration in the distribution network may rely on individual forecasts for each PV plant connected to the grid. This paper describes a probabilistic model for short-term forecasting of PV production which has been developed and tested under operational conditions in the frame of the Nice Grid demonstrator project in France. Detailed results on the performance of the forecasting tool are presented both in terms of deterministic and probabilistic forecasts for a portfolio of 35 PV installations. The results show that in general, even at the household producer level, the forecasts yield good performance.

International audience; Potential assessment has served various objectives in the development of renewable energies. However, the prospective nature of this type of assessment sometimes makes it difficult to evaluate and compare estimation results based on different data and modeling. To facilitate this comparison, uncertainty estimates need to be systematically provided. Since potential assessment sometimes relies on numerous parameters, this may first require determining the most important inputs to focus on. In this paper, we propose a sensitivity analysis methodology based on Sobol indices so as to identify the main inputs from a nonlinear assessment model. We illustrate the proposed methodology through analyzing sensitivity in an onshore wind and ground solar photovoltaic (PV) potential assessment covering two French regions. As a result, we show that, when estimating the potential of these renewable energy sources, parameters defining surface availability are more prevalent than those related to technology.

International audience; This poster gives an overview of the IEA Wind Task for Wind Power Forecasting. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task will run for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts, and one or more benchmark studies implemented on the Windbench platform hosted at CENER. Additionally, spreading of relevant information in both the forecasters and the users community is paramount. The poster also shows the work done in the first 9 months of the Task, e.g. the collection of available datasets and the learnings from a public workshop on 9 June in Barcelona on Experiences with the Use of Forecasts and Gaps in Research. Participation is open for all institutions in member states of the IEA Annex on Wind Power, see ieawind.org for the up-to-date list.

Advanced data-driven methods can facilitate the participation of renewable energy sources in competitive electricity markets by leveraging available contextual information, such as weather and market conditions. However, the underpinning assumption is that data will always be available in an operational setting, which is not always the case in industrial applications. In this work, we present a feature-driven method that both directly forecasts the trading decisions of a renewable producer participating in a day-ahead market, and is resilient to missing data in an operational setting. Specifically, we leverage robust optimization to formulate a feature-driven method that minimizes the worst-case trading cost when a subset of features used during model training is missing at test time. The proposed approach is validated in numerical experiments against impute-then-regress benchmarks, with the results showcasing that it leads to improved trading performance when data are missing.

In a context of massive renovation of residential housing, stakeholders need decision-support tools based on knowledge of the current building stock and an accurate simulation of energy demand. For this purpose, we developed a val- idation/calibration method on a territorial/national scale in order to represent the real consumption of housing. This methodological approach provides (1) more reliable identification of energy-saving measures (changes in technology or behaviour) and (2) improved knowledge of the energy simulation tool and its post-calibration performance for optimisation issues. The main contribution of the calibration method described in this paper is the geographical scale concerned: all French residential housing has been modelled, simulated and calibrated with national data (geometries and attributes) on buildings. Furthermore, some occupants' socio-professional characteristics have been taken into account to reflect their actual energy behaviours. This is different from traditional approaches that focus only on a few buildings or archetypes. This paper also describes the application of this methodology on an Open Source simulation software in order to be easily verifiable and usable. This linear model will also be used to optimise renovation solutions at territorial scale in future work. All data used in this paper are Open Data and thus available to the scientific community. This method enabled more than 18 million buildings to be calibrated while reducing the Normalized Root Mean Square Error, between simulated and real energy annual consumption, from 52% to 24% for gas and from 24% to 15% for electricity. In addition, the user of the method is free to prioritise either the maximum error reduction or the number of calibration coefficients if a simpler model is desired. This paper also discusses the results obtained from this method for future improvement.

International audience; The detailed modeling of distribution grids is expected to be critical to understand the current functionality limits and necessary retrofits to satisfy integration of massive amounts of distributed generation, energy storage devices and the electric consumption demand of the future. Due to the highly dimensional non-convex characteristics of the power flow equations, convex relaxations have been used to ensure an efficient calculation time. However, these relaxations have been proven to be inexact during periods of high RES injection. In this paper additional linear constraints were introduced in the power flow formulation to guaranty an exact relaxation. This convex relaxation is then applied within a multi-temporal algorithm in order to evaluate the benefits of storage grid integration. The case study of a French medium voltage feeder is studied to evaluate the maximum capacity of the grid to host RES sources and the advantages of storage systems in reducing curtailment of RES.

Ce livrable décrit l'ensemble des travaux d'aménagement et de développement une plate-forme expérimentale de PERSEE pour « Contrôleurs Régulateurs Embarqués pour des Systèmes Utilisant l'énergie Solaire » au travers de la réalisation d’une maquette expérimentale d’un contrôleur prédictif pour chauffe-eau solaire individuel CESI.

The European Digital Single Market initiative will ensure that Europe's economy, industry and employment take advantage of digital technologies as a key driver for growth. All industrial sectors need to integrate these new technologies and manage the transition to a smart industrial society. This report discusses use cases and opportunities of digitalization of the energy system.

Ouvrage disponible sur HAL : https://hal-mines-paristech.archives-ouvertes.fr/hal-01411582; International audience; Probabilistic forecasting of wind generation, forecasting of extremes and optimal use of forecasts in power system operations and markets. Overview of the ANEMOS, ANEMOS.plus and SafeWind projects

International audience

International audience; The interest for photovoltaic (PV) generation has grown in recent years, while some areas start to witness significant penetration of PV production in the grid. However, the power output of PV plants is characterized by an important variability since it depends on meteorological conditions. Accurate forecasts of the power output of PV plants is recognized today as a necessary tool to facilitate large scale PV penetration. In this paper, we propose a statistical method for very short-term forecasting (0-6 hours) of PV plants power output. The proposed method uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve the forecasts. It uses as input only production data of the geographically distributed power plants, while its computational requirements are small making it appropriate for large-scale application.

International audience

International audience; The Dynamic Line Rating (DLR) of overhead lines allows the increase of its ampacity, accordingly with the weather conditions. The dynamic ampacity can reach a value of 250% of the static rating. This can help solving congestion issues and brings several benefits for the grid operation: it improves the reliability of the grid, it allows economic savings and it facilitates the introduction of renewable energies. However, real-time rating is not sufficient to fully exploit DLR. For taking into account the DLR in the energy planning, day-ahead prediction must be developed. For security reasons, those predictions should have a high degree of reliability. The real ampacity of the line must be inferior to the predicted one during a limited time, in order to minimize risks which can be mortal. In this presentation is presented a methodology for probabilistic DLR forecasting and the evaluation of the models inspired from the existing practice developed for wind power prediction. The problem of DLR prediction can be briefly characterized as follows. Regarding the inputs of the problem, DLR depends on a series of meteorological parameters such as wind speed, wind direction, air temperature and solar radiation. Regarding the output, the ampacity of a conductor cannot be measured, being a limit seldom reached by the line, and must be estimated through a thermal models and/or thermal measurements on the conductor. The evaluation is based on three main characteristics of probabilistic forecasts: Reliability (the ability to inform about the probability of each event), Sharpness (the ability of the model to concentrate the information about the outcome) and Resolution (the ability to generate reliable forecasts depending on different forecast conditions). The three parameters are then used to allow the comparison of several models.

International audience; Wind power forecasting is a recognized means of facilitating large-scale wind power integration into power systems. Recently, there has been focus on developing dedicated short-term forecasting approaches for large and sharp wind power variations, so-called ramps. Accurate forecasts of specific ramp characteristics (e.g., timing and probability of occurrence) are important, as related forecast errors may lead to potentially large power imbalances, with a high impact on the power system. Various works about ramps’ periodicity or predictability have led to the development of new characterization approaches. However, a thorough analysis of these approaches has not yet been carried out. Such an analysis is necessary to ensure the reliability of subsequent conclusions on ramps’ characteristics. In this paper, we propose a comprehensive framework for evaluating and comparing different characterization approaches of wind power ramps. As a first step, we introduce a theoretical model of a ramp inspired from edge-detection literature. The proposed model incorporates some important aspects of the wind power production process so as to reflect its non-stationary and bounded aspects, as well as the random nature of ramp occurrences. Then, we introduce adequate evaluation criteria from signal-processing and statistical literature, in order to assess the ability of an approach for reliably estimating ramp characteristics (i.e., timing and intensity). On the basis of simulations from this model and using the evaluation criteria, we study the performance of different ramp detection filters and multi-scale characterization approaches. Our results show that some practical choices in wind-energy literature are inappropriate, while others, namely, from signal-processing literature, are preferable

International audience

International audience; Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Forecasting for Wind Energy tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, …), operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions.

International audience

International audience; In this paper we propose a method for the simulation of errors in renewable energy sources generation forecasting (photovoltaic and wind) for use in power system planning studies. The proposed methodology relies on 5 elementary simulation steps. The first step is the simulation of photovoltaic plant and wind farm power production, with a sufficient spatial and temporal resolution (few km and hourly time step), the second is the simulation of the localisation of production sites, the third step is the generation of forecast errors using historic data of numerical weather predictions, and the last step is the simulation of intra-hourly variations of photovoltaic production. Finally, it is discussed how these simulation tools can assist the evaluation of the required tertiary reserves in a power system with a large share of renewable energies into the mix.

International audience

International audience

International audience

International audience; Challenging times for European electricity security have recently brought light to the importance of a robust power sector with well-functioning electricity markets. Increased uncertainty disrupts the standard practice of decision-making in energy systems. The increased penetration of Renewable Energy Sources (RES) such as wind and photovoltaic plants adds to this uncertainty due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods. However, decision-makers of the energy sector, especially in high-risk situations, need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, climate change, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of extreme events. Therefore, our research focuses on optimizing understandable symbolic representations of data-driven decision-aid models for human operators in the energy sector.

International audience; The European Horizon 2020 project Smart4RES (http://www.smart4res.eu), which started in 2019 and runs until April 2023, aims at improving modelling and forecasting of weather variables necessary to optimize the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar) into power systems and electricity markets. It gathers experts from several disciplines ranging from meteorology, data science, power systems a.o. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond.This presentation has a double objective:(1) To present a comprehensive overview in terms of KPI improvements of the final results obtained by the project. These results cover thematic objectives including:Improvement of weather and RES forecasting;Streamlined extraction of optimal value from the data through data sharing, data market places, and novel business models for the data;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). The results obtained are numerous. Without being exhaustive, they include: improved forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras); A seamless approach to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions; Advances from ultra-high resolution NWPs based on Large Eddy Simulation; Approaches for RES production forecasting aiming at efficiently combining highly dimensionally input (various types of satellite images, NWPs, spatially distributed measurements etc.); Seamless probabilistic RES forecasting covering multiple time frames and data inputs; Resilient energy forecasting. In the front of applications methods are proposed to optimally use forecasts for the management of storage systems coupled with renewables, for the optimal trading of renewables in multiple markets and for grid management optimization and dynamic security assessment. Prescriptive analytics and explainable AI methods are proposed to optimize decision making. A cost benefit analysis is performed to assess the contribution of different types of data in forecasting problems.(2) To present hierarchized proposals for future research directions. An international workshop is organized by the project (14/04/2023), where experts are invited to assess where RES predictability stands today and propose research directions for the future. In this presentation we will present the conclusions of this workshop. This will be a useful insight for academics, industrials as well as policy makers in the field.Smart4RES Team:Simon Camal, Georges Kariniotakis, Dennis van der Meer, Konstantinos Parginos, Luka Santosuosso, Akylas Stratigakos [MINES Paris, PSL University, Centre PERSEE, France]; Gregor Giebel, Tuhfe Göçmen, Liyang Han [DTU, Denmark]; Pierre Pinson [Imperial College, UK]; Ricardo Bessa; Carla Goncalves, Rui Sousa [INESC TEC, Portugal]; Ivana Aleksovska, Bastien Alonzo, Marie Cassas, Quentin Libois, Marie-Adèle Magnaldo, Laure Raynaud [Meteo France, France]; Gerwin van Dalum, Gerrit Deen, Daan Houf, Remco Verzijlbergh [Whiffle, The Netherlands]; Matthias Lange, Björn Witha [Energy and Meteo Systems, Germany]; Niklas Blum, Jorge Lezaca, Bijan Nouri, Stefan Wilbert [DLR, Germany]; Maria Ines Marques, Catarina Mendes Martins, Manuel Silva [EDP, Portugal]; Wouter De Boer, Marcel Eijgelaar, Ganesh Sauba [DNV, The Netherlands]; John Karakitsios, Theodoros Konstantinou, Dimitrios Lagos, George Sideratos [NTUA/ICCS, Greece]; Theodora Anastopoulou, Efrosini Korka, Christos Vitellas [DEDDIE, Greece]; Stephanie Petit, Clementine Coujard [Dowel Innovation, France]

Standard practice of decision-making in energy systems relies largely on complex modeling chains to address technical constraints and integrate numerous sources of uncertainty. The increased penetration of Renewable Energy Sources (RES) such as solar and wind plants adds complexity due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods. However, decision-makers of the energy sector need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, weather and meteorological conditions, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of an extreme event. Therefore, our research focuses on understandable representations of data-driven decision-aid models for human operators in the energy sector. In order to enhance the interpretability of the AI models, a technique borrowed from the computer science domain is explored and further developed. Genetic programming and more precisely Symbolic Regression is used to derive a symbolic representation for the data-driven model that can take the form of a single equation. This equation results according to a specific reward function. The optimal solutions are selected naturally mimicking the biological theory of survival of the fittest. The main outcome is the production of symbolic representations of the AI models that require minimum changes when applied to different case studies. In this presentation a real-world use case is considered, to demonstrate the added value of the proposed tools for decision-making when trading the production of wind and solar power plants to the day-ahead market. An annual period of data is considered to train and test the proposed model. The typical modeling chain involves as many as 12 models for forecasting RES production, weather and meteorological conditions, together with stochastic optimization to derive trading decisions. A single AI-based model here replaces this complex chain. Such simplification is a significant enhancement to the modeling chain interpretability and facilitates trust to the human decision-maker. This work is carried out in part in the frame of the European project Smart4RES (Grant No 864337) supported by the H2020 Framework Program and in part in the frame the Marie-Curie COFUND project Ai4theSciences (Grant No 945304)

International audience; A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (" WIRE ") with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

National audience

International audience

National audience

National audience

International audience; Increasing penetration of distributed generation on the distribution network demands for a more flexible and efficient utilization of distributed energy resources. The development and deployment of Smart Grids technologies and solutions may provide solutions for significant changes in distribution operation within the near future. The Nice Grid project aims at implementing and testing a distributed energy resource management system, supporting hierarchical operation and control for a microgrid with high concentration of photovoltaic generators. The paper focuses on one use case of Load Reduction for TSO, from dispersed resources such as controllable loads and electrical storage units, which can be scheduled and operated through intermediaries, i.e. commercial aggregators. The business process, the project geographical footprint, the various supported standards and the overall architecture of the system are detailed in the following sections, with a strong emphasis on the transaction mechanism that supports the coordination process. The issues and lessons from the first two years are also discussed.