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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 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; 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; 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.

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; Predictions of wind power production for horizons up to 48-72 h ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the conditional expectation of the wind generation for each look-ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from non-parametric methods, and then take the form of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind power. These distributions are defined by a number of quantile forecasts with nominal proportions spanning the unit interval. The relevance and interest of the introduced evaluation framework are discussed.

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.

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International audience; EcoSD network is a French association whose main objective is to encourage collaboration between academic and industrial researchers so they may create and spread advanced and multidisciplinary knowledge in the eco-design fields at national and international levels. Several actions are proposed by the EcoSD network with the support from the French Environment and Energy Management Agency (ADEME), from the French Ministry of Higher Education and Research as well as the Ministry of Industry: Structuring EcoSD research activities in France to take advantage of the expertise from more than 200 members of this research network, Developing knowledge among researchers regarding the field of eco-design, particularly better training of PhD students by organizing relevant training courses over different themes in eco-design, Elaborating new methods, new tools and new databases to achieve complex systems design, compatible with the principle of sustainable development, Initiating the EcoSD label to acknowledge the quality and inclusion of sustainable development in trainings, research programs, research projects and symposiums, Helping interactive collaboration between researchers and industrial partners through the organization of quarterly research seminars in Paris and an annual workshop. The workshop was organized around three main topics: What is at stake with consequential LCA? Key methodological issues through the analysis of the state of the art, Paving the way towards a common terminology, framework and methodology: illustrations through case studies. Around 50 researchers from industry, academia and governmental institutions participated in the workshop and had the opportunity to exchange with experts.

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.

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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.

International audience; This paper presents the description of an algorithm for the management of a portfolio of distributed energy storage systems able to provide flexibility services to the distribution system operator. The algorithm will be referred to as Network Battery Aggregator (NBA). The work described in this paper is realized within the framework of the NiceGrid, a demonstration of the Grid4EU project. The project aims at developing a smart solar neighbourhood in an urban area near the city of Nice, France, and to combine controllable distributed electricity and thermal storage devices with forecasts of solar power production and load in a local energy management system. The local network energy manager (NEM) developed in this project will provide voltage control at the distribution level and congestion control at the transmission level.

International audience; With the growing environmental and energetic concern, the issue of adapting our energy systems is paramount. This paper deals with the issue of new optimal energy mixes in a high renewable energy share context, and capacity investment in such a framework. We use the "screening curve" method to determine competitors' investments and deduce the benefit of a conventional generator. This work is carried out using a robust approach i.e. we determine a threshold of benefit we want to reach with a defined probability no matter the actual demand and renewable penetration. To determine this solution, we consider four demand scenarii and levels of installed capacity both for wind and for photovoltaic energy. This work is undertaken for the French case. Some results are shown for a coal power plant with 2030 scenarii for demand, wind and solar capacity.

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International audience; Chapter 2 = Introduction - Control Functions - The Role of Information and Communication Technology - Microgrid Control Architecture - Centralized and Decentralized Control - Forecasting - Centralized Control - Decentralized Control - State Estimation - Conclusions. Book description : Microgrids are the most innovative area in the electric power industry today. Future microgrids could exist as energy-balanced cells within existing power distribution grids or stand-alone power networks within small communities. A definitive presentation on all aspects of microgrids, this text examines the operation of microgrids - their control concepts and advanced architectures including multi-microgrids. It takes a logical approach to overview the purpose and the technical aspects of microgrids, discussing the social, economic and environmental benefits to power system operation. The book also presents microgrid design and control issues, including protection and explaining how to implement centralized and decentralized control strategies.

The increasing wind power penetration in power systems represents a techno-economic challenge for power producers and system operators. Because of the variability and uncertainty of wind power, system operators require new solutions to increase the controllability of wind farm output. On the other hand, producers that include wind farms in their portfolio need to find new ways to boost their profits in electricity markets. This can be done by optimizing the combination of wind farms and storage so as to make larger profits when selling power (trading) and reduce penalties from imbalances in the operation. The present work describes a new integrated approach for analysing wind-storage solutions that make use of probabilistic forecasts and optimization techniques to aid decision making on operating such systems. The approach includes a set of three complementary functions suitable for use in current systems. A real-life system is studied, comprising two wind farms and a large hydro station with pumping capacity. Economic profits and better operational features can be obtained from the proposed cooperation between the wind farms and storage. The revenues are function of the type of hydro storage used and the market characteristics, and several options are compared in this study. The results show that the use of a storage device can lead to a significant increase in revenue, up to 11% (2010 data, Iberian market). Also, the coordinated action improves the operational features of the integrated system. This work was performed in the framework of project ANEMOS.plus (contract no 038692) funded in part by the European Commission under the 6th RTD Framework Program. The author Ricardo Bessa acknowledges Fundação para a Ciência e a Tecnologia (FCT) for PhD Scholarship SFRH/BD/33738/2009. The author Jesus Lugaro acknowledges Fundación Caja Madrid for Postgraduate Scholarship ‘Engineering-2010’. The authors Edgardo Castronuovo and Julio Usaola acknowledge the Ministry of Science and Technology of Spain (Projects IT2009-0063 and ENE2010-16074).

International audience; This paper describes preliminary findings of research on the use of Distributed Energy Storage devices for Renewable Energy integration. The primary objective is to describe the effect of different storage scheduling strategies, and namely the benefits from intraday intraday scheduling on the storage performance in renewable energy integration. Optimal schedules of Distributed Energy Storage devices are based on forecasts of Renewable Energy production, local consumption and prices, along with other criteria. These forecasts tend to have a higher uncertainty for higher time horizons, resulting in losses due to errors and to the underutilization of the assets. The use of frequent schedules updates can reduce part of these drawbacks and this paper aims at quantifying this reduction. The importance of the quantification of the benefits arising from different rescheduling frequencies lies in its influence on the ICT infrastructure necessary to implement it and its cost.

International audience; The continuous growth of solar power capacity raises challenges to distribution system operators regarding power quality and security of supply. Network management systems must be enhanced with short-term forecasting functionalities able to predict the solar plants production in the next hours or days. The provision of individual forecasts for each solar plant on the network is often required. To that purpose, historical measurements are needed for tuning the forecasting models. The situation is challenging for new plants for which long history of measurements is not yet available. In that case, models able to provide accurate production forecasts based on few historical production data, are required. In this paper, we investigate the performance of state-of-the-art short-term PV forecasting models as a function of the historical data available for tuning. We compare the results with those obtained by a reference model whose utilization does not require more than one day of past production data. Our analysis relies on production data from a 200 kWc solar plant located in the south-east of France. It shows that satisfactory performances can be expected from state-of-the-art models, when calibrated with no more than one or two weeks of training data.

International audience; This paper is motivated by the question of the impact that uncertainty in PV forecasts has in forecast-based battery schedule optimisation in microgrids in presence of network constraints. We examine a specific case where forecast accuracy can be impacted by the lack of enough data history to finetune the forecasting models. This situation can be expected to be frequent with new PV installations. A probabilistic PV production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size of the learning dataset of the forecast algorithm is modified in order to simulate the application of the system to new plants and the impact on the performance in the management of the battery is analysed

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