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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; 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; 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.

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; 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; 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.

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.

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.

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

International audience; The ability to predict wind power production over the next few hours to days is prerequisites for the secure and economic operation of power systems with high wind power penetration. From the point of view of a producer participating in the day-ahead electricity market, lack of predictability at a wind power production site results in imbalance costs. This paper aims at quantifying the impact on market revenue of, respectively, the predictability and the capacity factor of a wind farm or a cluster of wind farms. This is done through a real-life case study in West Denmark, including wind farm production data and market data. Finally, we make a prospective analysis under the assumption that the imbalance price settlement mechanism will remain the same

International audience; This paper presents the new International Energy Agency Wind Task 36 on Forecasting, and invites to collaborate within the group. 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 meteorological centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, MetOffice, met.no, DMI,...), 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. As first results, an overview of current issues for research in short-term forecasting of wind power is presented.

Phase change materials (PCM) are promising technology to store thermal energy at a constant temperature. A large amount of energy can be stored or released in latent heat form during the transition of material from one phase to another. Despite the great benefits, most PCMs have their own limitations i.e., low phase change enthalpy, poor specific heat and thermal conductivity, supercooling, volume change, phase segregation, etc. Consequently, efficient thermal energy storage requires improving the thermophysical properties of PCMs. The present study is a comprehensive review of existing techniques for PCMs thermophysical properties enhancement. The research progresses on adding zero, one, two, and three-dimensionally structured additives to PCM is assessed to improve the thermal transport by enhancing the PCM effective thermal conductivity. The enhancement of latent heat of fusion and specific heat using various additives is also discussed. Further, the latest techniques on supercooling and phase segregation reduction are also presented. Last, the modelling of the novel composite materials formed by combining a PCM with other materials is presented. Despite the fact that the majority of these methods are still in the research and development stage, some of them have the potential to be commercialized in the near future. Reliable and efficient PCMs are exceptionally useful for storing solar energy and industrial waste heat, especially for constant temperature applications.

Depletion of fossil reserves and environmental concerns arising from their use has led to the development of renewable energy technologies. Concentrated solar power (CSP) is one of the promising alternative energy solutions that can be built in higher capacity (hundreds of megawatts) and provide higher thermal efficiencies compared to other technologies. However, almost all CSP systems require thermal energy storage (TES) to improve the capacity factor and bridge the gap between supply and demand. In the current work, a reduced-order one-dimensional mathematical model of a phase change material (PCM) based TES is proposed and experimentally validated on different sets of data including different PCM, operating ranges and storage size per geometrical unit of the CSP. The model considers single EPCM as a lumped thermal mass without much compromise on the accuracy having less than 9% overall error in terms of predicting energy storage capacity when compared to experimental data. Additionally, an experimental setup for encapsulated-PCM (EPCM) is developed using NaNO3-KNO3 as the PCM. This system has a storage capacity of 9.7 MJ and a storage time of 6 hours is required to completely charge the system at selected conditions. An analysis of the system performance is presented under different experimental conditions. The model development, experimental analysis and validation shall provide reliable design estimates while designing EPCM-TES for large-scale applications. Therefore, the current simplified mathematical model best fits such applications where a design engineer needs to make quick decisions regarding the selection of an EPCM-TES system or where a reduced-order model of entire CSP plant is required.

In this paper, we propose and experimentally validate a scheduling and control framework for distributed energy resources (DERs) that achieves to track a day-ahead dispatch plan of a distribution network hosting controllable and stochastic heterogeneous resources while respecting the local grid constraints on nodal voltages and lines ampacities. The framework consists of two algorithmic layers. In the first one (day-ahead scheduling), we determine an aggregated dispatch plan. In the second layer (real-time control), a distributed model predictive control (MPC) determines the active and reactive power set-points of the DERs so that their aggregated contribution tracks the dispatch plan while obeying to DERs operational constraints as well as the grids ones. The proposed framework is experimentally validated on a real-scale microgrid that reproduces the network specifications of the CIGRE microgrid benchmark system., This work has been accepted for publication in IEEE Transactions on Energy Conversion

International audience; Real-time current-carrying capacity of overhead conductors is extremely variable due to its dependence on weather conditions, resulting in the use of traditionally conservative static ratings. This paper proposes a methodology for exploiting the latent current-carrying capacity of overhead transmission lines taking into account line ampacity forecasts, power flow simulations and the network operator's risk aversion. The procedure can be described as follows: Firstly, probabilistic forecasts for the current rating of transmission lines are generated, paying particular attention to the reliability of the lower part of the distribution. Secondly, a cost benefit analysis is carried out by solving a bilevel stochastic problem that takes into account the reduction in generation costs resulting from a higher power transfer capacity and the increased use of reserves caused by forecast errors. The risk appetite of the network operator is considered in order to accept or penalize high-risk situations, depending on whether the network operator can be described as risk neutral or risk averse.

The building sector is responsible for a significant part of global energy consumption and greenhouse gas emission. Nowadays, there is a growing interest in the so-called super-insulating materials, particularly Aerogels. The present work examines the performance of novel aerogel blankets obtained thanks to an innovative ambient drying process. Based on these aerogel blankets, two thermal insulation systems are developed: one as external thermal insulation composite system (ETICS) and another one as internal thermal insulation multi-layer system (ITI). The objectives of the current work are to assess the performance of the core material (aerogel blanket) as well as the two developed systems. At a material (core layer) scale, thermal, hydric, and mechanical performance characterizations are carried out. At a system level, in-situ hygrothermal, acoustic, and fire performance characterizations are carried out in real scale test cells. Hygrothermal characterization revealed that the thermal conductivity of the studied blanket is about 0.0165 W/(m.K) in ambient and dry conditions but can increase by up to 40% in a very humid environment (RH > 90%). The integration of the aerogel blanket panels into an interior insulating system, tested in a test-cell located in south of France, show great thermal performance. The U value of the wall decreased from 0.63 W/(m2.K) to 0.33 W/(m2.K)after retrofitting. The in-situ hygrothermal results showed that the external insulation system protects the wall against the moisture risks. For the internally insulated wall, the relative humidity remains lower than 85% even when high moisture generation is present inside the test cell. Concerning the thermal performance, the envelope's thermal transmittance is reduced by more than 80% after applying the insulation systems. The ITI system was tested in-situ to airborne sound insulation (facade), reporting significant acoustic improvement in acoustic insulation: +7 dB, equal to a noise reduction to one quarter compared to the base wall without insulation. The fire test of the ETICS proceeded without any material ignition, while the fire behavior of the internal insulation system depends significantly on the adhesion stability between all single components.

International audience; The paper contributes to improving the computational performance of controls of distributed energy resources (DERs) in distribution grids for efficient real-time control and short-term scheduling. The considered setting is a distribution grid with heterogeneous DERs controlled with model predictive control (MPC) to track a dispatch plan at its grid connection point (GCP) subject to DERs’ and grid’s constraints. The MPC control is first expressed as a quadratic programming (using linearized grid models) and, then, solved with several state-of-the-art alternating direction optimization methods: AMA, ADMM, AADMM (ADMM with adaptive penalty parameter) and their accelerated variants: FAMA, FAADMM, and FAADMM with restart rule. Performance is tested in terms of computational performance, constraints satisfaction, and optimality against the centralized MPC. The case studies are the CIGRE and IEEE benchmark grid for low- and medium voltage systems hosting different numbers of controllable DERs.

International audience; The participation of renewable generators in electricity markets involves employing a number of forecasting and decision-making tools. The standard approach consists in forecasting power output and market quantities, and then inputting the results into an optimization problem to derive optimal decisions. Typically, forecasting models are trained to optimize accuracy without considering the subsequent decision-making process. In this paper, we consider training forecasting models with a value-oriented approach that aims to minimize the suboptimality of decisions induced by a set of predicted inputs. We consider a risk-aware renewable generator participating in a day-ahead market subject to imbalance costs, and train ensembles of decision trees to forecast the imbalance penalty by directly minimizing trading costs for the provided strategy. The results indicate that our innovative approach leads to improved trading performance, compared to the standard method in which forecasting models are trained to minimize prediction errors.

International audience

International audience; Thermal energy storage devices are vital for reducing the inconsistency between energy supply and demand as well as for enhancing the performance of solar thermal systems. The present study investigates the melting process in metallic honeycombed heat exchangers filled with n-octadacane as phase change material (PCM). Further, a parametric study is conducted for four different honeycomb cell sizes, fin thicknesses, and angles of inclinations of the computational domain. The findings are described in the form of heated surface average temperature variation, melt fraction of the PCM, and thermal energy stored. Results show that the honeycomb fin structure considerably improves the heat transfer in the PCM. Further, it was observed that the honeycomb cell size and fin thickness greatly influence the variation of the average front surface temperature, melt fraction, and energy storage rate, whereas the inclination angle doesn’t have a significant effect when cell size is less than 0.01 m. From the study, it was also observed that when the computational domain is horizontal without honeycomb cells, the pure conduction in the PCM results in much higher front surface temperature, which is greatly reduced in presence of the honeycomb cells.

International audience; In the present study, a series of thermochemical equilibrium modeling was conducted to assess the thermodynamic potential of biomass conversion to ammonia using thermal and nonthermal plasma at small- and large-scale production. The system was designed and evaluated for five different locations in Australia including the Northern Territory, South Australia, Western Australia, and New South Wales using local biomass feedstock. The equilibrium modeling showed that the pathway of biomass to biomethane using an anaerobic digestion reactor, biomethane to hydrogen using a thermal plasma reactor, followed by conversion of hydrogen to ammonia via a nonthermal plasma reactor is a plausible route, by which the exergy efficiency of the process can be as high as ~60%. It is identified that the thermal plasma reactor required two distinct zones at 3000°C

International audience; Power systems with large shares of converter-interfaced renewables may be characterized by low grid inertia due to the lack of frequency containment provided by synchronous generators. Battery energy storage systems (BESSs), which can adjust their power output at much steeper ramping than conventional generation, are promising assets to restore suitable frequency regulation capacity levels. BESSs are typically connected to the grid with a power converter, which can be operated in either grid-forming or grid-following modes. This paper quantitatively assesses the impact of large-scale BESSs on the frequency containment of low inertia power grid and compares the performance of grid-forming and grid-following control modes. Numerical results are provided considering a detailed dynamic model of the IEEE 39-bus system where fully characterized models of stochastic demand and generation are taken into account. In order to assess the performance of the BESS control modes in a practical operative context, daily long simulations are considered where reserve levels for frequency containment and restoration are allocated considering the current practice of a transmission system operator in Europe. Numerical analyses on various metrics applied to grid frequency show that grid-forming outperforms grid-following converter control mode.

International audience; Variable renewable energy has a growing impact on electricity markets and power systems in many regions of the world. In this context, the 17th International Conference on the European Energy Market EEM20 set up a competition to develop probabilistic forecasting tools of wind production at a regional level. This paper proposes an adaptive approach for regional wind power forecasting. A physics-oriented pre-processing of the data delivers analog weather patterns and wind-power-related variables, then a k-means clustering of wind farms further reduces the dimension of the problem. The generated representative features feed a Quantile Regression Forests model that produces sharp and reliable predictions. As a result, our model won the competition with a relative improvement of the average pinball loss of 6.7% and 14.7%, compared to the teams ranked second and third respectively.

International audience; Due to environmental concerns, renewable energy sources (RES) play an increasingly important role in the energy mix. In France, from 2018 to 2019, an increase of 21.2% and 7.8% of energy production was observed for wind and solar respectively [1]. RES are characterized by high variability and limited predictability, mostly due to their dependence on meteorological factors. This variability presents challenges for RES integration into grids and electricity markets: as the penetration of RES increases, power system balancing becomes more complex, and congestions may occur in the grid. This lack of predictability can also have financial consequences. In some European countries, energy producers have to pay penalties proportional to the forecasting error of the injected power. To address these challenges, it is important to accurately predict the future amount of energy production. In this paper we propose a spot statistical forecasting model for very short-term time horizons (from a few minutes up to 6 hours ahead). This model is based on a combination of heterogeneous inputs with a conditioned learning approach. Spatio-temporal inputs (measurements from geographically distributed PV sites and satellite images) are used to enhance short-term predictability, while a weather analog approach enables adaptability to changes in meteorological conditions by considering the most relevant past observations. The performance evaluations are carried out on a case study featuring nine PV plants located in France, over a one-year period.

In this paper, we propose a re-dispatch scheme for radial distribution grids hosting stochastic Distributed Energy Resources (DERs) and controllable batteries. At each re-dispatch round, the proposed scheme computes a new dispatch plan that modifies and extends the existing one. To do so, it uses the CoDistFlow algorithm and applies a receding horizon control principle, while accounting for hard time computation constraints that impact on the instantaneous update of a dispatch plan. CoDistFlow handles stochastic DERs and prosumers uncertainties via scenario-based optimization and the non-convexity of the AC Optimal Power Flow by iteratively solving suitably defined convex problems until convergence. We perform numerical evaluations based on real-data, obtained from a real Swiss grid. We show that, with our proposed re-dispatch scheme, the daily dispatch tracking error can decrease more than 80%, even for small battery capacities, and if re-dispatch is frequent enough, it can be eliminated. Finally, we show that re-dispatch should be performed as often as the market allows and the performance continues to improve.

The aim of this paper is to present the objectives, research directions and first highlight results of the Smart4RES project, which was launched in November 2019, under the Horizon 2020 Framework Programme. Smart4RES is a research project that aims to bring substantial performance improvements to the whole model and value chain in renewable energy (RES) forecasting, with particular emphasis placed on optimizing synergies with storage and to support power system operation and participation in electricity markets. For that, it concentrates on a number of disruptive proposals to support ambitious objectives for the future of renewable energy forecasting. This is thought of in a context with steady increase in the quantity of data being collected and computational capabilities. And, this comes in combination with recent advances in data science and approaches to meteorological forecasting. Smart4RES concentrates on novel developments towards very high-resolution and dedicated weather forecasting solutions. It makes optimal use of varied and distributed sources of data e.g. remote sensing (sky imagers, satellites, etc), power and meteorological measurements, as well as high-resolution weather forecasts, to yield high-quality and seamless approaches to renewable energy forecasting. The project accommodates the fact that all these sources of data are distributed geographically and in terms of ownership, with current restrictions preventing sharing. Novel alternative approaches are to be developed and evaluated to reach optimal forecast accuracy in that context, including distributed and privacy-preserving learning and forecasting methods, as well as the advent of platform-enabled data-markets, with associated pricing strategies. Smart4RES places a strong emphasis on maximizing the value from the use of forecasts in applications through advanced decision making and optimization approaches. This also goes through approaches to streamline the definition of new forecasting products balancing the complexity of forecast information and the need of forecast users. Focus is on developing models for applications involving storage, the provision of ancillary services, as well as market participation.

AbstractOver the past years, environmental concerns have played a key role in the development of renewable energy sources (RES). In Europe, the installed capacity of photovoltaic (PV) has increased from around 10 GW in 2008 to nearly 119 GW in 2018 [1]. Due to this high penetration rate and the intermittent nature of RES, several challenges appear related to the economic and secure operation of a power system. To overcome these challenges, it is necessary to develop reliable forecasts of RES, and namely of PV production, for the next hours to days to adjust production planning, while intra-hourly forecasts may contribute to optimize operation of storage units coupled to RES plants.The aim of this paper is to present a novel spatio-temporal (ST) spot forecasting approach able to use multiple heterogeneous sources of data as inputs to forecast short-term PV production (i.e. from 15 minutes up to a day ahead).First, we consider measured production data from nearby power plants as input to forecast the output of a specific PV plant. These data permit to exploit the correlation between the production data of spatially distributed PV sites. The classical ST approach in the literature, based only on this source of data [2], permits to improve predictability for the next few minutes up to 6 hours ahead.Then, we extend the model by the use of satellite images (i.e. global horizontal irradiance (GHI)) which provide meaningful spatial information at a larger extent.Finally, we consider Numerical Weather Predictions (NWPs) as input, which permit to extend the applicability of the model to day-ahead lead times, so that, overall, the resulting model covers efficiently horizons ranging from a few minutes to day ahead.The spatio-temporal relationships being dependent on the particular meteorological situation of the day at hand, we apply an analog ensemble approach, to condition the learning process with historical observations corresponding to similar meteorological situation. We used the analogue approach to select a subset of similar historical situations over which a dynamical calibration of the forecasted model is done, as it was for example suggested by [3,4]. In our paper we extend the analogs ensemble approach by considering geographically distributed observations of the physical variables of interest (as suggested by [4] for hydrological issues) rather than only those at the level of the PV plant.The performance of the proposed ST model with heterogeneous inputs is compared with reference models and advanced ones such as the Random Forest model. Historical production data collected from 9 PV plants of CNR are considered. The power units, located in the South-East France, exhibit relevant spatial correlations which make them suitable for the proposed ST model. References[1] IRENA - https://www.irena.org/Statistics/Download-Data [2] Agoua, Girard, Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy , IEEE, 2018, 9 (2), pp. 538 - 546. https://doi.org/10.1109/TSTE.2017.2747765 [3] Alessandrini, Delle Monache, Sperati, Cervone. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015. https://doi.org/10.1016/j.apenergy.2015.08.011 [4] Bellier, Bontron, Zin. Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resources Research, 2017. https://doi.org/10.1002/2017wr021245

The aggregation of multiple renewable plants located in distinct climate zones, using different energy sources, enables to reduce the production uncertainty when compared to the production of a single plant. Such aggregations, controlled by a Virtual Power Plant (VPP) system, are good candidates for the provision of ancillary services. Stochastic optimization models are available to optimize biddings on ancillary services and energy markets (see for instance [1]). These models require trajectories of the renewable VPP production that anticipate production uncertainty and reproduce correctly the temporal correlations observed in the production signal. This is particularly important in ancillary services markets, where a reserve bid must be guaranteed over a production duration or validity period during which power fluctuations are significant (e.g. lasting currently 24 hours on the European common market for Frequency Containment Reserve, with a foreseen evolution to 4 hours by July 2020 [2]). Production trajectories may be obtained by coupling probabilistic forecasts and a model of temporal dependencies between forecast horizons [3] and possibly spatial dependencies in the case of a multivariate forecast at the scale of a region or a portfolio [4]. In the case of a renewable VPP, the aggregated production is primarily of interest. In this work, we propose a methodology to generate trajectories of aggregated production from probabilistic forecasts obtained with decision-tree based models or neural networks. A copula models the dependency between forecast horizons and the space defined by the plants contained in the aggregation. The model is tested in a day-ahead forecasting configuration on a 54 MW VPP comprising 15 plants with 3 different energy sources (Photovoltaics, Wind, Hydro). The comparison of trajectories generated from a direct forecast of the aggregated production and from forecasts at lower levels of the aggregation shows that the latter solution reproduces with more accuracy the temporal variability of the aggregated production over the whole horizon range, especially when Photovoltaics dominates the production capacities in the aggregation (15 % improvement of the Variogram Score). [1]: Soares, T., & Pinson, P. (2017). Renewable energy sources offering flexibility through electricity markets. Technical University of Denmark.[2]: ENTSO-E. (2018). TSO’s proposal for the establishment of common and harmonised rules and processes for the exchange and procurement of Balancing Capacity for Frequency Containment Reserves (FCR) TSOs’ proposal for the establishment of common and harmonised rules and pro-c, (October), 1–9.[3]: Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B. (2009). From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy, 12(1), 51–62. [4]: Golestaneh, F., Gooi, H. B., & Pinson, P. (2016). Generation and evaluation of space–time trajectories of photovoltaic power. Applied Energy, 176, 80–91.

International audience; In the prospect of a large scale deployment of Renewable Energy for electricity production, plasmas will definitively be a major option to get tuneable, high temperature enthalpy sources without direct CO2 emissions. This paper focuses on the direct decomposition of methane for the simultaneous synthesis of hydrogen and high value-added carbon black. After a review of gas phase carbon particle nucleation and growth physico-chemical phenomena, a new original model for the plasma decomposition of methane is presented. The model solves a reactive turbulent flow in a 3D geometry. The nucleation is based on a detailed reaction mechanism and the particle growth is handled by a sectional method. This model opens the way towards a better understanding of carbon particles gas phase nucleation and growth and consequently to a fine control of high value-added carbon black grades.

International audience; In the last decades, renewable energy sources have been increasing their shares in the world energy market. In addition to the ecological benefits, this trend can have adjunct benefits, for example for distribution system operators: a gain in their grid sizing. Indeed, installation of decentralized production, when used in a self-consumption approach, can lead to reduction of the consumption peaks. This work is willing to quantify what grid sizing reduction a distribution system operator can expect, knowing the renewable energies penetration rate on a MV feeder. To do so, a description of the actual sizing strategy is first described. Estimation of electricity demand is performed using a bottom-up simulation method while photovoltaics and wind power production are evaluated with reanalysis data coupled with a new method to inject variability to the smooth curves. This procedure leads to a new sizing power which can be used, guaranteeing an equivalent quality of supply for consumers. For the tested MV feeders, a maximum reduction of about 4 % of the sizing power is observed. Lastly, an analysis of the under-sizing risk is carried out, characterizing the error in the new sizing power estimation with the number of scenarios taken into account.

International audience; New hybrid organic-inorganic benzoxazine aerogel blankets for space applications have been synthesized and studied. Aerogel blankets were produced using a one-pot synthesis method with a PET unwoven fibrous network core, resorcinol, formaldehyde and a silica source (APTES with MTES or MTMS as silica co-precursors). Modifying the composition of the sol significantly impacts the physical characteristics of the resulting material such as texture (density and porosity), hydrophilicity, and thermal conductivity. The apparent density of the materials decreases when the percentage of solid in the sol (%solid) decreases and the molar ratio nMTES/nAPTES increases. Within the density range studied, apparent density and effective thermal conductivities are inversely proportional. By replacing a part of APTES by MTES or MTMS the density of the aerogels decreases notably whilst maintaining the level of thermal conductivity. This replacement is detrimental to water adsorption which increases for most conditions studied. When MTMS is used as silica co-precursor instead of MTES no significant differences in water adsorption and thermal conductivity were found. Using the RF-APTES-MTES chemical system we were able to obtain a 3%solid flexible hybrid blanket with both low apparent density (0.04 g cm−3) and relatively low effective thermal conductivity (0.027 W m−1 K−1 in room conditions). A proof of concept with large panels of this material was done by evaluating them as prototype materials composed of encapsulated aerogel blanket in vacuum and low pressure atmosphere. The blankets show a combination of key characteristics representing the right mix for thermal insulation in space applications operating in non-vacuum environments.

International audience; In the life cycle assessment (LCA) context, global sensitivity analysis (GSA) has been identified by several authors as a relevant practice to enhance the understanding of the model's structure and ensure reliability and credibility of the LCA results. GSA allows establishing a ranking among the input parameters, according to their influence on the variability of the output. Such feature is of high interest in particular when aiming at defining parameterized LCA models. When performing a GSA, the description of the variability of each input parameter may affect the results. This aspect is critical when studying new products or emerging technologies, where data regarding the model inputs are very uncertain and may cause misleading GSA outcomes, such as inappropriate input rankings. A systematic assessment of this sensitivity issue is now proposed. We develop a methodology to analyze the sensitivity of the GSA results (i.e. the stability of the ranking of the inputs) with respect to the description of such inputs of the model (i.e. the definition of their inherent variability). With this research, we aim at enriching the debate on the application of GSA to LCAs affected by high uncertainties. We illustrate its application with a case study, aiming at the elaboration of a simple model expressing the life cycle greenhouse gas emissions of enhanced geothermal systems (EGS) as a function of few key parameters. Our methodology allows identifying the key inputs of the LCA model, taking into account the uncertainty related to their description.

Distributed energy resources (DERs) installed in active distribution networks (ADNs) can be exploited to provide both active and reactive power reserves to the upper-layer grid (i.e., sub-transmission and transmission systems) at their connection point. This paper introduces a method to determine the capability area of an ADN for the provision of both active and reactive power reserves while considering the forecast errors of loads and stochastic generation, as well as the operational constraints of the grid and DERs. The method leverages a linearized load flow model and introduces a set of linear scenario-based robust optimization problems to estimate the reserve provision capability (RPC) area of the ADN. It is proved that, under certain assumptions, the RPC area is convex. The performance of the proposed method is tested on a modified version of the IEEE 33-bus distribution test system.

International audience; Demand-side management (DSM) represents a potential way to improve the profitability of renewable energy systems. In thispaper, power management including a new DSM strategy in a stand-alone hybrid Photovoltaic (PV)/ Diesel/ Battery system with multiplecustomers has been studied. A new probabilistic model of the consumer behavior based on Bayesian network and Monte Carlo simulation hasbeen carried out so as to capture the real-time and stochastic aspect of the demand. The analysis has been made by means of a one-year periodsimulation of the whole system. Statistical data on consumers and meteorological observation data have been used to set the simulation’sparameters. Numerical results showed that with the implementation of DSM, energy costs are reduced by 11.3% for equal total consumption, andthe use of solar energy resources rose to 54%. This provides insight on the significant performance enhancement offered by a DSM scheme insuch a system.

Isbn : 978-1-5386-8218-0/19/; International audience; Electrical demand in the world especially residential demand is increasing. Instead of building new conventional power plants, distributed generation based on renewable resources are an alternative solution to cope the demand. Simultaneously, government of some countries release their regulation to encourage households be active player in the market. However, in several years now, Feed in Tariff (FiT) is decreased by the regulation. Demand side management strategies especially load shifting are used to reduce the electric cost and trying to handle household satisfaction. To reach the similar goal in the community, coordination of energy management is necessary where customers maximize their self-consumption through PV sharing. Using energy contract and Particle Swarm Optimization it is expected to help the costumers to find their optimal cost, and to reach a maximum self-consumption rate.

Open access article available at : https://iopscience.iop.org/article/10.1088/1742-6596/1343/1/012029/meta; International audience; Reduction of the energy consumption is a key lever to tackle climate change, but identification of the retrofit actions to undertake within a building stock remains a challenging scientific problem. This paper presents a complete methodology able to design action plans at a territory level with a building resolution. The economic and energetic modeling of the retrofit context is detailed before introducing a linear 0-1 optimization formulation which is used to arbitrate between both building envelope insulation and heating system replacement measures. A 500 buildings territory study case is then presented to illustrate the potential of the developed tool.

International audience; Anticipating electricity prices on the day-ahead market has become a key issue for both risk assessment and revenue optimization. In this paper, we propose to generate time series of prices with an hourly resolution using a structural model that simulates a simplified market clearing process. The aggregated supply curves in this model are composed of orders based on the available capacity of generation units. The ask prices are parametrized, and the calibration is performed by applying statistical learning to historical market and power system data. To reflect the strategic behavior of market participants, these prices depend on the scarcity of power at the national level. The model's performance is assessed based on the case of France with a one-year horizon and data from 2013-2015. This approach illustrates how open data on the electric power system enable links to be drawn between technical constraints and price formation.

International audience; The flexibility of distributed energy resources (DERs) accommodated in active distribution networks (ADNs) can be aggregated and then used to provide ancillary services to the transmission system. In this context, this paper presents a linear optimization method for the transmission system operator (TSO) to allocate its required active power reserve from aggregated resources installed in active distribution systems (ARADSs) as well as dispatchable bulk power plants (DBPPs). It consists in a linear optimization problem that minimizes the sum of the expected cost of active power reserve allocated from all possible providers (including ARADSs and DBPPs) and the expected cost of load not served over a desired time horizon. The value of lost load (VOLL) index is used as a criterion to realize an economical balance between the expected cost of allocated reserve and expected cost of load not served. The method leverages scenarios of power system contingencies and forecast errors of loads and renewable generation to represent typical operational uncertainties. A simulation proof-of-concept using real-data from the transmission system operator of Switzerland, Swissgrid, is provided to illustrate the performance of the method. Index Terms-Active distribution network (ADN), active power reserve, aggregated resources of active distribution system (ARADS), energy storage system (ESS), distributed energy resource (DER).

International audience; The paper presents the results of a study on replicability and scalability carried out within the SENSIBLE project. In this study a simplified methodology for simulating the behaviour of a market based distributed energy storages coordination has been implemented. Attention has been paid to capture the core of the problems without falling into excessive details of the different solutions (centralised, decentralised) developed. After this, data have been gathered or simulated for a large number of necessary datasets, including electricity consumption in different neighbours, electricity wholesale and retail prices, in six European countries. Central to this stage has been the use of BUS (Bottom Up Simulator) a load profiles simulator developed at MINES ParisTech which has been used to simulate load curves in the different European countries considering historical local weather conditions. Index Terms-Energy storage, Batteries, Power system management I. INTRODUCTION Scalability and replicability are relevant concepts for the evaluation of the benefits of innovative solutions. The attention on these concepts is especially high in R&D projects, where solutions are developed and tested in limited demonstrators. In order to understand the potential of the solutions tested, an evaluation of their performance in other conditions is therefore necessary, but extremely difficult due to the complex nature of the problem. According to [1] both problems can be seen as the result of the interaction of four quite diverse factors: 1) technical, 2) economic, 3) regulatory and 4) acceptance. For this reason evaluations of replicability and scalability have been conducted though surveys or discussions with experts such as in [1], [2] and [3].