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This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

The energy transition requires rethinking how we produce and consume energy. Energy communities (EC) provide a recent legal framework for sharing energy, aiming to reduce energy bills and the environmental footprint of their participants. One of the challenges is adapting economic models to this technological upheaval. In this context, cooperative games, based on game theory, are valuable tools for modeling energy management through cooperation. However, despite their promising characteristics, cooperative games are limited by their computational complexity. The required computation time to solve cooperative games increases exponentially with the number of participants, restricting their application in energy management. This paper aims to propose a solution to apply cooperative game theory tools to larger communities using a multidisciplinary approach. For this purpose, a game subdivision approach based on the specific properties of energy communities is proposed. This methodology will be shown to be efficient in terms of computation time. While the game theory concepts are depreciated by limiting computing time, the sub-games method can become an interesting tool in energy management. Advantages and drawbacks in terms of energy management and game theory are discussed in this paper.

Bifacial photovoltaic (PV) modules, capable of capturing solar energy from both sides of the cells, are becoming increasingly popular as their manufacturing costs approach those of traditional monofacial modules. Accurate estimation of their power generation capacity is essential for optimizing their use. This study evaluates a power production model for bifacial PV modules using local irradiance data from Razon+ in Sherbrooke, Canada, and Solcast irradiance data derived from satellite imagery and weather models. The model's performance was assessed throughout the year, with particular attention to the impact of snow coverage during winter. To address computational efficiency, the study evaluated ray tracing and a 2D view factor model, selecting the more time-efficient method. Experimental validation showed that, using local irradiance data, the model achieved Normalized Root Mean Square Errors (NRMSE) of 18.77%, 4.94%, 3.93%, and 6.22% for winter, spring, summer, and fall, respectively. With Solcast data, the NRMSEs were 22.76%, 15.32%, 14.72%, and 17.78% for the corresponding seasons. While the model performed satisfactorily in spring, summer, and fall, it was less accurate in winter. To enhance winter accuracy, the model incorporated snow coverage, using snow depth as a metric to detect snow on the front surface. This adjustment improved the accuracy by 51.1%. Key words: Photovoltaic / bifacial PV panels / power model / raytracing technique / view factor approach / snow losses

Climate change is requiring changes in energy use. The integration of renewable energy sources into the energy mix is a viable solution for electricity generation, but their intermittence forces us to imagine new ways to consume and distribute electricity. Thus, new economic models and new organizations have to be established. Energy communities are a recent legal solution to share renewable energy among local actors. These structures facilitate the set-up of collective self-consumption operations. Collective self-consumption is an interesting tool to increase local renewable energy consumption and limit energy losses on the distribution grid. Consequently, energy communities are pursuing a dual objective: reducing energy bills and the environmental footprint of the participants. However, the distribution of gains generated through collective self-consumption is one of the main obstacles to the implementation of energy communities. An interesting possibility comes from game theory, especially from cooperative games, as solutions concepts already exist to solve this kind of problem. In this paper, a payoff distribution is proposed through a collaborative game. The aim of the study is to explore ways to distribute gains generated by collective self consumptions on individual self consumptions. These gains are 8% on the total energy bill and 25% on the self-consumption rate, assuming a collective objective based on the self-consumption rate and individual objectives based on the price of energy. In order to distribute the financial gains, two major concepts in cooperative game theory are studied in this paper: the Shapley Value and the Nucleolus. The advantages and drawbacks of these concepts in an energy community context are identified, as these concepts are based on two different philosophies: The properties of individual fairness of the Shapley Value and collective fairness of the Nucleolus for energy management are therefore discussed. Furthermore, a methodology is proposed to enable a fairer distribution of payoffs according to specific energy management parameters, such as efficiency and flexibility.

The energy sector is undergoing a profound transformation to tackle climate change issues rapidly and efficiently. The development of decentralized renewable energy and the electrification of the transport sector are two major aspects affecting electrical grids. This paper presents a novel energy management strategy that supervises controllable loads like electric vehicles and electric water heaters in a distribution grid to reduce energy transmission costs and promote the local consumption of renewable energy. The proposed strategy uses day-ahead forecasts of energy production and consumption to schedule future load profiles. It also considers potential grid constraints using a real-time co-simulation approach. The estimated constraints are mitigated by modifying the controllable loads’ dispatch or adjusting their power set-points. The system’s performance is finally evaluated on a real distribution grid model during days that present the most voltage constraints in the year.

This paper proposes a methodology to design a supervision system (SS) based on Fuzzy and Boolean logics. In the first stage, a graphical modeling tool is used to facilitate the analysis and the determination of Fuzzy–Boolean algorithm linked to the test system. To improve the performance of the proposed SS a genetic algorithm (GA) is implemented in the second stage. The SS objective is used to control electric vehicles (EVs) load in order to minimize the energy transmission costs (ETC) of the distribution system operator (DSO). To achieve this goal, it is necessary to promote local consumption of wind and photovoltaic (PV) power by coordinating them with EVs load, maximize EVs charging during cheaper energy periods and reduce subscribed power exceeding. The performance of the SS is shown by numerical simulation results using Matlab/Simulink. Finally, a Matlab–PowerFactory co-simulation framework is proposed in order to assess supervision system influence on the technical aspects of a real test grid.

he aim of the paper is to propose an approach for statistical assessment of the potential of plug-in electric vehicles (PEV) for vehicle-to-grid (V2G) ancillary services, where it focuses on PEVs doing daily home-work commuting. In this approach, the possible ancillary services (A/S) for each PEV fleet in terms of its available V2G power (AVP) and flexible intervals are identified. The flexible interval is calculated using a powerful stochastic global optimization technique so-called ‘‘Free Pattern Search” (FPS). A probabilistic method is also proposed to quantify the impacts of PEV’s availability uncertainty using the Gaussian mixture model (GMM), and interdependency of stochastic variables on AVP of each fleet thanks to a multivariate modeling with Copula function. Each fleet is analyzed based on its aggregated PEV numbers at different level of distribution grid, in order to satisfy the ancillary services localization limitation. A case study using the proposed approach evaluates the real potential in Niort, a city in west of France. In fact, by using the proposed approach an aggregator can analyze the V2G potential of PEVs under its contract.

Hybrid Energy Sources based on Storage Systems (HES) are increasingly used to improve the grid integration of renewable energy generators, or to improve the energy efficiency and the reliability of transport systems. This paper proposes a survey on the methodologies to design fuzzy logic based supervision strategies of this new kind of energy generating systems. Different ways to manage energy storage system are particularly discussed. A graphical modeling tool is used to facilitate the analysis and the determination of fuzzy control algorithms adapted to complex hybrid systems. The methodology is divided in different steps covering the design of a supervisor from the system work specifications to an optimized implementation of the control. An Experimental Design Methodology (EDM) combined with optimization algorithms is used to tune a set of parameters suitable for a fuzzy supervisor in order to optimize power, energy, efficiency, voltage quality, economic or environmental indicators. The application of this methodology to the supervision of different topologies of HES, based on renewable energy in a grid or in building applications or dedicated to transport systems, illustrates the performance and the systematic dimension of the approach.

This paper presents an energy management strategy for a commercial building in supermarket application. Some objectives are established as load shedding, to reduce the electricity bill and the CO2 emissions of commercial building, using photovoltaic (PV) and storage systems. An energy management supervision strategy based on the rules of the electricity bill will be presented. This paper focuses on the supervision strategy with the help of fuzzy logic and a graphical methodology to build it. It is shown, with the help of simulations and some economic and ecological indicators that the energy bill cost and the CO2 emissions can be reduced by using the proposed solution

Le problème majeur induit par l’intégration en grand nombre d’éoliennes dans les réseaux est dû à leur source primaire difficilement prévisible et très fluctuante qui rend difficile leur participation aux services système (réglage de la tension et de la fréquence, black start, fonctionnement en îloté,…). L’augmentation du taux de pénétration des éoliennes sera donc conditionnée par leur participation à ces différents services, ce qui nécessitera de leur associer des systèmes de stockage de l’énergie électrique. Dans cet article, après une brève synthèse des techniques de stockage de l’énergie adaptées aux réseaux électriques, trois exemples d’étude montrant l’intérêt d’associer du stockage inertiel à des éoliennes sont présentés. Il s’agit d’un couplage éolien-diesel, d’un émulateur temps réel d’une éolienne à vitesse variable associé à du stockage inertiel et de la participation aux services système d’une petite ferme d’éoliennes à vitesse variable intégrant du stockage. Différents apports du stockage sont illustrés par des simulations numériques ou des essais expérimentaux : le lissage de puissance, le réglage de la fréquence et de la tension et l’îlotage.

The growing use of electric vehicles (EVs) is key to reducing greenhouse gas emissions and promoting sustainable transportation. However, this rapid growth creates challenges, such as managing peak charging demand, maintaining power grid stability, and ensuring efficient use of charging stations. This study examines EV charging behavior within a potential local energy community in France, using data from September 2021 to August 2023 from 23 charging terminals. The analysis explores patterns in charging times, session durations, and energy consumption, uncovering trends and inefficiencies that impact power grid management. To improve energy planning, the study uses three machine learning models to forecast day-ahead EV charging demand. Gradient Boosting outperformed the others, achieving the lowest errors and highest R² (0.664), demonstrating the potential of advanced forecasting techniques for optimizing energy management systems. The results show how accurate forecasts can help balance energy demand and supply, making charging more efficient. This study demonstrates the value of integrating advanced forecasting techniques into energy management systems to support the growing adoption of EVs, reduce energy costs and improve power grid stability.

This paper presents a comprehensive methodology for integrated TSO/DSO modeling, aimed at enhancing coordinated management of transmission and distribution networks. The proposed approach allows to perform a unified numerical load-flow model that effectively integrates both transmission and distribution electrical systems, accounting for technical constraints of each grid operator. The validation of the detailed joint TSO/DSO model is carried out using real-world scenarios provided by grid operators, ensuring its practical applicability.

This paper presents a comparative study of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for optimal sizing of two hybrid renewable energy systems: Solar with Battery and Grid, and Solar with H2 System and Grid. Real-time energy consumption data from a university is used to model these systems, aiming to minimize costs while meeting energy demands and ensuring reliability. The performance of GA and PSO is compared based on solution quality, convergence speed, and computational efficiency. Results show that GA provides robust configurations, while PSO offers faster convergence. These findings support efficient and practical hybrid system design.

The increasing need for sustainable energy sources around the globe requires innovative approaches for power distribution and generation. This paper presents an hybrid micro-grid that integrates photovoltaic (PV), battery storage system and hydrogen systems which contains a fuel cell, the electrolyser, and H2 storage tank with grid effectively. Its design significantly lowers carbon emissions and increases costeffectiveness while maintaining a consistent energy supply, even in emergency situations. Its simulation under Matlab/Simulink analyzes energy consumption, production, total energy costs, and carbon emissions both locally in France and globally, emphasizing the advantages of integrating renewable energy sources. This emphasizes the importance of regional considerations and optimizing the integration of renewable energy for cost efficiency, reliability, and environmental benefits. The article further contributes by exploring the hybridization of battery and hydrogen storage, and studying multiple scenarios using both economic and ecological approaches. An overview of the results indicates significant improvements in energy efficiency and sustainability. The hybrid system shows a notable reduction in carbon emissions and overall energy costs. Index Terms—Renewable energy integration, Hybrid microgrid, system, Energy storage, Photovoltaic system, Carbon emissions reduction

To resolve the global problem of Climate change is one of the most important occupations goals for most countries in the world. Many solutions are proposed such as Sustainable Development including energy transition. Among the new and developing practices, we find individual and collective self-consumption. The individual as a participant in this type of operation must have an awareness of this policy through several actions such as education and training on the one hand and the implementation of information measures to the public from children to young people to adults, in the other hand. However, the question that arises is: “do the actors or the energy consumers know these different actions of energy management and environmental protection?” Therefore, this paper aims to investigate the level of awareness among individuals regarding collective self-consumption policies. Based on a new method of energy sharing using a collaborative game, this paper studies the acceptability of this system of sharing by actors using data analysis. Through this survey, we propose a synthesis of the answers of different topics treated throughout the questionnaire such as: background information, awareness and acceptability of the proposed energy sharing method. Therefore, we closely determine the different profiles of prosumers to better guide energy control strategies considering consumer behavior. Our study targets students and staff in the university community to integrate sustainable development especially climate change action into their daily behavior by studying their awareness. Through a survey the behavior of individuals is studied, and their opinions about the system of energy sharing considered. Keywords: Collective self-consumption, survey, data analysis, awareness, university community

While integrating renewable energy sources into the energy mix is effective in mitigating climate change, their intermittent nature requires new ideas on how to use and distribute electricity. It is therefore necessary to imagine new organizations and economic models. With an engineering approach, demand-side management becomes crucial to address the constraints on the distribution grid. Optimal energy behaviors are in, this context, often regulated by the price. Moreover, new economic models based on market logic are directed by the deregulation of the energy markets. A more recent legal way for local actors to exchange renewable energy is through energy communities often combined with collective self-consumption. They open a new approach to energy management based on ressource pooling and cooperation among consumers. This work proposes to explore a new methodology of demand-side management based on a cooperative game. It will be proved that a collective objective based on prioritizing local energy production in community consumptions can lead to individual costs and environmental impact reduction. The work integrates several motivations of stakeholders in their participation in a collective self-consumption operation. A new methodology based on the LCA method is deployed to assess environmental impacts. Ethical distributions of gains based on game theory will also be discussed. Several game-theoretic concepts are explored, opening up philosophical questions such as: Should we reward those who participate the most, but who are also often the wealthiest? Should we encourage the most vulnerable? From a technical point of view, other questions are discussed, such as: Which behaviors should be rewarded in the context of energy management? Should energysaving behavior be rewarded? Should flexibility be rewarded? The aim of this paper is to propose a novel energy management system able to meet the environmental and social challenges of the energy transition.

This paper introduces a novel method for generating Typical Meteorological Year (TMY) data for microgrids that employ bifacial photovoltaic modules. The method applies Principal Component Analysis (PCA) to five meteorological parameters that influence the power output of bifacial modules: ambient temperature, Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), albedo, and snow depth. The method selects the most representative months from a 10-year historical data set based on the correlation between the principal components and the original data. The performance of the proposed method is compared with existing methods; Sandia method and NSRDB TMY Method (TMY3), in terms of capturing the interannual variability of the meteorological data and minimizing the average deviation. The comparison is based on the z-score metric and the interannual energy gap, which measure the deviation and the variability of the annual energy yield of a microgrid with 60 bifacial modules. The results show that the proposed method yields a lower z-score (0.20) and a smaller interannual energy gap (5.3%) than the other methods, indicating a closer alignment with the average interannual energy and a higher reliability and accuracy of the optimal sizing and design of microgrids that use bifacial modules.

Climate change is requiring changes in energy use. The integration of renewable energy sources into the energy mix is a viable solution for electricity generation, but their intermittence forces us to imagine new ways to consum and to distribute electricity. Thus, new economic models and new organizations have to be established. Energy communities are a recent legal solution to share renewable energy among local actors. Joining a community can allow participants to reduce their energy bill and environmental footprint. However the distribution of gains generated through collective self consumption is one of the main obstacles. An interesting possibility comes from the game theory, especially from cooperative game, as solutions concepts already exist to solve this kind of problem. In this paper, a payoff distribution is proposed through a collaborative game. Two parrallel optimizations are established in order to compare the benefits of collective self consumption to individual self consumption. It is shown that the coordination of flexible devices to maximize the collective self-consumption rate generates gains on the electricity bill and environmental footprint. The aim of the study is then to explore ways to distribute these gains. Two major concepts in cooperative game theory are studied in this paper, the Shapley Value and the Nucleolus. The advantages and drawbacks of these concepts in an energy community context will be established. A methodology is proposed to permit a fairer distribution of payoffs according to specific energy management parameters such as efficiency and flexibility.

Environmental issues are leading to rethink the way energy issues are approached. The integration of renewables energies into the energy mix is one of the long-term solutions for electricity production. Their intermittency forces utility companies to reconsider the way energy is consumed. Indeed, consumers will not remain regarding the electricity networks but will be asked to participate in the daily management of electricity. Several solutions are emerging around smart grids and demand side management, but they are mainly focused on technological solutions. Human behaviors are rarely considered in energy management algorithms. Similarly, the environmental impacts of the whole system are rarely quantified. Recently, legal incentives such as energy communities have appeared. These aim to include different actors to participate in economic operations regarding energy while reducing their environmental impacts. In this context, energy sharing, and self-consumption become at the heart of energy issues. In this paper, we propose to integrate hypotheses from the social and human sciences into an energy management of a community. Its environmental impact will also be integrated into this management through calculations of carbon emissions based on LCA methods. The gain/loss of stakeholders if they participate in a collective self-consumption operation regarding their sensibilities on energy consumptions will be discussed.

Accurate modeling of bifacial module energy production is conditioned to the correct modeling of the front and rear irradiance. This paper compares the existing approaches used to estimate the incident irradiance on the back side and the front side of a photovoltaic (PV) bifacial module, by studying the performance of each model in terms of accuracy and computation time. In this study, we have selected three software with different approaches. We started with Bifacial_radiance which uses the ray-tracing technique. The second software is Sandia model which is a three-dimensional implementation of view factor method under MATLAB™. We complete our study with pvfactors that employs a two-dimensional configuration factor model. This study aims to propose the most time-efficient way to compute the irradiances received by bifacial panels, which will serve to predict the energy production of power plants. Having a fast model allows to develop efficient real-time management strategies for power supply systems that use bifacial modules. According to this study, pvfactors has the lowest execution time and gives almost the same output results as Bifacial_radiance and Sandia model that use complex algorithms.

In a context where smart grids are emerging, energy management strategies play a major role in tackling different challenges facing their development. This paper proposes a predictive supervision strategy to manage the activation of Electric Vehicles and Electric Water Heaters within a distribution grid. To do so, the energy production and consumption are forecasted using Artificial Neural Networks based on historical measurements and meteorological data. Then, these forecasts are used to determine controllable load profiles that minimize energy transmission costs. The system’s performance is finally tested through simulation using real-time measurements collected from a distribution substation, showing an important reduction in energy transmission costs and also an increase in local renewable energy consumption.

The growing integration of renewable energy production and important loads such as electric vehicles bring a lot of challenges to today’s electric systems. Demand-side management strategies are often presented as one of the solutions to these problems. In this study, a fuzzy logic supervision system is proposed to minimize energy transmission costs in distribution grids by controlling electric vehicle charging and electric water heater activation. The supervision system takes into account the production of renewable energy within the grid, the load consumption and other parameters including user’s constraints. A genetic algorithm is used to optimize the supervisor’s parameters. Finally, the system’s performance is confirmed through simulation by testing it on real distribution grid data.

The subject addressed in this paper concern the involvement of stakeholders in the management of the energy in a smart-grid. Demand side management (DSM) is indeed of particular interest to adjust the load, relying on the participation of stakeholders on a daily basis, or at least during peak hours. We discuss therefore a new methodology aiming to extract the flexibility of the stakeholders in a smart grid, by including their profiles and therefore there sensitivities and objectives in the optimisation process of the energy balance. The contribution of this paper is therefore twofold: Firstly, by the development of a methodology to understand and segment real consumer profiles for energy management, and secondly by studying and testing how each considered stakeholder can increase his satisfaction in a day ahead Demand Response program.

Known for being a reliable alternative, microgrids have been widely deployed recently in power distribution field in order to guarantee a constant power supply especially in isolated zones. Moreover, microgrids have increasingly known the penetration of renewable energy as environmentally friendly energy sources. However, the intermittency of these sources oblige specialists to think about tools allowing determining their potential, over a predetermined time interval, in order to ensure energy security. For this reason, renewable energy forecasting is crucial. Thus, in this paper, a feed forward back-propagation neural network is used to forecast next 24 hours photovoltaic (PV) power of one of the catholic university buildings "îlot RIZOMM". The accuracy of the model built is evaluated with some performance metrics. Thereafter, prediction results of PV power are compared to those provided by SteadySat, an industrial solution developed by the company SteadySun. It is shown that the prediction Mean Absolute Errors (MAEs) of the model are of 3.05% in a clear sky day, 4.95% in a cloudy day and 5.98% in a partly cloudy day.

To address the new challenges arising from the higher penetration of renewable energy in electrical grid, Demand Side Management (DSM) and Demand Response (DR) aim to involve the residential as well as industrial consumers in the grid equilibrium. Ensuring benefits for both utility and users requires the consumers sensitivities to be understood and then included in the Energy Management System (EMS). For this purpose, the cost is the predominant and most often only factor taken into account in the literature, although in the residential sector other concerns influencing electricity consumption behaviour have been observed. This paper presents an EMS based on a neighbourhood of consumers modelled at the level of their appliance and incorporating 6 consumption profiles along three sensitivities: cost, environment and appliances shifting comfort. A multi-agent optimization is lead by a central aggregator but performed locally by the household using multi-pass Dynamic Programming (DP), thus ensuring privacy protection for the stakeholders.

The challenges raised by new energy technologies, clean energy sources and energy efficiency led us to reconsider the way we manage energy in electricity grids and the way the stakeholders are involved. To tackle these issues, the focus should emphasize not solely the technical side, but also the sociological and economic issues related to the development of these new energy solutions. This paper presents an Energy Management System (EMS) addressing the following question: how to take into account, from the grid point of view, a stakeholder willing to be involved in the grid ? Involvement-profiles amongst stakeholders, particularly the households, in terms of electricity consumption/production can be defined by a socio-economic approach, and we chose to use a potential game approach from the game theory to integrate those profiles and imagine the possible interactions among them, incorporating their sensitivities and objectives.

In this study, the load profile estimation is done using the profiling technic, and its performance is enhanced using the real consumption values of the consumers. The results of estimation are evaluated using the measured data at the level of HV/MV (High Voltage/Low Voltage) substation and also MV feeders containing industrial and residential consumers and wind power productions. Based on the gap between actual measured data and estimated profiles, a correction factor is introduced that is applicable at the level of single MV/LV substations. These factors will be used for grid simulation and planning studies where the results will be more reliable compared to the previous estimation technics.

This paper proposes a V2G charging scheduling scheme for energy invoice minimization of railway station parking lots hosting plug-in electric vehicles. Using a two layer optimization technique the daily load profile of the railway station is reduced in order to increase the load factor and minimizing the annual energy invoice (AEI) of the station. Binary linear programming is used as second layer for charging/discharging scheduling problem. The final results shows interests of using the proposed approach conducting its impact on minimizing annual optimum subscribed power, maximum demand power and annual energy invoice.

This paper presents the contribution of electric vehicles to the distribution grid and railway station parking lots and their uncontrolled charging impacts on these grids. The economic indicators have been chosen as performance criteria. The benefits and advantages of using the charging/discharging coordination in term of economic indicators have been evaluated by using mixed integer linear programming and interior point for nonlinear optimization. The impacts of different charging scenarios have been evaluated and the results show high interests of using coordination charging techniques and V2G technology for grid service providing.

In this paper, a traffic-based modeling for electric vehicle charging profile is proposed. An optimization process will find the best-fitted probability distribution function to the real traffic data. The impacts of electric vehicles charging profile on the regional distribution network, and a typical railway station, proposing EVs parking lots facilities, are discussed. The results show a significant increment of annual energy purchase price for the downstream consumers that would be possibly reduced by proposing supervision system aimed to charging coordination.

This paper explores the feasibility of electric vehicle contribution to the grid ancillary services. Thanks to a probabilistic approach, the technical possibilities of Vehicle-to-grid ancillary services are assessed. Different scenarios with respect to the number of EVs fleet and commuting behavior have been generated to assess the possibility of grid service support in sense of power, energy and appropriate time interval. Each scenario is dedicated to proper ancillary service, where the economic interest for each service has been discussed.

The electricity market liberalization and the development of the decentralized production induce many new scientific and technical problems. The major problem experienced with decentralized energy sources, and particularly renewable energy ones, is that they do not usually contribute to the power network management. Their rate of penetration is then limited (to 30 % of the consumption for instance according to some experience feedback) in order to be able to guarantee the network stability under acceptable conditions. Among these sources, wind energy is expected to grow quickly in Europe during the coming years. Increasing its rate of penetration will be thus possible if this type of source takes part in the ancillary services, and can work in islanding mode. To reach these objectives with wind generators, we consider a Variable Speed Wind Turbine (VSWT) coupled to a Permanent Magnet Synchronous Generator (PMSG) combined to an energy storage system. Due to important fluctuations of the wind generator power, conventional electrochemical batteries are not adapted. We consider then a Flywheel Energy Storage System (FESS. Owing to the use of power electronics, the generator and the storage system are electrically coupled via a DC link. To evaluate the possibility of the proposed generating system (GS) to participate to the ancillary services, we consider in this paper the connection of a wind farm including three generating systems with a network including passive loads and a power grid. Generating systems are working as voltage sources with the help of a frequency droop. It is shown in this paper, with the help of simulations, that the power generated by each generating system can be controlled by fixing the power set point when they are connected to the grid. It is also shown in case of islanding, that they can share the global load of the isolated network like Uninterruptible Power Supply (UPS) parallel connection. This paper focus particularly on the system behaviour working in islanding mode when load varies.

The dispersed generation increases into the distribution system. Until now, this generation has generally been considered as “negative loads” because it does not participate directly to the ancillary services (voltage and frequency control, islanding…). But this approach will not be acceptable in the future when the level of penetration of dispersed generation will be over 20% which will be the case in numerous countries by 2010-2020. Indeed, the network stability would be endangered which would increase the risk of black-out. It is then necessary to evaluate the ability of the dispersed generation to participate directly to the ancillary services, which are now priced on the open market. Moreover, the dispersed generation would have to be able to continue to generate in case of network failures like a voltage sag or an islanding. The dispersed generation would then have to be able to work without auxiliary source, ensures the generation-consumption balance, set the adequate frequency, the amplitude of the voltage wave, enhance the network safety and reliability. To reach these objectives with wind generation, we consider a Variable Speed Wind Turbine (VSWT) coupled to a Permanent Magnet Synchronous Generator (PMSG) combined to an energy storage system. Due to important fluctuations of the wind generator power, conventional electrochemical batteries are not adapted. We consider then a Flywheel Energy Storage System (FESS). Owing to the use of power electronics, the generator and the storage system are electrically coupled via a DC link. To evaluate the possibility of the proposed generating system (GS) to participate to the ancillary services, we consider in this paper the interconnection of two of them with a network including passive loads and a power grid. Generating systems are working as voltage sources with the help of a frequency droop. It is shown in this paper, with the help of simulations, that the power generated by each generating system can be controlled by fixing the power set point when they are connected to the grid. It is also shown in case of islanding, that they can share the global load of the isolated network like Uninterruptible Power Supply (UPS) parallel connection. Finally, it is shown that the proposed generating system is able to continue to generate power during an unbalanced 600ms voltage sag of 20%. It may be noticed that electrical quantities in the network connection of the generating system are controlled with the help of resonant controllers.

Les distributeurs de services de communication doivent installer des tours de télécommunication même dans les zones reculées afin d’assurer une couverture optimale du réseau de télécommunication sans « zones blanches » à leurs clients. Les zones reculées sont trop éloignées pour être desservies par le réseau électrique. L'une des solutions les plus utilisées est le générateur à combustible fossile (groupe électrogène). Le carburant produit beaucoup d'émissions de CO2 et le coût de l'énergie (COE) est élevé. Les systèmes hybrides utilisant des énergies renouvelables et du stockage peuvent s’avérer plus fiables pour fournir de l'électricité et plus respectueux de l’environnement dans ce cas. Ainsi, ce travail porte sur l'optimisation de la fourniture d'énergie par l'utilisation d'un système multi-source à un site télécom éloigné dans le sud-est du Québec. L'objectif principal est de proposer un système économique et à faibles émissions de CO2. Le logiciel HOMER Pro est utilisé pour concevoir et comparer les résultats pour différentes configurations. Trois sources d'énergie différentes ont été utilisées : l'énergie solaire photovoltaïque avec des panneaux bifaciaux, le groupe électrogène et les batteries Li-ion. Les résultats montrent qu’une configuration avec 60% de renouvelable et du stockage est un bon compromis entre le coût économique et le coût écologique (COE = $0,544/kWh, émissions de CO2 = 9 025 kg/an).

Ce manuel propose des résumés de cours, des exercices et problèmes corrigés couvrant l’ensemble des systèmes d’énergie renouvelable. Il inclut également des tendances actuelles telles que le stockage de l’énergie solaire et l’autoconsommation photovoltaïque ou éolienne. Les exercices et les problèmes sont classés par niveau de difficulté et par compétences et sont corrigés de manière détaillée. De nombreux exemples sont fournis avec les calculs et les graphiques aidant à visualiser les différentes technologies et méthodologies mathématiques. Cet ouvrage s’adresse aux étudiants de BTS, BUT, des cycles universitaires et des écoles d’ingénieurs.

Les enjeux énergétiques et environnementaux sont à l’origine d’une forte croissance de la production d’électricité à partir d’énergies renouvelables depuis le début du XXIème siècle. Le concept de développement durable et le souci des générations futures nous interpellent au quotidien permettant l’émergence de nouvelles technologies de production d’énergie, et de nouveaux comportements d’utilisation de ces énergies. L’émergence rapide de nouvelles technologies peut rendre la compréhension et donc la perception de celle-ci difficile. Ce livre a pour but de contribuer à une meilleure connaissance de ces nouvelles technologies de production d’électricité en s’adressant à un public varié. En effet, il présente les enjeux, les sources et leurs moyens de conversion en électricité suivant une approche générale et développe les notions scientifiques de base permettant d’en appréhender les principales caractéristiques techniques avec une vision d’ensemble. Les objectifs de cet ouvrage sont : - de présenter les systèmes de production d’électricité à partir de ressources énergétiques renouvelables des petites aux moyennes puissances (jusque 100 à 200 MW), - d’introduire les notions électrotechniques de base nécessaire à la compréhension des caractéristiques de fonctionnement de ces convertisseurs, - d’évoquer les contraintes et problèmes d’intégration dans les réseaux électriques de ces moyens de production, - de proposer quelques exercices pour s’autoévaluer. Contenu 1. La production d’électricité à partir d’énergie renouvelable, Benoît Robyns. 2. Le solaire photovoltaïque, Arnaud Davigny. 3. L’éolien, Bruno Francois et Benoît Robyns. 4. L’hydroélectricité terrestre et marine : houle et marées, Benoît Robyns et Antoine Henneton. 5. La production d’origine thermique, Jonathan Sprooten. 6. Problématique de l’intégration de la production décentralisée dans le réseau électrique, Benoît Robyns.

Les enjeux énergétiques et environnementaux sont à l’origine d’une forte croissance de la production d’électricité à partir d’énergies renouvelables depuis le début du XXIe siècle. Le concept de développement durable et le souci des générations futures nous interpellent au quotidien permettant l’émergence de nouvelles technologies de production d’énergie, et de nouveaux comportements d’utilisation de ces énergies. L’émergence rapide de nouvelles technologies peut rendre la compréhension et donc la perception de celle-ci difficile. Ce livre a pour but de contribuer à une meilleure connaissance de ces nouvelles technologies de production d’électricité en s’adressant à un public varié. Il présente les enjeux, les sources et leurs moyens de conversion en électricité suivant une approche générale et développe les notions scientifiques de base permettant d’en appréhender les principales caractéristiques techniques avec une vision d’ensemble. Des systèmes de production d’électricité à partir de ressources énergétiques renouvelables de petites et moyennes puissances sont présentés. La deuxième édition de cet ouvrage, revue et augmentée, met à jour les nombreuses données caractérisant le développement de ces énergies renouvelables, aborde les nouvelles technologies émergentes photovoltaïques et marémotrices exploitant les courants marins, le développement de l’éolien offshore, et les développements récents concernant l’intégration de ces sources dans le réseau électrique, ainsi que le développement de l’autoproduction et de l’autoconsommation. Plusieurs exercices sont proposés au lecteur à des fins d’évaluation. Contenu 1. La production d’électricité à partir d’énergie renouvelable, Benoît Robyns. 2. Le solaire photovoltaïque, Arnaud Davigny. 3. L’éolien, Bruno Francois et Benoît Robyns. 4. L’hydroélectricité terrestre et marine, Benoît Robyns et Antoine Henneton. 5. La production d’origine thermique, Jonathan Sprooten. 6. Problématique de l’intégration de la production décentralisée dans le réseau électrique, Benoît Robyns et Jonathan Sprooten.

Energy and environmental issues have caused a marked increase in electricity production from renewable energy sources since the beginning of the 21st Century. The concept of sustainable development and concern for future generations challenge us every day to produce new technologies for energy production, and new patterns of use for these energies. Their rapid emergence can make the understanding and therefore the perception of these new technologies difficult. This book aims to contribute to a better understanding of the new electricity generation technologies by addressing a diverse audience. It presents the issues, sources and means of conversion into electricity using a general approach and develops scientific concepts to understand their main technical characteristics. Systems of electricity generation from renewable energy resources of small to medium powers are presented. The second edition of this book, revised and expanded, updates the numerous data characterizing the development of these renewable energy sources, discusses new emerging photovoltaic and tidal technologies exploiting marine currents, the development of offshore wind power, and the recent developments concerning the integration of these sources into the electricity grid, as well as the development of self-production and self-consumption. Several exercises are provided to the reader for evaluation purposes. Contents 1. Electricity Production from Renewable Energy, Benoît Robyns. 2. Solar Photovoltaic Power, Arnaud Davigny. 3. Wind Power, Bruno Francois and Benoît Robyns. 4. Terrestrial and Marine Hydroelectricity, Benoît Robyns and Antoine Henneton. 5. Thermal Power Generation, Jonathan Sprooten. 6. Integration of the Decentralized Production into the Electrical Network, Benoît Robyns and Jonathan Sprooten.

Les développements actuels dans le domaine des énergies renouvelables, ainsi que la tendance à l'autoproduction et à l'autoconsommation d'énergie, ont suscité un intérêt accru pour les moyens de stockage de l'énergie électrique; un élément clé du développement durable. Ce livre présente le potentiel offert par le stockage de l’électricité dans le cadre des bâtiments, îlots de bâtiments, quartiers intégrés dans un large réseau électrique intelligent ou « Smart Grid » ou formant un micro réseau électrique intelligent ou « Micro Grid », et tout particulièrement concernant leur gestion et leur valorisation. Il montre les nombreux services que le stockage peut apporter, et examine les facteurs socio-économiques importants liés à l’émergence de bâtiments intelligents et de réseaux intelligents. Enfin, il présente les outils méthodologiques nécessaires à la mise en place d'un système de gestion de l'énergie de ces technologies de stockage, illustrés par des exemples concrets et pédagogiques. Table des matières: 1. Problématique du stockage de l’énergie électrique dans l’habitat: vers des bâtiments et des villes plus intelligents. 2. Stockage énergétique dans un bâtiment commercial. 3. Stockage énergétique dans un bâtiment tertiaire, couplé à de la production photovoltaïque et de l’éclairage LED. 4. Stockage hybride associé à du photovoltaïque dans le contexte de l’habitat en zone non interconnectée. 5. Changements économiques et sociologiques induits par les smart grids. 6. Mutualisation énergétique entre bâtiments tertiaires, résidentiels et producteurs d’énergie. 7. Gestion centralisée d’une communauté locale énergétique permettant une autoconsommation maximale de l’énergie photovoltaïque. 8. Charge réversible des véhicules électriques vers les réseaux et les bâtiments.

Current developments in the renewable energy field, and the trend toward self-production and self-consumption of energy, has led to increased interest in the means of storing electrical energy; a key element of sustainable development. This book provides an in-depth view of the environmentally responsible energy solutions currently available for use in the building sector. It highlights the importance of storing electrical energy, demonstrates the many services that the storage of electrical energy can bring, and discusses the important socio-economic factors related to the emergence of smart buildings and smart grids. Finally, it presents the methodological tools needed to build a system of storage-based energy management, illustrated by concrete, pedagogic examples. Content: 1. Storing Electrical Energy in Habitat: Toward “Smart Buildings” and “Smart Cities”. 2. Energy Storage in a Commercial Building. 3. Energy Storage in a Tertiary Building, Combining Photovoltaic Panels and LED Lighting. 4. Hybrid Storage Associated with Photovoltaic Technology for Buildings in Non-interconnected Zones. 5. Economic and Sociological Implications of SmartGrids. 6. Energy Mutualization for Tertiary Buildings, Residential Buildings and Producers. 7. Centralized Management of a Local Energy Community to Maximize Self-consumption of PV Production. 8. Reversible Charging from Electric Vehicles to Grids and Buildings.