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Decentralized energy production, particularly from photovoltaic (PV) systems, is becoming increasingly prevalent, leading to a rise in the number of energy producers and consumers, or ”prosumers”. These prosumers, equipped with their own energy generation and storage systems, are not just passive consumers but active participants in the energy market. They generate their own electricity, often from renewable sources, and can feed excess power back into the grid, store it for later use, or share it within a local energy community. This evolving energy paradigm presents new opportunities and challenges in terms of energy management and optimization, necessitating innovative approaches to ensure efficient and sustainable use of energy resources. This paper introduces an innovative storage management method for grid-connected photovoltaic (PV) systems. The method is designed to minimize either the economic or ecological cost, or to find an optimal balance between the two, under various tariff scenarios. This is achieved while adhering to a full self-consumption constraint imposed by the distribution system operator. The control strategy is underpinned by forecasts of electrical consumption, production, and CO2 emissions, which are developed using feedforward neural network models. These models are trained on data from a real-scale smart-grid demonstrator at the Catholic University of Lille, France. The results of the study offer a comparative analysis of the economic and ecological benefits of the three proposed strategies, demonstrating that the best compromise is achieved when considering the off-peak tariff option. Furthermore, a real-time controller was implemented on the Energy Management System (EMS) of the demonstrator and tested over a 24-hour period, yielding satisfactory results. This paper, therefore, presents a significant advancement in the field of storage management for grid-connected PV systems.

In this paper, we make a comparative study of fuzzy logic and boolean logic energy management strategies (EMSs) for an hybrid electric vehicle (HEV) with parallel architecture made up mainly of an electric motor (EM) and an internal combustion engine (ICE). EM is used as a main propulsion system for the vehicle. However, the ICE is used as a backup system. This study is developed to manage the energy flow between the two sources by ensuring a balance between the generated and consumed powers, injected or absorbed into the battery and minimizing the ICE operation in order to reduce the fuel consumption and CO2 emission. The purpose of this study is to investigate the different scenarios for fuzzy logic and classical logic strategies under the Normalized European Drive Cycle (NEDC), NEDC cycle for 1-hour, and combined cycle of Worldwide harmonized Light vehicles Test Procedures (WLTP) and Assessment and Reliability of Transport Emission Models and Inventory Systems (ARTEMIS) cycles. The change of test conditions from NEDC to WLTP and ARTEMIS was shown to lead to a significant reduction of the fuel consumption and CO2 emission using the fuzzy logic control with results that may attend 50% increase of CO2 emissions reduction VS the results of boolean logic control. Simulations are made using MATLAB/ Simulink software. Results of both strategies are presented and discussed in this paper.

This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into a two-stage unit commitment optimization. The planning objective consists in minimizing operating costs and/or equivalent carbon dioxide (CO 2 ) emissions. Based on distributions of forecasting errors of the net demand, a LOLP-based risk assessment method is proposed to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction and the prescribed OR requirement. In a second stage, possible future forecasting uncertainties are considered. Hence, a stochastic operational planning is optimized in order to commit enough flexible generators to handle unexpected deviations from predic-tions. The proposed methodology is implemented for a local energy community. Results regarding the available operating reserve, operating costs and CO2 emissions are established and compared. About 15% of economic operating costs and environmental costs are saved, compared to a deterministic generation planning while ensuring the targeted security level.

Abstract In electrical systems, the main objective is to satisfy the load demand at the least cost without having imbalance between generation and consumption. Thus, the uncertainty of photovoltaic (PV) power production must be considered in generation planning. In this paper, the optimal generation scheduling in an urban microgrid is made by taking in consideration operating reserve (OR) provision and under stochastic characteristics of PV power prediction. By considering a prescribed risk level of unbalancing, a dynamic programming (DP) algorithm sets the operational planning of conventional generators, so that the operational cost and available operating reserve can be calculated. Then, the effect of PV power uncertainty into the unit commitment is analysed by considering PV forecast intervals with a 95 % confidence level. The unit commitment is then recalculated with new generator set points and the same criteria. Finally, variations of the targeted minimized costs and obtained OR is analysed according to the considered uncertainty.

The development of local energy communities and collective self-consumption framework at a large scale requires new control methods that take into account users preferences. This article presents a model of such a community, with diverse actors (photovoltaic generators, electric vehicles, storage system and tertiary buildings). Game theory is used to model the preferences of each user and to build a mathematical framework where each user optimizes individually his power profile according to these preferences. An ADMM distributed algorithm (Alternating Direction of Method of Multipliers) is employed for practical implementation. Thus, a central agent is no longer needed to reach the system equilibrium, in which all users are satisfied while ensuring that the local energy community does not import more power from the grid than allowed. The simulations performed on real data for different scenarios representing diverse users behaviors show that the developed approach converges to a stable state, and leads to a maximization of local energy exchanges.

This study is interested in optimisation of both sizing and energy management system of a hybrid storage system (HSS) associated with photovoltaic panels. The battery (BT) considered as the principal storage organ and a super-capacitor used as the secondary storage system to improve the BT life span makes up the HSS. The main purpose of this study is to explore a novel optimisation approach to jointly optimise the sizing and the fuzzy logic energy management system (FLEMS). In fact, an optimisation function based on sequential quadratic programming algorithm is proposed. The optimisation methodology has been performed jointly and successfully for the sizing of the BT storage system and the membership functions parameters of the FLEMS in order to decrease the levelised cost of energy with a violation time by 5% of mean absolute percentage error score <1.5% throughout the year. According to the simulations results, a benefit analysis has been done to assess the associated financial impact.

INTRODUCTION: This paper presents a distributed approach to optimise self-consumption on a local energy community containing photovoltaic generators, electric vehicles, loads and a storage system. OBJECTIVES: The goal is to maximise energy sharing between users while preserving the indivual objectives of each user. METHODS: Game theory is employed to model users’ behavior and preferences. A distributed algorithm is used to solve the optimisation problem. In addition, a physical model of the grid is built to verify if the solutions respect grid constraints. Finally, a private blockchain environnement is deployed to concretely implement this distributed framework with a smart contract. RESULTS: It is shown that the proposed approach effectively leads to an increase of self-consumption rate on the local grid. CONCLUSION: The proposed distributed framework, combining game theory and blockchain, shows real potential to improve energy sharing on energy communities.

This article is interested in a controlled three‐phased voltage source inverter (VSI) for a grid‐connected wind power generator. The objective consists of participating in auxiliary services by enhancing the grid stability. An active wind generator associated with a battery/super‐capacitor storage system composes the studied renewable generator. It is related to a power consumer (load) to form a microgrid (MG) that can work according to grid stability in two modes: standalone and grid‐tied. Thus, the principal objective of this study is to develop a robust control method for MG connection with the grid to ensure the grid voltage and frequency regulation. This control has two control tasks: First, an adaptive droop control is implemented to adjust power flows exchanged with the grid to ensure its stability. It regulates its voltage and frequency. Second, a sliding mode control (SMC) with super‐twisting algorithm is put forward for the VSI to improve the regulation of the grid voltage and frequency under uncertainties. It improves control results concerning the reduction of harmonics caused by sudden variations of the load and of powers exchanged with the grid. It allows preventing the phenomenon of chattering created by classical sliding mode technique. Extensive simulation studies are carried out under MATLAB/Simulink. They prove the effectiveness of the suggested droop and SMCs compared with the classical proportional‐integral controller.

Due to the intermittency and fluctuation of wind speed, the integration of wind power into electric power system has been a relevant issue. Recently, Pumped-storage hydro plant (PHES) can be used to balance the unstable output of wind farm, as it can adjust its production to compensate wind power fluctuation. This paper investigated the combination of a wind farm and a PHES facility from the point of view of a generation company in a market environment. A joint operation model between the wind farm and PHES is proposed. An algorithm of energy management system (EMS) is proposed to identify the daily operational strategy to be followed in order to: 1) Minimize the penalty cost resulted from wind-PHES output imbalances; 2) Maximize the daily revenue profit taking into consideration all constraints of joint operation. Simulation results under MATLAB/SIMILINK®environment are presented and discussed.

Due to the variable nature of wind resources, the integration of wind power into electric power system has been a relevant issue. Recently, pumped hydro energy storage can be used to balance the unstable output of wind farm, as it can adjust its production to compensate wind power fluctuation. This article investigated the combination of a wind farm and a pumped hydro energy storage facility from the point of view of a generation company in a market environment. A joint operation model between the wind farm and pumped hydro energy storage is proposed. An algorithm of energy management system is proposed to identify the daily operational strategy to be followed in order to (1) minimize the penalty cost resulted from wind-pumped hydro energy storage output imbalances and (2) maximize the daily revenue profit taking into consideration all constraints of joint operation. Simulation results under MATLAB/Simulink® environment are presented and discussed.

This paper was focused on the implementation of experimental test bench for a wind power generator (WPG) connected to the electrical network. The objectives of this work are to validate the test bench functionality and to investigate in real time a high‐order sliding‐mode control (SMC) scheme applied to control the WPG. This study has three main purposes. The first one is the development of the SMC technique to ensure a control method insensitive against nonlinear behavior of wind systems. The second one is the experimental implementation of this SMC scheme. The third one is a synchronization technique that has been presented in a grid‐connected power inverter to adapt the WPG voltage to the grid voltage. A comparative study between the experimental and simulation results demonstrates the functionality of the test platform and the robustness of the SMC in real time.

Small-sized variable renewable energy sources (RES) live a large-scale development in urban electrical systems. They increase local high dynamic unbalancing and then can create instabilities on the inertia response. Thus, setting an adequate operating reserve (OR) power to compensate the unpredicted imbalance between RES generation and consumption is essential for power system security. Indeed, effective calculation and dispatching of OR considering inaccurate forecast of both RES and load demand can provide substantial cost reductions. Thus, to facilitate the energy management and system optimization in an urban microgrid (MG), a user-friendly tool for Energy Management System and Operational Planning has been developed. The tool provides a complete set of user-friendly graphical interfaces to study the details of photovoltaic (PV) and batteries, load demand, as well as micro gas turbines (MGTs). Furthermore, this energy management system allows system operators to properly model RES uncertainty. In addition, it could assist operators for the day-ahead energy management with an efficient information system and an intelligent management.

In this paper, a control and power supervisor for a flexible operation of a Renewable Distributed Generator (RDG) is introduced. This RDG consists of a combination of a wind system and a hybrid storage system made up of Batteries (BT) and Super-Capacitors (SC). RDG is associated with a load and a fluctuating grid to form an Active Generator (AG). According to the grid fluctuation, AG can operate in a grid-connected and standalone mode. The objective of this work is to investigate a novel control strategy for AG integrated into the grid in order to maintain its voltage and frequency in an allowable range and to ensure the continuity of the power supply in case of a grid fault. The structure of the proposed control strategy consists of a Fuzzy Logic Supervisor (FLS), an adaptive Fuzzy Logic Droop Control (FLDC) and a Fuzzy Logic Islanding Detection (FLID). FLS is developed to manage the power flows between the storage devices by choosing the optimal operating mode, thereby ensuring the grid stability and the continuous supply of the load by maintaining the state of charge of SC and BT at acceptable levels and to reduce stresses on BT and improve their life cycle. FLID is used to detect de standalone mode in case of grid failure. Finally, FLDC is used to control the active and reactive powers exchanged with the grid, ensuring its stability by maintaining its frequency and its voltage in optimal margins. The effectiveness of the proposed control method is validated by simulation results and compared with a generalized control technique.

This paper was interested in flexible control of an active generator (AG) that includes a wind turbine, battery‐supercapacitors hybrid storage system, and loads. This AG can operate according to grid stability in islanded, synchronization, and grid‐connected modes. The developed control strategy comprises two principal control tasks: the first one is a two‐layer power management algorithm (PMA). The response of two‐layer PMA enables to detect the islanding mode in case of grid fault and to monitor the renewable power generation into nine operating modes according to the state‐of‐charge of each storage system. The second task is droop control. This droop control aims to reduce grid voltage and frequency variations. It controls exchanged powers with grid to ensure its stability. It also ensures a continuous supply of load in case of grid fault. The system is simulated using MATLAB software, and results are provided in order to show the feasibility of this control strategy.

The purpose of this article is to provide a high performance control of a renewable distributed generator to guarantee an electric power quality and jointly reduce the mechanical stress despite any possible uncertainties such as the random nature of wind speed, the presence of parameters uncertainties, and external perturbations acting on the system (sudden load variation). The renewable distributed generator integrating a wind generator associated with a batteries module is considered as an energy storage device and a variable load. The proposed method is designed by a power management supervisor and a sliding mode control technique. First, the power management supervisor is used to monitor the power flows transferred between the different system devices depending on the load variation and on the intermittency of wind production. In fact, it enables to ensure the balance at the continuous, Direct Current (DC) bus between the powers supplied by the renewable distributed generator and those demanded by the load. In addition, it prevents batteries from exceeding its maximum or minimum state of charge (SOCbat) by keeping it at an acceptable level [30%, 90%]. Second, a second-order sliding mode control based on the super-twisting algorithm is suggested to control the two subsystems (generator side and load side converters). The main target of the first one is to extract the maximum wind power taking into account the parameter variations and the fluctuating nature of wind. The second one is to investigate a second-order sliding mode control of active and reactive load power quantities, which provides better results in terms of attenuation of the harmonics present in the load voltage and current while considering the sudden load variations. In addition, a proportional–integral controller is also designed and simulated to establish a comparison framework. According to the simulation results, the second-order sliding mode control successfully deals with the nonlinearity of the renewable distributed generator compared with the proportional–integral performance.

This paper proposes a comparative study between different control strategies of a wind energy conversion system. It aims to guarantee a robust control strategy which gives a good performance despite the external disturbances. Studied system comprises a wind turbine, a permanent magnet synchronous generator and two converters linked by a DC bus. The whole is connected to the grid through an RL filter. A classical vector control based on PI controller is applied to our system. Owing of the sensitivity of this control against external disturbances, a control strategy using first-order sliding mode has been proposed. This strategy provides good performance, such as insensitivity to nonlinearity of system. Yet, the theory of first sliding mode has faced the problem of chattering, which proved to be a major drawback. To overcome this problem, a control strategy using sliding mode of higher order was implemented on the basis of the super-twisting algorithm.

Setting an adequate operating power reserve (PR) to compensate unpredictable imbalances between generation and consumption is essential for power system security. Operating power reserve should be carefully sized but also ideally minimized and dispatched to reduce operation costs with a satisfying security level. Although several energy generation and load forecasting tools have been developed, decision-making methods are required to estimate the operating power reserve amount within its dispatch over generators during small time windows and with adaptive capabilities to markets, as new ancillary service markets. This paper proposes an uncertainty analysis method for power reserve quantification in an urban microgrid with a high penetration ratio of PV (photovoltaic) power. First, forecasting errors of PV production and load demand are estimated one day ahead by using artificial neural networks. Then two methods are proposed to calculate one day ahead the net demand error. The first perform a direct forecast of the error, the second one calculates it from the available PV power and load demand forecast errors. This remaining net error is analyzed with dedicated statistical and stochastic procedures. Hence, according to an accepted risk level, a method is proposed to calculate the required PR for each hour.

Renewable energy sources and storage units’ integration in the railway power substations is an alternative solution to handle the energy consumption, due to railway traffic increase and electricity market liberalization. To integrate this technology change in the railway network, an adapted energy management system has to be established. However, when considering only energy efficiency aspects on the energy management strategy, an economical viable solution cannot be ensured. This paper proposes a supervision strategy based on multi-criteria approach including energetic, environmental and economic constraints. The energy management objectives such as reducing the network power demand, favoring local renewable consumption and ensuring storage availability are treated in different time levels. Economic aspects are first integrated in predictive mode based on forecast data. Then a supervision strategy is developed based on fuzzy logic approach and graphical tool to build it. An optimization study of the supervision strategy is proposed in order to conclude on system performance. Simulation results are discussed for different scenarios cases and the reaction of the hybrid railway power substation is detailed. Results show that this methodology can be successfully applied for hybrid systems energy management in order to improve their energy efficiency.

Generally, battery lifespan depends on the number of cycles and depth of discharge (DOD). Nevertheless, in a renewable hybrid power system, charge and discharge cycles are random and not regular. Therefore, it is important to develop an aging model suitable to this case. Thus, in this paper, a pertinent way for aging lead–acid batteries connected to a stand-alone multi-source renewable system has been developed. It is based on the Rain Flow method for counting cycles and considers instantaneous DOD and average temperature. In fact, for each functioning year, a classification of the number of cycles according to the DOD is done. Then, based on these data, the battery degradation rate is estimated so that it is possible to draw conclusions about battery lifespan. The method has been successfully applied to a multi-source power system simulated dynamically under Matlab/Simulink. This last takes into account with good accuracy several inputs and elements such as sun irradiation, wind speed, load profile, photovoltaic generator, wind turbine, and diesel generator. Results show the influence of the DOD and the batteries nominal capacity on their lifespan. A mean of eight years’ life is detected. Finally, a reasonable over-sizing may favor battery longevity.

This paper is focused on the design and the implementation of a hybrid PV-wind power system with batteries. It aims to emulate the behavior of a hybrid power system in order to face load consumption variations. Final system includes relevant contributions such as quality of emulator (a large number of parameters are considered); capacity to study various impacts simultaneously, a fast dynamic and a set of experimental tests that have been achieved and validated with a test bench. Moreover, a relevant supervision strategy based on currents control and batteries State Of Charge (SOC) estimation has been successfully performed despite simplicity of converter controls.

In this paper, new robust fuzzy scheduler fault tolerant control is proposed to tackle multivariable nonlinear systems subject to sensor faults, actuator faults and parameter uncertainties. Takagi–Sugeno fuzzy model is employed to represent the nonlinear wind energy systems, and then a model-based fuzzy scheduler controller design use the concept of general-distributed compensation. Takagi–Sugeno fuzzy systems are classified into three families based on the input matrices and a fault tolerant control synthesis procedure is given for each family. In each family, sufficient conditions are derived for robust stabilization, in the sense of Lyapunov method and Taylor series stability, for the Takagi–Sugeno fuzzy system with parametric uncertainties, sensor faults, and actuator faults. The sufficient conditions are formulated in the format of linear matrix inequalities. The effectiveness of the proposed controller design methodology is finally demonstrated through a wind energy system with doubly fed induction generators to illustrate the effectiveness of the proposed method.

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.

As the proportion of grid-connected variable renewable energy (VRE), mainly wind and solar photovoltaic (PV), getting higher, the demand for flexibility in the power system increases intensely, posing unprecedented challenges to the economic and stable operation of the power system. In order to meet the growing flexibility needs, new technologies such as rapid grade climbing and highly flexible combined cycle gas turbine power generation will be required on the power supply side, as well as new technologies such as energy storages. In addition, the expansion of trans-regional power transmission capacity will be another important source of power system flexibility. Together, these technologies have created huge opportunities to help the power system to integrate more VRE. In this study, a practical framework study for flexibility assessment and its allocation was proposed to investigate the flexibility demand change with high penetration of VRE in the power system. By focusing on the various flexible resources available, an optimal portfolio of flexibility solutions to facilitate VRE integration is studied. The study also explores especially the potential of extend grid interconnection as a flexible resource to make the power system more economical and low carbon.

High levels of renewable generation incorporation increase the flexibility requirements of the electrical system in response to fast and large variations in load and renewable energy output. Advanced power system operational methods and algorithm are required to schedule the generating units with more reliability and efficiency. In order to deal with uncertainties in generation planning of an urban microgrid, this paper presents a storage control strategy for reserve provision in a multi-objective scenario-based stochastic optimization algorithm. Power reserve allocation is optimized while uncertainties from renewable energy and load demand are taken into account. Results confirm that the presented algorithm ensures that storage-hybridized RESs contributes to the primary source of reserve capacity, and the performance in terms of security level are highlighted.

The intermittent nature of solar energy creates a significant challenge for the optimization and planning of future smart grids. In order to reduce intermittency, it is very important to accurately predict Photovoltaic (PV) power generation. This work proposes a new prediction method based on the Recurrent Neural Network (RNN) for accurately predicting the yield of photovoltaic power generation systems. Our study used a Longe Short-Term Memory (LSTM) architecture. The LSTM approach can store information over time, which is valuable for time series prediction. The proposed prediction method is evaluated using real PV energy in Lille, France. Firstly, all solar time series data are divided into three main parts: 70% of the data are used to train the neural network, 20% of the data are used for verification and the other data are used for testing. The proposed prediction method has a good prediction quality in very short term (one-hour), which proves the reliability and cost-effectiveness of this method.

In this paper, we present a distributed approach to optimise self-consumption on a university campus grid. The grid contains photo- voltaic generators, electric vehicles, loads and a battery. We propose to solve the optimisation problem with a distributed method using game theory, where each element of the grid tries to reach its own objectives. In addition to this optimisation framework, we develop a physical model of the grid. This model uses real consumption and production data. We use it to simulate the production and consumption profiles obtained from the optimisation problem in order to check if these solutions respect the grid constraints. Finally, we propose to implement concretely this dis- tributed approach using a private blockchain, which stores production and consumption data. In addition, a smart contract is deployed on the blockchain to transcribe the game theory framework. The smart con- tract collects the preferences of each element of the grid and launches the optimisation process. Then the blockchain gathers the results and replaces the role of a central optimisation supervisor. We present some preliminary results to illustrate our method.

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.

In electrical systems, the main objective is to ensure that users demand is met at the least cost without having imbalance between generation and consumption. Thus, the uncertainty of photovoltaic (PV) power production must be considered in generation planning. In this paper, the optimal generation scheduling including the operating reserve (OR) provision are developed under stochastic characteristics of PV renewable energy in an urban microgrid. With a prescribed risk level of unbalancing, a dynamic programming (DP) algorithm sets the operational planning of conventional generators. Then, the operational cost and available operating reserve can be calculated. The effect of PV power uncertainty into UC is then analyzed by considering forecast intervals of PV forecasting. The proposed methods consider PV prediction uncertainties with a 95% confidence level. The unit commitment is then recalculated as well as new generator set points with same criteria. Hence, variations of the targeted minimized costs and obtained OR is analysed according to the considered uncertainty.

This study concerns a grid-tied Photovoltaic (PV) generator related to an hybrid storage system composed by lithium NCA battery (energy source) and Maxwell super-capacitor (power source). Two supervision algorithms have been proposed for Energy Management System (EMS): a Boolean and a fuzzy logic EMS. Moreover, a comparative study between both supervision algorithms based on Levelized Cost of Energy (LCOE) and the lifespan of the storage system has been suggested. The economic analysis is done with two different planned PV power production profiles: one with a "clear sky" bell curve and a second with an ideal forecast. The supervisor based on Boolean method is simple and easy for understanding, while the fuzzy logic method offers more flexibility in supervision. It improves a little batteries lifespan and system performances and reduces significantly the system penalties. The simulation results show for all scenarios the achievement of the planned aims in terms of respecting of the production program taking into account the constraints of the electrical network manager with a LCOE below 130 €/MWh.

Electrochemical energy storage is emerging as a predominant technology for smartgrids, ancillary services and renewable energies applications. Hybrid renewable energy sources (RES) with storages, especially batteries, are increasingly used to balance electrical system uncertainties and to increase control dynamics. In order to facilitate the system modeling, in this paper, different power models of lithium-Ion (Li-ion) batteries, connected to a photovoltaic (PV) system, have been studied. The thermal or electrochemical characteristics are not studied since the focus of this paper is about Li-ion battery power model. A selection analysis is proposed based on the comparison of both complicity and detailed precision of the models.

This paper gives a definition of collective self- consumption and introduces the current regulatory framework in some European countries. It proposes a review of relevant Demand Side Management (DSM) methods applicable to improve the collective self-consumption rate. It also introduces the concept of blockchain and its possible applications to collective self-consumption, with a focus on some current experimentations. Current blockchain applications include validation of measured data and energy transactions. New architectures propose a completely decentralized energy market and grid control based on the blockchain technology. However, a deeper analysis of the benefit of blockchain is required. The legal framework will also play a role on the future deployment of these applications.

Electrical system operators and automatic controllers use Operating Reserve (OR) power to mitigate the unexpected imbalance between power supply and load demand. This backup power should be carefully sized and dispatched to reduce operation costs while keeping a satisfying security level. With the integration of small sized intermittent renewable generators in distribution networks, the allocation of OR where they are connected is interesting in order to compensate directly these uncertainty sources and maintain the security and reliability. In this paper, we consider the provision of OR directly by distributed PV generators combined with energy storage systems. For an urban microgrid, comparisons are given with the OR provision by a micro gas turbine. A method for dynamic joint dispatching of OR power on both generator types is presented and tested. Results show new insights in the possibilities of OR dispatching with renewable energy sources.

In this paper, we propose a controller and a Power Management Supervisor (PMS) based on Fuzzy Logic (FL) for a grid-connected wind power system associated with Hybrid Energy Storage (HES) made up of Batteries (BT) and Supercapacitors (SC). Batteries are used to meet the energy requirements for long-term, while SC is used to meet the demand for instant power. The SC can act as a buffer against large magnitudes and rapid fluctuations of power. The PMS is developed to manage the energy flows between the storage devices by maintaining these State Of Charge (SOC) into acceptable levels and establishes the priority order between them. The main objective of this work is the combination of two storage technologies and FL supervision implementation to meet the expectations of various production scenarios for a wind generator, to keep stable the DC bus voltage, and to participate in ancillary services such as: respecting a production program, supporting the grid and optimizing storage elements lifespan. Simulation results prove the efficiency of proposed power control and supervision strategy.

This paper deals with Takagi-Sugeno fuzzy logic applied to an autonomous photovoltaic system for power maximization. It includes a DC-DC buck converter state model connected between the PV panel and the load in order to maximize the output power. The TS fuzzy MPPT controller is compared to the classical P&O and fuzzy Mandani algorithms. Results show that: thanks to its capacity to transform the non-linear system in a set of local linear sub-systems, Takagi-Sugeno approach leads to better performances.

In this paper, we are interested in a nonlinear control of a wind farm connected to the electrical network based on permanent magnet synchronous generator. This control has two main objectives. Firstly, since the major problem of wind energy is the fluctuation of its primary source, to solve this problem, we develop in this study a control approach applied to this wind farm to ensure balance between production and consumption without adding storage organs despite intermittent form of the wind speed. Secondly, a comparative study between a classical PI controller and a non-linear regulator sliding mode are used to control our farm has been proposed. Due to external and internal perturbations of wind systems and the high range of wind speed variation, a PI controller has limited performances. It is sensitive against uncertainties and fluctuations. The simulation results validate the control strategy developed and show superior behaviour of the sliding mode than the PI controller.

This paper focusses on the design and the optimization of a photovoltaic system that includes battery storage. We have developed an optimization method that takes into account Life Cycle Cost (LCC) and Loss of Power Supply Probability (LPSP). Batteries’ lifespan is considered at the design phase especially for replacement. This approach is rather recent in the literature and brings a significant contribution. For the improvement of the management system, other aspects such as the inclination of the panels as well as batteries charge or discharge are considered. Finally, the methodology will help developers and installers to design appropriate renewable systems, particularly in standalone areas.

This paper discusses a robust control of a wind energy conversion chain based on a Permanent Magnet Synchronous Generator. This command is evaluated in the context of controlling the speed of PMSG and to control the active and reactive power injected into the grid, to increase the power generated and thus improve the efficiency of wind power system. A non-linear controller was implanted. The control of wind systems mainly uses the Proportional Integral controller. However, this type of regulators does not give good control performance because of the disturbance and nonlinearity of the wind systems. Therefore, a nonlinear robust control strategy based on Second-Order Sliding Mode is implanted. In order to validate the proposed control strategy, a system dynamics model proposed was modeled and simulated in MATLAB / Simulink environment.

Energy storages are emerging as a predominant sector for renewable energy applications. This work concerns a photovoltaic system that combines two storage technologies (one for energy needs NCA batteries and another for power SuperCaps Maxwell). A smart supervision algorithm based on fuzzy logic has been successfully developed. Simulations show that it achieves the desired objectives in terms of compliance with production program while respecting the various constraints of electric grid manager (power smoothing, frequency regulation, etc.). In addition, a comparative study of different storage configurations especially in terms of storage components lifspan and system Levelized Cost of Energy (LCOE) has been carried out. A life cycle cost analysis has been made to help designers in their choice. Results shows that: to have a good frequency adjustment with respect of the production program and with a reasonable storage total cost, a power storage ≥ 50% installed PV power must be used. A ratio of one is recommended. In addition, the studied system have a LCOE below 250 €/MWh.

In this paper, a photovoltaic system with hybrid storage connected to the grid has been studied. A smart supervision algorithm based on fuzzy logic has been successfully developed. In addition, a comparative study of different hybrid storage possibilities in terms of batteries longevity has been done using rain-flow cumulative damage method.

In this paper, a photovoltaic system with super capacitors and batteries hybrid storage connected to the grid has been studied. A smart supervision algorithm based on fuzzy logic has been successfully developed. In addition, contribution of storage hybridization on batteries longevity has been proved using rain-flow cumulative damage method.

Due to renewable energy sources (RES) variable nature and their wide integration into power systems, setting an adequate operating power reserve is important to compensate unpredictable imbalance between generation and consumption. However, this power reserve should be ideally minimized to reduce system cost with a satisfying security level. Although many forecasting methodologies have been developed for forecasting energy generation and load demand, management tools for decision making of operating reserve are still needed. This paper deals with power reserve quantification through uncertainty analysis with a photovoltaic (PV) generator. Indeed, using an artificial neural network based predictor (ANNs), PV power and load have been forecasted 24 hours ahead, and also forecasting errors have been predicted. Through forecasting uncertainty analysis, the power reserve quantification is calculated according to various risk indexes.

Renewable energy sources have a variable nature and are greatly depending on weather conditions. The load is also uncertain. Hence, it is necessary to use power reserve equipment to compensate unforeseen imbalances between production and load. However, this power reserve must be ideally minimized in order to reduce the system cost with a satisfying security level. The quantification of power reserve could be calculated through analysis of forecasting uncertainty errors of both generation and load. Therefore, in this paper, a back propagation artificial neural network approaches is derived to forecast solar radiations. Predictions have been analyzed according to weather classification. Some error indexes have been introduced to evaluate forecasting models performances and calculate the prediction accuracy. Forecasting results can be used for decision making of power reserve for renewable energy sources system with some probability or possibility methods.

Renewable energy sources and storage units’ integration in the railway power substations is an alternative solution to handle the energy consumption, due to railway traffic increase and electricity market liberalization. To integrate this technology change in the railway network, an adapted energy management system has to be established. However, when considering only energy efficiency aspects in the energy management strategy, an economical viable solution cannot be ensured. This paper proposes a supervision strategy based on multi-criteria approach including energetic, environmental and economic constraints. The energy management objectives such as reducing the network power demand, favoring local renewable consumption and ensuring the storage availability, are treated in different time levels. Economic aspects are first integrated in predictive mode based on forecast data. Then a supervision strategy is based on fuzzy logic approach and graphical methodology to build it. Simulation results are discussed for different scenarios cases and the reaction of the hybrid railway power substation is detailed.

This paper focuses on modeling and simulation of a buck converter based on a PV standalone system. This advanced synthetic study includes PV generator modeling with parameters identification, an improved P&O (Perturb and Observe) algorithm with adaptive increment step and a detailed approach of DC-DC converter modeling. A thorough method is used to determine parameters for PI current controller. The set was used to simulate a whole photovoltaic conversion chain under Matlab/Simulink environment. A special focus has been dedicated to the influence of solar irradiation variations on converter dynamic and behavior as a range of PV current 0-8 A was considered. Simulation results confirm stability, accuracy and speed response of synthesized regulator and MPPT algorithm employed. Dynamic analyses show that the more restricting control concerns low values of current Ipv (low irradiation). This work will be useful for PhD students and researchers who need an effective and straightforward way to model and simulate photovoltaic systems.

Railway traffic increases and electricity market liberalization constrain the railway actors to consider new solutions to handle the energy consumption. Hence, a technology change in the railway electrical systems is considered through the integration of renewable energy sources and storage units. In this context, a relevant methodology is proposed here for optimal design and operation analysis of railway hybrid power substations. This method is useful for the analysis and improvement of future railway power network efficiency.

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).

Les sources renouvelables ont des caractéristiques qui posent un problème majeur pour l’équilibre du réseau et l’équilibre offre-demande de manière générale. Il s’agit en effet de sources d’énergie variables et intermittentes, puisque dépendantes de grandeurs météorologiques par nature variables. Une solution pour remédier à ce problème majeur est de combiner plusieurs sources de production (comme par exemple le soleil, le vent, …), l’ajout de systèmes de stockage pour pallier au caractère intermittent de ces énergies et la gestion intelligente de l’ensemble afin de mettre en adéquation la production avec l’usage de l’énergie. On parle alors d’un système énergétique complexe qui nécessite une conception, une commande et une gestion énergétique optimales et efficaces. La difficulté consiste à bien dimensionner l’ensemble et assurer une gestion énergétique efficace avec des commandes locales robustes tout en minimisant les pertes de puissance. C’est ma problématique de recherche concentrée autour du thème commun de l’« Optimisation » des systèmes énergétiques complexes comprenant des sources d’énergie renouvelable et du stockage. En effet, au cours de ces dix dernières années, j’ai orienté mes recherches sur le développement et l’application de méthodologies pour l’optimisation du dimensionnement, de la supervision et de la commande de ces systèmes. Ces activités de recherche ciblent un but précis : développer un outil pour l’optimisation du dimensionnement des systèmes multi-sources et multi-stockages. Cet outil intègre une démarche d’optimisation multicritères avec des méthodes de gestion énergétiques intelligentes et multiobjectifs (prise en compte du coût économique, du coût écologique, des contraintes de stockage, de la satisfaction des charges, etc.). Ces activités ont été menées via la participation à des projets de recherche, l’encadrement de doctorants, de post doctorants et de stagiaires de MASTER et ont donné lieu à plusieurs publications en revues et en conférences. Ils prouvent que j’ai acquis le niveau et les compétences me permettant de prétendre à la direction indépendante de recherches.

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.

This paper aims to form a fully comprehensive reference on photovoltaic systems control. It presents a compilation of information about photovoltaic system configurations and their control from a beginner’s perspective. In addition, an example of a detailed PV system modeling and simulation is presented to apply the concepts discussed.

This article presents the modeling, control design and simulation of a variable speed Wind Energy Conversion System (WECS). The WECS contains a wind turbine that drives a permanent magnet synchronous generator (PMSG). The wind turbine and the PMSG are connected to a DC bus voltage through AC/DC converter. The power at the DC bus voltage is injected to the power grid through an inverter and RL (resistance and inductance) filter. The purpose of the modeling is to apply a control design able to ensure the operation of the wind generator in the maximum power point, to keep the DC bus voltage stable in its reference value, and to independently control the active and reactive powers injected to the grid. In addition, a simulation test of the detailed WECS is presented to demonstrate the concepts discussed.

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.