This article presents a cost-based robust charging scheme for plug-in electric vehicle (PEV) integrated with the beneficial collaboration of photovoltaic (PV) system. The uncertainty of load/PV power and electricity price is incorporated into the proposed model. To handle the altering feature of the uncertainty resources and achieve a robust charging strategy, the information gap decision theory (IGDT) is utilized. Since the formulated IGDT-based charging model is inherently a multiobjective optimization problem, the nondominated sorting genetic algorithm type 2 (NSGA-II) is used to create the Pareto optimal solutions. To attain the best compromise solution, a fuzzy-oriented selection method is introduced. Moreover, to elucidate more effective constraints for the robust territories (RTs) associated with the uncertainty resources of the proposed model, an evaluation based on the deterministic and nondeterministic operation costs is discussed. To validate the proposed robust charging strategy, a power hardware-in-the-loop experimental setup is developed using OPAL-RT 5700. Furthermore, the effectiveness of the NSGA-II in terms of both robustness and convergence efficiency is validated through comparison with single-objective particle swarm optimization, multiobjective particle swarm optimization and GUROBI solver.

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This paper presents a grid-integrated multilevel inverter based on Packed E-Cell (PEC) structure for connecting renewable energy sources to the existing power grids. This dual source topology benefits from low number of semiconductor devices, simple robust control strategy, and smooth current of the capacitors. The shunt-capacitors of the converter are charged or discharged simultaneously to a quarter of the input DC source via horizontally extension design of the circuit. Thus, by applying only one voltage sensor and simple phase disposition pulse width modulation approach (PD-PWM), the operation of the inverter to generate 17-levels for the output voltage is ensured. For improving the dynamic performance of the converter, a closed-loop controller is designed and a precise mathematical analysis is presented as well. Accurate performance of the proposed structure is assessed by calculating theoretical analyses and simulating the inverter in MATLAB Environment.

The main challenge in hybrid wind-battery systems is the battery cost including investment and replacement. Thus, numerous studies have been conducted on cost minimization. Previous studies often focused on the battery sizing, while the battery performance has also the significant effect on the battery replacement and in turn battery cost. In this paper, a new approach is proposed to improve the battery performance and lifetime. We proposed an extended two-set battery model with new scenarios and formulation to achieve a balance between batteries and managing their power during prediction error. In this model, shallow charge/discharge cycles are eliminated, and batteries experience complete cycles. Furthermore, the battery availability is increased. The simulation results for a case study are presented and the results show the effectiveness of the proposed approach in comparison of conventional models.

In this paper, an adaptive fuzzy passivity-based control strategy is proposed to provide stable operation for a grid-tied nine-level packed E-Cell (PEC9) converter. Passivity theory is developed in the first step to enable the error dynamics of the PEC9 converter currents to reach a much more effective convergence rate using the proposed damping coefficients. Moreover, various operating margins are achieved for the damping coefficients through PEC9 current errors-based 3D curves. In order to establish online estimation for the damping coefficients, the fuzzy controller and Artificial Neural Network (ANN) are combined. Experimental and simulation results are employed to validate the accuracy of the proposed control technique under load variation, parameter mismatch and DC-link voltage variation.

Smart charging/discharging of Plug-in Electric Vehicles (PEVs) is definitely able to bring a significant cost benefit for both EV station owners and EV owners as well. To this end, this paper develops a Mamdani-based fuzzy strategy with the inputs including the forecasted and real PV generation data, electricity price, and the instantaneous state-of-charging (SOC). Moreover, the output power of PEVs is considered as the output of the proposed strategy. In order to attain further smooth and accurate power sharing for PEVs, varied output rules are assigned commensurate with the rated power of each PEVs. In addition, another fuzzy strategy is developed using the instantaneous SOC error to establish a specified SOC for PEVs at the departure time. While two PV panel profiles are taken into account, Simulation in MATLAB/Simulink environment presents accurate and acceptable results from SOCs of PEVs, PEVs output power, prices and grid power.

This is a fact that the battery life is inversely related to the number of discharge cycles. However, these days, the bidirectional smart charging scenario of plug-in electric vehicles (PEVs) has become a hot trend, using them as a storage source at peak load or price. Therefore, this paper aims to present a simple and novel technique in predictive-based linear programming for limiting the number of discharge cycles of PEV. In this regard, a specified number of cycles is defined by the user such that the predictive algorithm tries to use these cycles in the most optimal way. In this paper, first, the effect of the discharging cycle on the battery life available in the literature is presented. Then, the photovoltaic-assisted charging station configuration and desired optimization algorithm are introduced. Following, the main novelty of this work, i.e. a linear technique for limiting the number of discharging cycles will be described. Finally, the simulation results are provided to validate the performance of the proposed method.

An accurate reference current detection (RCD) algorithm with the ability of double-frequency oscillation cancellation (DFOC) is designed in this paper to control a nine-level packed E-cell (PEC9) as a single-phase active power filter (SPAPF). The reference current is detected through DFOC-based RCD and subsequently injected by the converter. The detected current contains all unnecessary components and is accurate enough to keep the grid current in sinusoidal form with the THD under the permitted threshold. The proposed PEC9-based SPAPF not only guarantees the power quality at the grid side, but also benefits all advantages of PEC multilevel converter. The effectiveness of the proposed SPAPF is assessed in comparison with conventional one and its performance is investigated via simulation studies by using MATLAB/SIMULINK software.

The complementary energy exchange between residential microgrids aims at decreasing the dependence on the main grid, reducing the size and cost of the energy storage units, and increasing energy efficiency. In this regard, the use of a power router interface to connect the microgrids to the power system is necessary for controlling bidirectional power and data flow, which will build the core of the future Energy Internet. In fact, the power router is served as an energy exchange center that enables energy flow between microgrids. One of the most important factors in determining multimicrogrid system performance is the energy routing algorithm strategy. In this article, a new routing algorithm is proposed based on the graph theory. The objective of the proposed algorithm is to minimize the overall power losses with respect to congestion and reliability. Therefore, the best set of loads, optimal sources, and no-congestion minimum loss paths are determined. Various analysis cases have been investigated to confirm the performance and flexibility of the proposed algorithm. A novel case study with multisources and multiloads is addressed, which has not been previously analyzed with the application of power routers. In addition, the developed algorithm is compared with other existing algorithms in terms of power loss minimization, different scenarios, congestion management, computation time, and complexity.

Nowadays, statistics show that more than 90% of renewable energy systems are connected to the European distribution networks inducing technical issues and costs increase for network reinforcement. However, network reinforcement may not be the most efficient solution from both energy and economic point of views. Therefore, new strategies need to be implemented in order to efficiently manage both grid infrastructure and renewable resources. These strategies should use flexible passive and active users, such as demand-response and storage systems. Hence, in this paper, a contract-based framework for capacity allocation of microgrid's flexibilities is discussed to implement an active management of distribution grids. Moreover, an optimization model for day-ahead congestion management scheduling based on generation and flexible load flexibilities in microgrids is proposed. At the end, the effectiveness of the proposed model for both distribution system operators and microgrids is tested with a case study and simulation results are reported.

This paper demonstrates a double layer optimization strategy to determine the optimum size of battery energy storage system (BESS) considering the EMS of a microgrid (MG). In the developed model, the BESS sizing problem is viewed as the outer optimal loop and the economic dispatch of MG based on BESS data from the outer loop is considered as inner loop. An iterative method and a dynamic programming (DP) method are utilized to solve the optimal problems for outer and inner models respectively. A simulator is built in MATLAB environment for an island microgrid is used to evaluate the efficiency of the proposed method.

The world is shifting towards Renewable Energy Sources (RES) to meet the increasing demand. With proportionally high penetration of RES, the inertia of the grid tends to fall due to the incapability of these sources to participate in control mechanism. Also, due to the indeterminacy in the generation pattern, they tend to feed small signal disturbances in the grid due to the imbalance of generation and demand, which affects the frequency stability. In this paper, the affect of inertia emulation (IE) in the grid is presented to mitigate the small signal disturbances caused due to the change in load or due to the penetration of the RES. This work has considered Electric Vehicles for the application of vehicle to grid (V2G) towards frequency control. A cooperative control method, with V2G and synchronous power controller (SPC) based HVDC links, has been proposed towards the control of the fluctuations and a considerable decrease in the deviations have been observed. Several case studies are conducted and the results are presented to proof the concept.

In this article an energy management strategy for a Modular Multilevel Converter is proposed, this converter is operating as a direct frequency changer. It is connected in three-phase configuration and the developed control strategy depends only on measurements made at the converter site without using neutral wire parameters information, which is convenient for high power applications. Developments are conducted to use the converter in Offshore System working at Low Frequency, but also it can interconnect 50 with 60 Hz systems. Simulation results prove the efficiency of the control strategy with almost inexistent low frequency harmonics without using any filter.

A control strategy to manage the circulating current content in a Modular Multilevel Converter in direct AC-AC topology is proposed in this article. The converter is connected in three-phase YDY configuration which forms a closed delta path for free current circulation while keeping outsider waveforms with low THD content. Circulating current correction becomes necessary face to any kind of unbalance or even to small power transition. Simulations results show multiple frequency content in waveforms using a resonant corrector and performances against system unbalances.

Permanent magnet synchronous motor (PMSM) is extensively used in industries. Recently, active disturbance rejection controller (ADRC) based control designs stand out due to advantages for a complex uncertain PMSM servo system. A general design procedure for the ADRC based PMSM control is discussed at the beginning. Then, the ADRC based control designs applied to PMSM servo systems are fully reviewed, in which several aspects are involved: position control, speed control, current control, cascaded ADRC control, and position identification. Moreover, to verify the effectiveness of the ADRC based control, simulations for the PMSM speed control are conducted in the Matlab/Simulink. Conclusions and the future research directions are indicated in the end.

Micro-hydropower has been paid more attention for its huge potential in the renewable energy section. The hydraulic turbine efficiency is sensitive to the water flow rates and rotation speed variations, which makes the variable speed operation preferable to ensure the maximum power point tracking (MPPT) in a micro-hydropower generation system (MHPGS). However, a MHPGS is a complex coupled nonlinear system disturbed by large internal dynamics and external disturbances, which bring negative influences on the speeding tracking performance. The active disturbance rejection control (ADRC) has advantages in dealing with large uncertainties, as it can estimate both internal and external disturbances and then compensate the influences in the closed loop dynamics in real time. To improve the anti-disturbance ability of the speed tracking process for a MHPGS, an ADRC based MPPT strategy is put up in this paper. Finally, simulation and experiment results have proved the high efficiency of the proposed strategy.

Non-intrusive load monitoring (NILM) deals with the identification and subsequent energy estimation of the individual appliances from the smart meter data. The state of the art applications typically runs once per day and reports the detected appliances. In this work, data driven models are implemented for two different sampling rates (10 seconds and 15 minutes). The models are trained for 20 houses in the Netherlands and tested for a period of 4-weeks. The results indicate that the disaggregation methods is applicable for both sampling cases but with different use-case.

This manuscript explores the selection of appropriate mixed strategies (MSs) in a Mixed Strategist Dynamics (MSD) application for load management of Plug-in Electric Vehicle (PEV) fleets. This selection is based on the convenience of PEV owners, aiming to choose those MSs that privilege early high (or fast) charging rates when it is possible. The previously published MSD and Maximum Entropy principle (MSD-MEP) approach is revised and illustrated with several examples, specially in the context of selection of MSs. This revision allows a wider understanding of the method, and aims to inspire new contributions on domains where distributed optimization methods are pertinent. Results obtained without any management structure are compared to those obtained with the MSD-MEP approach under different scenarios, where full sets of MSs and reduced sets of convenient MSs are applied. The performance of the method, using conveniently reduced sets of MSs, is tested with real historical active power measurements, provided by the SOREA utility grid company in the region of Savoie, France.

This paper presents the multilevel A-diakoptics methodology (diakoptics based on actors) for the dynamic load-flow simulation of hybrid distribution systems (DSs), which are power systems working at different base frequencies. In the development of the smart grid, several challenges have been identified, such as the connection of nonconventional loads, distributed generators, and interoperability between power systems working at different frequencies. These challenges have led to use simulations for designing and developing the future grid. Additionally, computer hardware architectures have evolved to allow modeling the real world more accurately. However, the existing simulation methods for power-flow analysis are not compatible with parallel and concurrent processing, subusing the existing computer power. Our approach called A-Diakoptics combines the power of Diakoptics and the Actor model to make any conventional power-flow analysis method suitable for multithread processing. As a result, the nature and complexity of the power system can be modeled without affecting the computing time, even if several parts of the power system operate at very far modes or bandwidths such as in the case of dc microgrids. This method is an advanced strategy for simulating large DSs in unbalanced conditions, covering the basic needs for the implementation of multiscale grid dynamics.

In this paper, an application of evolutionary game dynamics is proposed in order to profit from the desirable features of mixed strategist dynamics in the solution of distributed resource allocation problems. The key idea of this approach is to define mixed strategies that represent the vertices of the convex hull of the set of feasible solutions defined by the constraints of the local optimization problems. With this approach, the dynamics of the state vector is restricted to a subset of the simplex. Details of the defintion of appropriate mixed strategies, payoff functions, and the multi-population modeling followed for this problem are provided.

As the number of Electric Vehicles (EVs) has continuously increased in the last years, due to environmental issues, the interest is to find the maximal penetration rate of EVs, which can meet both, technical and environmental constraints. The technical impacts can be reduced by applying an Energy Management Strategy (EMS), which in the present study increases the maximal allowed EVs' penetration rate with over 41%. The energetic mix has a great importance concerning the environmental impacts. If the renewable energy is injected in the grid as distributed energy, it also has benefic impacts regarding the technical constraints. Consequently, a multi-objective and multi-variable optimization is performed, in order to find an optimal energetic mix for a maximal number of EVs, with an appropriate EMS.

The article tackles the issues related to the identification of electrical appliances inside residential buildings. Each appliance can be identified from the aggregate power readings at the meter panel. The possibility of applying a temporal multilabel classification approach in the domain of nonintrusive load monitoring is explored (nonevent-based method). A novel set of metafeatures is proposed. The method is tested on sampling rates based on the capabilities of current smart meters. The proposed approach is validated over a dataset of energy readings at residences for a period of a year for 100 houses containing different sets of appliances (water heater, washing machines, etc.). This method is applicable for the demand side management of households in the current limitation of smart meters; from the inhabitants or from the grid operator's point of view.

In this paper, the problem of grid integration of a kite generator system (KGS), is handled. The mechanical power generated by the kite's traction is translated into an electrical one via a permanent magnet synchronous machine. This power is then injected in the grid or used to supply an isolated load after passing a power electronics interface. Control schemes have been developed for grid connected or stand-alone operation and tested on a hardware-in-the-loop simulator.

Charging of a large number of electric vehicles (EV) at the same time raises several technical problems or can have significant impacts on power systems like high peak power consumption. During daytime, when EVs are connected at the different charging stations at the same time, they can cause a congestion problem in the network. The paper proposes charging strategies to reduce the peak load. These strategies are based on the interruption (on/off) or the modulation of EV charging power. By dividing the daytime in many intervals, a binary linear programming combined with the bisection scheme is used to manage the charging plan of the vehicles. The first application is used for reducing peak load in daytime. The second application is proposed to limit the charging power during a time defined by DSO (or TSO). The performance of the proposed strategies is validated by simulations for a charging station of 50 electric vehicles with a fast calculation time. The results obtained show that peak power can be reduced by more than 50 per cent.

This paper provides a linear approach to compute the voltages at any node on a residential grid based on the house instantaneous load and the presence of charging Plug-In Hybrid Electric Vehicles (PHEV) on the grid (and the corresponding instantaneous consumption or injection). Based on this linear operation, the paper provides a detailed Linear Programming (LP) formulation of the problem of charging the PHEVs while providing a voltage support service to the grid. The approach gives optimal charging schedules for each PHEV in a centralized way, looking for benefit on the customer's perspective. Multiple evaluation cases are included in order to test the ability of the approach to maintain voltages within safety limits and provide optimal consumption/injection policies. An additional case is included to prove the potential of the PHEVs to solve existing voltage technical issues on a residential grid. The formulation is proposed as a benchmark to identify possible benefits and elements that could be useful for more realistic applications.

This paper presents a new control strategy for Kite Generator System (KGS). The proposed feedback strategy is based on motion planning using the virtual constraint approach and ensures exponential orbital stability of the desired trajectory. The strategy is detailed, applied and tested via numerical simulations and showed good convergence to a desired periodic motion.

Energy management for residential homes and/or offices requires both identification and prediction of the future usages or service requests of different appliances present in the buildings. The aim of this work is to identify residential appliances from aggregate reading at the smart meter and to predict their states in order to minimize their energy consumption. For this purpose, our work is divided in two distinct modules: Appliance identification and future usage prediction. Both identification and prediction are based on multi-label learners which takes inter-appliance co-relation into account. The first part of the paper concerns the identification of electrical appliance usages from the smart meter monitoring. The main objective is to be able to identify individual loads from the aggregate power consumption in a non-intrusive manner. In this work, high energy consuming appliances are identified at 1-hour sampling rate using novel set of meta-features for this domain. The second part of the paper concerns future usage prediction. A comparison of algorithms for future appliance usage prediction using identification and direct consumption reading is presented. This work is based on a real residential dataset, called IRISE: 100 houses monitored every 10 minutes to one hour during one year (including weather informations).

Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.

Variable speed micro-hydropower energy systems have recently received significant attention in the renewable energy field, due to its overall efficiency and great potential available worldwide. This emergent technology combined with direct power control allows simplifying the burden classical governing mechanical systems, and avoiding the speed control loop. In this paper, a “run of river” variable speed micro-hydropower plant (MHPP) based on a semi-Kaplan turbine is employed and a direct power control combined to a MPPT technique is proposed. This allows high tracking quality due to superior dynamic response. This approach has been experimentally validated by using a dedicated benchmark.

This paper deals with a frequency-based energy sharing method between batteries and ultracapacitors (UC) as power sources within an electric vehicle (EV). A frequency splitter is used for routing the low-frequency content of power demand into the battery and its high frequencies into the UC system, taking profit from the UC as a peak power unit. The autonomy may thus be increased while also preserving the battery life. Real-time experiments on a dedicated test rig validate the proposed energy management approach.

Plug-in Hybrid Electric Vehicles (PHEVs) will strongly penetrate in the car fleet. Based on databases of the houses Daily Loads Profiles (DLPs) and on a probabilistic algorithm of PHEVs connections in residential electric grid areas, this paper determines the minimal charging current or PHEVs charging power which assures that the batteries reached the desired State of Charge (SOC) at the departure time without any charging restriction during peak hours. A dynamic optimization algorithm programming which defines an optimal constant charging current for the PHEV is developed. If charging occurs at home, the application of the proposed optimal algorithm on 10 000 cases shows statistically that a load power chargers for PHEVs charged in residential electric grid areas equals to 373W (230V×1.62A) ensures that 99.4% of the PHEVs batteries have a SOC equals to 100% for the next use.

Plug-in Hybrid Electric Vehicles (PHEVs) will strongly penetrate in the car fleet. Because of their capacity of energy storage and their connection point that will be uniform on the low voltage residential electric grid in the case of home-charged, PHEVs are very suitable to provide services to the system (or Vehicle-to-Grid application) and thus support electric grids especially for voltage plan control. Thus, this paper proposes a mathematical relationship that defines the ability to control the average voltage plan of a low voltage residential electric grid in function of charge power of all PHEVs connected to the electric grid.