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.

Pursuit-evasion game (PEG) problems are a type of dynamic differential games that received a lot of attention thanks to the ability of this framework to articulate many real-life applications such as in military, aerospace and mobile robotics. Several techniques are used to solve such games, but recently techniques relying on deep reinforcement learning (DRL) gained traction, in particular DRL techniques adapted for problems with continuous action spaces such as Deep Deterministic policy gradients (DDPG). This paper explores the case of a one versus one pursuit-evasion game in a constrained game area, using two twin delayed DDPG (TD3) agents that are trained simultaneously from scratch via self-play only. The simulation results show that agents were performing better than other conventional methods such as Non-linear Model Predictive Control (NMPC).

In a bounded error context, a secure set-valued state estimator for a class of systems described by a linear discrete-time difference inclusion is introduced in this contribution. The proposed design approach is based on set-valued computation combined with elimination by consistency techniques. More formally, we will show that a fusion between data provided by a set-valued predictor and those generated by a set-valued estimator allows one: (i) To obtain guaranteed state enclosures in the presence of additive and bounded state disturbance and measurement noise; (ii) To be able to detect faulty behaviors of the system and (iii) To be insensitive to a certain class of cyber-attacks. A numerical example is introduced to illustrate the performance of the proposed secure set-valued state estimator.

This paper focuses on designing a drone capable of cleaning solar panels. As renewable energy, particularly solar power, becomes increasingly important, the challenge of dust accumulation on solar panels is growing. Dust can significantly reduce the efficiency of solar panels by blocking sunlight. This study evaluates the effectiveness of drone-based cleaning methods through secondary research and develops a proof-of-concept PV-cleaning drone. The project demonstrates the feasibility of using drones for this purpose and highlights areas for improvement.

International audience

International audience

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.

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 paper investigates the trajectory generation problem for a fleet of Unmanned Aerial Vehicles (UAVs) performing escort missions with respect to a convoy with a time-varying velocity. Using camera-equipped UAVs, we propose a framework allowing to adapt the UAVs altitudes to achieve a trade-off between the size of the covered surface and the image resolution by solving an optimization problem. Furthermore, we perform polynomial refinement based on the generated planar way-points to yield a minimum-jerk time-parameterized trajectory with dynamical constraints on the virtual leader to follow. The numerical simulations presented in this paper show the effectiveness of the optimization algorithm in adapting the UAVs altitudes to satisfy a compromise between the size of the covered area and its corresponding image resolution.

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.

In this paper, a Nonlinear Model Predictive Control (NMPC) has been employed to solve point-stabilization problems with static and dynamic obstacles avoidance. The algorithm was implemented on a mobile robot with two differential drive wheels. In NMPC, a cost function is formulated to minimize an error between the reference and the current state of the system subject to constraints. The major drawback of NMPC is the computation time, which results from predicting the system’s state over a horizon. However, in this work, the resulting optimal control problem is converted to a discrete nonlinear programming problem using a recently developed toolkit. Dynamic obstacles avoidance is incorporated as a time-varying constraint and can be affected by a short prediction horizon. On the other hand, a long prediction horizon affects the computation time. For this, a terminal state penalty is added to the cost function to guarantee the stability of the control using a relatively shorter prediction horizon. The performance of the proposed controller achieving both static and dynamic obstacles avoidance is verified using several simulation scenarios.

This paper proposes a trajectory generation strategy of quadcopter formation for area surveillance and inspection using multiple cameras. The proposed technique exploits a minimal virtual-leader based network topology to generate the executable trajectories where generating trajectories for each agent is no longer necessary. A coverage path planning algorithm is employed to generate a set of planar waypoints, which are then optimised to yield a minimum-jerk time-parametrised trajectory with dynamical constraints of the virtual leader to follow. In order to establish a formation pattern, the desired distance among the agents is then computed based on the operation mode, size of the area to cover, and the number of deployed UAVs. Numerical simulations demonstrate the effectiveness of the proposed method.

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.

In this contribution, interval extensions for both full-order and reduced-order unknown input observers are proposed for uncertain discrete time linear systems. The introduced interval estimators do not rely on the positive systems property of the estimation errors. They are mainly based on numerical schemes conceived to characterize in a rigorous way the reached set of some classes of uncertain dynamical systems. Thus, only the classical existence conditions of unknown input observers are needed to design their interval extensions. Interval analysis is used as a convenient tool to implement the proposed state estimation algorithms. However, other bounded geometrical sets could be also applied. Numerical examples are studied to highlight the effectiveness of the introduced interval estimators in the presence of both system's uncertainties and unknown inputs.

In this paper, RobotMe, a new educational robotic platform is presented. It is dedicated to drone position control using various control designs. Different material and software elements employed in the platform are detailed. Some improvement guidelines are also proposed.

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.

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.

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.

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.

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.

The growing need of energy, global warming and recent nuclear power plant accidents have shown that renewable energies need to be developed for tomorrow's world. Wind energy is generally harvested using wind turbines. Unfortunately, these systems have some drawbacks such as their cost, and the amount of steel and concrete used for construction. As their size grows, their complexity increases exponentially. This paper studies an alternative solution for the production of wind energy, using a kite's traction force. The aim of this paper is to control the amount of energy produced by the kite, and to be able to fly it safely in the presence of strong wind gusts. Our theoretical work has been implemented in a scale model flying autonomously in a wind tunnel. The proposed control strategy has led to control the system output power with an accuracy greater than 95%, with unknown wind speeds varying from 7.5 to 9 m/s.

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.

A quaternion based feedback is developed for attitude stabilization of rigid body. The control design takes into account the input bounds and is based on cascaded saturation approach. The global stability is guaranteed. A simulation study of the proposed scheme is illustrated for the four-rotor helicopter.

This paper proposes a global asymptotic stabilizing control law for a quad-rotor helicopter with bounded inputs. The proposed control design exploits the technique based on the sum of saturating functions and is based on the global stabilization of multiple integrators with bounded inputs. The positiveness of the thrust and the boundedness of the control inputs are taken into account. Numerical simulations show the effectiveness of the proposed controller.