Autonomous Hybrid Research Articles

Quantifying impacts and benefits of advanced remote monitoring solutions for the operation of mini grids in sub-Saharan Africa

As of 2022, 675 million people do not have access to electricity globally. The large majority of this underserved population lives in Sub-Saharan Africa. Mini grids systems play a central role to close this energy access gap. The adoption of latest technologies such as remote monitoring solutions can contribute to strengthen mini grids business case by improving their operation, maintenance and performance. In this study we present the latest trends, opportunities and challenges of remote monitoring solutions for mini grids and we illustrate the potential of this technology in optimizing mini grids operations, making the case for wider adoption in the sector. The analysis is developed assessing primary data collected from seven mini grids developers, covering 49 photovoltaic autonomous hybrid rural mini-grids across seven sub-Saharan Africa countries. To better understand the advantages of remote monitoring solutions we present a classification based on their related benefits compared to manual/local monitoring options. Orientation toward the adoption of remote monitoring solutions by mini grids developers highlights the advantages from a market and from the operational and maintenance perspectives. Through a cost-benefit analysis of remote monitoring solutions, we show that can have a positive impact on mini grids operations, leading to an overall operational expenditures reductions of up to 17 %, with an investment payback time as short as 1 year. Finally, we provide an estimation of operational costs savings at country level by using remote monitoring solutions in future rural electrification efforts.

Study of Renewable Potential of the Republic of Armenia for Implementation of Hybrid Autonomous Power Supply System Using Solar Photovoltaic Modules

Introduction. Solar energy is one of the leading renewable energy industries. Solar photovoltaic modules are most widely used to create hybrid autonomous electrification systems, since photovoltaics are not limited to specific limitations and can be applied almost everywhere.Aim. To determine the potential of meteorological and geographical features of the Republic of Armenia for the implementation of autonomous hybrid renewable energy sources systems (AHRESS) using solar photovoltaics (PV). To simulate operating conditions for various AHRESS configurations providing electric power to a cellular communication base station.Materials and methods. Climatic, geographical, social and economic features of the region of the Republic of Armenia are investigated. Regions are evaluated based on the key factors of AHRESS implementation potential and compared with studies performed by the Effergy-Irsolav. The simulations and technical and economic analysis were performed using the HOMER Pro software in Ttsovinar region.Results. The most efficient configuration is found to be B1, with an energy cost of 0.322 $/kW·h. The parameters of winning system: PV array with capacity of 4.5 kW, 12V battery with 100 A·h capacity, diesel generator with 3 kW capacity. The main efficiency criterion is the low economic costs for the implementation and operation of this AHRESS configuration. Ten additional variants of AHRESS were modeled in order to optimize the component base of the configuration.Conclusion. It was found that, depending on the consumer load, solar photovoltaic converters are limited and can only perform as supporting source of electricity. The use of the existing consumption load profile in the form of BSSS made it possible to cover the widest possible range of configurations of the component base, which indicates the high accuracy of the simulated results and, as a result, the technical and economic relevance of the study.

Integrated Optimization of Power Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Integrated Optimization of Power Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Simulation Study on a New Hybrid Autonomous Underwater Vehicle with Elevators

This study aims to design a new hybrid twin autonomous underwater vehicle (HTAUV) consisting of dual cylinder hulls and analyze its pitching motion. The kinematic model for the HTAUV is established, followed by the execution of hydrodynamic simulation CFD of the HTAUV using Ansys Fluent. These simulations are conducted to obtain the hydrodynamic force equation of the HTAUV, which relates to the deflection angle of the elevator. Through the motion simulation of the HTAUV, under the same net buoyancy condition, notable differences emerge when the elevator is deflected. Specifically, parameters such as gliding speed, gliding angle, and pitch angle of the HTAUV are larger when the elevator is deflected, as compared to cases where no deflection is applied.

Load Frequency Control With Renewable Energy Sources Using Practical Swarm Optimization Based On PID

Future competitive solutions for the production of electricity may be provided by emerging renewable energy sources (RESs) technologies as wind, solar photovoltaic, and solar thermal power systems. In this study, simulation tests have been conducted on autonomous hybrid generating systems(HGSs) made up of diesel engine generators (Degs), fuel cells (FCs), wind turbine generators (Wpg), solar photovoltaic generators (Pvpg), battery energy storage systems (Bess), and flywheels (Fess). The abrupt fluctuations in load, generation, or both cause the power system's frequency to vary. PID controller tuning using particle swarm optimization (PSO) is used for the optimization of controllers’ gains of the proposed hybrid systems. The proposed method is compared with a genetic algorithm (GA) and a traditional PID controller. To test the proposed controller, three different scenarios were used: first, changing the load's value with a fixed value over a set period of time; second, changing the load's value with variable values; and third, changing the load's value with a variable value while changing the system parameters. The simulation results reveal that the suggested controller performs better and is more resilient against load and generational disruptions by reducing the maximum overshoot value and settling time by 70% and 65%, respectively, and by 14.2% and 5% when compared to a GA-PID control.

A cloud-based eco-driving solution for autonomous hybrid electric bus rapid transit in cooperative vehicle-infrastructure systems: A dynamic programming approach

Efficient public transportation has always intrigued extensive research. Aiming to improve the commuting efficiency and fuel economy of the autonomous hybrid electric buses in the Bus Rapid Transit (BRT), a cloud-based eco-driving solution adopting dynamic programming and model predictive control is proposed in this paper. This solution contains an upper-level cloud-based scheduling strategy and a lower-level onboard predictive energy management, which is conceived to function in a Cyber-physical system of the cooperative vehicle-infrastructure system. The scheduling model carefully considered coupled spatiotemporal constraints for the driving of autonomous BRT buses, including traffic lights, traffic regulations, stations, and ride comfort. The onboard energy management leverages the pre-planned scheduling information to achieve near-optimal fuel economy. The eco-driving solution is examined in three scenarios with intersections, stations, and ramps. Simulation results show that the proposed method can deal with different spatiotemporal limits along the route, with virtually no non-essential stops and sudden acceleration or braking, and achieves 97%–98% energy-saving potential compared with the baseline performance.

Improving the Energy Efficiency of a Ship’s Power Plant by Using an Autonomous Hybrid System with a PMSG

In ship systems, diesel power generators are used in various systems of autonomous power plants to ensure power supply security. This article presents an autonomous hybrid system of a ship power plant with a diesel generator with a permanent magnet synchronous generator and electricity storage in parallel topology (the permanent magnet synchronous generator is connected directly to the receiving network). The electricity storage through the active converter is connected to the receiving network in parallel with the permanent magnet synchronous generator. The decoupled control of the reactive and active power in the active converter enables stabilization of the voltage in the ship’s electrical power grid while ensuring the possibility of obtaining different operating regimes of the power plant at various stages of the ship’s operation. The innovative method of voltage stabilization presented in this article is based on the compensation of the unfavorable reaction of the permanent magnet synchronous generator armature through the optimal transmission of negative inductive reactive power from the active converter to the generator using the electromagnetic properties of the synchronous generator. The active converter controls the direction of the active power transmission between the DC source, the grid, and the permanent magnet synchronous generator. This paper proposes a hybrid autonomous power plant system using battery storage in place of a single diesel generator set against the commonly used two diesel generator sets, working in parallel, to increase the energy efficiency of the power plant by minimizing the specific fuel consumption of the diesel generator set. The article examines the possibilities of such a mechatronic system design based on analytical research and analysis of electromagnetic and energy characteristics using the Matlab-Simulink program.

Energy management of a hybrid energy system (PV / PEMFC and lithium-ion battery) based on hydrogen minimization modeled by macroscopic energy representation

This research work is designed for the management of the electric power of an autonomous hybrid system which generally integrates several subsystems, whose main source of production is solar energy (photovoltaic panels) coupled with a hydrogen fuel cell using a storage device (lithium battery).This energy coupling behavior is used in a wide range of operating conditions ensuring the originality of the exploitation of the energy produced to supply electricity to remote regions and isolated urban regions of southern Algeria, which will be modeled by a recent graphic formalism methodology macroscopic energy representation and controlled by a simple method the maximum control structure that takes into account all the inputs and outputs of the system. This hybrid system is controlled by an energy management strategy by acting on a common continuous bus with variable residential load via a DC/DC converter, allowing control of the amount of energy between the different energy resources to minimize the use of the fuel cell from which to minimize hydrogen consumption. Another is used to maintain the voltage of this bus at its reference via the battery by regulating the bidirectional DC/DC converter.

Introducing NSF’s Hammer Engineering Research Center: Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (Hammer)

Introducing NSF’s Hammer Engineering Research Center: Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (Hammer)

Evaluation of super-twisting SMC and NSC combination for power control in multi-source renewable power generation system

This work is based on the evaluation of a super-twisting sliding mode controller for power control in an isolated multi-source renewable generation system. The hybrid multi-source system taken into consideration, in this case, is made up of three different generation units. The main generation unit is a 22kW micro-hydro system with a self-excited induction generator. To this, a 7.5kW doubly fed induction generator-based wind energy conversion system is integrated. An 18kW solar PV system connected to a battery energy storage system is connected at the dc-link. All the above systems are integrated through a bidirectional nine-switch converter. The autonomous hybrid system's power control is solely done by the nine-switch converter. In this work, efforts have been made to combine the robustness and accuracy of super-twisting sliding mode controllers with the reliability and flexibility of the nine-switch converters. The performance evaluation of the above combination is done with various load and source side disturbances. The system's uninterrupted power supply capability has also been examined in this work. Using the MATLAB environment, the system's performance is evaluated, examined, and the same has been verified using an OPAL-RT 4510 real-time simulator.

An Efficient Supervisory Power Management Scheme for a Wind–Battery-Assisted Hybrid Autonomous System

This article presents an efficient power management and protection scheme for a wind–battery-assisted hybrid autonomous system. To operate the wind power generation system at the maximum power point (MPP), a drift-free perturb and observe algorithm is adapted. A unified controller for a bidirectional converter (BDC) is incorporated for the dc-bus voltage regulation along with the charge/discharge control of the battery. The small-signal analysis of the BDC and the buck converter is presented in detail. The performance of the proposed power management scheme (PMS) is validated through a time-domain simulation in MATLAB/Simulink along with the hardware implementation using the DS1103 digital platform. Initially, the PMS with only maximum power point tracking (MPPT) and pitch controller is unable to maintain the dc-bus voltage during the battery overcharge or deep-discharge condition. To address this issue, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -MPPT controller is proposed to regulate the dc-bus voltage. The effectiveness of all the operating modes of the proposed PMS is verified in the developed laboratory prototype, and the performance is also compared with that of the classical droop control method. The experimental results confirm the ability of the PMS to handle power sharing and dc-bus voltage fluctuation during wind speed and load change and satisfy the load demand. This ensures that the improved performance of the developed hybrid autonomous system and changeover from the MPP to the generator or battery protection mode is seamless and fast.

Off grid PV/Diesel/Wind/Batteries energy system options for the electrification of isolated regions of Chad

Access to reliable energy is fundamental for the development of any community. The electricity is produced in Chad solely from thermal plants that use fossil fuels, which are not environmentally friendly. In addition, the electrification rate of Chad is less than 11%. This work aims to propose some reliable electrification options for Chad, through hybrid energy systems. To achieve this objective, autonomous hybrid PV/Diesel/Wind/Batteries feasibility to meet the demand of electrical load in isolated regions of Chad is evaluated using HOMER software. The design is done considering three types of daily load profiles in each of the 16 regions that are not yet electrified in Chad; the low, medium and high community load profiles. From the simulation, it was observed that the optimal configurations were: PV/Battery, PV/Diesel/Battery and PV/Wind/Diesel/Battery for various consumers and sites. The COE was found to be in the range of 0.367 and 0.529 US$/kWh which shows that, the COE of some sites are less than the production cost of energy in Chad (0.400 US$/kWh) and therefore profitable. Using these hybrid systems, compared to single diesel generator will result in less CO2 emission per year (between 0 and 15670 kg/year). These results may guide investors and policies makers in the planning and implementation of various optimal feasible options that may be used to increase the electricity access rate of Chad, especially in remote areas.

Multi-physical cooperative control of plug-in hybrid electric vehicles via cyber hierarchy and interactional network

Cyber hierarchy and interactional network (CHAIN) concept control of autonomous plug-in hybrid electric vehicles is presented to fully release the potential of the vehicle components while guaranteeing the driving safety and stability, which was divided into cyber intelligent driving system (cyber-IDS) on the cyber-end and vehicle intelligent driving system (vehicle-IDS) on the vehicle-end. On the cyber-end, the state information of surrounding vehicles and infrastructures about traffic conditions are uploaded via vehicle-to-everything (V2X) and processed in the cloud to obtain future driving behavior via car following theory, achieving automatic following control. On the vehicle-end, the vehicle-IDS determines the set of its future control variables, and simultaneously provides an optimized control sequence in the prediction horizon by performing approximate global optimization instead of local optimization. The system global optimization needs the high coupling between the macro physical process(cyber-IDS) and the micro physical process(vehicle-IDS), and only the macro planning cannot be directly applied to the micro process control. The nonlinear system at the vehicle-end is decomposed into linear systems around each working point, and the dynamic characteristics between nodes are connected by neural network based on radial basis function of heterogeneous distribution coefficients. By introducing the genetic algorithm for time series, the CHAIN autonomous driving strategy capable of macro and micro coupling can realize the accurate optimal control under the actual driving conditions, which not only eliminates random fluctuations, but also enables a full-lifespan fuel saving of autonomous hybrid vehicles as well as no damage to battery health.

Hybrid Autonomous Vehicle (Aerial and Grounded)

This work discusses hybrid autonomous vehicles that are grounded and aerial vehicles that are utilized to select their course based on their environmental characteristics. It includes algorithms for path planning, obstacle avoidance, and trajectory planning. It also has a microcontroller, known as the PIXHAWK Flight Controller, for various transmissions and configurations. Calibration and testing are performed using Mission Planner software. This article shows the different problematic features of an autonomous vehicle with several functionalities.

Optimal sizing of autonomous hybrid microgrids with economic analysis using grey wolf optimizer technique

Integrating microgrids with existing distribution systems is a complex process that requires optimal design. This study seeks to develop a robust methodological framework to design optimal configurations of hybrid Microgrid systems (HMGs). Different configurations of hybrid Microgrids are proposed comprising various generating resources to meet the electrical load of small villages in Malaysia. Grey Wolf Optimizer (GWO) is employed to minimize the cost of energy COE ($/kWh) considering operation constraints. Four indicators are calculated to assess the reliability and performance of the hybrid system, which are loss of power supply probability (LPSP), renewable energy index (IRE), storage performance index (ISP), and excess energy index (IEE). These formations are subjected to two energy management strategies: load following (LFs) and cyclic charging (CCs). The results indicate that the energy cost of the optimal configuration was $0.24/kWh, whereas renewable resources contributed 75.3% of total energy production, and the percentage of unserved loads was 0.039. The results reveal that climatic conditions are essential in selecting generation resources. A genetic algorithm (GA) is applied to compare the results. This study provides essential information for electrical power designers.

Optimal energy scheduling of a standalone rural microgrid for reliable power generation using renewable energy resources

ABSTRACT Rural villages can be electrified using renewable power sources for the area to grow effectively. Microgrids powered by renewable energy is more environmentally friendly and practical choices for electrifying rural areas. This study attempts to ascertain the technological and financial viability of an autonomous hybrid energy source for a remote Indian area in order to provide a reliable power supply. The region’s load requirements can be met with the help of a solar energy system, biogas, wind energy, diesel generators, and batteries. Differential evaluation (DE) is used to optimize the proposed microgrid for grid-independent operation. The system’s ultimate objective is to reduce energy costs and recommend workable component configurations. Particle swarm optimization (PSO) and the genetic algorithm (GA) are used to compare the DE findings. The finding suggested the optimal energy cost of 0.22 $/kWh, a net current cost of $5,18,656, and a capital cost of $8,50,774. DE’s total net present cost is 4.3% less than PSO and 5.2% less than GA. However, it has been determined that the recommended approach is more practical and affordable for electrifying rural areas.

Design and Construction of Hybrid Autonomous Underwater Glider for Underwater Research

The main goal of this paper was to design and construct a hybrid autonomous underwater glider (HAUG) with a torpedo shape, a size of 230 cm in length and 24 cm in diameter. The control, navigation, and guidance system were executed simultaneously using a Udoo X86 minicomputer as the main server and three BeagleBone Black single-board computers as the clients. The simulations showed a controlled horizontal speed of 0.5 m/s in AUV mode and 0.39 to 0.51 m/s in glide mode with a pitch angle between 14.13∘ and 26.89∘. In addition, the field experiments under limited space showed the proposed HAUG had comparable results with the simulation, with a horizontal speed in AUV mode of 1 m/s and in glide mode of around 0.2 m/s. Moreover, the energy consumption with an assumption of three cycles of gliding motion per hour was 51.63 watts/h, which enabled the HAUG to perform a mission for 44.74 h. The proposed HAUG was designed to hold pressure up to 200 m under water and to perform underwater applications such as search and rescue, mapping, surveillance, monitoring, and maintenance.

Fuzzy Logic Control of Maximum Power Point Tracking Controller in an Autonomous Hybrid Power Generation System by Extended Kalman Filter for Battery State of Charge Estimation

Autonomous power generation systems are designed to operate independently from the public power grid. Batteries constitute the important element in stand-alone PV system. They are used to store electricity produced by solar energy at overnight or for emergency use during the non-constant load demand. This paper has three major parts.The first pertains the design of an intelligent method for maximum power point tracking based on fuzzy logic controller to improve the efficiency of a standalone solar energy system. The second part describes the battery state of charge (SOC). The proposed model, which better reflects the real SOC response of the lithium battery, is constructed by using the extended Kalman Filter (EKF) states estimator. This proposed method can be considered as a more accurate and reliable method to estimate the battery state of charge. The third part integrates a management system for the above two renewable energy sources. The performance of the proposed management system by using a fuzzy logic controller based maximum power point tracking FLC-MPPT and the EKF estimator have been simulated in Matlab/Simulink at different solar irradiation and temperature for a given no constant load energy request.

Endogenous and exogenous wireless multimodal light-emitting chemical devices.

Multimodal imaging is a powerful and versatile approach that integrates and correlates multiple optical modalities within a single device. This concept has gained considerable attention due to its potential applications ranging from sensing to medicine. Herein, we develop several wireless multimodal light-emitting chemical systems by coupling two light sources based on different physical principles: electrochemiluminescence (ECL) occurring at the electrode interface and a light-emitting diode (LED) switched on by an electrochemically triggered electron flow. Endogenous (thermodynamically spontaneous redox process) and exogenous (requiring an external power source) bipolar electrochemistry acts as a driving force to trigger both light emissions at different wavelengths. The results presented here interconnect optical imaging and electrochemical reactions, providing a novel and so far unexplored alternative to design autonomous hybrid systems with multimodal and multicolor optical readouts for complex bio-chemical systems.

Planning for Urban Energy Needs with A PV/WT Integrated Low-Carbon Infrastructure: A Techno-Economic and Environmental Study

This study presents a techno-economic feasibility analysis of implementing an autonomous hybrid renewable energy system in a university building in Saudi Arabia. The system utilizes a combination of solar panels and wind turbines to generate electricity and a storage system to manage excess energy. The economic analysis includes cost-benefit analysis, payback period, and net present value. The results show that the proposed system has a positive net present value and a payback period of fewer than 10 years. The implementation of the system is expected to reduce the university's reliance on fossil fuels and decrease its carbon footprint. Overall, the findings suggest that the adoption of an autonomous hybrid renewable energy system is a viable and financially feasible option for the university. Keywords: HOMER Software; Optimization; Solar energy; Wind energy; renewable energy. DOI: 10.7176/ISDE/12-6-05 Publication date: December 31 st 2022