SMART HIGHWAY WITH GREEN ENERGY

The increasing use of battery-powered electronic devices necessitates efficient and fast charging solutions. This research aims to optimize battery charging at a charging station using the Maximum Power Point Tracking (MPPT) method integrated with Proportional Integral Derivative (PID) control. This method is chosen to address overshoot issues and speed up the stabilization process. The study begins with a literature review to understand the basic theory of MPPT optimization and PID control. Subsequently, hardware and software for the battery charging station were designed and developed. An Arduino ATMega328 microcontroller was used as the main controller, with an LCD to display charging information. Tests were conducted on 12V batteries with capacities of 5Ah and 9Ah. The results showed that using PID control in MPPT optimization successfully increased battery charging efficiency. The fastest charging duration was achieved for a 12V 5Ah battery with parameters Kp=6, Ki=9, and Kd=4, where the battery was fully charged in 19 minutes, and for a 12V 9Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 27 minutes. Conversely, the longest charging duration occurred for a 12V 5Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 34 minutes, and for a 12V 9Ah battery with parameters Kp=2, Ki=5, Kd=1, with a charging time of 38 minutes. The designed system was able to reduce input current and increase input voltage to reach the 14.5V set point, ensuring that there was no excessive voltage and current that could prolong battery life. This research demonstrates that the PID method in MPPT optimization is effective in accelerating and optimizing battery charging. Future research is expected to further develop this method to improve battery charging efficiency and duration. Keywords: MPPT, PID, Optimization, Battery Charger, Optimal, PWM ABSTRAK Penggunaan perangkat elektronik yang bertenaga baterai semakin meningkat memerlukan solusi pengisian yang efisien dan cepat. Penelitian ini bertujuan untuk mengoptimalkan pengisian baterai pada stasiun pengisian dengan menggunakan metode Maximum Power Point Tracking (MPPT) yang diintegrasikan dengan kontrol Proportional Integral Derivative (PID). Metode ini dipilih untuk mengatasi masalah overshoot dan mempercepat waktu menuju keadaan stabil. Penelitian dimulai dengan studi literatur untuk memahami teori dasar optimasi MPPT dan kontrol PID. Selanjutnya dirancang dan dibuat perangkat keras serta perangkat lunak untuk stasiun pengisian baterai. Mikrokontroler Arduino ATMega328 digunakan sebagai pengendali utama dengan LCD untuk menampilkan informasi pengisian. Pengujian dilakukan pada baterai aki 12V dengan kapasitas 5Ah dan 9Ah. Hasil pengujian menunjukkan bahwa penggunaan kontrol PID pada optimasi MPPT berhasil meningkatkan efisiensi pengisian baterai. Durasi pengisian yang paling cepat dicapai baterai 12V 5ah dengan parameter Kp=6 Ki=9 dan Kd=4 dimana baterai terisi penuh dalam waktu 19 menit dan baterai 12V 9ah dengan parameter Kp=5, Ki=3, Kd=1 dimana baterai terisi penuh dalam waktu 27 menit. Sebaliknya durasi pengisian terlama terjadi pada baterai 12V 5ah dengan parameter Kp=5, Ki=3, Kd=1 baterai terisi penuh dalam waktu 34 menit dan baterai 12V 9ah dengan parameter Kp=2 Ki=5, Kd=1 dengan waktu pengisian 38 menit. Sistem yang dirancang mampu mengurangi arus input dan meningkatkan tegangan input untuk mencapai set point 14.5V menjaga agar tidak terjadi tegangan dan arus berlebih yang dapat memperpanjang umur baterai. Penelitian ini menunjukkan bahwa metode PID pada optimasi MPPT efektif dalam mempercepat dan mengoptimalkan pengisian baterai. Penelitian selanjutnya diharapkan dapat mengembangkan lebih lanjut metode ini untuk meningkatkan efisiensi dan durasi pengisian baterai. Kata kunci: MPPT, PID, Optimasi, Charger Baterai, Optimal, PWM

The maximum power point tracking technique is applied in wind power generation system to improve the utilization of wind energy. A review of existing maximum power point tracking methods is presented in this paper, based on which an intelligent maximum power point tracking is developed by the authors to improve the system performance and to facilitate the control implementation. The developed maximum power point tracking algorithm adopts variable duty cycle step size to maximize the output power of the wind generator, without the need for either knowledge of wind turbine characteristics or the measurements of mechanical quantities such as wind speed and turbine rotor speed. System simulation results have confirmed the functionality and performance of this method.

This paper deals with implementation of artificial neural network in the maximum power point tracking (MPPT) controller algorithm for modern household where electric vehicle (EV) was purchased. The proposed MPPT algorithm was designed to achieve the best possible efficiency of the MPP (maximum power point) tracking and the best possible energy harvesting to charge the EV’s battery. The artificial neural networks have strong advantage in fast input to output response of signals and the finding of MPP is faster than in commonly used algorithms. In this article, the optimised simulation model based on artificial neural network will be introduced. The proposed artificial neural network algorithm was designed for non-shielded photovoltaic panels.

Wind power as a renewable energy has the potential to become a clean energy source in almost all countries of the world but, there are lots of technical challenges that need to be addressed in advance. Wind speed variation (WSV) is one of the most important issues. Separate from its mechanical effects on wind turbines (WTs), it causes electrical power variations on WT as well. Doubly Fed Induction Generators (DFIGs) and Maximum Power Point Tracking (MPPT) system have been helpful so far but some problems such as inertia still needed to be solved. Inertia in the WTs causes a delay on MPPT. This paper proposes a novel method to increase WTs output power in which a DFIG equipped with an energy storage system such as Ultra-capacitor helps MPPT system to track the wind variations rapidly by absorption or injection energy during the wind changes. The ultra-capacitor and control system are modeled and simulated in Matlab/Simulink® environment. The simulation results proofs that the proposed system can improve electrical power and increase electrical energy during WSVs. The simulation model will lead to more penetration of wind power and also enables engineers to optimize the system.

10 - DC microgrids for electric vehicle wireless charging

The increasing need for sustainable and efficient water pumping solutions in remote and rural regions has led to the investigation of hybrid renewable energy systems. This document offers an extensive analysis of a grid-connected water pumping system that utilizes a combination of photovoltaic, wind, and diesel energy sources, powered by an induction motor. The suggested system utilizes the synergistic characteristics of photovoltaic (PV) and wind energy sources, augmented by a diesel generator to guarantee uninterrupted and dependable functionality. We commence by outlining the system architecture, which incorporates PV panels, a wind turbine, and a diesel generator linked via a shared DC bus, interfacing with an AC grid. The water pump is powered by an induction motor, selected for its durability and minimal maintenance requirements. Sophisticated power electronic converters and controllers are utilized to regulate energy transfer and ensure optimal operating conditions. The study’s essential components encompass the modeling and simulation of the hybrid system across diverse environmental and load scenarios. We evaluate performance indicators including energy efficiency, reliability, and cost-effectiveness. The control strategy integrates Maximum Power Point Tracking (MPPT) for both the PV and wind subsystems, thereby optimizing the use of available renewable resources to their fullest potential. Furthermore, the diesel generator serves as a supplementary power source during times of inadequate renewable energy, thus improving the overall reliability of the system. The findings indicate that the suggested hybrid system markedly decreases reliance on fossil fuels, reduces operational expenses, and offers a sustainable alternative for water pumping applications. The environmental benefits, including reduced greenhouse gas emissions, are also highlighted. The research culminates in a discourse regarding the scalability and practicality of implementing such systems across various geographical regions, as well as potential enhancements and avenues for future investigation.

Proposing and evaluation of MPPT algorithms for high-performance stabilized WIND turbine driven DFIG

In order to achieve maximum power point tracking (MPPT) of wind energy systems, the rotating speed of wind turbines (WTs) ought to be adjusted in the constant as indicated by wind speeds. However, fast wind speed varieties and heavy inertia bargain the MPPT control of WTs. This chapter proposes a fuzzy logic controller (FLC)-based MPPT strategy for Wind Energy Conversion Systems (WECS). The performance of the proposed MPPT strategy is analyzed mathematically and verified by simulation using MATLAB/PSIM/Simulink software. The proposed method improves the speed and accuracy of MPPT. Furthermore, the simulation results have been conducted to approve the performance of the proposed MPPT strategy, and all results have confirmed the adequacy of the proposed MPPT strategy.KeywordsMaximum power point tracking (MPPT)Wind energy conversion systems (WECS)Fuzzy logic controller (FLC)Wind turbine (WT)

Solar power is mostly influenced by solar irradiation, weather conditions, solar array mismatches and partial shading conditions. Therefore, before installing solar arrays, it is necessary to simulate and determine the possible power generated. Maximum power point tracking is needed in order to make sure that, at any time, the maximum power will be extracted from the photovoltaic system. However, maximum power point tracking is not a suitable solution for mismatches and partial shading conditions. To overcome the drawbacks of maximum power point tracking due to mismatches and shadows, distributed maximum power point tracking is utilized in this paper. The solar farm can be distributed in different ways, including one DC–DC converter per group of modules or per module. In this paper, distributed maximum power point tracking per module is implemented, which has the highest efficiency. This technology is applied to electric vehicles (EVs) that can be charged with a Level 3 charging station in <1 hour. However, the problem is that charging an EV in <1 hour puts a lot of stress on the power grid, and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate. Therefore, a Level 3 (fast DC) EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue. Finally, the simulation result is reported using MATLAB®, LTSPICE and the System Advisor Model. Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day, which is enough to fully charge ~120 EVs each day. Additionally, the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants. For example, instead of supplying EVs with power from coal-fired power plants, 1989 pounds of CO2 will be eliminated from the air per hour.

HighlightsWhat are the main findings?Ensuring energy efficiency through the use of the P&O MPPT algorithmThe integration of a PV-battery hybrid system for a smart street lighting systemThe use of internet-free LoRaWAN communication for a remote monitoring and control systemWhat is the implication of the main finding?The primary objective of this study is to present a design for a street lighting system based on LEDs, which is hybrid-powered by solar energy and batteries, thereby making it independent of the grid.It focuses on reducing energy consumption during times of low demand, managing energy according to the potential of energy sources, and enhancing system reliability by enabling monitoring and control of the entire system through the LoRaWAN communication method.The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure.

In this work, a torque controller for a variable rotational speed wind turbine has been modelled using Reinforcement Learning and considering the Optimal Torque - Maximum Power Point Tracking problem as one of optimization. The reward optimization function is designed as a non-linear function depending mainly on the rotor power variation. Based on this, an optimal action (electromagnetic torque variation) regulates the turbine rotational speed. A simulated 1.5 MW three bladed wind turbine operation is managed by the torque controller. It keeps the turbine working at optimal operational conditions after a successful training process, which is carried out using the Proximal Policy Optimization algorithm. For the controller training, the turbine confronts constant and then randomly staggered wind speed behaviour. Time series of rotor angular speed, torque and power are presented. Our results show that the modelled controller is able to reach and maintain the wind turbine operation at its optimal power generation conditions. This methodology avoids using some empirical parameter characteristic of the Optimal Torque - Maximum Power Point Tracking algorithm widely used in wind turbine control systems.

Aeronautic instruments

This paper proposes a maximum power point tracker (MPPT) for a photovoltaic panel, that is to be integrated with the panel during manufacturing. The MPPT is inexpensive, efficient and has few components that serve to increase the MPPT's mean time between failures (MTBF). The MPPT uses an inexpensive microcontroller to perform all of its functions. This includes maximum power point tracking, series battery voltage regulation, sensorless short circuit protection of the MPPT's converter and intelligent shutdown and wakeup at dusk and dawn. The MPPT can source 10 A to a 6-36 V lead-acid storage battery and can be connected in parallel or series with other MPPTs. The MPPT may be easily configured to perform output voltage regulation on passive and water pumping loads. It could also control the actuation of a diesel generator in a hybrid remote area power supply (RAPS). Energy transfer enhancements of up to 26%, compared to solar panels without MPPTs, have been measured, The complete component and materials cost of the MPPT is approximately 2.8% of the cost of photovoltaic panels with a peak power rating of 154 W. The integrated MPPT also consumes no stored energy at night.

SummaryCurrently, a maximum power point tracking (MPPT) unit implemented in a wind energy conversion system (WECS) extracts the maximum mechanical power from the wind turbine used in the WECS. Therefore, the MPPT unit acts as a maximum mechanical power tracker (MMPT) that performs optimal control of the wind turbine to extract the maximum mechanical power from the wind turbine. In this paper, the basic concept of a maximum electrical power tracker (MEPT) is presented both theoretically and technically. It is demonstrated that an MEPT implemented in a WECS maximizes the output electrical power of the WECS. Thus, in contrast with an MMPT, the proposed MEPT optimally controls the whole of the WECS, rather than the wind turbine, to extract the maximum electrical power from the WECS. Since, in the WECS, the power efficiency of the whole of the WECS, not the wind turbine, should be maximized to extract the maximum output electrical power from the WECS, the conventional MPPT unit acting as an MMPT should be replaced with the proposed MEPT, and this is the superiority of the proposed MEPT to an MMPT or a conventional MPPT unit. To provide experimental verifications, 2 novel MEPT and MMPT with simple structures and better performance compared to the MPPT techniques commonly used in WECSs have been constructed, which are presented in detail.

The increasing adoption of electric vehicles (EVs) necessitates robust and sustainable charging infrastructures, capable of minimizing environmental impact while providing reliable power. A solar-wind hybrid EV charging station integrates photovoltaic (PV) solar panels and wind turbines, ensuring stable power output even under variable environmental conditions. This hybrid system leverages renewable energy sources to address intermittency issues inherent to single-resource systems, supporting a continuous energy supply for EVs. MATLAB/Simulink is employed to design and simulate the proposed system, allowing the evaluation of critical performance metrics such as power output, load management, and system efficiency. Through simulation, various operational strategies, including maximum power point tracking (MPPT) for the solar array and pitch control for wind turbines, are optimized to enhance output stability. This setup holds potential to address challenges in off-grid areas, reduce dependency on fossil fuels, and mitigate grid instability during peak EV charging times. Results indicate that a well-optimized solar-wind hybrid system significantly improves power reliability and charging efficiency and offering a scalable solution for green energy-driven transportation.