Enhancing Renewable Energy Integration in Zimbabwe's Power Grid: Addressing Storage Challenges
Frequency Regulation of Multi Area Hybrid Power System with Electric Vehicle Integration
Load Frequency Control of Contemporary Power System with Honey Badger Algorithm Tuned Regulator
Enhancing Grid Connectivity and Power Quality through Reactive Power Management in DC-AC Converters
Examination of Current Developments in EV Battery Power Management Techniques
Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh
A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies
Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions
Design of a Grid Connected PV System and Effect of Various Parameters on Energy Generation
Comparative Analysis of Harmonics by Shunt Active Filter using Resonant Current Control in Distribution System
Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI
Development of Power Flow Controller for Grid Connected Renewable Energy Sources Using Lyapunov function
Detection and Location of Faults in Three Phase 11kv Underground Power Cables By Discrete Wavelet Transform (DWT)
Design of PV-Wind Hybrid Micro-Grid System for Domestic Loading
Applications of Artificial Neural Networks in various areas of Power System; A Review
Zimbabwe currently battles a mirage of challenges chief among them being energy poverty which has seen the nation experiencing outrageous 8 – 12 hour power cuts. However, advances with research and technology in other countries has led to increased energy sufficiency, and reliable energy ecosystems which effectively support sustainable energy development including load balancing, seamless integration of renewables, and diverse energy mix adoption. The growing interest in energy storage fuelled by the growth of renewable energy technology has provided research with innovative solutions including improved adaptation of pumped hydro-storage, battery energy storage, thermal energy storage, and storage of electrical energy by utilizing off-peak power to produce hydrogen from waste or coal. This study therefore proposes adoption of the technologies as part of the solution towards sustainable energy supply in Zimbabwe. This not only directly improves power supply but also reduces carbon emissions, encourages the adoption of electric vehicles, advances rural electrification, and strengthens the economy. The study is expected to contribute significantly to the nation's development strategy and support efforts toward affordable and clean energy as well as climate action.
This study explores frequency regulation in large hybrid power systems by leveraging electric vehicles (EVs) to enhance grid stability. A novel control strategy, termed the Donkey and Smuggler Technique (DST), is proposed to optimize a Three- Degree-of-Freedom Proportional-Integral-Derivative (3DOFPID) controller. The system model incorporates real-world challenges such as communication delays and sudden variations in power demand. Extensive simulations were conducted to evaluate the system's performance under various scenarios, including controller effectiveness, the impact of communication latency, and the influence of EV integration. Results demonstrate that incorporating EVs significantly improves frequency stability. The proposed controller exhibited fast settling times and minimal frequency deviations. Overall, the approach shows strong potential for application in future smart grids, highlighting the valuable role of EVs in maintaining grid reliability.
This study focuses on enhancing Load Frequency Control (LFC) in contemporary interconnected power systems using a novel optimization-based approach. A Two-Degree-of-Freedom Proportional-Integral-Derivative (2DOFPID) controller is proposed and its parameters are optimally tuned using the Honey Badger Optimization Algorithm (HBOA), a recent nature-inspired met heuristic known for its robust global search capability. The controller is evaluated on a Multi- Area Diversified Multi-Fuel (MADMF) system model subjected to various step load perturbations. To increase operational realism, system nonlinearities such as generation rate constraints, communication delays, and governor dead-band effects are incorporated. The analysis is extended by integrating a High Voltage Direct Current (HVDC) tie-line to study its effect on frequency dynamics and inter-area oscillation damping. Comparative simulations demonstrate that the HBOA-tuned 2DOFPID controller significantly outperforms traditional PI, PID, and fuzzy PID controllers by minimizing frequency deviations, overshoot, and settling time. The system exhibits strong robustness even under varying load conditions. The findings confirm that the proposed controller, when combined with HVDC infrastructure, offers a reliable and efficient solution for maintaining frequency stability in modern power grids.
Solar energy, contributing significantly to the global renewable capacity by 2019, has seen rapid adoption due to substantial cost reductions, including major drops in utility-scale PV LCOE and crystalline PV module prices. These advancements demand innovative technologies like multilevel converters and transformer less systems to overcome challenges in traditional inverter topologies, such as bulky transformers. PV systems, using semiconductor materials like silicon, are categorized as stand- alone or grid-linked, with grid-linked systems dominating due to their ability to exchange energy with the grid. Multilevel converters enhance power quality and reduce system issues like high- frequency switching damage, making them ideal for high-power applications. Widely used in grid and off-grid setups, PV systems continue to expand in applications such as rural electrification and water pumping, cementing their role in sustainable energy.
Electric Vehicles (EVs) have occurred as a good solution to help the environmental impact of traditional internal combustion engine vehicles. Key to the success of EVs is efficient and reliable battery power management systems. Further, central to the efficiency and longevity of EVs are battery power management systems. Recent advances in battery technology, along with sophisticated power management algorithms, have significantly enhanced the performance, range, and lifespan of EV batteries. This paper provides a comprehensive review of the fundamentals in battery power management for EVs. It discusses advancements in Battery modelling, Battery state estimation (The state of charge (SOC) and state of health (SOH)), cell equalizers for voltage balancing and improved safety. Furthermore, it discusses the fault diagnosis methods. Additionally, it discusses predictive maintenance techniques and battery health monitoring systems to ensure the long-term reliability of EV batteries. The integration of artificial intelligence (AI) and machine learning (ML) algorithms for real-time data analysis and optimization of battery performance is also highlighted.