Jagadeesh Pasupuleti

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies.

This study presents an innovative integrated control system to enhance photovoltaic (PV) efficiency in arid regions by addressing two critical challenges: temperature-induced performance degradation and dust accumulation. Focusing on Kirkuk, Iraq, the proposed system integrates two distinct intelligent subsystems powered by IoT technology: an activated water-based cooling mechanism and an activated water-driven dust removal system. Both subsystems employ real-time data from IoT sensors (temperature, humidity, dust density, irradiance) to autonomously optimize operations through a centralized cloud platform. The cooling subsystem utilizes activated water circulated through microchannel networks embedded in PV panels, dynamically triggered by AI algorithms to maintain optimal temperatures. Simultaneously, the dust removal subsystem employs pressurized activated water sprays, activated during the night periods to minimize energy loss, with computer vision algorithms identifying dust distribution patterns for targeted cleaning. This research highlights the synergy between IoT-driven automation, activated water technologies, and dual-control optimization, offering a scalable model for renewable energy systems in arid climates. The framework aligns with sustainable development goals by balancing energy efficiency, water conservation, and cost-effectiveness. Field experiments in Kirkuk demonstrated a 27% increase in energy output and a 40% reduction in maintenance downtime compared to conventional systems. The intelligent scheduling of activated water usage reduced overall water consumption by 30% while achieving 95% dust removal efficiency. Economic analysis confirmed a 22% reduction in operational costs due to adaptive resource management and prolonged PV lifespan.

Direct Methanol Fuel Cell (DMFC) is a powerful system for generating electrical energy for various applications. However, there are several limitations that hinder the commercialization of DMFCs, such as the expense of platinum (Pt) at market price, sluggish methanol oxidation reaction (MOR) due to carbon monoxide (CO) formation, and slow electrooxidation kinetics. This work introduces carbon nanocages (CNCs) that were obtained through the pyrolysis of polypyrrole (Ppy) as the carbon source. The CNCs were characterized using BET, XRD, HRTEM, TEM, SEM, and FTIR techniques. The CNCs derived from the Ppy source, pyrolyzed at 750 °C, exhibited the best morphologies with a high specific surface area of 416 m²g⁻¹, allowing for good metal dispersion. Subsequently, PtRu catalyst was doped onto the CNC-Ppy750 support using chemical reduction and microwave-assisted methods. In electrochemical tests, the PtRu/CNC-Ppy750 electrocatalyst demonstrated improved CO tolerance and higher performance in MOR compared to PtRu-supported commercial carbon black (CB), with values of 427 mA mg⁻¹ and 248 mA mg⁻¹, respectively. The superior MOR performance of PtRu/CNC-Ppy750 was attributed to its high surface area of CNC support, uniform dispersion of PtRu catalyst, and small PtRu nanoparticles on the CNC. In DMFC single-cell tests, the PtRu/CNC-Ppy750 exhibited higher performance, approximately 1.7 times higher than PtRu/CB. In conclusion, the PtRu/CNC-PPy750 represents a promising electrocatalyst candidate for MOR and anodic DMFC applications.

In recent years, research communities have shown significant interest in solar energy systems and their cooling. While using cells to generate power, cooling systems are often used for solar cells (SCs) to enhance their efficiency and lifespan. However, during this conversion process, they can generate heat. This heat can affect the performance of solar cells in both advantageous and detrimental ways. Cooling cells and coordinating their use are vital to energy efficiency and longevity, which can help save energy, reduce energy costs, and achieve global emission targets. The primary objective of this review is to provide a thorough and comparative analysis of recent developments in solar cell cooling. In addition, the research discussed here reviews and compares various cooling systems that can be used to improve cell performance, including active cooling and passive cooling. The outcomes reveal that phase-change materials (PCMs) help address critical economic goals, such as reducing the cost of PV degradation, while enhancing the lifespan of solar cells and improving their efficiency, reliability, and quality. Active PCMs offer precise control, while passive PCMs are simpler and more efficient in terms of energy use, but they offer less control over temperature. Moreover, an innovative review of advanced cooling methods is presented, highlighting their potential to improve the efficiency of solar cells.

The development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO ₂ ) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO ₂ emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multi-objective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG’s optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO ₂ emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO ₂ emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO ₂ emissions as against the first scenario.