Ievgen Zaitsev

2 months ago | nature.com | Muzaffar Iqbal |Junhai Ma |Naveed Ahmad |Muhammad Yousaf |Wajid Khan |Bashar Tarawneh | +3 more

Energy efficiency (EE) in the construction sector is crucial for sustainable development, particularly in emerging economies like Pakistan, where the industry accounts for a large share of energy consumption and environmental degradation. Despite its economic significance, Pakistan’s construction sector suffers from inefficiencies in energy use, with limited comprehensive assessments to guide improvements. This research introduces a novel, integrated approach combining Data Envelopment Analysis (DEA) and Tobit regression to evaluate and enhance EE in construction projects. DEA was applied to data from 120 construction firms, revealing an average technical efficiency (TE) of 84.4%, pure technical efficiency (PTE) of 93.2%, and scale efficiency (SE) of 90.4%, highlighting notable inefficiencies. The Tobit regression analysis identifies key factors influencing EE, including contractor training, access to loans, experience, and project site distance. This dual-method framework not only measures EE but also provides actionable insights to address inefficiencies, offering practical implications for policymakers and industry stakeholders. The findings emphasize the need for targeted interventions, such as government-supported financing and contractor training programs, to promote energy-efficient practices in Pakistan’s construction sector. This approach provides a replicable model for other developing economies seeking sustainable construction practices.

Dec 30, 2024 | nature.com | Navneet Singh |Mohit Bajaj |Ievgen Zaitsev |Paulson Samuel |Isha Chandra

The rapid global adoption of electric vehicles (EVs) necessitates the development of advanced EV charging infrastructure to meet rising energy demands. In particular, community parking lots (CPLs) offer significant opportunities for coordinating EVs’ charging. By integrating energy storage systems (ESSs), renewable energy sources (RESs), and building prosumers, substantial reductions in peak load and electricity costs can be achieved, while simultaneously promoting environmental sustainability. This paper presents a novel three-stage real-time Energy Management System (EMS) designed to coordinate EV charging in CPLs, integrating solar photovoltaics, wind energy, ESSs, and building backup units. The proposed EMS operates in three stages: (1) day-ahead scheduling of energy generation and consumption, (2) real-time power management to address deviations between forecasted and actual power generation and demand, and (3) priority-based EV charging, which considers EV state of charge (SOC) and owner preferences. The system is evaluated through MATLAB® simulations under four different scenarios and based on six performance indices: daily electricity bills, cost savings, self-sufficiency, self-consumption, carbon emissions, and fairness in EV charging. The results demonstrate that the proposed EMS can reduce electricity bills for parking lot operators (PLOs) by up to 45%, with a corresponding decrease in carbon emissions by 40% compared to uncoordinated charging scenarios. Additionally, the EMS improves the self-sufficiency ratio by up to 75% and increases the self-consumption ratio to 85%. The system also ensures fairness in charging, achieving a fairness index of 0.82, thus addressing the needs of both PLOs and EV owners. This research underscores the potential of CPLs to optimize energy use, lower costs, and contribute to broader sustainability goals by integrating renewable energy and intelligent charging strategies.

Dec 30, 2024 | nature.com | R. Siva Kumar |Faisal Alsaif |Mohit Bajaj |Ievgen Zaitsev |K. Reddy Madhavi |Arvind R. Singh

The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.

Jun 7, 2024 | nature.com | Venkatesan Ramakrishnan |Pradeep Vishnuram |Tiansheng Yang |Mohit Bajaj |Rajkumar Singh Rathore |Ievgen Zaitsev | +3 more

Wireless charging of Electric Vehicles (EVs) has been extensively researched in the realm of electric cars, offering a convenient method. Nonetheless, there has been a scarcity of experiments conducted on low-power electric vehicles. To establish a wireless power transfer system for an electric vehicle, optimal power and transmission efficiency necessitate arranging the coils coaxially. In wireless charging systems, coils often experience angular and lateral misalignments. In this paper, a new alignment strategy is introduced to tackle the misalignment problem between the transmitter and receiver coils in the wireless charging of Electric Vehicles (EVs). The study involves the design and analysis of a coil, considering factors such as mutual inductance and efficiency. Wireless coils with angular misalignment are modelled in Ansys Maxwell simulation software. The proposed practical EV system aims to align the coils using angular motion, effectively reducing misalignment during the parking of two-wheelers. This is achieved by tilting the transmitter coil in the desired direction. Furthermore, micro sensing coils are employed to identify misalignment and facilitate automatic alignment. Additionally, adopting a power control technique becomes essential to achieve both constant current (CC) and constant voltage (CV) modes during battery charging. Integrating CC and CV modes is crucial for efficiently charging lithium-ion batteries, ensuring prolonged lifespan and optimal capacity utilization. The developed system can improve the efficiency of the wireless charging system to 90.3% with a 24 V, 16 Ah Lithium Ion Phosphate (LiFePO4) battery at a 160 mm distance between the coils.