Bhaskaran Gopalakrishnan

The expansion of public electric vehicle (EV) charging infrastructure requires facility-level planning methods that account for uncertain vehicle arrivals, charger availability, queueing behavior, electrical demand, and tariff-based operating cost. This study presents a simulation-based decision support framework for estimating the feasibility of installing Electric Vehicle Supply Equipment (EVSE) in public facilities. The method integrates a Monte Carlo arrival model with a discrete-event charging simulation and a demand-sensitive cost model. Daily EV arrivals are generated using weather severity and calendar-based demand factors, split by vehicle type, and converted into hourly annual arrival schedules. The discrete-event model routes vehicles to compatible chargers, represents charger occupancy, waiting, charging delay, release, vehicle departure without charging, and records service and utilization statistics. The resulting charging-event and hourly-utilization data are then used to estimate energy consumption, monthly peak demand, energy charges, demand charges, and total operating cost. A representative facility case is used to evaluate weather-severity sensitivity and charging-station configuration sensitivity. Results show that monthly demand peaks and operating costs are sensitive not only to the number and power rating of chargers but also to the coincidence between EVSE charging activity and existing facility load. The framework supports preliminary EVSE planning by allowing public facility managers to compare charger-count scenarios, estimate utility-bill impacts, and identify months with elevated peak-demand risk before investment decisions are finalized.

This study presents the electrification plan of a school bus (SB) fleet and examines its potential in vehicle-to-grid (V2G) applications. The data collected includes the efficiency of a 120 kW EV charger, energy consumption of a 40-foot electric school bus (ESB), and a diesel bus operating on the same route. The energy consumption data of the ESB and diesel school bus (DSB) were processed to derive the yearly average distance-specific energy consumption of 0.37 mile/kWh (0.60 km/kWh) grid electricity and 5.55 MPG (2.36 km/L), respectively. The energy consumption ratio of the ESB over the DSB is 14.92 kWh/gallon (3.94 kWh/L) diesel. Based on the CO₂ intensity, 1.956 lb/kWh (0.887 kg/kWh) of electricity produced in WV and that of diesel fuel, the distance-specific CO₂ emissions of the ESB were 5.38 lb/mile (1.52 kg/km), which are higher than the 4.08 lb/mile (1.15 kg/km) from the diesel bus operating on the same route. This study also presents the V2G potential of the proposed electrical school bus fleet. Based on the estimated grid-to-vehicle battery (G2VB) efficiency of 92% and vehicle battery-to-grid (VB2G) efficiency of 92%, the grid–vehicle battery–grid (G2VB2G) efficiency is 84.64%. The application of V2G technology is associated with a loss of electricity. Based on the 20% to 80% battery charge, and the estimated 92% VB2G efficiency, the proposed ESB fleet has the potential to provide 14,929 kWh electricity, 55.2% of the ESB fleet battery capacity. The increased cost associated with the implementation of the proposed V2G is about USD 7.5 million, a 400% increase compared to the charger satisfying the operation of ESBs when V2G is not used. The V2G application also is expected to increase the charging cycles, which raises concerns about battery degradation and its replacement during SB service lifetime. Accordingly, more research work is needed to address the increased cost and grid capacity demand, and battery degradation associated with V2G applications.

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024.

The Paris Agreement’s pressing global mandate to limit global warming to 1.5 degrees Celsius above pre-industrial levels by 2030 has placed immense pressure on energy-consuming industries and businesses to deploy robust, advanced, and accurate monitoring and tracking of carbon footprints. This critical issue is examined through a systematic review of English-language studies (2015–2024) retrieved from three leading databases: Scopus (n = 1528), Web of Science (n = 1152), and GreenFILE (n = 271). The selected literature collectively highlights key carbon footprint tracking methods. The resulting dataset is subjected to bibliometric and scientometric analysis after refinement through deduplication and screening, based on the PRISMA framework. Methodologically, the analysis integrated the following: (1) evaluating long-term trends via the Mann–Kendall and Hurst exponent tests; (2) exploring keywords and country-based contributions using VOSviewer (v1.6.20); (3) applying Bradford’s law of scattering and Leimkuhler’s model; and (4) investigating authorship patterns and networks through Biblioshiny (v4.3.0). Further, based on eligibility criteria, 35 papers were comprehensively reviewed to investigate the emerging carbon footprint tracking technologies such as life cycle assessment (LCA), machine learning (ML), artificial intelligence (AI), blockchain, and data analytics. This study identified three main challenges: (a) lack of industry-wide standards and approaches; (b) real-time tracking of dynamic emissions using LCA; and (c) need for robust frameworks for interoperability of these technologies. Overall, our systematic review identifies the current state and trends of technologies and tools used in carbon emissions tracking in cross-sectors such as industries, buildings, construction, and transportation and provides valuable insights for industry practitioners, researchers, and policymakers to develop uniform, integrated, scalable, and compliant carbon tracking systems and support the global shift to a low-carbon and sustainable economy.

As structures age, air leaks naturally form and can remain undetected for years, resulting in increased utility bills and, in severe cases, structural damage. The traditional method to determine if a structure has developed these leaks is through an energy audit, including blower door testing, which is costly and disturbs normal use of the building. A numerical approach to indicate the presence of air leaks, for instance, a simple mathematic formula requiring only simple building information available to any layman, would be of great value. Within this paper, a formula was developed for climate Zone 5 regions using multiple linear regression models to infer the presence of air leaks using only four input variables. To validate the model, this framework was applied to a series of 700–770 square foot (65.03–71.54 m²) apartment units in Morgantown, West Virginia, USA. The model was determined to be able to accurately estimate the energy consumption of a given unit within this size range with 20% accuracy, which can then be used to ascertain sub-optimal, and thus unsustainable, consumption of energy. This framework can be applied in additional climate zones to create a more robust and generally applicable formula.

Highly energy-efficient buildings have generated remarkable interest over the last few years. There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devices, as... | Find, read and cite all the research you need on Tech Science Press

Decarbonizing fossil-fuel usage is crucial in mitigating the impacts of climate change. The burning of fossil fuels in boilers during industrial process heating is one of the major sources of CO₂ in the industry. Electrification is a promising solution for decarbonizing these boilers, as it enables renewable energy sources to generate electricity, which can then be used to power the electric boilers. This research develops a user-driven simulation model with realistic data and potential temperature data for a location to estimate boilers’ current energy and fuel usage and determine the equivalent electrical boiler capacity and energy usage. A simulation model is developed using the Visual Basic Application (VBA)^® and takes factors such as current boiler capacity, steam temperature and pressure, condensate, makeup water, blowdown, surface area, and flue gas information as input. Random numbers generate the hourly temperature variation for a year for discrete-event Monte Carlo Simulation. The simulation generates the hourly firing factor, energy usage, fuel usage, and CO₂ emissions of boilers for a whole year, and the result compares fossil-fuel and electrical boilers. The simulated data are validated using real system data, and sensitivity analysis of the model is performed by varying the input data.

Industrial process heating furnace operations consume considerable energy in the U.S. manufacturing sector, making it crucial to identify energy efficient strategies due to the growing need to minimize energy usage and emissions. It is important to identify the potential impact of these factors to enable process engineers to operate process heating systems at the maximum possible efficiency. This study examines and identifies the key impact factors that influence the efficiency of process heating systems using MEASUR (v1.4.0), the DOE software tools such as the insulation effectiveness, the burner stoichiometry, cooling medium, thermal storage, and atmospheric gases. Data from a two-fuel-fired heat treatment furnace and an electric arc furnace (EAF) for steelmaking were employed to establish the baseline heat balance models in MEASUR. The fractional factorial design experiment was developed with two-level parameter values and energy efficiency strategies for the heat input into industrial furnaces. The three most significant parameters for the heat input for a fuel-fired industrial furnace, Industrial Furnace A, are excess air percentage or the oxygen percentage in flue gas (OF), average surface temperature (ST), and combustion air temperature (CT). Similarly, for an electric industrial furnace, Industrial Furnace B, the parameters are charge temperature (CHT), average surface temperature (ST), and time open (TO). A comparative analysis was carried out for the fuel-fired and equivalent electric resistance furnaces to identify the prospect of electrification of industrial furnaces relying upon fossil fuels. The study aims to assist industries and designers in making informed decisions regarding industrial furnace upgrades, process optimization, and maintenance investments, resulting in substantial energy and cost savings, and a reduced environmental impact.

Building energy performance is a function of numerous building parameters. In this study, sensitivity analysis on twenty parameters is performed to determine the top three parameters that have the most significant impact on the energy performance of buildings. Actual data from two fully operational commercial buildings were collected and used to develop a building energy model in the Quick Energy Simulation Tool (eQUEST). The model is calibrated using the Normalized Mean Bias Error (NMBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)) method. The model satisfies the NMBE and CV(RMSE) criteria set by the American Society of Heating, Refrigeration, and Air-Conditioning (ASHRAE) Guideline 14, Federal Energy Management Program (FEMP), and International Performance Measurement and Verification Protocol (IPMVP) for building energy model calibration. The values of the parameters are varied in two levels, and then the percentage change in output is calculated. Fractional factorial analysis on eight parameters with the highest percentage change in energy performance is performed at two levels in a statistical software JMP. For building A, the top 3 parameters from the percentage change method are: Heating setpoint, cooling setpoint and server room. From fractional factorial design, the top 3 parameters are: heating setpoint (p-value = 0.00129), cooling setpoint (p-value = 0.00133), and setback control (p-value = 0.00317). For building B, the top 3 parameters from both methods are: Server room (p-value = 0.0000), heating setpoint (p-value = 0.00014), and cooling setpoint (p-value = 0.00035). If the best values for all top three parameters are taken simultaneously, energy efficiency improves by 29% for building A and 35% for building B.