Load Factor Improvement of the Electricity Grid Considering Distributed Energy Resources Operation and Regulation of Peak Load

Cerna, Fernando V.; Coelho, John; Fantesia, Mauricio; Naderi, Ehsan; Marzband, Mousa; Contreras, Javier

doi:10.1016/j.scs.2023.104802

Cited by 15 publications

(4 citation statements)

References 41 publications

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“…The other common energy benchmark, the load factor (Eq. 2), is a value that determines the building's electrical efficiency by comparing the peak demand to the total energy usage in a time frame [17]. As summarized in Table 1, for the maintenance facility, the EUI was ~404 kWh/m 2 , which is almost three times the CBECS benchmark of ~151 kWh/m 2 .…”

Section: Building Energy Benchmarking and Utility Billsmentioning

confidence: 99%

Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study

Higgins,

Ramnarayan,

Family

et al. 2024

Preprint

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A comprehensive Energy Audit of a Maintenance facility was performed to assess its energy performance and identify scope for improvement. The facility’s Energy Use Intensity (EUI) for 2022 was 404 kWh/m2 — more than double the Benchmark EUI for Maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the Load Factor for 2022 was 0.22, which is lower than the recommended minimum of 0.75 for an efficient building. The audit encompassed an in-depth evaluation of the building's structural and operational characteristics, comprising HVAC systems, lighting, building envelope, and energy-intensive machinery. An energy model of the building was developed to emulate the baseline building energy performance for 2022. Following the energy model's development and validation, an analysis was conducted to identify energy-intensive areas and opportunities for optimization. Energy Efficiency Measures were then formulated, focusing on improving energy efficiency while focusing on energy consumption reduction and GHG emission reduction. Results demonstrated the potential of Energy Audits and Modeling to enable significant reductions in energy consumption and promote sustainable building practices. Among the Energy Efficiency Measures considered, re-sizing and decarbonizing HVAC equipment contributed the most to energy savings, with a 100% decrease in natural gas and 37% decrease in electricity use annually.

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Section: Building Energy Benchmarking and Utility Billsmentioning

confidence: 99%

Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study

Higgins,

Ramnarayan,

Family

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Literature on load and peak forecasting can be clustered according to time, into short, medium and long term [34]. Another distinction can be made in the following categories: a) the conditional modeling approach, generally based on macroeconomic variables like inflation, GDP, Forex, etc.. [8][9][10][11][12], b) the system indicators of the electrical distribution and transmission system, such as the number of connections, machinery capacity etc., [13][14][15][16][17][18], c) the historical modeling approach [9,19] and d) hybrid models [20,21]. Finally, literature can be clustered around the method used, a distinction used by Weron [22], in the disciplines of a) time series analysis-statistics [8,21,23,24], b) informatics or computational intelligence and c) hybrid models [25][26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

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Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

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“…Energy benchmarks are quantified using the fundamental metric of energy use intensity (EUI) measured in kWh/m 2 (Equation (1)), which measures a facility's overall energy consumption over its gross floor area [27]. The other common energy benchmark, the load factor (Equation ( 2)), is a value that determines the building's electrical efficiency by comparing the peak demand to the total energy usage in a time frame [28]. As summarized in Table 1, for the maintenance facility, the EUI was ~404 kWh/m 2 , which is almost three times the CBECS benchmark of ~151 kWh/m 2 .…”

Section: Building Energy Benchmarking and Utility Billsmentioning

confidence: 99%

Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study

Higgins,

Ramnarayan,

Family

et al. 2024

Energies

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A comprehensive energy audit of a light rail maintenance facility was performed to assess its energy performance and identify potential scope for improvements. The facility’s energy use intensity (EUI) for 2022 was 404 kWh/m2—more than double the benchmark EUI for maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the load factor was 0.22—significantly lower than the recommended minimum of 0.75 for an efficient building. The energy audit encompassed an in-depth evaluation of the facility’s structural and operational characteristics, comprising HVAC systems, lighting, the building envelope, and energy-intensive machinery. An energy model of the facility was developed to emulate the facility’s energy performance in 2022. Following the energy model’s validation, an analysis was conducted to identify opportunities for improving energy efficiency. Post-implementation of energy efficiency measures for the facility, the projected annual reductions are 1086 MWh of electricity, 5034 GJ of natural gas, utility savings of USD 162,402, and net GHG emissions reductions of 584 metric tons of CO2e. A subsequent 30% reduction in EUI to 283.6 kWh/m2 could be achieved with an 86% improvement in load factor, that is, increasing it from 0.22 to 0.41. This study emphasizes the need for energy audits and modeling for maintenance facilities to reduce Scope 1 and 2 emissions.

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Load Factor Improvement of the Electricity Grid Considering Distributed Energy Resources Operation and Regulation of Peak Load

Cited by 15 publications

References 41 publications

Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study

Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study

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