Agent-based machine learning assessment on real data for improvement of the daily load factor using demand response program DOI

Mohammad Hassan Nikkhah,

Mahdi Samadi, Hossein Lotfi

et al.

Sustainable Energy Grids and Networks, Journal Year: 2024, Volume and Issue: unknown, P. 101584 - 101584

Published: Dec. 1, 2024

Language: Английский

Capabilities of compressed air energy storage in the economic design of renewable off-grid system to supply electricity and heat costumers and smart charging-based electric vehicles DOI

Farshad Khalafian,

Nahal Iliaee,

Ekaterina Diakina

et al.

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 78, P. 109888 - 109888

Published: Dec. 14, 2023

Language: Английский

Citations

129
Overview of improved dynamic programming algorithm for optimizing energy distribution of hybrid electric vehicles DOI
Xueqin Lü, Songjie He, Yuzhe Xu

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 232, P. 110372 - 110372

Published: April 8, 2024

Language: Английский

Citations

14
A reinforcement learning approach using Markov decision processes for battery energy storage control within a smart contract framework DOI
Mansour Selseleh Jonban, José Luis Romeral Martínez, Mousa Marzband

et al.

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 86, P. 111342 - 111342

Published: March 19, 2024

Language: Английский

Citations

11
Optimizing power and energy loss reduction in distribution systems with RDGs, DSVCs and EVCS under different weather scenarios DOI Creative Commons

Chava Hari Babu,

R. Hariharan,

T. Yuvaraj

et al.

Sustainable Energy Technologies and Assessments, Journal Year: 2025, Volume and Issue: 75, P. 104219 - 104219

Published: Jan. 31, 2025

Language: Английский

Citations

1
Hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade DOI

Xiangyu Kong,

Haixuan Zhang,

Delong Zhang

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 234, P. 110533 - 110533

Published: June 18, 2024

Language: Английский

Citations

4
Regret-aware optimization of hydrogen-assisted congestion control in a renewable-dominated reconfigurable distribution network DOI
Xin He,

Baohai Zhang,

H. Y. Jia

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 123084 - 123084

Published: April 1, 2025

Language: Английский

Citations

0
A novel analytical method for optimal management of network congestion caused by electric vehicle charging stations DOI

Mohmmad Hossein Atazadegan,

Jaber Moosanezhad,

Mustafa Habeeb Chyad

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 239, P. 111203 - 111203

Published: Nov. 8, 2024

Language: Английский

Citations

2
Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm DOI Creative Commons
Liyuan Sun,

Yuang Lin,

Nan Pan

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(23), P. 7714 - 7714

Published: Nov. 22, 2023

With the rapid development of new power systems, usage stations are becoming more diverse and complex. Fine-grained management demand-side load has become increasingly crucial. To address accurate forecasting needs for various consumption types provide data support in stations, this study proposes a sequence noise reduction method. Initially, wavelet is performed on multiple sequences collected by system. Subsequently, northern goshawk optimization employed to optimize parameters variational mode decomposition, ensuring selection most suitable modal decomposition different sequences. Next, SSA–KELM model independently predict each sub-modal component. The predicted values component then aggregated yield short-term prediction results. proposed method been verified using actual from terminals. A comparison with popular methods demonstrates method’s higher accuracy versatility. average results industrial can reach RMSE = 0.0098, MAE 0.0078, MAPE 1.3897%, R2 0.9949. This be effectively applied providing reliable basis decision-making.

Language: Английский

Citations

4
Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study DOI Open Access
Jordan Higgins, Aditya Ramnarayan,

Roxana Family

et al.

Published: March 8, 2024

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

Language: Английский

Citations

1
Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method DOI Creative Commons
Petros Theodorou, Demetris Christopoulos

Green and Low-Carbon Economy, Journal Year: 2024, Volume and Issue: 2(4), P. 310 - 324

Published: May 29, 2024

Price spikes in electricity markets are very frequent, posing tremendous burden on household income and manufacturing cost. Electricity demand (load) can be divided two parts, energy (MWh) peak (MW) most of time is responsible for the price spikes. Literature review while devoting discussion to energy, lags investigation peak. In this research, a model analysis forecasting developed. The based portfolio cluster extreme value (C-EVA) methods using unit invariant knee, extremum distance estimator, weighted scale load innovations optimal determination clusters daily peaks divulgence. C-EVA method consists Clustering part number classification day month peak, Extreme Value Analysis computation statistical confidence interval maxima. after all currently available maxima, estimates statistically expected worst-case scenario loads. Load will determined by EVA an estimated bimodal distribution signaling prompt probability extremes. added proposed that does not reject values as methodologies do. maxima minima provide estimators highest lowest hourly load, giving return level optimization selection rolling window, period. It was found distributed generation renewables create camel effect which increases sharpness. methodology solved issue opening ground future research role storage, batteries well virtual power plants integrated generation. Received: 18 December 2023 | Revised: February 2024 Accepted: 19 May Conflicts Interest authors declare they have no conflicts interest work. Data Availability Statement database supports findings study made upon request only specific Excel format. Author Contribution Petros Theodorou Demetris Theodoros Christopoulos: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, curation, Writing - original draft, & editing, Visualization, Supervision, Project administration.

Language: Английский

Citations

1