4.6 Article

Multinodes interval electric vehicle day-ahead charging load forecasting based on joint adversarial generation

Related references

Note: Only part of the references are listed.
Article Engineering, Multidisciplinary

Hydrogen Energy Storage System for Demand Forecast Error Mitigation and Voltage Stabilization in a Fast-Charging Station

Ting Wu et al.

Summary: This article focuses on the application of hydrogen energy storage system (HESS) in hydrogen-integrated transportation and power systems. The combination of wavelet transform and long short-term memory neural network is used to accurately predict the nonstationary traffic flow. A queueing theory-based model is then developed to convert the predicted traffic flow to charging demand, considering the limitations of charging service and driver behaviors. The HESS components are scheduled based on the charging demand prediction error, taking into account their properties and operational limits, to address the charging demand forecast error and voltage deviation.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2022)

Article Thermodynamics

A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features

Fei Ren et al.

Summary: This paper proposes a hybrid method based on SARIMA and deep learning for power demand prediction of electrical vehicles. The method extracts the linear trend and non-linear components of power demand, and combines periodic features for prediction. Experimental results demonstrate that the proposed method achieves higher prediction accuracy compared to other data-driven models.

ENERGY (2022)

Article Engineering, Electrical & Electronic

Coordinated management of aggregated electric vehicles and thermostatically controlled loads in hierarchical energy systems

Guozhong Liu et al.

Summary: This paper introduces an energy management model for electric vehicles and thermostatically controlled loads in intelligent energy systems, utilizing a hierarchical management strategy to address energy demand management. It involves distributed optimization and peer-to-peer trading, improving end-users' information privacy protection and providing economic and environmental benefits.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

Article Energy & Fuels

Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China

Nantian Huang et al.

Summary: This study combines a solar-load uncertainty model and economic analysis to assess the financial impact of adding a reused-battery energy storage system to a photovoltaic system in China under multi-tariff policies. The results show that generated scenarios effectively describe the uncertainty of photovoltaic output and residential load, guiding residential customers in installing reused-battery energy storage systems. The economic feasibility and sustainable development of photovoltaic with reused-battery energy storage system depend on market tariff policy regulations.

JOURNAL OF ENERGY STORAGE (2021)

Article Energy & Fuels

An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations

Lubos Buzna et al.

Summary: This paper presents a methodology for probabilistic electric vehicle load forecasting for different geographic regions, using a hierarchical approach to decompose the problem at low-level regions and forecast the aggregate load at a high-level geographic region through an ensemble methodology. Experimental results show that hierarchical approaches increase the skill of probabilistic forecasts up to 9.5% compared with non-hierarchical approaches.

APPLIED ENERGY (2021)

Article Engineering, Electrical & Electronic

Probabilistic Load Forecasting Based on Adaptive Online Learning

Veronica Alvarez et al.

Summary: This paper introduces a method for probabilistic load forecasting based on adaptive online learning of hidden Markov models, which effectively addresses the issues of traditional load forecasting techniques in evaluating intrinsic uncertainties and capturing dynamic changes. Experimental results demonstrate that the proposed method significantly improves the performance across various scenarios.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

Article Automation & Control Systems

Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model

Xian Zhang et al.

Summary: This article predicts traffic flow using deep learning and proposes a new probabilistic queuing model for charging load forecasting. Experimental results show that this method can comprehensively learn the uncertainties of electric vehicle charging load, indicating significant potential for practical applications.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

Article Engineering, Civil

Risk Assessment of an Electrical Power System Considering the Influence of Traffic Congestion on a Hypothetical Scenario of Electrified Transportation System in New York State

Hongping Wang et al.

Summary: This paper proposes an integrated risk assessment framework for evaluating the risks in the electric power system related to EV charging technology in the transportation system of New York State. The framework models the propagation of transportation system scenarios onto the power system and quantifies the consequences, demonstrating the interactions between the two systems.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Article Automation & Control Systems

Compensating Delays and Noises in Motion Control of Autonomous Electric Vehicles by Using Deep Learning and Unscented Kalman Predictor

Yanjun Li et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2020)

Article Engineering, Electrical & Electronic

How many electric vehicles can the current Australian electricity grid support?

Mengyu Li et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2020)

Article Engineering, Multidisciplinary

Price Incentive-Based Charging Navigation Strategy for Electric Vehicles

Xuecheng Li et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2020)

Article Chemistry, Multidisciplinary

Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Juncheng Zhu et al.

APPLIED SCIENCES-BASEL (2019)

Proceedings Paper Energy & Fuels

Deep Learning method to predict Electric Vehicle power requirements and optimizing power distribution

Nectar Jinil et al.

2019 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENERGY SYSTEMS (ICEES 2019) (2019)

Article Computer Science, Information Systems

Combined Probability Prediction of Wind Power Considering the Conflict of Evaluation Indicators

Nantian Huang et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

Charging Demand Forecasting Model for Electric Vehicles Based on Online Ride-Hailing Trip Data

Qiang Xing et al.

IEEE ACCESS (2019)

Article Engineering, Electrical & Electronic

Probabilistic modeling of electric vehicle charging pattern in a residential distribution network

Azhar Ul-Haq et al.

ELECTRIC POWER SYSTEMS RESEARCH (2018)

Article Thermodynamics

Electric vehicle fast charging station usage and power requirements

Thomas S. Bryden et al.

ENERGY (2018)

Article Engineering, Electrical & Electronic

Optimal Routing and Charging of an Electric Vehicle Fleet for High-Efficiency Dynamic Transit Systems

Tao Chen et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

Article Engineering, Electrical & Electronic

Integrating EV Charging Stations as Smart Loads for Demand Response Provisions in Distribution Systems

Omar Hafez et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

Article Energy & Fuels

Electric vehicle charging demand forecasting model based on big data technologies

Mariz B. Arias et al.

APPLIED ENERGY (2016)

Article Energy & Fuels

Forecasting the EV charging load based on customer profile or station measurement?

Mostafa Majidpour et al.

APPLIED ENERGY (2016)

Article Engineering, Electrical & Electronic

Charging demand for electric vehicle based on stochastic analysis of trip chain

Tao Shun et al.

IET GENERATION TRANSMISSION & DISTRIBUTION (2016)

Article Engineering, Electrical & Electronic

Probabilistic estimation of plug-in electric vehicles charging load profile

Nima H. Tehrani et al.

ELECTRIC POWER SYSTEMS RESEARCH (2015)

Article Thermodynamics

Probabilistic wind power forecasting with online model selection and warped gaussian process

Peng Kou et al.

ENERGY CONVERSION AND MANAGEMENT (2014)

Article Engineering, Electrical & Electronic

Statistical Charging Load Modeling of PHEVs in Electricity Distribution Networks Using National Travel Survey Data

A. Rautiainen et al.

IEEE TRANSACTIONS ON SMART GRID (2012)

Article Computer Science, Artificial Intelligence

FSIM: A Feature Similarity Index for Image Quality Assessment

Lin Zhang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2011)

Article Green & Sustainable Science & Technology

Aggregated Impact of Plug-in Hybrid Electric Vehicles on Electricity Demand Profile

Zahra Darabi et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2011)