Next Article in Journal
Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm
Previous Article in Journal
Application of Spectral Kurtosis to Characterize Amplitude Variability in Power Systems’ Harmonics
Article Menu
Issue 1 (January-1) cover image

Export Article

Open AccessArticle
Energies 2019, 12(1), 195; https://doi.org/10.3390/en12010195

A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method

1,* , 2,3
and
4
1
School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
2
State Grid Beijing Electric Power Company, Beijing 100031, China
3
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
4
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Received: 27 November 2018 / Revised: 23 December 2018 / Accepted: 1 January 2019 / Published: 8 January 2019
Full-Text   |   PDF [4081 KB, uploaded 9 January 2019]   |  

Abstract

To reduce the fluctuation of renewable energy (RE) supply and improve the economic efficiency of the power grid, the energy local area network (ELAN), which is a subnetwork of the energy internet (EI), plays an important role in specific regions. Electric vehicles (EVs), as virtual energy storage (VES) in ELANs, are helpful to decrease the fluctuations of RE supply. However, how to use EVs in ELANs is a complex issue, considering the uncertainties of EVs’ charging demand, the forecast data errors of RE sources, etc. In this paper, a typical ELAN structure is established, taking into account RE sources, load response system, and a distributed energy storage (DES) system including EVs. A two-step optimization framework for ELAN scheduling problem is proposed. A global optimization model based on forecast data is built to maximize the income of ELAN, and an online local optimization model is introduced to minimize the correction cost utilizing prior knowledge. Finally, the proposed two-step optimization framework is applied to a series of real-world ELAN scheduling problems. The results show that DES system with EVs can reduce the volatility of RE supply evidently, and the proposed method is able to maximize the income of the ELAN efficiently. View Full-Text
Keywords: energy local area network scheduling; virtual energy storage; two-step optimization framework; day-ahead scheduling strategy; online local optimization; prior knowledge energy local area network scheduling; virtual energy storage; two-step optimization framework; day-ahead scheduling strategy; online local optimization; prior knowledge
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, X.; Zhang, X.; Fan, Y. A Two-Step Framework for Energy Local Area Network Scheduling Problem with Electric Vehicles Based on Global–Local Optimization Method. Energies 2019, 12, 195.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top