Abstract

Active network management, such as economically shifting flexible electric vehicle (EV) load over time, can help relieve network congestion caused by excessive wind output with the least wind power curtailment. This paper proposes an enhanced EV load shifting strategy by considering the uncertainty associated with wind and load forecasting. Due to wind and load forecasting error, there will be risks in expected benefits from EV shifting over differing time horizon. The longer the time horizon, the higher will be the risk. Such tradeoffs between benefits and risks in EV shifting for mitigating network congestions are not considered in existing literature. Besides, the general method to analyze uncertainty is based on Monte Carlo simulation, which is time-consuming. This paper addresses the challenge by adopting risk adjusted return on capital concept, which is widely used in the financial sector for assessing returns under differing risk levels. The proposed strategy converts the operational benefits, generated from EV shifting under different uncertainty levels into an equivalent benefit value under per unit uncertainty level, i.e., 'mitigating' the impact of uncertainties in the optimization of EV load shifting. As demonstrated in this paper, the proposed strategy assesses the impacts of wind and load forecasting error on the expected operational benefit in an analytical and scalable way, thus extending the traditional deterministic network operation to stochastic network operation.

Original languageEnglish
Article number7438890
Pages (from-to)2694-2701
Number of pages8
JournalIEEE Transactions on Smart Grids
Volume8
Issue number6
Early online date22 Mar 2016
DOIs
Publication statusPublished - 1 Nov 2017

Fingerprint

Active networks
Network management
Electric vehicles
Wind power
Uncertainty

Keywords

  • Electric vehicle
  • generation curtailment
  • load shifting
  • risk adjusted return on capital
  • wind forecasting

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Active Network Management Considering Wind and Load Forecasting Error. / Zhou, Lin; Li, Furong; Tong, Xing.

In: IEEE Transactions on Smart Grids, Vol. 8, No. 6, 7438890, 01.11.2017, p. 2694-2701.

Research output: Contribution to journalArticle

@article{6d3a061b4cd8440895440bd9c8ae99a9,
title = "Active Network Management Considering Wind and Load Forecasting Error",
abstract = "Active network management, such as economically shifting flexible electric vehicle (EV) load over time, can help relieve network congestion caused by excessive wind output with the least wind power curtailment. This paper proposes an enhanced EV load shifting strategy by considering the uncertainty associated with wind and load forecasting. Due to wind and load forecasting error, there will be risks in expected benefits from EV shifting over differing time horizon. The longer the time horizon, the higher will be the risk. Such tradeoffs between benefits and risks in EV shifting for mitigating network congestions are not considered in existing literature. Besides, the general method to analyze uncertainty is based on Monte Carlo simulation, which is time-consuming. This paper addresses the challenge by adopting risk adjusted return on capital concept, which is widely used in the financial sector for assessing returns under differing risk levels. The proposed strategy converts the operational benefits, generated from EV shifting under different uncertainty levels into an equivalent benefit value under per unit uncertainty level, i.e., 'mitigating' the impact of uncertainties in the optimization of EV load shifting. As demonstrated in this paper, the proposed strategy assesses the impacts of wind and load forecasting error on the expected operational benefit in an analytical and scalable way, thus extending the traditional deterministic network operation to stochastic network operation.",
keywords = "Electric vehicle, generation curtailment, load shifting, risk adjusted return on capital, wind forecasting",
author = "Lin Zhou and Furong Li and Xing Tong",
year = "2017",
month = "11",
day = "1",
doi = "10.1109/TSG.2016.2535269",
language = "English",
volume = "8",
pages = "2694--2701",
journal = "IEEE Transactions on Smart Grids",
issn = "1949-3053",
publisher = "IEEE",
number = "6",

}

TY - JOUR

T1 - Active Network Management Considering Wind and Load Forecasting Error

AU - Zhou, Lin

AU - Li, Furong

AU - Tong, Xing

PY - 2017/11/1

Y1 - 2017/11/1

N2 - Active network management, such as economically shifting flexible electric vehicle (EV) load over time, can help relieve network congestion caused by excessive wind output with the least wind power curtailment. This paper proposes an enhanced EV load shifting strategy by considering the uncertainty associated with wind and load forecasting. Due to wind and load forecasting error, there will be risks in expected benefits from EV shifting over differing time horizon. The longer the time horizon, the higher will be the risk. Such tradeoffs between benefits and risks in EV shifting for mitigating network congestions are not considered in existing literature. Besides, the general method to analyze uncertainty is based on Monte Carlo simulation, which is time-consuming. This paper addresses the challenge by adopting risk adjusted return on capital concept, which is widely used in the financial sector for assessing returns under differing risk levels. The proposed strategy converts the operational benefits, generated from EV shifting under different uncertainty levels into an equivalent benefit value under per unit uncertainty level, i.e., 'mitigating' the impact of uncertainties in the optimization of EV load shifting. As demonstrated in this paper, the proposed strategy assesses the impacts of wind and load forecasting error on the expected operational benefit in an analytical and scalable way, thus extending the traditional deterministic network operation to stochastic network operation.

AB - Active network management, such as economically shifting flexible electric vehicle (EV) load over time, can help relieve network congestion caused by excessive wind output with the least wind power curtailment. This paper proposes an enhanced EV load shifting strategy by considering the uncertainty associated with wind and load forecasting. Due to wind and load forecasting error, there will be risks in expected benefits from EV shifting over differing time horizon. The longer the time horizon, the higher will be the risk. Such tradeoffs between benefits and risks in EV shifting for mitigating network congestions are not considered in existing literature. Besides, the general method to analyze uncertainty is based on Monte Carlo simulation, which is time-consuming. This paper addresses the challenge by adopting risk adjusted return on capital concept, which is widely used in the financial sector for assessing returns under differing risk levels. The proposed strategy converts the operational benefits, generated from EV shifting under different uncertainty levels into an equivalent benefit value under per unit uncertainty level, i.e., 'mitigating' the impact of uncertainties in the optimization of EV load shifting. As demonstrated in this paper, the proposed strategy assesses the impacts of wind and load forecasting error on the expected operational benefit in an analytical and scalable way, thus extending the traditional deterministic network operation to stochastic network operation.

KW - Electric vehicle

KW - generation curtailment

KW - load shifting

KW - risk adjusted return on capital

KW - wind forecasting

UR - http://www.scopus.com/inward/record.url?scp=84961615991&partnerID=8YFLogxK

U2 - 10.1109/TSG.2016.2535269

DO - 10.1109/TSG.2016.2535269

M3 - Article

VL - 8

SP - 2694

EP - 2701

JO - IEEE Transactions on Smart Grids

JF - IEEE Transactions on Smart Grids

SN - 1949-3053

IS - 6

M1 - 7438890

ER -