Evaluating the robustness of an active network management function in an operational environment

Alberto Venturi; Michael James Dolan; Paul Clarkson; Paul Wright; Alistair Forbes; Xin-She Yang; Andrew Roscoe; Graham Ault; Graeme Burt

Evaluating the robustness of an active network management function in an operational environment

Alberto Venturi, Michael James Dolan, Paul Clarkson, Paul Wright, Alistair Forbes, Xin-She Yang, Andrew Roscoe, Graham Ault, Graeme Burt

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

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Abstract

This paper presents the integration process of a distribution network Active Network Management (ANM) function within an operational environment in the form of a Micro-Grid Laboratory. This enables emulation of a real power network and enables investigation into the effects of data uncertainty on an online and automatic ANM algorithm's control decisions. The algorithm implemented within the operational environment is a Power Flow Management (PFM) approach based around the Constraint Satisfaction Problem (CSP). This paper show the impact of increasing uncertainty, in the input data available for an ANM scheme in terms of the variation in control actions. The inclusion of a State Estimator (SE), with known tolerances is shown to improve the ANM performance.

Original language	English
Publication status	Published - 10 Jun 2013
Event	CIRED 2013, 22nd International Conference on Electricity Distribution - Stockholm, Sweden Duration: 10 Jun 2013 → 13 Jun 2013

Conference

Conference	CIRED 2013, 22nd International Conference on Electricity Distribution
Country/Territory	Sweden
City	Stockholm
Period	10/06/13 → 13/06/13

Keywords

active network management
power flow management
constraint satisfaction

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title = "Evaluating the robustness of an active network management function in an operational environment",

abstract = "This paper presents the integration process of a distribution network Active Network Management (ANM) function within an operational environment in the form of a Micro-Grid Laboratory. This enables emulation of a real power network and enables investigation into the effects of data uncertainty on an online and automatic ANM algorithm's control decisions. The algorithm implemented within the operational environment is a Power Flow Management (PFM) approach based around the Constraint Satisfaction Problem (CSP). This paper show the impact of increasing uncertainty, in the input data available for an ANM scheme in terms of the variation in control actions. The inclusion of a State Estimator (SE), with known tolerances is shown to improve the ANM performance.",

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T1 - Evaluating the robustness of an active network management function in an operational environment

AU - Venturi, Alberto

AU - Dolan, Michael James

AU - Clarkson, Paul

AU - Wright, Paul

AU - Forbes, Alistair

AU - Yang, Xin-She

AU - Roscoe, Andrew

AU - Ault, Graham

AU - Burt, Graeme

PY - 2013/6/10

Y1 - 2013/6/10

N2 - This paper presents the integration process of a distribution network Active Network Management (ANM) function within an operational environment in the form of a Micro-Grid Laboratory. This enables emulation of a real power network and enables investigation into the effects of data uncertainty on an online and automatic ANM algorithm's control decisions. The algorithm implemented within the operational environment is a Power Flow Management (PFM) approach based around the Constraint Satisfaction Problem (CSP). This paper show the impact of increasing uncertainty, in the input data available for an ANM scheme in terms of the variation in control actions. The inclusion of a State Estimator (SE), with known tolerances is shown to improve the ANM performance.

AB - This paper presents the integration process of a distribution network Active Network Management (ANM) function within an operational environment in the form of a Micro-Grid Laboratory. This enables emulation of a real power network and enables investigation into the effects of data uncertainty on an online and automatic ANM algorithm's control decisions. The algorithm implemented within the operational environment is a Power Flow Management (PFM) approach based around the Constraint Satisfaction Problem (CSP). This paper show the impact of increasing uncertainty, in the input data available for an ANM scheme in terms of the variation in control actions. The inclusion of a State Estimator (SE), with known tolerances is shown to improve the ANM performance.

KW - active network management

KW - power flow management

KW - constraint satisfaction

UR - http://www.cired.net/publications-all/cired-main-conferences

UR - http://www.cired2013.org/

M3 - Paper

T2 - CIRED 2013, 22nd International Conference on Electricity Distribution

Y2 - 10 June 2013 through 13 June 2013

ER -

Evaluating the robustness of an active network management function in an operational environment

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Keywords

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