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DISTRIBUTION NETWORK AUTOMATION FOR MULTI-OBJECTIVE OPTIMISATION

Zhang, Boyi

[Thesis]. Manchester, UK: The University of Manchester; 2018.

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Abstract

Asset management and automation are acknowledged by distribution utilities as a useful strategy to improve service quality and reliability. However, the major challenge faced by decision makers in distribution utilities is how to achieve long-term return on the projects while minimising investment and operation costs. Distribution automation (DA) in terms of transformer economic operation (TEO), distribution network reconfiguration (DNR), and sectionalising switch placement (SSP) is recognised as the most effective way for distribution network operators (DNOs) to increase operation efficiency and reliability. Automated tie-switches and sectionalising switches play a fundamental role in distribution networks. A method based on the Monte Carlo simulation is discussed for transformer loss reduction, which comprises of profile generators of residential demand and a distribution network model. The ant colony optimisation (ACO) algorithm is then developed for optimal DNR and TEO to minimise network loss. An ACO algorithm based on a fuzzy multi-objective approach is proposed to solve SSP problem, which considers reliability indices and switch costs. Finally, a multi-objective ant colony optimisation (MOACO) and an artificial immune systems-ant colony optimisation (AIS-ACO) algorithm are developed to solve the reconfiguration problem, which is formulated within a multi-objective framework using the concept of Pareto optimality. The performance of the optimisation techniques has been assessed and illustrated by various case studies on three distribution networks. The obtained optimum network configurations indicate the effectiveness of the proposed methods for optimal DA.

Bibliographic metadata

Type of resource:
Content type:
Administered thesis
Form of thesis:
Traditional
Type of submission:
Doctoral level ETD - final
Thesis title:
DISTRIBUTION NETWORK AUTOMATION FOR MULTI-OBJECTIVE OPTIMISATION
Degree type:
Doctor of Philosophy
Degree programme:
PhD Electrical and Electronic Engineering
Publication date:
2018-04-11T10:26:46
Institution:
The University of Manchester
Location:
Manchester, UK
Total pages:
224
Abstract:
Asset management and automation are acknowledged by distribution utilities as a useful strategy to improve service quality and reliability. However, the major challenge faced by decision makers in distribution utilities is how to achieve long-term return on the projects while minimising investment and operation costs. Distribution automation (DA) in terms of transformer economic operation (TEO), distribution network reconfiguration (DNR), and sectionalising switch placement (SSP) is recognised as the most effective way for distribution network operators (DNOs) to increase operation efficiency and reliability. Automated tie-switches and sectionalising switches play a fundamental role in distribution networks. A method based on the Monte Carlo simulation is discussed for transformer loss reduction, which comprises of profile generators of residential demand and a distribution network model. The ant colony optimisation (ACO) algorithm is then developed for optimal DNR and TEO to minimise network loss. An ACO algorithm based on a fuzzy multi-objective approach is proposed to solve SSP problem, which considers reliability indices and switch costs. Finally, a multi-objective ant colony optimisation (MOACO) and an artificial immune systems-ant colony optimisation (AIS-ACO) algorithm are developed to solve the reconfiguration problem, which is formulated within a multi-objective framework using the concept of Pareto optimality. The performance of the optimisation techniques has been assessed and illustrated by various case studies on three distribution networks. The obtained optimum network configurations indicate the effectiveness of the proposed methods for optimal DA.
Keyword(s):
Thesis main supervisor(s):
Thesis co-supervisor(s):
Degree grantor:
Language:
en

Institutional metadata

University researcher(s):
Academic department(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:314160
Created by:
Zhang, Boyi
Created:
11th April, 2018, 09:26:46
Last modified by:
Zhang, Boyi
Last modified:
2nd May, 2018, 13:49:02

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