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Hybrid immune-genetic algorithm method for benefit maximisation of distribution network operators and distributed generation owners in a deregulated environment
Alternative Title
Hybrid Immune-Genetic Algorithm Method for Benefit Maximization of DNOs and DG Owners in a Deregulated Environment
Date Issued
2011-01
Date Available
2014-11-25T11:14:03Z
Abstract
In deregulated power systems, distribution network operators (DNO) are responsible for maintaining the proper operation and efficiency of distribution networks. This is achieved traditionally through specific investments in network components. The event of distributed generation (DG) has introduced new challenges to these distribution networks. The role of DG units must be correctly assessed to optimise the overall operating and investment cost for the whole system. However, the distributed generation owners (DGOs) have different objective functions which might be contrary to the objectives of DNO. This study presents a long-term dynamic multi-objective model for planning of distribution networks regarding the benefits of DNO and DGOs. The proposed model simultaneously optimises two objectives, namely the benefits of DNO and DGO and determines the optimal schemes of sizing, placement and specially the dynamics (i.e. timing) of investments on DG units and network reinforcements over the planning period. It also considers the uncertainty of electric load, electricity price and wind turbine power generation using the point estimation method. The effect of benefit sharing is investigated for steering the decisions of DGOs. An efficient two-stage heuristic method is utilised to solve the formulated planning problem and tested on a real large-scale distribution network.
Type of Material
Journal Article
Publisher
Institute of Engineering and Technology (IET)
Journal
IET Generation, Transmission and Distribution
Volume
5
Issue
9
Start Page
961
End Page
972
Copyright (Published Version)
2011 The Institution of Engineering and Technology
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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