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Title: Automated distribution network planning with active network management
Author: Conner, Steven
ISNI:       0000 0004 7232 2359
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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Renewable energy generation is becoming a major part of energy supply, often in the form of distributed generation (DG) connected to distribution networks. While growth has been rapid, there is awareness that limitations on spare capacity within distribution (and transmission) networks is holding back development. Developments are being shelved until new network reinforcements can be built, which may make some projects non-viable. Reinforcements are costly and often underutilised, typically only loaded to their limits for a few occasions during the year. In order to accommodate new DG without the high costs or delays, active network management (ANM) is being promoted in which generation and other network assets are controlled within the limits of the existing network. There is a great deal of complexity and uncertainty associated with developing ANM and devising coherent plans to accommodate new DG is challenging for Distribution Network Operators (DNOs). As such, there is a need for robust network planning tools that can explicitly handle ANM and which can be trusted and implemented easily. This thesis describes the need for and the development of a new distribution expansion planning framework that provides DNOs with a better understanding of the impacts created by renewable DG and the value of ANM. This revolves around a heuristic planning framework which schedules necessary upgrades in power lines and transformers associated with changes in demand as well as those driven by the connection of DG. Within this framework a form of decentralised, adaptive control of DG output has been introduced to allow estimation of the impact of managing voltage and power flow constraints on the timing and need for network upgrades. The framework is initially deployed using simple scenarios but a further advance is the explicit use of time series to provide substantially improved estimates of the levels of curtailment implied by ANM. In addition, a simplified approach to incorporating demand side management has been deployed to facilitate understanding of the scope and role this may play in facilitating DG connections.
Supervisor: Harrison, Gareth ; Djokic, Sasa ; Wallace, Robin Sponsor: Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: renewable energy ; distributed generation ; distribution networks ; active network management ; network planning tools