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Multi-objective network planning tool for networks containing high penetrations of DER

Inglis, Steven and Ault, Graham and Alarcon Rodriguez, Arturo Daniel and Galloway, Stuart (2010) Multi-objective network planning tool for networks containing high penetrations of DER. In: 45th International Universities Power Engineering Conference (UPEC), 2010. IEEE, pp. 1-6. ISBN 9781424476671

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Abstract

The integration of Distributed Energy Resources (DER) in power systems presents substantial challenges to network planners. Assessing accurately the impact that DER will have is critical, as the specifics of the installed DER may affect: control of the network within limits; quality of supply; losses and financial objectives. The optimal placement and size of DER in a distribution network is desirable when the primary aim of network planning is to keep the system in a secure state; and the secondary aim is to receive the optimum benefits from DER (generation, responsive demand side and stored energy including electric vehicles). Within the Supergen Highly Distributed Power Systems (HDPS) consortium a multiple objective planning framework and tool was developed based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This tool is able to evaluate the impact of different DER configurations, and to optimise the location, type and operation of DER for an optimal development of the system. The tool includes an outer optimisation, which optimises DER location and type, and an inner optimisation which uses Optimal Power Flow to optimise the operation of DER. This paper describes how this tool can be extended for the planning of networks which have various levels of penetration of DER and to represent the wider set of DER options now emerging such as electric vehicles (EVs) which are the focus of this paper. Different network configurations will be represented within the outer loop investment optimisation problem to allow various network architectures to be selected to complement the decentralised energy resource portfolio.

Item type: Book Section
ID code: 35405
Keywords: batteries, planning , optimization, evolutionary computation, electricity , density estimation robust algorithm, biological cells , Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 01 Nov 2011 15:37
    Last modified: 09 May 2014 05:22
    URI: http://strathprints.strath.ac.uk/id/eprint/35405

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