Identifying Microgrids in Rural Areas Using a Genetic Algorithm

Manou Rosenberg1, James Fletcher2, Mark Reynolds3, Lyndon While4, Tim French5

1 The University of Western Australia, 35 Stirling Highway, WA, 6009, manou.rosenberg@research.uwa.edu.au 

2 The University of Western Australia, 35 Stirling Highway, WA, 6009, james.fletcher@research.uwa.edu.au 

3 The University of Western Australia, 35 Stirling Highway, WA, 6009, mark.reynolds@uwa.edu.au 

4 The University of Western Australia, 35 Stirling Highway, WA, 6009, lyndon.while@.uwa.edu.au 

5 The University of Western Australia, 35 Stirling Highway, WA, 6009, tim.french@.uwa.edu.au

The partitioning of networks in order to optimise one or more given objectives is a highly researched topic with many real-world applications, such as dividing water supply networks into so-called district meter areas, or partitioning transport system networks for distributed traffic management. In this research project the aim is to find an optimal distributed network topology for rural electricity networks.

In larger towns and cities the electricity network often has a meshed infrastructure such that most power lines are backed up by other connections in the network. In rural regions an electricity network often spreads over a large area servicing a relatively small number of electricity customers as is the case in many parts of Western Australia. These networks are expensive to build and maintain, and prone to several environmental risks such as bushfires or storms. In the South-West of WA the customers in remote areas are connected to the main grid by long power lines causing a large amount of the infrastructural costs for electricity network operators. As previous research has shown, it can be beneficial to take some of the customers off the interconnected system in order to form nano- or microgrids, where energy is provided by renewable energy sources or small scale generators. The aim is to identify those electricity customers and apply the developed methods to problem instances of Australian electricity customer loads and locations. This could provide a more reliable, cost effective electricity network infrastructure that incorporates a larger percentage of renewable energy resources.

A problem-specific genetic algorithm approach has been developed to determine those network parts, where the electricity customers could be clustered into self-sustaining microgrids. Given a set of electricity customer loads and locations, the microgrid and stand-alone power system formations are identified in order to minimise the total network costs over a certain time period.

The proposed poster will present an overview of background information on electricity networks and microgrids, a brief introduction to microgrids, a description of the evaluated cost function, and an outline of the problem-specific genetic algorithm used for optimising the network. First results for a real-world test instance will be shown as well.

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The Computing Research and Education Association of Australasia, CORE, is an association of university departments of computer science in Australia and New Zealand. Prior to 2004 it was known as the Computer Science Association, CSA.

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