Assessment of the impact of different energy mixes in local decentralised energy networks

作者：Anderson Lucy^*; Galloway Stuart; Stephen Bruce

来源：Proceedings of the Institution of Mechanical Engineers - Part A: Journal of Power and Energy , 2013, 227(1): 105-114.

DOI：10.1177/0957650912466782

• Energy hubs
• multiple energy carriers
• micro-generation
• least cost optimisation
• distributed energy resources

Proposed future UK energy supply infrastructures harbour ambitions for achieving a sustainable and reliable energy network, which can potentially be realised through a decentralised and flexible approach to electricity generation. Such an energy system will be expected to deliver these objectives through the widespread use of distributed energy resources, thereby contributing to the international aim of a low carbon future. Existing research in the area of distributed energy resource integration has developed the energy hub methodology [Geidl M, Koeppel G, Favre-Perrod P, et al. The energy hub - a powerful concept for future energy systems. In: Proceedings of the third annual Carnegie Mellon conference on the electricity industry, Tepper School of Business, Carnegie Mellon University, 13-14 March 2007, pp.1-10.] to formally model flows within an energy network, abstracting generation from multiple energy carriers (e.g. electricity, wind and natural gas) and allowing a modular energy network construction that facilitates optimal placement of distributed energy resources within each hub. This article will present the energy hub approach to modelling a decentralised energy network, illustrated with two case studies from the East and West coasts of Scotland. The resulting analysis will show the impact of two scenarios with different energy mixes available whilst reflecting the subtle differences in demands across areas with different weather profiles. This is a fundamental progression of existing distributed energy resource research to date in that individual characteristics and costs of a mixture of technologies are modelled. Such analysis will be pivotal in planning future networks to ensure that they function optimally and also in directing policy to encourage consumers to adopt a different approach to energy generation.