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Abstract

Globally diesel generator sets account for the majority of generation into remote and off-grid power systems. While diesel generator sets have proven to be a reliable and accessible technology, their downside involves the expense and environmentally emissions linked to diesel fuel consumption. In response diesel generation alternatives are becoming both available and cost competitive, specifically via the integration of renewable energy technology (RET). Hybrid power systems (HPS), those adopting both diesel and RET are increasingly employed to reduce both cost and environmental emissions. As renewable penetration increases within HPS a potential conflict arises, with diesel generation unable to lower output below minimum load set points. These load set points are predetermined to ensure engine efficiency and preserve engine condition. Under medium to high renewable penetration, diesel load set points compete with renewable generation to produce surplus energy. This surplus energy must be absorbed by the system, and while various ancillary technologies, such as demand management, energy storage and dump loads can perform this role, such technologies add expense and complexity. This paper investigates low load diesel (LLD) as one solution to minimising surplus generation within HPS. HPS modelling is used to explore removal of energy storage systems (ESS’s) under LLD application. Model validation, undertaken against both kW and MW scale operational diesel generator data sets is referenced to support the findings of both models. Results show LLD to reduce both system establishment and operational costs for high RET penetration HPS.

Item Type:	Conference Publication
Authors/Creators:	Hamilton, JM and Negnevitsky, M and Wang, X
Keywords:	hybrid power systems, low load diesel, variable speed diesel, remote area power, energy storage systems.
Journal or Publication Title:	Energy Procedia
Publisher:	Elsevier
ISSN:	1876-6102
DOI / ID Number:	https://doi.org/10.1016/j.egypro.2017.03.107
Copyright Information:	Copyright 2017 The Authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) http://creativecommons.org/licenses/by-nc-nd/4.0/
Related URLs:	Google Scholar: Negnevitsky, M UTAS Profile: Negnevitsky, M Google Scholar: Wang, X UTAS Profile: Wang, X
Item Statistics:	View statistics for this item

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