Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

217 Pages·2017·2.48 MB·English

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Theses: Doctorates and Masters Theses
2015
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Adel Brka
Edith Cowan University
Follow this and additional works at: https://ro.ecu.edu.au/theses
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RReeccoommmmeennddeedd CCiittaattiioonn
Brka, A. (2015). Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent
techniques. https://ro.ecu.edu.au/theses/1756
This Thesis is posted at Research Online.
https://ro.ecu.edu.au/theses/1756
Edith Cowan University
Research Online
Theses: Doctorates and Masters Theses
2015
Optimisation of stand-alone hydrogen-based
renewable energy systems using intelligent
techniques
Adel Brka
Edith Cowan University
Recommended Citation
Brka, A. (2015).Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques. Retrieved from
http://ro.ecu.edu.au/theses/1756
This Thesis is posted at Research Online.
http://ro.ecu.edu.au/theses/1756
USE OF THESIS

The Use of Thesis statement is not included in this version of the thesis.
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OPTIMISATION OF STAND-ALONE HYDROGEN-BASED RENEWABLE ENERGY SYSTEMS
USING INTELLIGENT TECHNIQUES

by
Adel Z. S. Brka
A thesis with publications presented to Edith Cowan University in fulfilment of the
requirement for the degree of Doctor of Philosophy

SCHOOL OF ENGINEERING
FACULTY OF COMPUTING, HEALTH AND SCIENCE
EDITH COWAN UNIVERSITY
December 07, 2015

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The declaration page
is not included in this version of the thesis
ABSTRACT
Wind and solar irradiance are promising renewable alternatives to fossil fuels due to
their availability and topological advantages for local power generation. However, their
intermittent and unpredictable nature limits their integration into energy markets.
Fortunately, these disadvantages can be partially overcome by using them in
combination with energy storage and back-up units. However, the increased complexity
of such systems relative to single energy systems makes an optimal sizing method and
appropriate Power Management Strategy (PMS) research priorities.
This thesis contributes to the design and integration of stand-alone hybrid renewable
energy systems by proposing methodologies to optimise the sizing and operation of
hydrogen-based systems. These include using intelligent techniques such as Genetic
Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three
design aspects: component sizing; renewables forecasting; and operation coordination,
have been investigated. The thesis includes a series of four journal articles.
The first article introduced a multi-objective sizing methodology to optimise stand-
alone, hydrogen-based systems using GA. The sizing method was developed to
calculate the optimum capacities of system components that underpin appropriate
compromise between investment, renewables penetration and environmental footprint.
The system reliability was assessed using the Loss of Power Supply Probability (LPSP)
for which a novel modification was introduced to account for load losses during
transient start-up times for the back-ups.
The second article investigated the factors that may influence the accuracy of NNs
when applied to forecasting short-term renewable energy. That study involved two
NNs: Feedforward; and Radial Basis Function in an investigation of the effect of the
type, span and resolution of training data, and the length of training pattern, on short-
term wind speed prediction accuracy. The impact of forecasting error on estimating the
available wind power was also evaluated for a commercially available wind turbine.
The third article experimentally validated the concept of a NN-based (predictive) PMS.
A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated
renewable power source; battery bank; and hydrogen fuel cell coupled with metal
hydride storage, satisfied the dynamic load demand. The overall power flow of the
constructed system was controlled by a NN-based PMS which was implemented using
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MATLAB and LabVIEW software. The effects of several control parameters, which are
either hardware dependent or affect the predictive algorithm, on system performance
was investigated under the predictive PMS, this was benchmarked against a rule-
based (non-intelligent) strategy.
The fourth article investigated the potential impact of NN-based PMS on the economic
and operational characteristics of such hybrid systems. That study benchmarked a
rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel
cell optimisation using PSO, when applied in the context of predictive PMS was also
investigated. The comparative analysis was based on deriving the cost of energy, life
cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser
units. The effects of other parameters such the LPSP level, prediction accuracy were
also investigated.
The developed techniques outperformed traditional approaches by drawing upon
complex artificial intelligence models. The research could underpin cost-effective,
reliable power supplies to remote communities as well as reducing the dependence on
fossil fuels and the associated environmental footprint.

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ACKNOWLEDGEMENTS
Completion of this Doctoral research would not have been possible without the support
of many people throughout my candidature as a student at Edith Cowan University
(ECU). I would like to thank my principal supervisor, Dr Ganesh Kothapalli, and Co-
principal Dr Yasir Al-Abdeli. Their support, provided to me throughout the duration of
the research project has been invaluable.
The generous support awarded to me in the form of an ECU International Postgraduate
Research Scholarship (ECU-IPRS) is acknowledged and my gratitude and appreciation
is extended to the staff of ECU for their support and assistance during the development
of the research project.
I would like to express my profound gratitude to my wife for her understanding, endless
patience and encouragement when it was most required during difficult times. Also,
special thanks to my daughters and sons, Noura, Abier, Mohamed and Haithem, who
have always been my motivator for success. I would also like to thank my family from
Libya for their unconditional love and support. Finally, I am grateful to all my friends
including those who are from the school of engineering, ECU.

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LIST OF SYMBOLS
(cid:1827)(cid:1842)(cid:1831) Absolute percentage error
(cid:1828) Atmospheric extinction coefficient
(cid:1828) Battery state of charge
(cid:3020)(cid:3016)(cid:3004)
(cid:1828) Minimum battery state of charge
(cid:3040)(cid:3036)(cid:3041)
(cid:1828) Maximum battery state of charge
(cid:3040)(cid:3028)(cid:3051)
(cid:1829) Sky diffusion factor
(cid:1829) Capital cost per unit
(cid:3030)(cid:3028)(cid:3043)
(cid:1829) Replacement cost per unit
(cid:3045)(cid:3032)(cid:3043)
(cid:1829) Operation and maintenance cost per unit
(cid:3042)(cid:428)(cid:3040)
(cid:1829)(cid:1844)(cid:1832) Capital recovery factor
(cid:1829) Hydrogen conversion coefficient,
(cid:3009)(cid:3118)
(cid:1831) Initial energy of hydrogen storage
(cid:3009)(cid:2870)(cid:481)(cid:2868)
(cid:1831) Energy of hydrogen storage
(cid:3009)(cid:2870)
(cid:1831) Maximum energy capacity of hydrogen storage
(cid:3009)(cid:2870)(cid:481)(cid:3040)(cid:3028)(cid:3051)
(cid:1831) Initial energy of battery
(cid:3029)(cid:3028)(cid:3047)(cid:481)(cid:2868)
(cid:1831) Battery energy
(cid:3029)(cid:3028)(cid:3047)
(cid:1831) Maximum energy of battery
(cid:3029)(cid:3028)(cid:3047)(cid:481)(cid:3040)(cid:3028)(cid:3051)
(cid:1831)(cid:1831) Excess energy
(cid:1831) Energy converted or stored by components
(cid:3036)
(cid:1831) Bandgap energy
(cid:3034)
(cid:1831)(cid:1870)(cid:1870)(cid:1867)(cid:1870) Prediction error
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