Academic literature on the topic 'Electrical energy generation (incl. renewables, excl. photovoltaics)'

"... the research presented in this thesis identifies the key sources of power loss while connecting wind farms to the existing grid through HVAC transmission system. The findings will ultimately assist in establishing wind power as a key source of renewable energy and help developing a green Australia"--Abstract.

A recent issue of increasing public focus is the need for robust, sustainable and climate friendly power transmission and distribution systems that are intelligent, reliable and green. Current power systems create environmental impacts as well as global warming due to utilisation of fossil fuels, especially coal, as carbon dioxide is emitted into the atmosphere. In contrast to fossil fuels, renewable energy offers alternative sources of energy which are in general pollution free, technologically effective and environmentally sustainable. It is therefore a fundamental concern today to be able to bring higher percentages of renewable electricity into the energy mix due to the variable nature of many of these resources. The intermittent nature of power output from renewable energy sources, in particular wind and solar, introduces potential technical challenges that affect quality of power observed including voltage fluctuation, power system transients and harmonics, reactive power, switching actions, synchronisation, long transmission lines, low power factor, storage system, load management, and forecasting and scheduling. Therefore, the major aim of this research study on the “Experimental investigation and assessment of renewable energy integration into the grid” is to investigate the strategic impacts of integrating renewable energy sources with the grid, including analysis of the potential barriers and possible deployment integration issues of renewable energy into the grid so as to develop a clean-energy system for a sustainable future. The study was divided into five major sections. Firstly, a feasibility study was undertaken to analyse the potentialities of renewable energy sources in Australia, in particular wind and solar energy. The second part of the study predicted the availability and characteristics of variable wind speed and solar radiation as well as typical variations of energy production from renewable sources for adequate management of the power systems with large-scale renewable energy integration. This part also explored the usefulness of integrating renewable energy sources with the power systems, analysing the benefits and outcomes for a typical Australian power network within the subtropical climate of Central Queennsland. From the feasibility study, it can be evident that Australia has significant potential for renewable energy generation that reduces the cost of energy generation and global warming significantly. Forecasting model predicts the solar radiation and wind speed as well as possible energy generation from solar and wind sources in advance in the Capricornia region that can be used by the grid operators for grid management purposes. The major objective of the study is to investigate the strategic impacts of integrating renewable energy sources into the grid. Initially, rigorous experimental analysis was conducted using the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Renewable Energy Integration Facility. Once experimental analysis was performed, a power system model that mimicked the experimental setup was developed using the power system design and analysis simulation tool PSS SINCAL to investigate the strategic impacts of renewable energy integration into the grid. However, to investigate the impacts of large-scale integration of renewable energy sources into the grid, a section of Ergon Energy’s Distribution Network was developoed, in particular the Rockhampton power network. It can be evident from the experimental analysis that integration of solar photovoltaic (PV) energy introduces harmonics and voltage fluctuations into the network and the level of these impacts increases with the increase of solar PV energy utilisation. Similar findings were also perceived from the simulation analyses. Model analysis clearly indicated that large-scale integration of renewable sources, in particular solar PV and wind, into the grid causes uncertainties in the power network due to the intermittent nature of these sources and the level of impacts that exceeded the regulatory standard defined by the local distribution network service provider (DNSP) for some of the studied case scenarios. The major adverse impacts are voltage regulation, overloading of transformers, poor power factor regulation and current and voltage harmonic distortion into the network which require to be reduced to provide a smooth power supply to the customers. Finally, the study explores possible mitigation measures to reduce the potential adverse impacts listed above and it was observed that integration of optimised STATCOM and energy storage improves the overall power quality of the power network as it enhances voltage regulation and improves power distribution and transformer utilisation and reduces total harmonic distortion of the power network. This vital study will be offered a path toward significant environmental improvement which will assist to reduce global warming and CO2 emissions substantially. This study will also be helpful for the power utilities and communities to develop a climate-friendly sustainable power system for the future.

This thesis examines the use of matrix converter (MC) in voltage regulators intended for use in the low voltage (LV) distribution network. Voltage balance and voltage regulation can be controlled by adding a series compensation voltage with a transformer. The MC supplies the injection transformer with an appropriate voltage which may be significantly unbalanced. Prior-art MCs have been predominantly applied in motor drives which have relatively balanced voltages and currents. This thesis shows the existing methods are not ideal for very unbalanced situations. It is shown that a traditional MC supplying an unbalanced load develops current harmonics at its input terminal. This thesis provides an improved MC modulation method that can eliminate input harmonics. This result is confirmed by simulation and experimental work. The thesis extends the traditional 3× 3 matrix into four wire compatible MC topologies including 3×4, 4×3 and 4×4 converters. For these cases, and generalised cases, solutions are presented for switch commutation. These are implemented with a field programmable gate array (FPGA). An experimental study is made of switch commutation for silicon and silicon carbide metal oxide semiconductor field-effect transistors (MOSFETs).

The penetration level of photovoltaic (PV) power in the low voltage (LV) distribution networks is rapidly increasing. The European Photovoltaic Industry Association reported the installed capacity of PV systems globally reached 177GW at the end of 2014. The annual rate of installations, 38.7GW in 2014, continues to increase. A large part of this is installed as residential systems connected to LV networks. The Australian Clean Energy Regulator has published a report showing the total installed capacity of PV in Australia exceeded 4.5GW in 2015. The impacts of high PV penetrations in LV residential distribution networks include voltage rise, voltage unbalance, and reverse power flow which may limit the level of photovoltaic penetration within the LV distribution networks. A four-leg compensator based on a unified power flow controller (UPFC) concept is proposed for simultaneous voltage and current compensation in LV distribution systems. As a voltage compensator, this compensation device is shown to be capable of regulating the positive, negative and zero sequence voltage in LV distribution networks under high PV penetrations. At the same time, as a current compensator, the device is capable of power factor correction, zero sequence or neutral current compensation, harmonic current compensation and a degree of negative sequence current compensation. Instantaneous reactive power theory shows that DC-bus capacitor power will fluctuate at twice mains frequency during any unbalanced operation of the regulator. Real and instantaneous power balance of the UPFC can be maintained by allowing the input shunt converter to draw a small positive and negative sequence current respectively. Instantaneous power balance with negative sequence current makes it possible to reduce the DC bus capacitance which allows long life ceramic or polypropylene capacitors to replace electrolytic capacitors. The operation of the proposed UPFC based four-leg compensator has been demonstrated by extensive simulation studies. These confirm that the device can perform the full range of series and parallel compensation duties for the compensation of voltage and current respectively. It has also been demonstrated that the DC bus voltage can be controlled using relatively small DC bus capacitors. The simulation work is further confirmed by experimental work with a small scale laboratory model. The laboratory system used commercial inverter modules rated at 600V and 15A per phase to construct two inverters with four phase legs. These were configured as a parallel connected inverter and a series inverter with series voltage injection transformers. The four-leg inverters shared a common DC bus and were equipped with voltage and current transformers and controlled using a Texas Instruments Delfino processor.

Energy expenditure is one of the significant overheads in the lifespan of multistoreyed buildings. Reliable and proficient functions of Heating, Ventilation and Air Conditioning (HVAC) systems are further imperative as a result of the climbing price of electricity. This research recommends that the compounded energy utilisation to meet the demand from high humidity and temperature could be minimised by adopting the alternative high-performance building envelope and low emission cooling method along with the optimised control of additional operational parameters. The core purpose of this research is to computationally evaluate the performance of various alternative building envelopes and low energy cooling methods to determine the best performing envelop and cooling method to enhance the energy efficacy and human comfort in buildings in a subtropical climate in Australia. Firstly, a detailed energy assessment of the current building systems is undertaken on a selected case study building in Rockhampton, Central Queensland. Then, a comprehensive energy simulation model is developed, employing a building energy simulation algorithm. The modelled energy and comfort data of the building systems are validated by means of on-site recorded data. The substantiated model is then expanded to evaluate the efficacy of several alternative building envelopes such as bio-phase change material (BioPCM), cavity wall, Trombe wall, building integrated photovoltaic (BIPV) and low emission cooling methods such as, cooled Beam, ground source heat pump, variable air volume, variable refrigerant flow system to secure better comfort and energy savings in both summer and winter months. Furthermore, an extensive multicriteria based optimisation is undertaken to determine combined alternative envelope and cooling method for retrofitting of the existing systems that will meet the requisite of the present and the future depending on the potential climate change scenario. This study found that both cooled beam and ground source heat pump as low energy high-performance cooling alternatives, and BioPCM as high-performance building envelope have the higher potential for energy conservation and better thermal comfort based on the present and future weather conditions. Through multi-criteria optimisation, the study found that BioPCM and Cooled Beam as an integrated mechanism can be successfully incorporated into buildings in subtropical climate to improve the energy efficiency by 30% and human comfort which have not been evaluated in any other studies in the past. Furthermore, the use of the combined optimised approach, i.e. integration of BioPCM and Cooled Beam, produces significantly less emission (21%) per year at the same time ensures the comfortability of the occupants which is the utmost consideration in the study. Finally, the study offered a net positive energy operating method to ensure that carbon footprint is minimised considering the present and future weather conditions. Overall, a practical thermal simulation orientated optimisation framework is developed and executed that unites the objective of minimising energy consumption of building systems as well as maintaining superior comfort of the people based on the present and future weather conditions.

The supply of petroleum sources is finite, non-renewable and, at the current rate of consumption, it will become severely depleted by 2050. Furthermore, the use of petroleum fuel increases greenhouse gas (GHG) emissions, leading to global warming which is harmful. Thus, there is an urgent need to find alternative sources of energy that are renewable, cost effective and can be produced in a sustainable manner. Non-edible feedstock biodiesels are a promising alternative fuel for reducing most petroleum fuel related environmental problems. They are attracting increasing attention due to their abundant availability and similar physicochemical properties as petroleum-derived diesel. This study carefully investigated six major non-edible vegetable oils (papaya seed oil, stone fruit kernel oil, jatropha oil, rapeseed oil, beauty leaf tree oil and waste cooking oil), that are locally available, out of 350 oil-bearing crops that could be potentially used to produce biodiesel. Four multiple criteria decision analysis (MCDA) methods with twelve physicochemical properties of biodiesel feedstocks and three different weightage (%) determination methods were used to rank these six feedstocks, with the view to find the best performing biodiesel feedstocks. The overall results show that the stone fruit kernel oil (SFO) was ranked as the best performing feedstock on the basis of engine performance amongst the six locally available feedstocks examined, papaya seed oil (PSO) came out as the second best, and the waste cooking oil was the worst performing biodiesel. Alkali catalysed transesterification reaction is the most widely used method for producing biodiesel from oil/animal fats due to its higher conversion efficiency in a short reaction time (30-60 min). The current study was undertaken to optimise the transesterification process for PSO and SFO with the view to increasing the efficiency of biodiesel conversion. A response surface method (RSM) based Box-Behnken design was employed to optimise biodiesel conversion processes for both PSO and SFO. Biodiesel conversion efficiencies of 96.5% and 95.8% were found for PSO and SFO at their respective optimum operating conditions. These PSO and SFO biodiesels were evaluated using a 4-cylinder, 4-stroke Kubota diesel engine. In general, both PSO and SFO blends decreased engine performance slightly compared to diesel as expected, however, SFO biodiesel blends gave about 3% better performance compared to PSO blends. On the other hand, PSO blends (20%) decreased most of the engine emissions by up to 34% except for an increase of about 5% in nitrogen oxide (NOx) compared to diesel. These emission performances are up to 14% better than the corresponding SFO emissions. Although the SFO biodiesel blends have slightly better engine performance than PSO biodiesel blends, the PSO biodiesel blends proved to be a better overall choice due to their excellent environmentally friendly attributes as they can reduce exhaust emissions to a great extent. Therefore, PSO was chosen subsequently to develop interactive relationships between three operating parameters of PSO, namely biodiesel blends, engine load, and engine speed and four responses of brake power (BP), torque, brake specific fuels consumption (BSFC), and brake thermal efficiency (BTE) for engine testing and emissions behaviour. Analysis of variance (ANOVA) and a statistical regression model show that load and speed were the two most important parameters that affect all four responses. The biodiesel blends parameter had a significant effect on BSFC. The engine load and engine speed were the two most important parameters that affect four of the responses (NOx, hydrocarbon (HC), particulate matter (PM) and carbon monoxide (CO)). In-cylinder peak pressures for PSO biodiesel blends were higher than for diesel irrespective of engine speed. Heat release rates of PSO biodiesel blends were found to be lower than for diesel due to lower ignition delays and lower caloric values of biodiesel. The maximum cylinder temperatures of PSO biodiesel blends were higher (3.73%) than that of diesel. To minimise the exhaust emissions, PSO biodiesel blends were mixed with two oxygenated additives, namely diethylene glycol dimethyl ether (diglyme) and n-butanol, to make ternary blends. These blends were tested for both engine performance and emissions. The addition of oxygenated additives increased the BP, torque and BTE values of PSO biodiesel ternary blends and it lowered the average BSFC by 0.5% and 17.7% compared with diesel and PSO blends (20%), respectively. PSO-diglyme-diesel ternary blend performed better than all other binary blends as well as the PSO-n-butanol-diesel ternary blend. The average reductions of HC, CO, NOx and PM of PSO-diglyme-diesel ternary blends compared with diesel were 32.4%, 61%, 0.64% and 47.4% respectively, whereas a 2.8% increase in carbon dioxide (CO2) emission was observed. The average increase of NOx, and CO2 for PSO blends (20%) compared with diesel were 4.1% and 4.5%, respectively. In conclusion, this study provided a solid base of new knowledge regarding biodiesel feedstock selection and optimisation techniques for PSO and SFO, assessed the suitability of PSO and SFO as alternatives to petroleum diesel and analysed how the emissions from these biodiesels could be reduced. These are very useful information for engine manufacturers, Government, stakeholders and policy makers to eliminate the lack of awareness of using second-generation biodiesel in Australia

Renewable energy sources, particularly solar energy, play a vital role for generating environment-friendly electricity. Foremost advantages of solar energy sources are: nonpolluting, free in terms of availability and renewable. The renewable green-energy sources are becoming more cost-effective and sustainable substitutes to conventional fossil fuels. Nonetheless, power generation from Photovoltaic (PV) systems is unpredictable due to its reliance on meteorological conditions. The effective use of this fluctuating solar energy source obliges reliable and robust forecast information for management and operation of a contemporary power grid. Due to the remarkable proliferation of solar power generation, the prediction of solar power yields becomes more and more imperative. Large-scale penetration of solar power in the electricity grid provides numerous challenges to the grid operator, mainly due to the intermittency of the sun. Since the power produced by a PV depends decisively on the unpredictability of the sun, unexpected variations of a PV output may increase operating costs for the electricity system as well as set potential threats to the reliability of electricity supply. Nevertheless, the prediction accuracy level of the existing prediction methods for solar power is not up to the mark that is very much required to deal with the forthcoming sophisticated and advanced power grid like Smart Grid. Therefore, accurate solar power prediction methods become very substantial. The main goal of this thesis is to produce a novel hybrid prediction method for more accurate, reliable and robust solar power prediction using modern Computational Intelligence (CI). The hybrid prediction method which is mainly composed of multiple regressive machine learning techniques will be as accurate and reliable as possible, to accommodate the needs of any future systems that depend upon it for generator or load scheduling, or grid stability control applications. In this thesis, research on the experimental analysis and development of hybrid machine learning for solar power prediction has been presented. The thesis makes the following major contributions: 1) It investigates heterogeneous machine learning techniques for hybrid prediction methods for solar power 2) It applies feature selection methods to individually improve the prediction accuracy of previous machine learning techniques 3) It investigates possible parameter optimisation of computational intelligence techniques to make sure that individual predictions are as accurate as possible 4) It proposes hybrid prediction by non-linearly integrating the discrete prediction results from various machine-learning techniques. Performance characteristics of the hybrid machine learning over individuals was carried out through experimental analysis and the results are justified by various statistical tests and error validation metrics which confirmed the maximum achievable accuracy of the developed hybrid method for solar power prediction. It is expected that the outcome of the research will provide noteworthy contribution to the relevant research field as well as to Australian power industries in the near future.