THE UNIVERSITY OF

Bachelor of Engineering and Master of Engineering (BE/ME) Thesis

Analysis of a hybrid system including a CST plant utilising a supercritical CO2 power cycle for off-grid power generation

Student Name: Vishak BALAJI

Course Code: ENGG7290

Supervisor: Professor Hal GURGENCI

Submission Date: 28 June 2018

A thesis submitted in partial fulfilment of the requirements of the Bachelor of Engineering and Master of Engineering (BE/ME) degree in Mechanical and Materials Engineering

Faculty of Engineering, Architecture and Information Technology Executive Summary The electrification of remote locations that lack grid-connectivity is a global challenge. In , off-grid electricity accounts for 6% of total generation. At present these needs are met through the use of fossil fuels, resulting in a high electricity cost, and environmental consequences. With the present drive towards reducing gas emissions and increasing the use of renewable energies, there is an opportunity to transition from the use of fossil fuels in remote locations to the use of renewables. In this regard, the hybridisation of renewable technologies with diesel generators has been shown to improve reliability and increase penetration.

This project aims to explore the use of a hybrid renewable energy system consisting of solar photovoltaic (PV) with battery storage, concentrating solar thermal (CST) with thermal storage and diesel generators for off-grid power generation. The analysis considers two major consumer groups, viz. mining sites and communities, at three locations – Newman, and Halls Creek. In particular, the solar thermal system explored utilises a supercritical carbon dioxide power cycle, due to the suitability of this technology at scales appropriate for off-grid use. This is necessitated due to the fact that existing CST plants typically utilise a steam Rankine cycle, which suffers reduced efficiencies at small scales. Based on the existing literature, the proposed technologies have been found to present excellent suitability to this application.

A key area of interest with regards to the CST plant is the turbine inlet temperature, due to higher temperatures presenting a higher power cycle efficiency, which offers a route to reduce energy costs. However, these higher temperatures increase the power cycle and receiver costs, due to the requirement of higher performance materials. Increases in receiver losses are also seen at higher temperatures. Similarly, the CST plant also presents increasing field losses with increasing size. In this analysis, three turbine inlet temperatures (namely 650°C, 800°C and 1000°C) are explored.

A Python program was developed, encapsulating the thermodynamics and operating characteristics of each technology, to simulate the performance of the hybrid system over the course of one year, based on weather data and a synthetic load profile. This simulation was then used along with an optimisation function to identify the optimal mix of technologies in the hybrid system based on the minimum levelised cost of energy (LCOE). Using this program, results were generated for community and mining consumer groups at the three locations, and it was found that in both cases the optimal mix consists of CST as the primary source of baseload power, with 12- 15 hours of thermal storage. While PV and diesel generators were both shown to be necessary, they represent a smaller fraction of the total energy production and serve as supplementary sources of power. Notably, none of the optima included battery storage, highlighting the high costs of this technology as compared to CST with thermal storage vis-a-vis providing baseload power.

All of the scenarios explored presented a relatively high renewable fraction, ranging from 83-93%, and provide significant cost reductions over the diesel base case. This serves as an indication of the excellent solar resources available at all three sites. By comparing the three turbine inlet temperatures, the 650°C case presented the lowest LCOE. Although overall efficiency gains were seen at higher temperatures, with the power cycle efficiency increase outweighing the receiver losses, these gains were insufficient to compensate for the increased capital costs. In addition, the ratio of storage capacity to field capacity was found to be an important consideration with regards to minimising spilled energy from the CST plant. These results were consistent with previous work and expectations based on theory, and hence there is a high level of confidence in their veracity.

Of the three locations, Halls Creek presented the lowest LCOE for both the community and mining cases ($0.158/kWh and $0.136/kWh respectively), with a cost reduction of 34% and 43% over the diesel base case ($0.24/kWh). As such, Halls Creek was deemed the optimal site for implementation. The compositions of these systems were 60%-32%-8% and 69%-24%-7% (CST- PV-diesel) in the community and mining case respectively, with a life-cycle emissions analysis revealing annual reductions in CO2 emissions of 88% and 89% over the diesel base case. Based on a sensitivity analysis, it was found that the LCOE was most sensitive to the CST capital costs and the discount rate (interest rate charged on loans), with the PV capital cost and diesel cost having a smaller but still notable influence.

Based on these results, concept designs of the CST systems at Halls Creek were then produced using System Advisor Model (SAM), including optimised specifications and field layout. The overall requirements for the CST plants at Halls Creek were:

• 1 MW power cycle capacity, 13 hours storage and solar multiple of 2.36 for the community. • 10 MW power cycle capacity, 13.2 hours storage and solar multiple of 2.59 for the mine. • A turbine inlet temperature of 650°C.

The results of this analysis demonstrate that remote off-grid locations in Australia present excellent potential for the deployment of a CST-PV-diesel hybrid renewable energy system. The proposed systems are capable of uninterrupted power supply and have been shown to provide significant economic and environmental benefits over the conventional systems based on diesel generators. These hybrids therefore present a viable solution for achieving lower cost and environmentally friendly electricity for remote users.

Contents 1 Introduction ...... 1 1.1 Context ...... 1 1.2 Aims and expected outcomes ...... 2 1.3 Scope boundaries ...... 3 1.4 Goals ...... 4 2 Project plan ...... 5 2.1 Methodology ...... 5 2.2 Deliverables and milestones ...... 6 2.3 Key resources ...... 6 2.4 Risks and opportunities ...... 7 2.4.1 Risks ...... 7 2.4.2 Opportunities ...... 8 3 Literature review ...... 10 3.1 Planning ...... 10 3.1.1 Scope ...... 10 3.1.2 Key resources ...... 10 3.2 Overview of the candidate technologies...... 11 3.2.1 Solar photovoltaic and battery storage ...... 11 3.2.2 Concentrating solar thermal ...... 14 3.2.3 Diesel generators ...... 16 3.3 Hybrid renewable energy studies ...... 17 3.3.1 Overview of studies ...... 17 3.3.2 Study methodologies ...... 19 3.3.3 Feasibility of technologies ...... 21 4 Methodology ...... 27 4.1 Candidate technologies ...... 27 4.1.1 Concentrating solar thermal plant ...... 27 4.1.2 Photovoltaic array with battery storage ...... 32 4.1.3 Diesel generators ...... 34 4.2 Candidate locations ...... 35 4.2.1 Load profile ...... 35 4.2.2 Characterisation of solar resources ...... 37 4.3 Economic analysis ...... 37 4.4 Environmental assessment ...... 38 4.5 Sensitivity of modelling variables ...... 38 4.6 Simulation program outline ...... 38

4.6.1 Optimisation ...... 41 5 Results ...... 42 5.1 CST efficiency charts ...... 42 5.2 Optimal hybrid systems ...... 43 5.2.1 Community scenario...... 43 5.2.2 Mining scenario ...... 46 5.2.3 Optimal site for further analysis ...... 49 5.3 Sensitivity analysis ...... 50 5.3.1 Halls Creek community case ...... 50 5.3.2 Halls Creek mining case ...... 51 5.4 Life cycle emissions analysis ...... 51 6 CST concept design in SAM ...... 52 6.1 SAM inputs ...... 52 6.2 Results ...... 53 7 Discussion ...... 55 7.1 CST efficiency charts ...... 55 7.2 Optimal hybrid system results...... 55 8 Conclusions ...... 62 9 Recommendations for further work ...... 63 10 Reflection on professional development ...... 64 References ...... 66 Appendix A ...... 74 Appendix B ...... 75 Appendix C ...... 76 Appendix D ...... 78 Appendix E ...... 79 Appendix F ...... 80

List of Tables

Table 1 Project goals ...... 4 Table 2 Project methodology ...... 5 Table 3 Project deliverables ...... 6 Table 4 Key resources ...... 6 Table 5 Risk analysis ...... 7 Table 6 Scope of literature review ...... 10 Table 7 Power cycle efficiency ...... 30 Table 8 Additional CST assumptions...... 31 Table 9 CST material and costs ...... 31 Table 10 CST cost breakdown ...... 32 Table 11 PV panel specifications ...... 33 Table 12 Battery storage assumptions ...... 33 Table 13 PV and battery costs ...... 34 Table 14 Diesel generator costs ...... 35 Table 15 Average daily energy consumption ...... 36 Table 16 Emissions of each system ...... 38 Table 17 Newman community case configuration ...... 43 Table 18 Port Augusta community case configuration ...... 44 Table 19 Halls Creek community case configuration ...... 45 Table 20 Newman mining case configuration ...... 46 Table 21 Port Augusta mining case configuration ...... 47 Table 22 Halls Creek mining case configuration ...... 48 Table 23 Base case parameters for sensitivity analysis ...... 50 Table 24 Annual emissions ...... 51 Table 25 SAM inputs ...... 52 Table 26 Specifications obtained from SAM ...... 53 Table 27 Community economic analysis raw results ...... 78 Table 28 Mining economic analysis raw results ...... 78

List of Figures

Figure 1 Study locations ...... 17 Figure 2 CST subsystems ...... 28 Figure 3 Cosine loss diagram (Holl, 1978) ...... 29 Figure 4 Candidate locations (Google Maps, 2018) ...... 35 Figure 5 Community load profile ...... 36 Figure 6 Community seasonal load variation ...... 36 Figure 7 Receiver efficiency vs temperature ...... 42 Figure 8 Field optical efficiency vs capacity ...... 42 Figure 9 Newman community case energy production ...... 44 Figure 10 Newman community case spilled energy ...... 44 Figure 11 Port Augusta community case energy production ...... 45 Figure 12 Port Augusta community case spilled energy ...... 45 Figure 13 Halls Creek community case energy production ...... 46 Figure 14 Halls Creek community case spilled energy ...... 46 Figure 15 Newman mining case energy production ...... 47 Figure 16 Newman mining case spilled energy ...... 47 Figure 17 Port Augusta mining case energy production ...... 48 Figure 18 Port Augusta mining case spilled energy ...... 48 Figure 19 Halls Creek mining case energy production ...... 49 Figure 20 Halls Creek mining case spilled energy ...... 49 Figure 21 Halls Creek community case sensitivity analysis ...... 50 Figure 22 Halls Creek mining case sensitivity analysis ...... 51 Figure 23 Emission fractions ...... 51 Figure 24 Community case heliostat field layout ...... 54 Figure 25 Mining case heliostat field layout ...... 54 Figure 26 Project Gantt chart ...... 74 Figure 27 Newman weather data ...... 76 Figure 28 Port Augusta weather data ...... 76 Figure 29 Halls Creek weather data ...... 77

1 Introduction 1.1 Context The electrification of geographically isolated remote communities, where conventional approaches such as grid extension are not viable, is a global challenge (Byrne et al., 2015). According to AECOM (2014), off-grid electricity represents ~6% of total generation in Australia, with the largest consumers being mining sites and remote communities (Baig et al., 2015). At present the main sources of energy for off-grid electricity generation are fossil fuels, primarily natural gas and diesel (AECOM, 2014). High fuel costs in remote locations greatly increases off- grid electricity costs, making it the most expensive electricity source in Australia (AECOM, 2014). These fuel costs are susceptible to fluctuations and supply chain disruptions, and are expected to increase over time (AECOM, 2014).

The use of fossil fuels also comes with an environmental cost due to the emission of greenhouse gases (GHG), which has become a major global concern (Bahadori et al., 2013). This reliance on fossil fuels has been a major contributor to Australia presenting one of the world’s highest GHG emissions per capita, with 30-50% of GHG emissions resulting from electricity generation (Yusaf et al., 2011, Baig et al., 2015, Bahadori et al., 2013). Like many other countries, Australia is committed to reducing GHG emissions, with an aim to achieve 20% reduction per capita by 2020, and 50% reduction by 2030 (Department of the Environment and Energy, 2015). Therefore, there is a critical need for environmentally and economically friendly electricity generation to overcome these problems. This has spurred overwhelming interest in renewable energy throughout the world and has created a move towards a more sustainable energy mix. The off-grid energy market in Australia has also experienced an increased interest in renewables as a means to reduce electricity costs (AECOM, 2014). Initiatives such as the Federal Government’s Renewable Energy Target policy, designed to ensure that 33,000 GWh of Australia’s electricity comes from renewable sources by 2020, have served as strong drivers of growth in the renewable energy market.

Australia is fortunate to have one of the highest average solar radiation per square metre in the world (Bahadori et al., 2013). Several of the areas with the best solar resources in Australia are remote locations, such as the . Despite this, these areas present a low penetration of renewables, with AECOM (2014) estimating that renewables contribute to only 1% of off-grid electricity generation, in contrast to the 17.3% contribution of renewables to Australia’s overall electricity generation (Clean Energy Council, 2016). Similarly, while residential solar photovoltaic (PV) installations in urban centres have increased year on year, the same increases have not been seen in larger scale solar deployments as yet, with medium and large scale PV accounting for only

1

0.19% and 0.21% of overall electricity generation respectively, and solar thermal accounting for 0.01% (Clean Energy Council, 2016).

Large scale solar deployments are now gaining traction, with solar accounting for fifteen of the thirty large scale renewable energy projects that commenced in 2017 (Clean Energy Council, 2016), many of which are driven by funding from the Regional Australia’s Renewables (RAR) program by the Australian Renewable Energy Agency (ARENA), which supports investment in off-grid renewables. As such, is expected to play an increasingly important role in the Australian energy sector, and there are clearly opportunities to transition from fossil fuels towards incorporating large scale solar PV and solar thermal systems in remote mining sites and communities. Furthermore, hybridising variable renewable energy such as PV, with solar thermal and/or diesel generators has been recommended to increase reliability and penetration (Choros, 2016). Hybrid systems integrating renewables with existing diesel generator systems are gaining traction internationally as they provide a suitable route to decrease reliance on fossil fuels and attain lower energy costs (Adaramola et al., 2017). However, the viability of such systems in Australia is still an active area of investigation (Shafiullah, 2016), particularly with regards to the incorporation of solar thermal technology.

1.2 Aims and expected outcomes This project aims to develop a method to identify the optimum combination of three power generation techniques to be used in a hybrid system to supply power to remote communities and mining sites in Australia. These are concentrated solar thermal (CST) with thermal storage

(specifically CST that utilises a supercritical carbon dioxide (sCO2) power cycle), solar photovoltaic with battery storage, and diesel generators. The combination of technologies used in the hybrid system will be optimised primarily through an economic analysis, and in this process, the optimal size of each candidate technology will be established. As an additional measure, the environmental impacts of each technology will be assessed. This technique will be used to select the most suitable configuration for a number of potential locations (namely Newman, Port Augusta and Halls Creek). Furthermore, this project aims to explore the effect of varying critical parameters of the CST system on the viability of the system by considering the changes in the thermodynamics and costs of the subsystems, with the expected outcome of identifying key functional requirements for future sCO2 CST power plants.

2

1.3 Scope boundaries The scope for this project is limited to the aforementioned technologies, and as such other technologies such as wind or hydroelectric will not be considered in the analysis. Since the focus of this project is on off-grid systems, analysis of larger scale grid connected systems is out of scope. One of the main considerations with regards to off-grid systems is the concept of spilled energy, whereby energy that is generated but unused or not stored is wasted. It is therefore important to carefully configure off-grid energy generation with demand. This is not a concern for grid connected systems as excess energy is simply fed into the grid. Similarly, the option of building new or stronger transmission lines, which may be applicable in certain fringe-of-grid locations is also ignored. Furthermore, while there is international potential for hybrids, the analysis here will be limited to a remote Australian context. In addition, only existing knowledge and literature in this space is expected to be leveraged in this project, and as such additional independent experimentation is outside the scope of this project.

3

1.4 Goals The expected outcomes for this project can be divided into two overarching goals, each consisting of a number of sub goals, as follows:

Table 1 Project goals

Major Goal 1 Identify the optimal combination of the candidate technologies (CST, PV, battery storage and diesel generators) for remote Australian locations. Sub goal 1 Conduct a thorough literature review on the candidate technologies, and similar hybrid renewable energy projects in Australia and globally to establish technical background, current research progress and to identify gaps in the literature. Sub goal 2 Develop a simulation methodology for each technology, to simulate the performance of a given hybrid system over the course of one year. Sub goal 3 Devise economic and environmental metrics to assess and compare the candidate technologies. Sub goal 4 Based on these metrics, develop an algorithm to identify the optimal combination of the technologies in the hybrid system for each potential location. The outputs will include the fraction of total power provided by each technology, the sizes of each technology, and the operating conditions for the CST plant.

In addition, there is an extended analysis into the CST system with goals as follows. Note that these goals are connected to the goals from Major goal 1.

Major Goal 2 Identify key functional requirements for future sCO2 CST systems.

Sub goal 1 Carry out research into sCO2 CST systems to understand the thermodynamics and key operating parameters for this technology. Sub goal 2 Simulate a number of operating conditions of the CST system and explore the effect of the variations in thermodynamics and costs on the optimal technology mix, to identify ideal operating conditions (tied to Sub goal 4 in Major goal 1). These optima will form the basis of the functional requirements for CST systems.

Sub goal 3 Produce a concept design for a sCO2 CST power plant at one of the proposed locations.

4

2 Project plan 2.1 Methodology In order to facilitate the outcomes outlined in Section 1.4, a number of steps are necessary. The sequence of research activities followed is presented below.

Table 2 Project methodology

Item Description 1. Proposal • Clarify the scope and goals for the project, confirm the deliverables and outcomes, and establish a comprehensive project timeline. 2. Literature • Carry out research on the candidate technologies to establish the technical review background.

• Research sCO2 CST implementation, with particular emphasis on the thermodynamic and economic influences of critical operating parameters. • Explore feasibility studies of similar hybrid implementations, in Australia and globally. 3. Develop • Based on the findings in the literature review, identify appropriate economic and methods environmental metrics to assess the candidate technologies, and formulate an economic objective function. • Formalise the economic and thermodynamic trade-offs arising from different CST operating conditions, and methods to approximate these. • Identify up to date costs for each technology. • Source electricity consumption and weather data for the candidate locations. 4. Analysis • Develop a method using Python to simulate the performance of a hybrid system. program • Embed this simulation in an optimisation function to identify the optimal configuration for the system based on an economic analysis. 5. Generate • Utilise the Python program to identify the optimal technology mix and the results functional requirements for the CST plant by simulating the performance of a range of configurations of the hybrid plant over the course of one year, at each location. • Assess the sensitivity of the results to the input parameters. • Assess the environmental impact of the proposed systems. 6. Concept • Based on the results from the Python program, produce a concept design of the design CST power plant (consisting of field layout and component sizing).

5

2.2 Deliverables and milestones The key deliverables and milestones for the project are summarised below. A detailed breakdown and timeline is presented in the Gantt chart in Appendix A.

Table 3 Project deliverables

Task Deliverable Delivery date 1 Project proposal 8th March 2018 2 Comprehensive literature review 21st March 2018 3 Finalising evaluation metrics 25th March 2018 4 Analysis program 13th April 2018 5 Analysis results 28th April 2018 6 Interim report 3rd May 2018 7 Concept design 1st May 2018 8 Oral presentation 15th June 2018 9 Final report 28th June 2018

2.3 Key resources Since this project involves leveraging existing knowledge and literature, the literature databases and search engines outlined in Section 3.1 are an important resource. In addition, a strength of this project is the guidance of Professor Hal Gurgenci, a leading academic in this field at the University of Queensland, and this is a key resource for this project. A map of the main resources corresponding to the analysis stages of the project are presented below.

Table 4 Key resources

Task Resources Application Python simulation • Python software (Anaconda, Python is used in the analysis to compare 2018) and optimise the technology candidates. • Weather data (AUSTELA, Hourly weather data from each location is 2014) used as an input to the Python code to simulate the hybrid system. Concept design • System Advisor Model SAM is used for generating a concept (SAM) (NREL, 2018b) design of the CST system. Modelling the CST • DELSOL (Kistler, 1986) Portions of the source code of this program plant have been adapted for use in the Python program.

6

2.4 Risks and opportunities 2.4.1 Risks An analysis was carried out to identify the potential risks presented by this project. Since this project does not necessitate any experimentation or practical activities, occupational health and safety risks were deemed to be negligible. As such, the risks identified pertain primarily to potential impediments to the research and analysis process. The risks, along with the proposed strategies to manage the risks and are presented below.

Table 5 Risk analysis

Risks to project completion Proposed preventative strategy Unsuitable scope / changes to • Engage in a discussion with the supervisor early and clearly scope, leading to delays in the outline the boundaries to the scope and the expected outcomes project timeline. from the project. Insufficient detail in the • Carry out a comprehensive literature search to establish the modelling of the technologies, technical background and key operating parameters, and use this leading to less accurate results. to establish a thorough modelling framework prior to proceeding to subsequent stages. • Make effective use of industry contacts to gain a comprehensive overview of the current state of the art and modelling paradigms. Lack of technical or economic • Communicate with the project supervisor to identify important data on one or more of the information sources and gain knowledge outside of published candidate technologies, which academic literature. could result in delays to project • Use well-reasoned engineering approximations where no data is outcomes or lower quality available (this is especially necessary in the case of CST). results. Bugs and inaccuracies in the • Formalise the methodology and equations in Excel first, to Python program, which could enable comparison of intermediate results. result in poor accuracy of results. • Segment the Python code to ensure ease of debugging. • Thorough validation and checking of results against existing literature and expectations from theory. Inaccurate weather data used in • Make use of reliable data sources. the analysis. This may be due to • Ensure data is representative of the location by utilising data that fluctuations in weather or is averaged over a number of years where possible. corrupted data files and can lead to inaccurate results.

7

Risks to project completion Proposed preventative strategy Lacunae or issues with SAM, • Gain an understanding of the functionality of these programs in which could lead to project advance through help forums. delays. • Adjust scope of utilisation of these programs by understanding their limitations. Loss or corruption of project • Maintain a backup of all project files using Dropbox. files. • Ensure appropriate version and document control. Project running over schedule. • Review progress weekly against the proposed timeline and adjust activities as necessary. Problems with project • Meet weekly with the project supervisor and evaluate progress / communication, which can lead research directions. to delays or misdirected analysis.

2.4.2 Opportunities In undertaking this project, there are a number of key opportunities as follows. Note that many of these points are substantiated in the literature review.

• The research outcomes from this project are expected to address the gaps in the literature and improve on the knowledge in this research space. This could potentially lead to new publications in a research journal relating to this field of work.

• Given that there is a gap in the literature with regards to studies exploring CST hybrids for small scale off-grid use, positive results from this research could serve as a motivator for

further research in this area, particularly for hybrids involving sCO2 CST systems.

o In this regard, considering the lack of flexibility in market available software with regards to CST modelling, the Python program which has been developed may also serve as a useful reusable tool for future analysis.

• The research principles and methodology used in this project could be adapted for use in an international context, with many overseas rural communities and mining sites offering a market for .

• The outcomes from this research could have a direct impact on ongoing CST projects in Australia through industry collaboration.

• This work creates avenues for further research, and these are highlighted in Section 9.

8

Furthermore, given that this project addresses a pertinent problem in remote Australia, it is important to note that positive research outcomes could prove beneficial in furthering the penetration of renewables and lowering energy costs in these areas. There are also opportunities for commercialisation if the proposed system is shown to be economically competitive with existing solutions.

9

3 Literature review 3.1 Planning 3.1.1 Scope This section aims to review literature relevant to this space, to gain an understanding of the technical background for the candidate technologies and develop an awareness of similar hybrid studies in Australia and globally. The boundaries to this literature review are summarised below.

Table 6 Scope of literature review

Within scope Outside of scope Solar PV, along with battery storage Other renewable technologies such as wind CST, along with Renewable and fossil fuel combined thermal plants Diesel generators Main grid connectivity / grid extension Hybrid renewable energy systems Alternate storage systems

3.1.2 Key resources In order to ensure a high standard of relevance and credibility in the findings of this literature review, a number of high quality sources were used. These include but are not limited to:

• Databases such as Scopus, Web of Science, Science Direct and Google Scholar • SolarPACES solar project database (NREL, 2018a) • Publications by key organisations in the renewable energy space, including the National Renewable Energy Laboratory (NREL), International Renewable Energy Agency (IRENA) and ARENA.

Additionally, a screening process was incorporated to identify reputable sources. In this process, priority was given to articles published in peer reviewed journals. Journals with an impact factor greater than 3 were deemed to contain articles with a significant influence in this field. Furthermore, articles published within the last decade and highly cited articles (preferably greater than 20 citations) were prioritised.

10

3.2 Overview of the candidate technologies This section provides an overview of the technical background for each technology. This theory forms the basis for the simulation methodology and is developed further in Section 4.

3.2.1 Solar photovoltaic and battery storage Solar photovoltaic systems supply electricity through solar panels that absorb and convert sunlight into electricity. The Global Horizontal Irradiance (GHI), which is the total solar irradiation per unit area that is received by a surface horizontal to the ground, is the resource used by PV (McEvoy et al., 2012). Worldwide, the use of PV technology has grown significantly over the past decade, and PV currently supplies between 1.3-1.8% of the global electricity demand (International Energy Agency, 2017). This increase in capacity has been spurred largely by decreases in the cost of PV panels due to technological advances in manufacturing and economies of scale in production. In a report by the NREL, it was found that the cost of utility scale PV had decreased from $4.57/W1 in 2010 to $1.03/W in 2017, with and hardware costs being the primary drivers of cost reduction (Fu et al., 2017).

From a technological standpoint, there has been significant progress over the past two decades in the space, and three generations of solar cells have been developed. Of these, generation one cells are predominantly used in industry, accounting for ~90% of the total production of solar modules worldwide (McEvoy et al., 2012). To best align with industry practices and facilitate comparison with existing research, this project will also make use of PV panels based on generation one solar cells.

In Australia, solar PV has been harnessed primarily at a small-scale residential capacity, with small-scale PV accounting for 16% of renewable energy generation (Clean Energy Council, 2016). The ease of installation and maintenance, absence of GHG emissions, low capital costs, and government incentives such as the solar credits scheme (Clean Energy Regulator, 2017), have played a role in the increasing popularity of small-scale PV.

Medium and large-scale PV have not enjoyed the same rate of growth, together accounting for only 2.3% of renewable generation, and contributing to 0.19% and 0.21% of overall electricity generation respectively (Clean Energy Council, 2016). A major reason for these low levels of deployment is the fact that PV is intermittent, due to the dependence on sunlight and sensitivity to weather conditions. This dependence also means that PV is incapable of generating electricity at night. As such, a pure PV system is incapable of providing baseload power, viz. a continuous

1 Please note that the costs throughout this report are presented in USD unless otherwise specified. 11 supply of power above a minimum requirement. This serves as an impediment to the use of PV, with utility scale systems requiring baseload power capabilities (McEvoy et al., 2012).

3.2.1.1 Battery storage To overcome these limitations and provide a stable energy supply, PV must be coupled with supplementary power generation (such as diesel generators, explored in Section 3.2.3) or with a form of energy storage. Batteries have been the most widely adopted form of storage (Zafirakis, 2010). Lead-acid batteries are the most mature form of battery storage, with established performance characteristics, a reliable market and relatively low maintenance requirements (Zafirakis, 2010). However, they also present significant drawbacks, including self-discharge, low energy density and environmentally unfriendly content. Furthermore, a major drawback is the fact that lead-acid batteries (particularly the commonly used flooded lead-acid battery) require frequent replacement, which constitutes a large portion of the lifetime cost of the system (Jaiswal, 2017). Given that the lead-acid battery is a century old technology, further cost reductions are expected to be minimal (Jaiswal, 2017, Matteson and Williams, 2015).

Lithium-ion (li-ion) batteries are a newer technology, and present significant advantages over lead- acid batteries, including a higher energy density, conversion efficiency (~90%) and number of cycles to failure (Zafirakis, 2010). Li-ion batteries have historically presented a much higher capital cost than lead acid batteries, and this was long considered prohibitively expensive for large scale systems. Yet over the years, driven by academic and industrial research, particularly in the electric vehicle sector, the cost of li-ion batteries has decreased (Branco et al., 2018, Diouf and Pode, 2015). This coupled with the fact that lithium-ion cost is currently decreasing at 8-16% per annum, has greatly reduced the investment barrier (Paul Ayeng'o et al., 2018). This is highlighted in recent studies by Diouf and Pode (2015), Parra and Patel (2016) and Zubi et al. (2016), that concluded that the longer lifespan of li-ion batteries made PV systems with li-ion batteries more cost effective than lead-acid. With li-ion expected to replace lead-acid as the major energy storage in off-grid PV (Diouf and Pode, 2015), this study will make use of li-ion batteries.

Battery storage is manufactured in nominal capacities, representing the maximum amount of charge the battery storage can hold. The state of charge (SOC), is the charge quantity in the battery at a given time. The SOC is bounded by a minimum value, which is dependent on the depth of discharge (DoD), whereby minimum SOC = 100% – DoD (Ogunjuyigbe et al., 2016). This minimum value is necessitated because of the degradation and reduction in battery life that occurs when batteries are deep cycled (fully charged and discharged). For lead-acid batteries, a minimum SOC value of ~50% is common (Kolhe et al., 2015, Shezan et al., 2017). Similarly, a maximum SOC is also recommended to minimise stress on the battery. The main consequence of this is 12 reduced runtime, and therefore battery systems need to be oversized to account for maintaining a minimum SOC (10kWh battery with a 50% minimum SOC has a useful capacity of only 5kWh). This is a major advantage of li-ion batteries, as they present significantly less degradation at low SOC values. Jaiswal (2017) estimates that for most li-ion chemistries a minimum SOC of ~10% is sufficient (90% DoD).

It is interesting to note that Brisbane based battery manufacturer Redflow offers a scalable zinc- bromine flow battery (ZMB2), that is capable of high temperature operation and 100% DoD (Redflow, 2015), with zero degradation over the course of 10 years (typical life for utility scale batteries). Similarly, Tesla offers a utility scale li-ion battery, the Tesla Powerpack, which is also capable of sustained operation at 100% DoD (Tesla, 2018). To gain insights into the battery storage industry in Australia, contact was made with Redflow, and it was found that the ZMB2 was unable to compete on a cost basis in the solar energy space with the more mature li-ion batteries (Parkinson, 2017). This is not limited to only Redflow, with an analysis by Lazard (2017) echoing a similar industry-wide trend for flow batteries. Despite this, the ZMB2 offers the advantage of retaining storage capacity for the entire 10 year warranty period with 100% DoD, making it an attractive choice for applications such as telecommunications (Redflow, 2015). In contrast, Tesla products which have recently gained significant traction in Australia, most notably through the 129 MWh Powerpack deployment in (Harmsen, 2017), are noted to retain only 70-80% of their nominal capacity at the end of the 10 year life (Shahan, 2015). This in turn necessitates that the capacity fade be considered in the analysis.

Rather than a year-based life, it is more suitable to characterise battery life in terms of charge cycles to failure, where one charge cycle involves charging the battery to its nominal capacity and fully discharging (fractional charges/discharges are fractional cycles). Typically, a higher DoD results in fewer cycles to failure. For the ZMB2, Redflow guarantees 1000 cycles at 100% DoD before failure (Redflow, 2015). On the other hand, the reports in the literature for the cycles to failure at a given DoD for li-ion batteries present significant spread (Jaiswal, 2017, Diouf and Pode, 2015), reflecting the varying chemistries and the rapid developments in this technology in recent years with regards to improving performance at higher DoD. The Tesla Powerpack is likely the best representation of the current state of the art on the market with regards to utility scale storage (Saving with solar, 2017), and hence will be used as a representative battery in this project.

A typical charging curve for a li-ion battery consists of two stages, with an initial linear increase in charge with time, following which the charging rate slows to zero as the charge approaches 100%. A general rule of thumb is to avoid the second region (known as the saturation zone), by setting an upper limit on the SOC, typically ~90% (Cadex Electronics, 2017). The efficiency of li- 13 ion batteries varies based on the charge and discharge rate. Fast charging reduces efficiency due to lower charge acceptance and heat generation, and slow charging lowers efficiency due to self- discharge. The charge rate also has an influence on the life of the battery, and a maximum rate is generally defined by the manufacturer (Hesse et al., 2017). For instance, Tesla specifies an 88% round-trip efficiency for a Powerpack system that can charge or discharge fully in 2 hours (Tesla, 2018). The charge rate is an important consideration for off-grid systems, since excess energy that cannot be stored is spilled. The modelling process for the batteries is discussed in Section 4.1.2.2.

3.2.2 Concentrating solar thermal Similar to PV, CST also makes use of sunlight to generate electricity. This is achieved by using mirrors to concentrate sunlight onto a receiver. The Direct Normal Irradiance (DNI), which is the solar radiation per unit area received on a plane normal to the , is the solar resource used by CST (Liu et al., 2016). This heat energy is then used to drive a heat engine which produces electricity. CST technologies exist in four key forms, namely, power towers, linear Fresnel reflectors, parabolic troughs and dish systems (Mehos et al., 2016). These technologies share similar advantages, including zero GHG emissions and efficient energy storage potential. Of these, parabolic trough systems are the most mature, and consist of a linear parabolic reflector that concentrates sunlight onto a receiver tube that is placed along the focal line. Having been in commercial operation since 1984, these systems are the most proven and represent the majority of the deployed capacity globally (Mehos et al., 2016). However in recent years, power tower systems have gained significant traction, owing to their ability to achieve higher temperature operation which can yield efficiency gains (14-20% as compared to 13-15% for parabolic trough), and lower cost energy storage (Mehos et al., 2016). Cekirge and Elhassan (2015) also note that power towers present 15-20% less operating and maintenance expenses, reduced piping system size (which reduces energy losses and material costs), and a lower investment cost per unit output when a storage system is incorporated. It is also noted that unlike parabolic troughs, power towers do not make use of an environmentally hazardous heat transfer medium (thermal oil). Hinkley et al. (2013) note that power towers should be targeted for ongoing deployment in Australia, and this interest is mirrored in recently announced projects such as the 150 MW Aurora project in Port Augusta, South Australia (Solar Reserve, 2017). As such, power tower technology is the most viable for this project.

To provide baseload power, CST must be coupled with thermal energy storage (TES) – typically 5-15 hours of storage. While TES increases the capital cost of CST projects, thermal storage is generally very efficient (~99%), offers flexibility of output, and decreases spilled energy (Mehos et al., 2016). It is also noted that the use of TES increases revenue, which generally outweighs the 14 increased capital cost, decreases energy cost, and allows for a quicker pay-back time (Dowling et al., 2017). The current state of the art in TES is sensible energy storage in the form of molten salt comprising of sodium nitrate and potassium nitrate (Liu et al., 2016).

The power blocks used in CST plants are sub-critical steam Rankine cycles, which are a mature technology, and present notable economies of scale in terms of cost and performance (Mehos et al., 2016). However, these cycles present an efficiency of only 35-40%, and suffer from further reduced efficiency at small scales, having been typically used in utility scale applications with greater than 200 MW output (Meybodi et al., 2017). Gurgenci (2014) notes that the minimum economical size for current steam Rankine CST technologies is 50 MW, with the major constraint being the power block. This size is impractical for off-grid applications in Australia, which are generally smaller than 10 MW (Gurgenci, 2014). Given that a power block efficiency greater than 50% is a target set by the U.S. Department of Energy as part of the SunShot initiative which aims to make solar energy competitive with conventional sources by 2020 (Mehos et al., 2016), an alternative power block that offers higher efficiency and increased suitability for small scale applications is necessary.

An alternate system consisting of supercritical CO2 as the working fluid for the power block, in a closed-loop Brayton cycle has been proposed for use in next generation CST systems. This system presents notable advantages over the conventional steam Rankine system, including compact power blocks, simpler plant configurations, and a higher efficiency (Ahn et al., 2015, Gurgenci,

2014). Multiple authors including Ma and Turchi (2011) and Turchi (2009) have proposed sCO2

Brayton cycles as an effective alternative to the steam power block. sCO2 systems have also been shown to provide efficiency gains as compared to steam cycles at scales appropriate for CST applications in Australia (Meybodi et al., 2017). The development of sCO2 power blocks for CST use has become a SunShot goal, since the increased efficiencies offer a route to reduce solar field size and in turn decrease capital costs. In Australia, projects such as that by CSIRO (2017) have demonstrated the capabilities of sCO2 CST systems, and it has been noted that sCO2 power blocks can be built at small sizes (1-10 MW), with minimal cost or efficiency penalties (Ma and Turchi, 2011).

To achieve these higher cycle efficiencies, high operating temperatures are needed, with the turbine inlet temperature being the key parameter due to its significant influence on cost and performance (Mehos et al., 2016, Meybodi et al., 2017). This involves an increase in turbine inlet temperature to above 600°C (up to 1000°C), in comparison with current systems operating between 290-565°C (Liu et al., 2016). This temperature increase has a number of key consequences. Current molten salt storage undergoes decomposition above ~600°C (Ahn et al., 15

2015), and is unsuitable for use at higher temperatures. Several high temperature storage alternatives are currently being investigated, including thermochemical storage, phase change materials and solid particles. Corgnale et al. (2016) suggest that these next generation systems can be developed to be more cost effective than current systems. It is difficult to select a front-runner, due to the research area still being very new, with no commercialisation as yet (Mehos et al., 2016). As such, due to the experimental nature of these solutions and their unknown performance, this project will retain the use of molten salt for the purpose of gauging the performance of high temperature CST plants.

The losses in a CST system are also related to the operating temperatures. The major loss areas, other than the aforementioned power cycle loss, are the heliostat field (which presents a dependence on field size), and the receiver, consisting of thermal and absorptive losses (Hinkley et al., 2013). Size effects and thermal losses in the receiver are of particular interest for this project. This is because thermal losses increase to the fourth power of temperature (Hinkley et al., 2013), and so higher temperatures can lead to more losses in the CST system. Similar effects are also observed with increasing field size (necessitated for larger capacities).

Finally, higher operating temperatures necessitate high performance materials to prevent degradation, resulting in higher system costs (Meybodi et al., 2017). The main areas of cost increase are the power cycle and the receiver, and these costs depend primarily on the material used (Meybodi et al., 2017). There is agreement in the literature that stainless steel is ideal for 650°C and below, and above this point appropriate nickel based alloys with a sufficiently high service temperature are required (Glatzmaier, 2011).

Exploring the interplay between the efficiencies and costs is the main purpose of this analysis into the CST system, and the methods used to explore this are described further in Section 4.1.1.

3.2.3 Diesel generators Diesel generators (DG) make use of a compression engine along with diesel fuel to generate electricity. Remote towns and mining sites in Australia that are located away from the grid typically make use of diesel generators for on-site power generation (Gurgenci, 2014). Despite their many disadvantages, including high fuel costs and GHG emissions, diesel generators have been widely adopted (Bahadori et al., 2013), due to their ease of deployment, low capital and ability to scale with power demands. In analysing diesel generators, the fuel price is the main driver of energy costs (Lazard, 2016), and in Australia, there are fuel tax exemptions extended to the mining and power generation industries, which makes diesel less costly (Gurgenci, 2014). For this

16 project, diesel generators will provide supplementary power when the renewable sources are unable to meet the electricity demand.

3.3 Hybrid renewable energy studies A comprehensive literature review was carried out to gain an understanding of the feasibility of hybrid renewable power generation systems. This section aims to develop an understanding of the motivators, methods and outcomes of these studies, and to identify the gaps in the literature.

3.3.1 Overview of studies 3.3.1.1 International study locations In order to first understand the motivation behind these studies, the locations where the key studies took place were explored. This is presented in Figure 1.

Figure 1 Study locations It is evident that many of these studies have taken place in developing, or semi-developed regions. Rural and remote electrification in these regions has been noted to be a major challenge, and significant populations often have sparse or no access to electricity. For instance, Lambani et al. (2017) note that access to electricity in Sub-Saharan Africa’s rural populations is only 35.3%. With grid-extension to these remote locations being ineffective, off-grid solutions typically make use of diesel generators, which present notable aforementioned disadvantages. With global initiatives having identified that access to electricity is a key factor in economic growth, there is a growing need for electricity, and a drive towards the use of renewables for these off-grid communities (Lambani et al., 2017). This, and the fact that many of these locations such as Sub-Saharan Africa are rich in renewable resources serves as a strong motivator for research into renewable hybrid solutions. Other studies, such as that by Palone et al. (2017) which explores an island off the coast of Italy are more similar to Australian conditions, in that while electricity is available, it is provided through expensive diesel generators, and hence cost reductions and environmental concerns serve as motivators for research into renewables. 17

3.3.1.2 Technologies considered in the studies Of the 28 key studies reviewed, 16 focused on a combination of PV and wind. The combination of PV, wind, battery storage and diesel generators was the most common area of study. One reason for this is the low cost of wind energy (Pramanik and Ravikrishna, 2017), and the quickly declining costs of PV (Mandelli et al., 2016). However, the major reason is the ability to increase reliability and uniformity through the combined use of these technologies, particularly in conjunction with battery storage. This is because of the natural synergism between solar and wind energy, since wind speeds are usually higher during the nights and winter, when sunlight is lower (Kolhe et al., 2015). Given that intermittency is a barrier to the implementation of renewables, the ability to improve uniformity serves as a strong motivator for studies in this space.

No studies explored CST, PV and diesel hybrids, viz. the proposed technology for this project. While there were studies such as by Platzer (2016) and Starke et al. (2016) which explored CST- PV hybrids, these studies explored large grid connected CST systems (>50 MW capacity). As Cocco et al. (2016) note, there is a gap in the literature with regards to CST hybrids aimed at smaller off-grid applications. This is likely because 50 MW is the minimum economical size for present steam Rankine CST systems (Gurgenci, 2014), making them unsuitable for small scales.

It is this gap in the literature that this project aims to address through the incorporation of a sCO2 CST system. Despite the lack of studies addressing CST hybrids in an off-grid context, the studies performed by Starke et al. (2016), Rashid et al. (2017) and Platzer (2016) indicate that hybridised CST solutions could offer a cost effective route to increase reliability and provide baseload power. These studies serve as confirmation of the viability of hybrid CST systems, and justify the inclusion of CST technology in this project.

3.3.1.3 Analysis techniques employed Most of the studies explored in this review make use of the Hybrid Optimisation Model for Multiple Energy Resources (HOMER) software in their analysis. However, for this project HOMER is unsuitable due to its inability to model CST with thermal storage. This is evidenced in the studies into CST hybrids opting for other options, such as SAM (Pan and Dinter, 2017), TRYNSYS and SAM (Starke et al., 2016), and TRYNSYS and MATLAB (Starke et al., 2018).

Platzer (2016) notes that modelling CST hybrids with classical tools is not straight forward. Mohamed et al. (2015), noting that flexible implementation of hybrid systems is not possible with market software such as HOMER and TRYNSYS developed a simulation program in MATLAB. A similar approach involving a self-developed simulation program was taken by Cocco et al. (2016) to model a CST-PV hybrid, and by Gan et al. (2015) to model a hybrid system consisting of wind, PV, battery storage and diesel generators. The developmental nature of the sCO2 CST 18 system and thermal storage that will be explored in this project necessitates a high level of flexibility in analysis. With previous authors having demonstrated the effectiveness of a self- developed simulation for this application, this project will also make use of a similar method.

3.3.2 Study methodologies Broadly, studies evaluating the feasibility of renewable hybrids follow the below sequence in their methods (Amutha and Rajini, 2016). These areas are investigated further in this section.

1. Load assessment for the location 2. Identification and characterisation of available resources 3. Techno-economic analysis using tool of choice a. Evaluation of feasibility and selection of optimal hybrid configuration

3.3.2.1 Load profile estimation Given the intermittent nature of renewables, in order to minimise excess production or overreliance on supplementary diesel power, it is important to ensure a close match between electricity demand and production. In order to do this, an assessment of the load profile for the target location is necessary. The studies reviewed here have taken several approaches to accomplish this. Gan et al. (2015) note that the most accurate method for load profile estimation is using historical measurements of consumption in the region and potentially incorporating load growth factors, as was done by Fazelpour et al. (2016) for a stand-alone establishment, and Ansong et al. (2017) for a remote mine in Ghana. However, in the absence of useful historical data many studies have turned to estimation methods.

The simplest form of estimation is used by Bianchini et al. (2015), who estimated the load profile by taking a reported daily profile and assuming it be constant throughout the year. A similar constant profile was also utilised by Gan et al. (2015), Lambani et al. (2017) and Kolhe et al. (2015). However, this is unlikely to be an accurate representation of the load profile, as seasonal variation in consumption is expected (Adaramola et al., 2017). In the study by Adaramola et al. (2017), the minimum electricity levels set by the United Nations (UN) were used as the benchmark for the load profile, due to low levels of electrification in the region rendering any prior measurements unusable. In this process, the load profile was estimated based on the projected consumption of households and community utilities should the UN targets be met. The authors aimed to account for daily, monthly and seasonal variation by incorporating random variations (15-20%) in the load profile. While this is an improvement over the assumption of constant loads, random variations are unlikely to account for the consistent variations seen with seasonal changes.

19

Amutha and Rajini (2016), who explored the feasibility of a renewable hybrid for use in a rural village in India, took this method to a higher level of granularity, by estimating the load profile based on a breakdown of the population, number of people per household, household appliances, community facilities and industrial activity in the region. Estimations with similar levels of granularity are also seen in the works of Mamaghani et al. (2016) and Rahman et al. (2016). Seasonal variation was accounted for by splitting the domestic consumption based on estimated usage during different seasons, with peak consumption during the summer months – primarily due to household cooling needs. Seasonal splitting of the load profile and the assumption of peak consumption in summer was also seen in the studies by Rahman et al. (2016) and Rehman et al. (2016). It is interesting to note that only study in this review where the assumption of a constant load is justified is that by Kolhe et al. (2015), who substantiate this with the fact that Sri Lanka’s position near the equator renders seasonal variations negligible. These studies indicate that there is merit to incorporating variations in the load profile, but nevertheless several authors have not done so, indicating that it may not be integral to accurate results. In the context of this project, the seasonal split approach is likely to be important to account for seasonal variations in Australia.

Both Gan et al. (2015) and Lambani et al. (2017), found that the peak daily loads occurred in the morning (~9 am), while people got ready for work, and in the evening (~6 pm) as people returned from work, with minimal load during work hours. A similar result was also observed by Mamaghani et al. (2016). This is due to the fact that these studies focus on supplying electricity primarily for domestic purposes and do not account for productive use for industrial, business or community purposes (a significant oversight, considering that these are key drivers of socio- economic development). In contrast, the study of a mine in Ghana (Ansong et al., 2017) found that the load profile was uniform, due to the mine being the primary consumer, and monthly variability was also found to be minimal. This result indicates that industrial loads are more uniform and less prone to seasonal variability than households, and serves to corroborate the approach of Amutha and Rajini (2016), who varied only domestic loads seasonally. Unlike the studies which focused heavily on a single consumer group, mixed communities were studied by Sen and Bhattacharyya (2014) and Shahzad et al. (2017) who modelled domestic and industrial loads separately, with both studies demonstrating a load driven primarily by industrial activity during the day, and domestic activity in the evenings. The clear take away from these studies is that the load profile needs to be tailored to consider the key electricity consumers in each region.

3.3.2.2 Characterisation of local resources Given the reliance of renewables on the availability of renewable resources, it is no surprise that the design of a hybrid system depends heavily on the resources available at the target location. The 20 literature is consistent in the methods used to characterise local resources, wherein solar resources are assessed based on the DNI and GHI in the location. The majority of papers reviewed make use of satellite data obtained from NASA Surface Meteorology through HOMER, for example Adaramola et al. (2017) and Ansong et al. (2017), who cite an unavailability of ground-based measurements as the reason for using satellite data. Other authors have also acquired similar data from their local meteorological organisations, such as Fazelpour et al. (2016) in Iran. Green et al. (2015) improve on this approach, by using ground measurements to validate the data they procured from a commercial supplier.

Green et al. (2015) note that aggregated data such as the average annual DNI can be misleading because it fails to consider day-to-day or seasonal variability and the degree to which “sunny days” are consecutive. Accordingly, the data used by most authors takes the form of hourly values, which has sufficient granularity of account for these factors (e.g. Fazelpour et al. (2016), Green et al. (2015)). Despite this, authors such as Kolhe et al. (2015) and Rahman et al. (2016) made use of monthly averages, which neglects variability at the day and week levels.

There are numerous examples in the literature that highlight the influence of resource availability on the optimal technology choice. Lambani et al. (2017) studied the optimal configuration of a PV-biomass-wind hybrid system for use in two villages in Africa. Based on an analysis of local resources, it is found that a combination of wind and PV was optimal for the coastal village due to the high availability of both resources. Instead, a combination of PV and biomass, with no wind, was found to be optimal for the inland village, due to poor wind resources, and an availability of biomass from livestock. Another example is Mamaghani et al. (2016), who explored two villages in Colombia and found that the optimal combination varied across the two sites, with the first site presenting with PV-wind-diesel-battery as the optimal combination, and the second with PV- diesel-battery, with no wind contribution due to poor wind speeds.

3.3.3 Feasibility of technologies 3.3.3.1 Technical and economic feasibility Renewable energy hybrids are assessed from both technical and economic standpoints. The most common measure of economic performance is the levelised cost of energy (LCOE), which considers the time value of money. Renewable hybrids are benchmarked against the existing source of power, most commonly diesel generators. As noted in a recent review paper (Goel and Sharma, 2017), many examples in the literature have shown that hybrid renewable energy systems can be more economically viable and reliable than single energy sources for off-grid use. Kolhe et al. (2015) found that a PV-wind-diesel-battery system was the most economical for remote use in

21

Sri Lanka, with a LCOE of $0.34/kWh. In comparison, a diesel system presented a LCOE of $0.56/kWh, costlier than even a 100% renewable system ($0.51/kWh). Li and Yu (2016) compared combinations of PV-diesel-battery systems in China and found that with a LCOE of $1.105/kWh, the PV-diesel-battery system was the most economical, whereas the diesel power system was found the be the most expensive ($2.847/kWh). Fazelpour et al. (2016) concluded that a wind- diesel-battery system was the most economical system for deployment in Iran. Amutha and Rajini (2016) found that for a rural town in India, a 100% renewable PV-wind-hydro-battery system presented the lowest LCOE, with diesel incorporation increasing the LCOE.

Despite this apparent consensus, there are also studies that have found hybrids to be less cost effective. Some studies benchmarked against the national grid, and found that the hybrid solutions were three times (Fadaeenejad et al., 2014), and four times (Adaramola et al., 2017) as expensive. However this comparison is flawed, because as Adaramola et al. (2017) note, the hybrid solution is not displacing grid power, but rather diesel generators. An important observation by Adaramola et al. (2017) is the low load factor in rural electricity projects, which results in a high LCOE. It is further noted that the main reason for this is the idle power during work hours. This illustrates the need to take into account productive use as mentioned earlier, and explains why authors such as Amutha and Rajini (2016) who did so, achieved lower LCOE values. A more apt comparison was carried out by Rahman et al. (2016) in Canada, who found that a 0% renewable system comprising of only diesel generators had a lower LCOE than any configuration that incorporated solar or wind. The closest alternative was a 21% renewable fraction PV-wind-battery-diesel system. However, this is likely due to the capital costs assumed for the renewables, most notably a cost of $3570/kW for PV panels. This is the highest value seen across all the studies in this review, and seems unreasonable given that costs in North America fell to ~$1500/kW by the close of 2016 (IRENA, 2018). This is acknowledged by Adaramola et al. (2017), who note that a cost of $1500/kW would have been more appropriate than the $3000/kW used in their own study. Another study also found diesel systems to be less expensive than any renewable hybrid configuration, but acknowledged that this was facilitated only by favourable diesel costs in Saudi Arabia (Rehman and Al- Hadhrami, 2010). As such, the economic feasibility of renewable hybrids depends strongly on the costs of the renewable components and on the cost of the diesel alternative.

Given that hybrid systems aim to reduce intermittency, the reliability of the system in meeting load demands must be assessed, since a lack of reliability can result in frequent black outs and power shortages. On the other hand, as Lambani et al. (2017) note, too high of a focus on reliability can lead to an overdesigned system, which adds unnecessary capital and maintenance costs. The excess electricity (difference between energy produced and energy consumed) is used as an indicator of 22 overdesign. As previously mentioned, this metric is particularly important for off-grid applications. A common trend in the studies reviewed here is an increase in excess generation with renewable fraction (Rahman et al., 2016). Some authors, such as Amutha and Rajini (2016) reported a high excess generation, but noted that this was due to the fact that generation capacity was oversized to facilitate zero unmet load. Lambani et al. (2017) allowed for some unmet load in their configurations and observed that 100% renewable PV-wind solutions typically overproduced in the summer and failed to meet the load during winter. An important point to note is that the addition of battery storage decreased excess production considerably, in line with the findings of many other studies (Amutha and Rajini, 2016, Li and Yu, 2016, Rahman et al., 2016). This highlights the role of storage in increasing reliability without needing to overdesign.

Based on these criteria, a number of papers have evaluated the feasibility of CST hybrids (albeit not for small off-grid use). Petrollese and Cocco (2016) studied CST-PV hybrid plants and established that the capital cost of the CST plant had the largest influence on LCOE, with battery storage accounting for much of the cost of the PV plant. The authors concluded that the viability of the plant depended on the number of hours each day for which baseload power was required. For 8 hours or less, PV was found to have the lowest LCOE. For all-day production (as in the case of this project), when the ability of CST plants to decouple power generation from sunlight was exploited, the CST-PV hybrid presented a lower LCOE than both plants individually, with thermal storage becoming much more cost effective than battery storage. It was found that the LCOE was sensitive to the unmet load fraction, highlighting the extra costs involved in making a solar power plant an effective base load plant. Significant cost reductions were possible with an increase in unmet load from 5% to 15%, a load that could have been more easily met with supplementary diesel generation. Starke et al. (2016) evaluated the performance of a CST-PV hybrid in Chile, with the conclusion that the high irradiation in the region rendered the hybrid a cost effective solution for baseload power to mining sites. Parrado et al. (2016) forecasted the performance of a CST-PV hybrid from 2016 to 2050, and found that in all the predicted scenarios, the hybrid plant outperformed singular implementations of either technology. Pan and Dinter (2017) demonstrated the ability for a similar hybrid to provide cost effective baseload power in South Africa. The viability of these solutions is evidenced by the announcement of three such CST-PV hybrid plants in Chile (Platzer, 2016). A number of authors have explored CST-wind hybrids, as Pramanik and Ravikrishna (2017) note, wherein the synergies between CST and wind allow for meeting evening and night power requirements with fewer storage hours. The incorporation of CST also reduced the uncertainty of wind power output and allowed for better matching the load profile.

23

These results are important, as they demonstrate the viability of CST when used in a hybrid system to provide baseload power. However, none of them considered the incorporation of supplementary diesel generation.

3.3.3.2 Environmental and social considerations Given that environmental considerations are a major reason for the recent drive towards renewables, an environmental analysis is a key aspect of feasibility. The main measure is the quantity of GHG emitted, particularly CO2. Rahman et al. (2016) demonstrated that GHG emissions decrease as renewable fraction increases. This is an intuitive result, considering that the only active source of emissions in a hybrid system is the diesel generator (Amutha and Rajini, 2016). Li and Yu (2016) calculated their GHG reduction based on the “barrels of crude oil not consumed” (BONC). However, these approaches do not consider the embodied CO2 in the renewables. This is an oversight, with PV panels and batteries noted to have significant embodied

CO2 values, and therefore a life-cycle assessment is a more appropriate measure (Chow and Ji, 2012). While most authors simply quantified GHG reduction as an additional measure, others such as Fazelpour et al. (2016) and Rahman et al. (2016) tied in the environmental benefit with the economic analysis by including a GHG penalty cost, which penalised the use of diesel. In the case of Rahman et al. (2016), the incorporation of this penalty cost allowed a hybrid solution to compete with the diesel alternative.

3.3.3.3 Sensitivity analyses The sensitivity of a hybrid system’s performance to the initial inputs has been studied by several authors. Rehman et al. (2016) explored the effect of diesel cost and resource availability on their PV-wind-battery-diesel system. The authors found that the LCOE increases with increasing diesel fuel cost and decreases with increasing wind speed and radiation. In a similar analysis by Rahman et al. (2016), the LCOE was found to be most sensitive to the diesel cost, likely due to the fact that the system had a renewable fraction of only 21%. Bianchini et al. (2015) explored the effect of varying battery storage capacity for a similar system and found that increasing battery size resulted in more long term savings, and a corresponding decrease in the use of diesel fuel. Ansong et al. (2017) varied the capital costs and diesel cost for a PV-fuel cell-diesel-battery system and found that for a diesel cost of greater than $1.5/litre, the 85% renewable fraction hybrid system presented a lower LCOE than the diesel only alternative. Note that despite the high renewable fraction, diesel cost still had a moderate influence on the LCOE. Considering that the increasing cost of diesel is a driver for the adoption of renewables, Li and Yu (2016) examined the effect of the fuel cost escalation rate. While the effect on LCOE was not explored, the net present value (NPV) was found to increase with fuel cost escalation rate. Mehrpooya et al. (2018) took a similar approach 24 and forecasted performance based on three potential diesel prices and found that even at costs equal to half that of the EU average, the PV-diesel-battery system had a lower LCOE than the diesel system. Given the rapidly changing costs of renewables, and the increasing price of diesel, a sensitivity analysis is important to evaluate the long-term effectiveness of a hybrid solution.

3.3.3.4 Feasibility studies conducted in Australia A number of studies have been conducted to explore the feasibility of hybrid renewables in Australia. An early study Karutz and Haque (2013) explored the possibility of integrating renewables with existing diesel generator systems to provide power to a remote community in South Australia. Through a HOMER optimisation and cash flow analysis, it was found that a PV- wind-battery-diesel system could provide electricity at a lower cost than the existing diesel system. This is a promising result, considering that a high PV capital cost of $4000/kW was used, corresponding to the costs in 2013. At the end of the 20 year project life, the authors predicted total savings of AU$2.2 million in comparison with using diesel. The authors concluded that this hybrid solution would decrease government expenditure that is used to subsidise diesel for off- grid consumers under the Remote Areas Energy Supplies (RAES) scheme. Orhan et al. (2014) evaluated the feasibility of a hybrid for use in Kangaroo Island and Christmas Island. It was found that the lowest LCOE ($0.285/kWh) was achieved by pairing the renewables (PV-wind-battery) with the existing diesel generators, with a renewable fraction of 98%, as compared to $0.6/kWh for the existing system, and $0.416/kWh for a renewable only system. Other authors such as Shafiullah (2016), Jamal et al. (2016) and Shezan et al. (2017) have also shown that the incorporation of renewables (PV and wind) and battery storage into existing diesel systems can generate lower cost electricity. The Ti Tree, Kalkarindji and Lake Nash (TKLN) hybrid system which supplies electricity to three remote townships serves as working proof of this concept in an Australian setting (Chaudhary et al., 2014).

The approaches used to quantify the reduction of emissions with renewable use are similar to those mentioned in the international studies. Orhan et al. (2014), Shezan et al. (2017) and Shafiullah (2016) calculated GHG reductions based on the reduction in the use of diesel fuels, in line with the approach commonly used in the global studies. An economic element was introduced by Karutz and Haque (2013) who incorporated a carbon emission tax to penalise the use of diesel. As such, there is scope for improvement in this analysis, by considering a life-cycle assessment and taking into account the embodied CO2 of the renewable components (Chow and Ji, 2012).

Jamal et al. (2016) note that the inherent characteristics of remote Australian power networks are similar to those seen in African and Asian rural networks, and hence the findings from studies in

25 these regions are highly relevant in an Australian context. Therefore, it is expected that the findings from international studies in this literature review will hold in the context of this project.

Given that there is a lack of studies exploring CST hybrids for off-grid use, with no such studies in an Australian context (or specifically the CST-PV-diesel-battery combination), there is sufficient novelty in this project. While the viability of large scale CST-PV hybrids has been demonstrated, it is unknown whether these results will hold true at the smaller scales necessary for off-grid implementation in Australia. Similarly, while it has been shown that some form of storage is necessary for hybrid systems to be viable, the interplay between battery storage and thermal storage which perform similar roles has again not been explored at this scale. It is expected that this project will add value to the research space by addressing these gaps in the existing literature.

26

4 Methodology

While the complete methodology used to simulate and analyse the hybrid system is outlined in Section 4.6, for context it is useful to note up front that the simulation involves the following steps for each location, based on the load profile and the availability of solar resources.

• An optimisation function is used to simulate a range of sizes of the CST plant and PV plant, along with a range of thermal storage and battery capacities, to determine the combination of these parameters that results in the lowest energy cost. For each set of values: o Calculate the contribution to total power generation of a CST plant with a given configuration and turbine inlet temperature, by sizing subsystems and determining the corresponding system efficiencies. o Calculate the contribution to total power generation of a PV array of given size and battery storage capacity. o Fill in any remaining power requirements using diesel generators. o Calculate the costs associated with these systems. • The configuration of the hybrid system with the lowest LCOE is deemed optimal. • These steps are carried out for three CST temperatures, namely 650°C, 800°C and 1000°C.

4.1 Candidate technologies In this section, the modelling approach and assumptions (technical and economic) are introduced, following which the analysis program in Python incorporating these elements is outlined.

4.1.1 Concentrating solar thermal plant The CST plant consists of four key components, with a simplified process flow shown in Figure 2. In this analysis, the parameters to be varied are the turbine inlet temperature, number of storage hours, and the field capacity, which are key operating parameters for the CST plant. Of these, the first two are varied directly in the simulation. The field capacity is varied implicitly using the “storage fill time”, with net field capacity (thermal power absorbed at the receiver) defined as storage capacity divided by number of hours specified to fill the storage. The fill time is also closely related to the solar multiple, and the derivation for this relationship is given in Appendix E. To explore the thermodynamic implications of these parameters, the efficiency of each process must be approximated, and the methods used to do so are described in this section. The steps taken to model the subsystems in the Python program are described in Section 4.6.

27

Figure 2 CST subsystems The overall efficiency of the CST system can be approximated as the product of the efficiency in each process and is described as follows.

휂표푣푒푟푎푙푙 = 휂푟푒푓푙푒푐푡𝑖푣𝑖푡푦휂푠ℎ푎푑𝑖푛푔휂푎푡푡푒푛푢푎푡𝑖표푛휂푐표푠𝑖푛푒휂푟푒푐푒𝑖푣푒푟휂푝표푤푒푟 푏푙표푐푘 (1) In the literature review it was found that of these, the attenuation, cosine, receiver and power block efficiencies varied with operating temperature or mirror field size. Exact determination of the cosine and receiver efficiency is calculation intensive and generally requires the use of a modelling tool such as DELSOL (Kistler, 1986). As this is not feasible for the optimisation stage of this analysis, approximations for these quantities will be used instead. It is important to note that despite being approximations, it is expected that the results obtained will still be indicative of the relative performance of the CST systems, and therefore will be sufficient to determine the optimal combination of the technology candidates. Note that the product of all the efficiency terms prior to the power block constitutes the net “sun to heat” efficiency.

4.1.1.1 Atmospheric attenuation loss Atmospheric attenuation losses account for the scattering of reflected radiation from the heliostat as it passes through the air towards the receiver. This loss increases with distance, thereby placing a limitation on the mirror field size. Vittitoe and Biggs (1978) approximate that the atmospheric transmittance decreases with distance (S) from the receiver as given by:

푇푟푎푛푠푚𝑖푡푡푎푛푐푒 (푐푙푒푎푟 푑푎푦) = 0.99326 − 0.1046푆 + 0.017푆2 − 0.002845푆3 (2) 푇푟푎푛푠푚𝑖푡푡푎푛푐푒 (ℎ푎푧푦 푑푎푦) = 0.98707 − 2748푆 + 0.03394푆2 (3) While this expression was derived for a specific site altitude, authors such as Wagner (2008) and Venus (2016) note that the trend is expected to hold for other sites also, due to the dependence on elevation being minimal compared to the dependence on visibility. This is also evidenced by the use of a similar third order approximation in DELSOL, a reputable program used to model CST plants (Kistler, 1986). Therefore, assuming clear days (defined as 23 km visibility), Equation 2 can be safely used in this analysis to calculate the average attenuation loss. Note that the total field area is approximated based on the calculated mirror field area and a mirror land usage factor of 0.2 as recommended by Holl (1978) and Becker et al. (1992).

28

4.1.1.2 Cosine loss The cosine loss involves a reduction in the effective area of reflection and occurs due to the angle between the incident radiation and a vector normal to the heliostat surface. Cosine loss has a geometric dependence on the position of the sun and the location of a heliostat with respect to the central receiver (Wagner, 2008). This effect is shown in Figure 3, which notes that for a CST field in the northern hemisphere, cosine losses are minimised with north oriented . In the southern hemisphere, the opposite is true. Cosine losses scale with the ratio between the distance from the heliostats to the central tower, and the height of the tower, or “number of tower heights” (Holl, 1978). A nominal tower height of 60 metres is assumed for this analysis.

Figure 3 Cosine loss diagram (Holl, 1978) Since cosine loss is a function of the position of the heliostat relative to the tower, a comprehensive calculation of the cosine loss requires the detailed design of a heliostat field, and a summation of the cosine loss from each heliostat. This is beyond the scope of this analysis and would require interfacing with an external program to generate an optimised heliostat field for each design condition. For the purpose of this analysis the cosine loss is approximated by interpolating the average efficiency values outlined at each distance by Holl (1978), shown graphically in Figure 3.

4.1.1.3 Receiver losses The losses from the receiver consist of reflective losses, which is the reflection of radiation from the receiver surface (characterised by absorptivity), spillage, wherein a portion of the radiation from the field fails to intercept the receiver, and thermal losses, due to radiation and convection from the receiver. The increase in thermal losses with temperature is an important consideration in this study. In CST systems, the operating temperature is a function of the receiver design and the concentration ratio (ratio between mirror and receiver area). Therefore, the concentration ratio required to achieve each operating temperature (assuming receiver collection temperature equal to turbine inlet temperature) considered in this analysis must be determined. An approximate method 29 is outlined by Xu et al. (2011) for calculating the required concentration ratio for a given receiver temperature, desired power, and known receiver design. A nominal cavity receiver was designed in SAM and used for the purpose of this analysis. This method has been shown to be a realistic representation of the relationship between the power, temperature and concentration ratio through experimental validation (Li et al., 2010), and is also found to match SAM quite well. There is therefore confidence in the use of this method. Based on this concentration ratio and the calculated mirror field area, the external area of the receiver can be computed. The net efficiency of the receiver is then approximated using the relation outlined by Bellos and Tzivanidis (2018). The reader is directed to Appendix B for full details of these calculations, and Section 5.1 for efficiency charts that are intended to provide an indication of the change in efficiency with temperature.

The value of the intercept factor, which governs spillage loss, is a function of heliostat surface condition, tracking accuracy and beam spread, and requires calculation of the receiver flux distribution for exact determination (using DELSOL). Therefore, the intercept factor is simplified as a constant multiplier (0.9) of the thermal efficiency, as outlined by Bellos and Tzivanidis (2018). Absorptivity is treated as a constant (0.93) as usual (Kistler, 1986, Bellos and Tzivanidis, 2018).

4.1.1.4 Other sources of losses The other losses associated with the field, viz. heliostat reflectivity, and shading/blocking are assumed to be constant with values of 0.91 and 0.95 respectively. This is reasonable based on the approaches taken by other authors (Bellos and Tzivanidis, 2018, Kistler, 1986, Eddhibi et al., 2015). Losses associated with TES are assumed to be negligible (Zhang et al., 2013).

4.1.1.5 Power cycle

As mentioned in the literature review, the efficiency of the sCO2 Brayton cycle increases with increasing turbine inlet temperature. The power cycle efficiency at the three temperatures considered in this analysis is presented below (Meybodi et al., 2017, Clementoni et al., 2015, Yoon et al., 2012).

Table 7 Power cycle efficiency

Temperature (°C) Efficiency 650 0.49 800 0.54 1000 0.604

30

4.1.1.6 Further assumptions In addition to the assumptions outlined in previous subsections, a number of assumptions are needed to model the CST plant. The design point DNI is a key assumption, as this value is used to size the mirror field. For this analysis, the DNI at noon on the summer solstice is used (Wagner, 2017).

Table 8 Additional CST assumptions

Quantity Description Value Design point DNI value at which the plant performs at design output 950 W/m2 DNI capacity. Found to be very similar at all three locations Thermal storage Safety factor applied to the thermal storage capacity 1.2 buffer Storage fill time Time taken to fully fill the TES Variable System life Number of years until total replacement becomes 25 years necessary

4.1.1.7 Costs The changes in CST component costs at higher temperatures necessitate a deeper analysis than simply assuming an overall cost per unit capacity. As such, a greater level of granularity is provided in this analysis through a cost breakdown for the major components. From the literature review, it is primarily the power cycle and receiver costs that increase with temperature. To approximate the receiver costs, it is assumed that the cost will scale linearly based on material costs. The material required at each service temperature is assumed to be as follows (Glatzmaier, 2011, Brun et al., 2017), with costs obtained from Granta Design Limited (2018). Brun et al. (2017) note that costs for the sCO2 power block are still early-stage estimates and present uncertainty. For this analysis, the power block costs are a top down estimate based on a recent costing2 by Meybodi et al. (2017) with appropriate scaling (NETL, 2017).

Table 9 CST material and costs

Temperature Material Cost ($/kg) Multiplier of base cost 650°C (base case) Stainless steel 316 3.69 1 800°C Nickel-Fe-Cr Incoloy 840 6.12 1.66 1000°C Nickel-Cr Hastelloy X 13.4 3.63

2 Note that the 650°C power block cost is assumed to be similar to the reported 610°C cost due to materials requirements being very similar (both low enough for stainless steel). The 1000°C cost is directly reported. The cost for the 800°C case needed to be approximated and is hence scaled based on the material costs. 31

The costs for the CST plant at each temperature are assumed to be as follows, based on the costs outlined by Meybodi et al. (2017) and the segmentation by Hinkley et al. (2013). On top of these, 25% indirect costs and 10% contingency are assumed (Turchi and Heath, 2013).

Table 10 CST cost breakdown

Component Cost at 650°C Cost at 800°C Cost at 1000°C Power block ($/kWe) 1440 2400 4360 Balance of plant ($/kWe) 355 355 355 General Site ($/m2) 20 20 20 Heliostat field ($/m2) 120 120 120 Central receiver ($/kWth) 115 190 417 Tower ($/kWth) 44 44 44 Thermal storage ($/kWth) 30 30 30 Operation & maintenance (O&M) ($/kW/year) 50 50 50

4.1.2 Photovoltaic array with battery storage 4.1.2.1 Photovoltaic array The main objective when installing a PV array is to maximise power generation. In order to ensure that the PV panels intercept maximum irradiation, the panel must be oriented such that it is perpendicular to the sun’s rays. One solution is tracking systems that follow the sun’s trajectory, but due to high costs and limited applicability, fixed installations are more common (Mehleri et al., 2010). For applications in the northern hemisphere, the optimal orientation is due south. In the southern hemisphere the opposite is true (Mehleri et al., 2010). The optimal tilt angle varies seasonally, with a general rule of latitude ± 20° for winter and summer respectively, or equal to the latitude for a permanent fixed tilt system (Soulayman, 1991). For this analysis, a due north orientation, and tilt angle equal to the latitude are assumed. The methods used to calculate the net incident radiation on a titled PV panel are extensively documented by other authors and the equations outlined by Yang et al. (2012) are used in the Python program. In order to calculate the power output the method used in HOMER is followed (HOMER Energy, 2018), which involves the following relation:

푁푒푡 𝑖푛푐𝑖푑푒푛푡 푟푎푑𝑖푎푡𝑖표푛 푃표푤푒푟 = 푅푎푡푒푑 푐푎푝푎푐𝑖푡푦 × × 퐷푒푟푎푡𝑖푛푔 푓푎푐푡표푟 (4) 퐼푛푐𝑖푑푒푛푡 푟푎푑𝑖푎푡𝑖표푛 푎푡 푡푒푠푡 푐표푛푑𝑖푡𝑖표푛푠 This requires the assumption of a representative solar panel, which is taken to be the SunPower E20-435-COM panel. The specifications of this panel are as follows (SunPower, 2017).

32

Table 11 PV panel specifications

Quantity Value Rated capacity 435 W Incident radiation at test conditions 1000 W/m2 Panel efficiency 0.203 Derating factor (a typical value used to account for soiling, module heating 0.77 (Enphase, 2014) and inverter inefficiencies) Plant life (based on product warranty) 25 years

4.1.2.2 Battery storage It is assumed that the PV array is used in conjunction with li-ion battery storage, namely the Tesla Powerpack. As noted in the literature review, there are several key considerations when modelling battery storage. Based on specifications for the Powerpack, these are assumed as follows.

Table 12 Battery storage assumptions

Quantity Value Nominal capacity 210 kWh per Powerpack (Tesla, 2018) Depth of discharge 90% (Tesla, 2018, Jaiswal, 2017) Maximum SOC 90% (Cadex Electronics, 2017) Charge cycles to failure 1500 (Tesla, 2015) Round trip efficiency 0.88 (Tesla, 2018) Maximum charging rate 0.5 Capacity/hour (Tesla, 2018) Maximum discharging rate 0.5 Capacity/hour (Tesla, 2018) End of life retained capacity 0.7 of nominal capacity (Shahan, 2015)

Note that although Tesla states a 100% DoD for the Powerpack (Tesla, 2018), 90% is assumed as a conservative measure (Jaiswal, 2017, Albright et al., 2012), so as to allow comparison with generic li-ion storage that may not sustain 100% DoD. Furthermore, given that li-ion batteries face degradation with use, it is important to model its effects. To do so, a linear capacity fade from the nominal capacity to the end of life retained capacity is assumed, in accordance with SAM.

4.1.2.3 Costs Despite the rapidly decreasing costs of PV (down from $1450/kW in 2016 to $1030/kW in 2017), these changes are well documented and up to date by reputed sources such as the NREL (Fu et al., 2017). The cost of li-ion battery storage on the other hand presents significant spread, from estimates of $300/kWh (Boussetta et al., 2016) to $600/kWh (McKinsey & Company, 2018).

33

Multiple industry experts note that these costs are declining rapidly, with some expecting a cost of $200/kWh by 2020 (McKinsey & Company, 2018, IRENA, 2015). For this project, the recent comprehensive analysis by Lazard (2017) which notes a range of $576-720/kWh, is deemed reliable, and the lower end of this estimate, which corroborates the estimate by McKinsey & Company is used. The costs associated with the systems are assumed to be as follows.

Table 13 PV and battery costs

Item Cost PV capital cost $1030/kW (Fu et al., 2017, Jamal et al., 2016) PV operation and maintenance cost $40/kW (Jamal et al., 2016) Battery storage capital cost $576/kWh (Lazard, 2017) Battery storage replacement cost $576/kWh (Lazard, 2017) Battery storage operation and $15/kWh/year (Jamal et al., 2016) maintenance cost

4.1.3 Diesel generators As mentioned in the literature review, the key driver of overall costs for diesel generators is the fuel price, with capital and O&M costs being less important. While the fuel consumption of a diesel generator varies between manufacturers, and with generator size and operating load, an estimate is presented by Arun et al. (2008), who propose a linear relationship for fuel consumption (FC) as follows:

퐹퐶 (푙𝑖푡푟푒푠/ℎ표푢푟) = 0.01841 × 푅푎푡푒푑 푐푎푝푎푐𝑖푡푦 + 0.2088 × 퐿표푎푑 (5) There is a high level of confidence in this relation, as it has been validated with manufacturer fuel consumption data (Arun et al., 2008, Skarstein and Uhlen, 1989), and appears in the work of multiple other authors (Roy and Kulkarni, 2016, Rohani and Nour, 2014). In this analysis, the rated capacity is calculated based on the peak load requirement from the diesel generators, and the hourly load is taken to be the average annual hourly load satisfied by the diesel generators (in hours where the generators are required). Both quantities are calculated as required in the Python program. The costs and parameters associated with the diesel generators are assumed to be as follows.

34

Table 14 Diesel generator costs

Quantity Value Capital cost (assumed equal to replacement $800/kW (Lazard, 2016) costs) Operations and maintenance cost $30/kW/year (Lazard, 2016) Generator life 15,000 hours (Jamal et al., 2016, Karutz and Haque, 2013) Diesel fuel cost $0.70/litre3 (Jamal et al., 2016)

4.2 Candidate locations For the purpose of this study, the three locations identified in a previous analysis (Swanson, 2017) as being ideal sites for implementation of a hybrid system have been chosen. These are Port Augusta, Halls Creek and Newman.

Figure 4 Candidate locations (Google Maps, 2018) 4.2.1 Load profile To assess opportunities for off-grid applications, two remote consumer groups are explored, viz., communities and mining sites. Note that in both cases it is assumed that the hybrid system will satisfy the entire load (no capacity shortage). This is especially important in the mining case, as downtime can lead to revenue losses.

4.2.1.1 Community scenario A synthetic load profile with daily hourly load was assumed as in Figure 5. This load profile was then scaled based on population data from the Australian Bureau of Statistics (2017) and the

3 Note that is value corresponds to an estimate of 0.90 AUD/litre, to reflect remote area transportation costs as well as the excise tax exemption (Gurgenci, 2014). 35 average household consumption in each location based on the Australian Government (2018) estimates, assuming five per household. Seasonal variability was incorporated to account for increased consumption in summer, based on the Australian Energy Regulator (2018) seasonal peak demands in summer and winter. The assumed consumption values are as follows.

Table 15 Average daily energy consumption Location Population Annual average daily household Location average total daily consumption (kWh/day) consumption (kWh/day) Port 15000 17.4 52,200 Augusta Halls Creek 3338 22.9 15,288 Newman 5500 22.9 25,190

The unscaled load profile and seasonal variability are assumed to be as in the following figures. Note that these were scaled using the values in Table 15 prior to being used in the Python program.

Community daily load profile 100% 80% 60% 40% 20% 0% 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Percentage Percentage of load peak Hour

Figure 5 Community load profile

Community seasonal load variation 1.5

1

0.5

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Multiplier average of load Month

Figure 6 Community seasonal load variation 4.2.1.2 Mining site scenario Each of the locations selected is near a mining site. These are the Carrapateena mine near Port Augusta, the Nicholson mine near Halls Creek and the Mount Whaleback mine near Newman.

36

Mining sites generally present a more uniform load profile due to full day operation and as such, a flat load profile with a uniform load of 10 MW is assumed for the mining scenario.

4.2.2 Characterisation of solar resources Weather measurements have been used to characterise the availability of solar resources in these locations. Specifically, the data used contains DNI and GHI values at hourly intervals over a year. To account for year to year variability, data collected over several years and averaged was deemed ideal, so that unusual weather in one year would not introduce bias into the data. However due to unavailability, single year data from AUSTELA (2014) is used – Port Augusta (2012), Newman (2006) and Halls Creek (2012). Nevertheless, this is not expected to have a significant influence on the results.

4.3 Economic analysis As evidenced in the literature review, the most common metric used to evaluate the cost competitiveness of hybrid systems is the LCOE, viz. the cost per kWh of energy produced. To calculate this, the capital costs are first converted to an annual figure using the equivalent annual cost (EAC), which is the annualised cost of owning an asset over its entire lifetime, and is given by:

푇표푡푎푙 푎푠푠푒푡 푣푎푙푢푒 × 퐷𝑖푠푐표푢푛푡 푟푎푡푒 퐸퐴퐶 = (6) 1 − (1 + 퐷𝑖푠푐표푢푛푡 푟푎푡푒)푁표. 표푓 푦푒푎푟푠 The total annual cost is then obtained by adding the annual O&M and fuel costs to the EAC, and the LCOE is calculated by dividing the total annual cost of the project by the total useful annual energy production. In this analysis it is assumed that all costs over the lifetime of the project are borrowed. As such, the discount rate (interest rate charged on loans) becomes a key parameter in the analysis. In the Australian studies explored, a discount rate of ~7% was commonly seen (Meybodi et al., 2017, Shafiullah, 2016). This is mirrored in the ten-year average of 6.82% in the solar energy sector by the Australian Energy Council (2016), and therefore a discount rate of 7% is assumed. While there is potential for leveraging funding or subsidies for this project, these are not considered, as they still present a financial burden on the government or external body, and it is important that the project be feasible on its own merits. Moreover, although these locations likely already contain diesel generation assets, the condition and age of these are unknown, and hence it is assumed that new generators are purchased. The cost performance of the hybrid systems will be benchmarked against a 100% diesel generator base case. AECOM (2014) estimates that diesel generated off-grid power is typically provided at a cost of $0.24-0.45/kWh. Although these costs can vary, the lower end of this estimate, $0.24/kWh is used as the benchmark in this analysis.

37

4.4 Environmental assessment As established in the literature review, an analysis of emissions across the entire life cycle of the systems is the most robust way of assessing environmental impact. As such, the reduction in emissions is calculated not only based on the reduction in the use of diesel, but also includes the embodied emissions of the renewable components. The average equivalent CO2 emission of each technology is found to be as follows.

Table 16 Emissions of each system

System Emissions (gCO2eq/kWh) Diesel generators 778 (IPCC, 2014) PV (utility scale) 48 (IPCC, 2014) Battery storage 150 (Romare and Dahllof, 2017) CST 27 (IPCC, 2014)

4.5 Sensitivity of modelling variables The uncertainty in the input modelling variables is addressed through a sensitivity analysis, by systematically altering the values of parameters to understand their effects on system performance. This analysis is also expected to provide insight into the factors that could serve as impediments or drivers to the future uptake of renewables. Given that the module costs for the renewables are likely to continue to decline due to technological advancements, learning effects and increasing economies of scale, it is important to explore the implications of these cost reductions. In addition, to account for potentially higher current costs than those assumed in this analysis, an increased cost is also considered. Changes in these variables of up to 30% are studied. The base case values are tabulated along with the results for the reader’s convenience.

4.6 Simulation program outline The methods outlined throughout this section were implemented through a Python program. The entire Python script is included in Appendix F. The structure, functionality, requirements and outputs of the program are outlined in this section.

1. The program draws dependencies from the packages Math, Xlrd, NumPy and SciPy. 2. Classes are used to model the operation of the CST and PV-battery systems. The assumptions listed previously for each system are embedded within these classes. a. A class “solarthermal” is used to model the CST plant and calculate the sizes of the components within the system based on 5 arguments (design load, storage hours, storage fill time, design point DNI, and operating temperature). This involves:

38

i. Defining the thermal storage capacity (kWh), based on the design load (kW) multiplied by the number of storage hours and divided by cycle efficiency. ii. Calculating the field capacity based on this storage capacity and the storage fill time. In this process the necessary mirror area is set based on the design point DNI. The field capacity is decoupled from the storage capacity by varying the fill time, with net field capacity defined as storage capacity divided by number of hours specified to fill the storage (note that this is divided by field and receiver efficiencies to get gross capacity). iii. A method to add energy to storage based on the DNI value for a given hour, the mirror area and the net sun to heat efficiency. iv. A method that calculates the maximum power output suppliable by the system based on the power cycle efficiency and the amount of thermal storage currently available. v. A method to subtract from thermal storage based on the power supplied. vi. A method to track the spilled energy. vii. A method that calculates the total capital cost of the CST system, based on the costs per unit mentioned earlier and the sizes of the subsystems. b. A class “photovoltaic” is used to model the PV and battery systems based on two arguments (number of storage hours and total PV array size). This involves: i. Defining the nominal battery storage capacity (kWh), based on the PV array size (kW) multiplied by the number of storage hours. ii. A method to calculate the optimal panel tilt based on the site location and the corresponding irradiation on the tilted panel at a given GHI. iii. A method to calculate the power generated by the PV array over a given hour, based on the current GHI and the panel tilt. iv. A method to add a storage amount to the battery storage based on the maximum SOC and charging rate. v. A method to calculate the maximum output from the battery system in a given hour based on the amount of available storage, the minimum SOC, discharge rate and the round-trip efficiency. vi. A method to subtract from the storage, considering the minimum SOC. vii. A method that models capacity fade by linearly reducing the storage capacity with charge cycles. The capacity is reset to the nominal value when the end of life capacity (70%) is reached (1500 cycles). viii. A method to track the excess energy (energy not stored in a given hour). 39

3. A function is used to simulate the operation of the hybrid plant over the course of one year, in hourly steps. This function requires eight inputs. Firstly, the arguments for the solarthermal (storage hours, storage fill rate, design point DNI and temperature) and photovoltaic classes (PV array size, storage hours), and secondly, a data file containing the hourly load, and another containing hourly DNI and GHI values over one year. a. The function first calculates the mean peak load from the load file, this is used as the baseload in the solarthermal class. b. Instances of the solarthermal and photovoltaic classes are created. c. The simulation itself involves the following steps for each hour of the year: i. Add to the CST thermal storage based on the DNI in that hour. ii. Calculate the output from the PV plant based on the GHI. iii. Check if PV alone can cover the entire load in that hour. If so, add the load to the total contribution from the PV plant. If there is excess, add as much as possible to the battery storage. iv. If PV power is unable to cover the load alone, check if PV along with the amount stored in the battery system is sufficient. If so, increment the contributions accordingly. v. If PV along with the battery is unable to cover the load, check whether the addition of CST is sufficient. If so, increment the contributions of each system accordingly. vi. If all three systems together are unable to satisfy the load, the deficit is covered by the diesel generators, and the contributions of each system are incremented accordingly. d. The outputs of this simulation are: i. The total contribution (kWh) and the peak load (kW) from each technology. The power cycle capacity of the CST plant is set based on the peak load served by the CST plant. ii. The excess energy generated by the renewables. iii. The total runtime over the year for the diesel generators. 4. The simulation function and its inputs are then embedded into a function that calculates the LCOE for a given hybrid system configuration, which takes in the four key configuration variables as arguments - viz. thermal storage hours, storage fill time (linked to field capacity), PV size and battery storage hours. The annual costs associated with each system are first calculated. The total annual cost is then divided by the total useful energy production to calculate the LCOE. 40

4.6.1 Optimisation Optimisation is carried out on the LCOE function to find the hybrid configuration that yields the minimum LCOE. The four variable optimisation is carried out as follows.

1. Firstly, an exhaustive search is carried out, to yield an estimate of the optimal configuration (corresponding to the global minimum point). The search ranges used for the four variables are as follows: a. Thermal storage hours – defined as the number of hours of CST baseload power that can be supplied using the thermal storage: 0 to 15 hours b. Storage fill time – defined as the time taken to fill the thermal storage, and determines the ratio between storage and field capacity: 1 to 15 hours c. PV size (installed capacity): 0 MW to 20 MW d. Battery storage hours – defined as the number of storage hours that can provide the installed capacity of the PV plant: 0 to 15 hours 2. This estimate is used as the initial guess in the Simplex minimisation function (part of the SciPy package) which uses a simplex algorithm to locate the exact optimum. a. The thermal storage hours, storage fill time, PV size and battery storage hours corresponding to the optimum are reported. 3. This process is carried out for each of the three turbine inlet temperatures.

41

5 Results 5.1 CST efficiency charts The following efficiency charts were produced based on the approximations outlined in Section 4.1.1 to characterise the behaviour of the temperature and size dependent loss terms. Note that to generate these charts, representative conditions must be assumed, and these are mentioned below. The variation in the thermal efficiency of the receiver with temperature is shown in Figure 7. Note that this includes only the thermal losses, which are temperature dependent, and not the other fixed components that contribute to the overall receiver efficiency. This chart has been produced assuming a DNI of 950 W/m2. Similarly, the variation in the field optical efficiency (encompassing reflectivity, shading, attenuation and cosine loss), with plant capacity is shown in Figure 8. This chart has been generated assuming 12 hours of storage, three fill times (3, 5 and 7 hours), and a turbine inlet temperature of 650°C. These parameters affect the field optical efficiency through their influence on field size, with a smaller fill time resulting in a larger field, and a higher turbine inlet temperature allowing for a smaller field.

Receiver thermal efficiency vs temperature 0.9

0.85

0.8

0.75

650 700 750 800 850 900 950 1000 Receiver Receiver thermal efficiency Temperature (°C)

Figure 7 Receiver efficiency vs temperature

Field optical efficiency vs capacity 0.75 0.70 0.65 0.60 0.55

Field Field optical efficiency 1 2 3 4 5 6 7 8 9 10 Net output capacity (MW)

Field optical efficiency (3 hrs) Field optical efficiency (5 hrs) Field optical efficiency (7 hrs)

Figure 8 Field optical efficiency vs capacity

42

Since the above charts are specific to the conditions assumed and cannot be generalised, they are only intended to provide an indication of the overall changes in receiver efficiency with temperature, and field optical efficiency with size. The actual efficiencies calculated by the Python program for each of the CST configurations of interest are reported in the succeeding section.

5.2 Optimal hybrid systems The following results were obtained using the Python program for each location and scenario. Please note that battery storage does not feature in any of the optima, with results indicating zero hours of battery storage being optimal. To avoid repetition, this is excluded from the pursuing results. The LCOE for the diesel base case is assumed to be $0.24/kWh. The raw results from the economic analysis can be found in Appendix D.

5.2.1 Community scenario The optimal system configurations, LCOE and technology breakdown for the ideal systems in each location for the community scenario are as follows.

5.2.1.1 Newman community The optimal configurations, viz. the configurations that deliver the minimum LCOE, for the three temperatures are as follows.

Table 17 Newman community case configuration

CST Renewable CST CST fill CST cycle CST sun CST net PV DG size temp fraction store time capacity to heat efficiency size (kW) (°C) (%) (hrs) (hrs) (kW) efficiency 4 (kW)

650 88.5% 12.8 4.30 1466 0.524 0.257 700 1466 800 88.4% 13.9 4.70 1466 0.505 0.273 600 1466 1000 86.5% 14.4 5.00 1466 0.467 0.282 200 1466

The contribution of each technology to the total energy production and LCOE is as follows.

4 Net efficiency is the sun to heat efficiency multiplied by the power cycle efficiency. 43

Newman community production breakdown and cost

100.00% 83% 0.23 75% 77% 80.00% 0.213 0.21 60.00% 0.180 0.19 0.167 40.00% 0.17 14% 11% 12% 13%

20.00% 11% 4% 0.15

Energyfraction LCOE ($/kWh) LCOE 0.00% 0.13 650 800 1000 Temperature (°C)

CST PV Diesel LCOE

Figure 9 Newman community case energy production The excess energy generated by each system is as follows.

Newman community case spilled energy 2500 1923 2000 1805 25.0% 1323 1500 20.0% 1000 15.0% 500 0 0 0

0 10.0% Excess percentage Excess Excess Excess energy (MWhe) 650 800 1000 Temperature (°C)

PV Excess CST Excess Percentage of total renewable production

Figure 10 Newman community case spilled energy

5.2.1.2 Port Augusta community The results for the Port Augusta community case are as follows.

Table 18 Port Augusta community case configuration

CST Renewable CST CST fill CST cycle CST sun CST net PV DG size temp fraction store time capacity to heat efficiency size (kW) (°C) (%) (hrs) (hrs) (kW) efficiency (kW)

650 88.1% 13.7 4.30 3038 0.507 0.248 2400 3014 800 87.3% 14.2 4.60 3038 0.489 0.264 2200 3016 1000 86.0% 14.5 4.90 3038 0.452 0.273 2000 3017

44

Port Augusta community production breakdown and cost 100.00% 0.25 80.00% 65% 66% 0.232 0.23 0.197 67% 0.21 60.00% 0.183 0.19 40.00% 23% 21% 19% 0.17 13% 14%

20.00% 12% 0.15

LCOE ($/kWh) LCOE Energyfraction 0.00% 0.13 650 800 1000 Temperature (°C)

CST PV Diesel LCOE

Figure 11 Port Augusta community case energy production

Port Augusta community case spilled energy 6000 25.0% 3872 4000 3326 2900 20.0% 2000 3 0 0 0 15.0%

650 800 1000

Excess Excess energy (MWhe) Excess percentage Excess Temperature (°C)

PV Excess CST Excess Percentage of total renewable production

Figure 12 Port Augusta community case spilled energy

5.2.1.3 Halls Creek community The results for the Halls Creek community case are as follows.

Table 19 Halls Creek community case configuration

CST Renewable CST CST fill CST cycle CST sun CST net PV DG size temp fraction store time capacity to heat efficiency size (kW) (°C) (%) (hrs) (hrs) (kW) efficiency (kW)

650 92.3% 13 5.50 10005 0.539 0.264 1000 862 800 91.9% 13.9 5.90 1000 0.518 0.280 900 864 1000 91.2% 14 6.00 1000 0.479 0.289 800 867

5 Rounded in the simulation to 1 MW based on the minimum size required for sCO2 power cycles. Maximum load served is actually 884 kW. 45

Halls Creek community production breakdown and cost 100.00% 0.213 80.00% 60% 0.158 63% 0.174 65% 0.2 60.00% 40.00% 29% 26% 0.15 32%

20.00% 8% 9% LCOE ($/kWh) LCOE Energyfraction 8% 0.00% 0.1 650 800 1000 Temperature (°C)

CST PV Diesel LCOE

Figure 13 Halls Creek community case energy production

Halls Creek community case spilled energy 800 723 600 600 497 17.0% 400 12.0% 200 14 0 0 0 7.0%

650 800 1000

Excess Excess energy (MWhe) Excess percentage Excess Temperature (°C)

PV Excess CST Excess Percentage of total renewable production

Figure 14 Halls Creek community case spilled energy

5.2.2 Mining scenario The optimal system configurations, LCOE and technology breakdown for the ideal systems in each location for the mining scenario are as follows.

5.2.2.1 Newman mining The results for the Newman mining case are as follows.

Table 20 Newman mining case configuration

CST Renewable CST CST fill CST cycle CST sun CST net PV size DG temp fraction store time capacity to heat efficiency (kW) size (°C) (%) (hrs) (hrs) (kW) efficiency (kW) 650 87.3% 13.1 4.60 10000 0.470 0.230 4700 10000 800 85.7% 13.9 5.20 10000 0.454 0.245 2500 10000 1000 85.5% 14.9 5.50 10000 0.419 0.253 1000 10000

46

Newman mining production breakdown and cost

100.00% 83% 78% 81% 0.21 80.00% 0.19 60.00% 0.147 0.175 40.00% 0.153 0.17 9% 13% 14%

20.00% 14% 0.15 Energyfraction 5% 3% ($/kWh) LCOE 0.00% 0.13 650 800 1000 Temperature (°C)

CST PV Diesel LCOE

Figure 15 Newman mining case energy production

Newman mining case spilled energy

15000 13436 20.0% 10000 8366 7648 15.0%

5000 10.0% 0 0 0

0 5.0% Excess percentage Excess Excess Excess energy (MWhe) 650 800 1000 Temperature (°C)

PV Excess CST Excess Percentage of total renewable production

Figure 16 Newman mining case spilled energy

5.2.2.2 Port Augusta mining The results for the Port Augusta mining case are as follows.

Table 21 Port Augusta mining case configuration

CST Renewable CST CST fill CST cycle CST sun CST net PV DG temp fraction store time capacity to heat efficiency size size (°C) (%) (hrs) (hrs) (kW) efficiency (kW) (kW) 650 84.1% 13.5 4.30 10000 0.467 0.229 10000 10000 800 83.2% 13.8 4.50 10000 0.451 0.244 9000 10000 1000 83.7% 14.5 4.60 10000 0.415 0.251 8000 10000

47

Port Augusta mining production breakdown and cost 100.00% 0.23 80.00% 64% 65% 67% 0.2 0.21 60.00% 0.179 0.19 0.17 40.00% 0.17 20% 18% 17% 17% 16%

20.00% 16% 0.15

LCOE ($/kWh) LCOE Energyfraction 0.00% 0.13 650 800 1000 Temperature (°C)

CST PV Diesel LCOE

Figure 17 Port Augusta mining case energy production

Port Augusta mining case spilled energy 30000 20032 17477 30.0% 20000 16212

10000 25.0% 597 213 19 0 20.0%

650 800 1000

Excess Excess energy (MWhe) Excess percentage Excess Temperature (°C)

PV Excess CST Excess Percentage of total renewable production

Figure 18 Port Augusta mining case spilled energy

5.2.2.3 Halls Creek mining The results for the Halls Creek mining case are as follows.

Table 22 Halls Creek mining case configuration

CST Renewable CST CST fill CST cycle CST sun CST net PV DG temp fraction store time capacity to heat efficiency size size (°C) (%) (hrs) (hrs) (kW) efficiency (kW) (kW) 650 92.7% 13.2 5.10 10000 0.472 0.231 12000 10000 800 92.7% 13.9 5.30 10000 0.455 0.246 11000 10000 1000 92.6% 14.9 5.50 10000 0.42 0.254 9000 10000

48

Halls Creek mining production breakdown and cost 100.00% 0.18 74% 0.166 80.00% 69% 70% 0.144 0.16 60.00% 0.136 0.14 40.00% 24% 23% 19% 20.00% 7% 7% 0.12

7% ($/kWh) LCOE Energyfraction 0.00% 0.1 650 800 1000 Temperature (°C)

CST PV Diesel LCOE

Figure 19 Halls Creek mining case energy production

Halls Creek mining case spilled energy 15000 16.0% 11062 10464 9439 10000 14.0%

5000 12.0% 340 78 0 0 10.0%

650 800 1000 Excess percentage Excess Excess Excess energy (MWhe) Temperature (°C)

PV Excess CST Excess Percentage of total renewable production

Figure 20 Halls Creek mining case spilled energy

5.2.3 Optimal site for further analysis From the results in Sections 5.2.1 and 5.2.2, the 650°C case consistently presents the lowest LCOE amongst the three temperatures explored. Furthermore, the top location based on the LCOE for both the community and mining scenarios is Halls Creek, with LCOE of $0.158/kWh and $0.136/kWh respectively. Therefore, further analysis has been undertaken in the subsequent sections for a hybrid plant at the Halls Creek site, with a CST temperature of 650°C.

49

5.3 Sensitivity analysis A sensitivity analysis was performed for the hybrid systems at the Halls Creek site, with a CST temperature of 650°C. The sensitivity of the LCOE to the input parameters is indicated by the ensuing results. Note that for both cases, the top four parameters in terms of influence on the LCOE are reported, with the remaining inputs presenting negligible influence (< 2% change for a 30% change in the input). The base case values are reproduced for the reader’s convenience below.

Table 23 Base case parameters for sensitivity analysis

Parameter Base value PV capital expenditure (Capex) $1030/kW Diesel fuel cost $0.7/litre Discount rate 7% CST Capex Please refer to Section 4.1.1.7 since this was calculated using a cost breakdown.

5.3.1 Halls Creek community case

Halls creek community case LCOE sensitivity

CST Capex +/-30% 0.131 0.186 CST Capex +/-20% 0.14 0.177 CST Capex +/-10% 0.149 0.167

Discount rate +/-30% 0.136 0.183 Discount rate +/-20% 0.143 0.175 Discount rate +/-10% 0.151 0.166

PV Capex +/-30% 0.153 0.163 PV Capex +/-20% 0.155 0.162 PV Capex +/-10% 0.156 0.16 Low High Diesel cost +/-30% 0.154 0.162 Diesel cost +/-20% 0.156 0.161 Base LCOE ($/kWh) = 0.158 Diesel cost +/-10% 0.157 0.16 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 LCOE ($/kWh)

Figure 21 Halls Creek community case sensitivity analysis

50

5.3.2 Halls Creek mining case

Halls creek mining case LCOE sensitivity

CST Capex +/-30% 0.11 0.163 CST Capex +/-20% 0.119 0.154 CST Capex +/-10% 0.127 0.145

Discount rate +/-30% 0.116 0.158 Discount rate +/-20% 0.123 0.151 Discount rate +/-10% 0.129 0.143

Diesel cost +/-30% 0.132 0.14 Diesel cost +/-20% 0.134 0.139 Diesel cost +/-10% 0.135 0.138 Low High PV Capex +/-30% 0.133 0.139 PV Capex +/-20% 0.134 0.138 Base LCOE ($/kWh) = 0.136 PV Capex +/-10% 0.135 0.137 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 LCOE ($/kWh)

Figure 22 Halls Creek mining case sensitivity analysis

5.4 Life cycle emissions analysis

The equivalent CO2 emissions of the optimal hybrid systems at the Halls Creek site are as follows.

Note that all emissions are presented in kgCO2eq/year.

Table 24 Annual emissions

Case Diesel PV CST Total Diesel base case Reduction emissions emissions emissions emissions emissions Mining 4,984,598 1,026,737 1,614,673 7,626,008 68,152,799 89% Community 332,075 86,269 90,488 508,832 4,337,764 88%

The contribution of each technology is as follows.

Halls Creek CO2 equivalent emissions of each technology 100% 80% 65% 65% 60% 40% 22% 13% 18% 17% 20%

0% Emission Emission fraction Mining Community Scenario

CST Emissions PV Emissions Diesel emissions

Figure 23 Emission fractions 51

6 CST concept design in SAM System Advisor Model (SAM) was used to generate a concept design of the CST systems for Halls Creek, which has been found to be the optimal site.

6.1 SAM inputs The key inputs and assumptions made within the SAM user interface to model the CST systems are presented in Table 25. The values specified were set based on the assumptions made in the Python program for the CST plant, and the remaining parameters in the interface were left at SAM’s default values. Note that this analysis was performed on SAM legacy version 2014.1.14 (NREL, 2018b), due to subsequent versions lacking cavity receiver functionality.

Table 25 SAM inputs

Input / Parameter Community case Mining case Location and resource tab Weather file Halls Creek Halls Creek System design tab Design point DNI 950 W/m2 950 W/m2 Solar multiple6 2.36 2.59 Design turbine gross output 1 MW 10 MW Gross to net conversion factor 1 1 Cycle thermal efficiency 0.49 0.49 Full load hours of storage 13 hours 13.2 hours HTF hot temperature 650°C 650°C HTF cold temperature 290°C 290°C Tower and receiver tab Receiver type Cavity receiver Cavity receiver Tube outer diameter 40 mm 40 mm Tube wall thickness 1.25 mm 1.25 mm

6 Note that the solar multiple (ratio of net field capacity to nameplate power cycle capacity) is equivalent to the storage hours divided by the storage fill time and is hence set according to the results in Section 5.2. This derivation is given in Appendix E. 52

6.2 Results These assumptions were used in the SAM analysis to obtain optimised specifications for the two cases. The key results obtained are presented below.

Table 26 Specifications obtained from SAM

Parameter Community case Mining case Number of heliostats 95 645 Total heliostat reflective area (m2) 13,715 93,121 Solar field land usage (m2) 149,734 1,214,000 Tower height (m) 35 95 Receiver aperture width (m) 3.0 12.2 Receiver aperture height (m) 2.4 9.8 Receiver aperture lip height (m) 0.31 1.26 Receiver internal panel height (m) 2.64 10.76 Storage tank height (m) 20 20 Storage tank diameter (m) 2.5 8 Storage tank volume (Two tank) (m3) 98.4 999

Optimised heliostat field layouts were generated for each of the cases and are presented overleaf. Note that a darker shade of red indicates a higher heliostat density in a given zone, and white/grey indicates vacant areas.

53

N

Radial step = 19.9 m

Figure 24 Community case heliostat field layout

N

Radial step = 53.4 m

Figure 25 Mining case heliostat field layout

54

7 Discussion 7.1 CST efficiency charts Based on the efficiency charts presented in Section 5.1, it is apparent that the thermodynamics of the CST system align with the expectations based on theory, and while these charts were generated for specific conditions, they are sufficient to capture the overall behaviour of the efficiency terms. From the results obtained, it is evident that the thermal efficiency of the receiver decreases with increasing collection temperature, and the optical efficiency of the solar field decreases with increasing field size (due to increased attenuation and cosine losses). The decrease in optical efficiency is evidenced with both a direct increase in plant capacity, and with a decrease in the storage fill time, which in turn results in an increase in the field capacity.

Since these two terms were the only ones that had to be approximated, as opposed to the remaining efficiency terms which are constants, these charts were a necessary check, and the fact that the behaviour is as anticipated provides confidence that the efficiency terms have been approximated appropriately. As an interesting aside, during testing, these efficiency values were checked against those produced by SAM and were found to be in close agreement. While these details are not included for brevity, this increases confidence in the approximations. The actual sun to heat efficiencies and overall efficiencies calculated for each optimum are discussed in the succeeding section.

7.2 Optimal hybrid system results There are a number of important trends that are observable from the optimisation results. Most notably, it is evident that all the scenarios present a relatively high renewable fraction, ranging from ~83-93%, and provide significant cost reductions over the diesel base case. This serves as an indication of the high quality solar resources available at all three sites. A higher renewable fraction is accompanied by a lower diesel generator contribution, as expected, given that diesel generation was utilised only to provide supplementary power.

The optimal contribution of each technology varies substantially between the three sites. For instance, in the mining scenario for the 650°C CST case, the optimal configuration in Newman presents a 78%-9%-13% split of CST, PV and diesel respectively. Whereas at Port Augusta and Halls Creek, the split is 64%-20%-16% and 69%-24%-7%. As such, while CST is the main component at all the sites, it is clear that PV is more favourable at the Port Augusta and Halls Creek sites than Newman. Since the load profile used in the mining case was the same across all the sites, differences in optimal configuration are attributed solely to the quality of the solar resources. Note that the overall trend is mirrored in the community case as well, however there are

55 minor differences due to the influence of the load profile. Upon inspection of the weather data (Appendix C), it is evident that the DNI values for all three sites are quite high throughout the year. However, in the Newman site alone, the average DNI seen in the winter months is unusually high, as compared to the GHI which dips in these months. This most likely played a role in the higher levels of CST and low levels of PV seen in Newman. On the other hand, both the DNI and the GHI at Halls Creek are consistently high year-round, and hence while CST is still the primary component at this site, there is an increased contribution from PV. Port Augusta is the only site in which a pronounced dip in both DNI and GHI is seen in the winter months. This results in the renewable fractions at this site being the lowest of the three, due to reduced contribution from the renewables during winter. There is also a clear link between renewable fraction and the LCOE, with a higher renewable fraction yielding a lower LCOE. This is evidenced in Port Augusta consistently presenting the highest LCOE, and in Halls Creek presenting the highest renewable fractions and the lowest LCOE in both the mining and community cases. The excess energy production is also the lowest at Halls Creek, indicating that this site is the least overdesigned.

The power cycle capacities of the CST plant for the ideal systems across all the cases explored are between 1 MW and 10 MW. These are much smaller than the 50 MW minimum for conventional steam Rankine CST plants, further reiterating the merits of the sCO2 power cycle. Since these are above the 1 MW minimum for sCO2 power cycles, implementation of these systems is feasible.

Despite these differences, the CST plant accounts for the majority of the energy generation in all the cases. On the other hand, while PV is shown to be necessary in all the cases, the relative contribution of PV is much lower than CST. A notable absence is battery storage, which does not feature in any of the optima. This is an important result, as it indicates that CST with thermal storage provides a more optimal route to baseload power than PV with battery storage. It is important to note that the cost used for the li-ion batteries in this analysis was the lower end of the range outlined by Lazard (2017), and the actual costs could be significantly higher. As such, the fact that battery storage does not feature in the optima despite the low costs assumed serves to further support the point that PV with battery storage is an inferior source of baseload power compared to CST. This is not surprising, given that previous studies such as those by Jorgenson et al. (2016) and Petrollese and Cocco (2016) also found that CST-PV hybrids consisting primarily of CST with thermal storage, can provide lower cost baseload energy than PV-battery or singular implementations of either technology. Since these analyses were carried out at larger scales, the fact that the superiority of CST, and the overall viability of CST-PV hybrids still holds at small scales is an important finding. The fact that the trends in this analysis mirror the trends observed by previous authors increases confidence in the veracity of the results obtained. 56

The maximum renewable fraction seen across all the scenarios is 92.7%. This result corroborates the work of Petrollese and Cocco (2016), who found that the lowest energy costs were possible if the renewable fraction was limited to 85-95%, due to the difficulties associated with making CST the sole source of baseload power (due to the need to overdesign). Therefore, the results obtained indicate that there are indeed diminishing returns on increasing the renewable fraction beyond this point, and the remaining load is more easily met using diesel generators. This is primarily due to the fact that despite overall excellent solar resources, there are inevitably times during the year with poor sunlight. Hence incorporating a diesel generator allows for preventing electricity shortages without overdesigning the renewables. This is a key result, as it shows that even with excellent solar resources, there are strong merits to hybridisation with a diesel generator. These results corroborate the work of Swanson (2017), and indicate that despite differences in methodology and assumptions, the overall results observed are quite similar. Hence it may be posited that the results obtained here will extend well to tests under a different load profile, and with different CST configurations.

The sizes of each technology also reveal some key insights. In all the scenarios, the total diesel generation capacity is sized equal to or very close to the peak load. This was automatically sized in the simulation based on the load demands, and therefore indicates that there are times during the year, most likely at night, when the renewables produce negligible power, and hence the diesel generators cover the entire load. This result aligns with previous studies, which have indicated that in cases where 24 hour baseload power was necessary, the diesel generators must be sized equal to the peak load to ensure uninterrupted output (Fazelpour et al., 2016). In addition, given that hybrid projects typically involve incorporating renewables into existing diesel generation capacity, it is expected that the existing generators would already be sized to meet the entirety of the load. Therefore, this sizing strategy would simplify the integration of the proposed systems with existing assets. Based on the sizes and relatively low contribution of PV, it appears that PV serves to simply shave off some of the peak load during the day time. Similarly, the dominance of the CST system is evidenced in the net power cycle capacity (sized based on the largest load served by CST) taking on the value of the peak load in each scenario, indicating that there are hours in which CST covers the entire load itself. This makes intuitive sense, given the role of the CST system as the primary source of baseload power.

The LCOE values obtained in each of the cases are reasonable, and present significant reductions over the diesel base case. This is most notable at Halls Creek, where the 650°C hybrid system presents 34% and 43% reductions in the community and mining cases respectively, with compositions of 60%-32%-8% and 69%-24%-7% (CST-PV-diesel). Note that the cost assumed 57 for the diesel base case was the lower end of the estimate by AECOM (2014) and so there is a possibility that the real cost reductions would be even greater. Nevertheless, the proposed systems present clear economic benefits. Given that the CST system is the main contributor, it is important to note that the LCOE values fall within the range expected for CST (IRENA, 2018) (albeit on the lower side, but this is as anticipated since sCO2 presents a higher efficiency than the steam Rankine plants reported in literature).

Based on the sun to heat and overall efficiencies reported for each of the CST systems, it is apparent that the thermodynamics of the CST system are as expected. It is evident that the net sun to heat efficiency decreases with increasing turbine inlet temperature, and also with plant size, with the mining scenario presenting a consistently lower efficiency than the community case. This is as anticipated, based on the trends seen in the efficiency charts. The efficiency values seen for the 650°C base cases fall within the typical range (Zhang et al., 2013) for CST plants (~0.5). With an increase in temperature, although the sun to heat efficiency decreases due to the increased receiver losses, these losses are smaller than the efficiency gains in the power cycle, and hence overall efficiency increases. This result too is as expected, and is in line with the work of Meybodi et al. (2017). The close match with previous work provides confidence that the individual efficiency components have been calculated appropriately, and the relative performance at the three temperatures has been accurately captured. As a result of the efficiency gains, the same performance can be achieved with smaller subsystems, offsetting some of the costs in these areas. However, despite the increase in overall efficiency, a net increase in the LCOE is evident, indicating that the increase in power cycle and receiver costs seen at higher temperatures outweighs the efficiency gain and any reduction in component sizes. As such, the 650°C case consistently presents the lowest LCOE. This corroborates the results of the previous analysis by Meybodi et al. (2017) who found that of the 560°C, 610°C, 700°C and 1000°C cases, 610°C was optimal. This also supports the sCO2 power cycle temperature target of 610°C set by the Australian Solar Thermal Research Initiative (ASTRI), who aim to use this as an avenue to provide the next generation of cost reductions in CST technology (Meybodi et al., 2017).

The changes in the other CST variables seen with temperature increases are interesting. In all the cases, as temperature increases, the contribution of CST increases, and the contribution of PV decreases. In addition, the number of thermal storage hours, and the fill time both increase. This may seem like a counter intuitive result, since the increase in the cost of energy from CST at higher temperatures would be expected to make CST less attractive. While this is true, even in these cases, the optimisation reveals that CST is still the least expensive source of baseload power (as compared to diesel or PV-battery), and hence CST remains the favoured source of energy. The excess energy 58 production provides some key insights into these trends. In all scenarios, the excess energy production, which comes almost entirely from the CST system (with PV producing little or no excess due to its smaller size) is found to decrease with increasing temperature. This is very desirable, considering that the cost penalty of excess is higher for the higher temperature cases. As such, it appears that the changes in the optimum seen in the higher temperature cases represent an effort to minimise excess energy. This reduction is facilitated in three ways. Firstly, it is important to note that unlike solar and wind, which are synergistic, CST and PV are both solar, and hence reach their maximum production capabilities at the same time during the day. As a result, large sizes of the two systems (as seen in the 650°C cases) cause significant spilled energy (which comes mainly from the CST plant due to the order of dispatch whereby PV is dispatched first). Hence, the reduction in PV capacity allows for more CST usage during the daytime, thereby reducing excess. This reduction in PV with an increase in CST contribution means that the renewable fraction changes only minimally. Secondly, the increase in storage hours facilitates additional storage, resulting is less energy being spilled. Thirdly, there is an increase in the fill time, which causes a decrease in the solar field capacity relative to the storage capacity. As a result, the storage is not filled as quickly, leading to a further reduction in spillage. These trends reveal important steps that are necessary for hybrid plant viability at higher temperatures.

Based on these results, it is clear that the storage fill time, which controls the ratio of field capacity to storage capacity, plays a key role in reducing excess production and costs, and hence is an important consideration. This is a key result, as in a previous analysis by Swanson (2017) the fill time was treated as a constant to simplify the optimisation. This analysis has revealed that there are strong merits to including the fill time as an optimisation parameter, as it is directly linked to the excess production.

As such, while exact functional requirements for the CST plant will vary from site to site, the optima obtained in this analysis have revealed key overall requirements. These are as follows:

• Turbine inlet temperature of 650°C, which has been shown to produce the lowest LCOE. • 12-15 hours of thermal storage, which is necessary to make the CST plant the primary source of baseload power in the hybrid and exploit its load shifting capabilities. • Careful sizing of the field capacity with relation to the storage capacity.

59

As a base reference, the requirements for the CST plant at the optimal site, Halls Creek, are:

• 1 MW power cycle capacity, 13 hours storage and 5.5 hours fill time (implying a solar multiple of 2.36) for the community case. • 10 MW power cycle capacity, 13.2 hours storage and 5.1 hours fill time (implying a solar multiple of 2.59) for the mining case. • A turbine inlet temperature of 650°C.

The sensitivity analysis carried out for the Halls Creek systems in Section 5.3 reveals a number of important trends. Clearly, in both cases the LCOE is most sensitive to the CST capex. This makes logical sense, given the capital-intensive nature of CST, and the fact the CST is the primary component of the systems. The discount rate, which is the interest rate charged on loans, has the second largest influence, highlighting the importance of this parameter. Careful consideration of the discount rate would be necessary when analysing investment potential. The remaining two parameters, PV capex and diesel cost, have a smaller but still notable influence on the LCOE. The PV capex and diesel cost are the third and fourth most influential parameters in the community case and are transposed in the mining case. This difference is due to the composition of the two systems, with the community case presenting more PV than the mining case. It is interesting to note that although diesel generators provide a much smaller contribution to the total energy than PV, the sensitivity of the LCOE to diesel cost is very much comparable to the PV capex, highlighting the high costs associated with using diesel fuel, even in small proportions. Given the high sensitivity of the LCOE to the CST capex, it is important that the costs assumed present minimal uncertainty. In this regard, there is confidence in the costs assumed, as they were taken from recent and reputable analyses. Despite this, there is some uncertainty associated with the costs assumed for the power cycle, due to the sCO2 technology being fairly new. It is expected that these costs will become more definite as the technology is developed further.

From the emissions analysis in Section 5.4, the proposed hybrid systems at Halls Creek present reductions in total life cycle emissions of 89% and 88% in the mining and community cases respectively over the diesel base case. Based on the contributions of each technology to the total emissions, it is evident that despite only providing a small portion of the energy production, diesel generators contribute to 65% of the total emissions. Solar PV contributes the least to the emissions (13% for the mining case and 17% for the community), as expected, due to its low embodied emission value, and its relatively low contribution to the total production. It is interesting to note that although CST is by far the biggest contributor to the total energy production, its contribution to the total emissions is only slightly higher than PV (22% for the mining case and 18% for the

60 community), due to the fact that CST presents the lowest embodied emissions per unit output of all the technologies considered. With the reduction of emissions becoming an increasingly important consideration, these hybrid systems clearly present strong environmental benefits.

The concept designs of the CST plant for the mining and community cases generated in SAM present optimised component sizes and specifications, and an optimised heliostat field layout. In this analysis, the assumption of a cavity receiver was necessary to ensure consistency with the methods used to model the receiver in the Python program. This is important to note, because a cavity receiver places a limitation on the heliostat field span, as opposed to an external cylindrical receiver which allows for a 360° span. This is evidenced in the field layout obtained, with the heliostats oriented in front of the receiver. It is interesting to note that other receiver design concepts are currently being researched specifically with a view of facilitating more efficient collection at higher temperatures (Coventry et al., 2017). If results from these studies are found to be promising, it may be worthwhile revisiting this analysis with a more state of the art receiver design. Nevertheless, at this stage, the concept designs generated in SAM serve as specifications for future CST plants at the Halls Creek site.

61

8 Conclusions The purpose of this thesis has been to investigate the optimum combination of CST, PV, battery storage and diesel generators for off-grid power generation. The analysis considered two major consumer groups, viz. mining sites and communities, at three locations – Newman, Port Augusta and Halls Creek. To this end, a simulation program was developed in Python, which made use of a load profile and weather data to identify the optimum combination in each case based on the minimum LCOE. Furthermore, an extended analysis was carried out into the sCO2 CST system, to explore the effect of turbine inlet temperature on the thermodynamics and costs of the plant.

Based on the results obtained, the optimum systems consistently present a high renewable fraction, and rely on CST as the primary source of baseload power, with ~12-15 hours thermal storage. PV and diesel generators both serve as supplementary sources of power. Notably, none of the optima included battery storage, highlighting the high costs of this technology as compared to CST vis-a- vis providing baseload power. By comparing three turbine inlet temperatures (650°C, 800°C and 1000°C), it was found that although overall efficiency gains were achieved by the higher temperatures, the increases in costs outweighed these gains. As a result, the 650°C case consistently presented the lowest LCOE. Furthermore, it was found that the storage capacity to field capacity ratio was an important consideration with regards to minimising spilled energy from the CST plant. These results were congruent with previous studies and expectations based on theory, and hence there is a high level of confidence in their veracity.

The Halls Creek site was found to present both the highest renewable fraction and the lowest LCOE ($0.158/kWh and $0.136/kWh for the community and mining cases respectively), presenting a cost reduction of 34% and 43% over the diesel base case ($0.24/kWh). The composition of these systems was 60%-32%-8% and 69%-24%-7% (CST-PV-diesel) in the community and mining case respectively, with a life-cycle emissions analysis revealing annual reductions in CO2 emissions of 88% and 89%.

The results of this analysis demonstrate that remote off-grid locations in Australia present excellent potential for the deployment of a CST-PV-diesel hybrid renewable energy system. The proposed systems are capable of uninterrupted power supply and have been shown to provide significant economic and environmental benefits over the conventional systems based on diesel generators. These hybrid systems therefore present a viable solution for achieving lower cost, environmentally friendly electricity for remote users.

62

9 Recommendations for further work There are a number of factors that could be incorporated in future work to build on the results of this analysis. These are as follows:

• Making use of real electricity consumption data from the communities or mining sites of interest, to better guide investment decisions. • Incorporating the start-up, shut down and ramping penalties for the technologies in the Python simulation. • Using a more rigorous costing for the solar thermal system, especially with regards to the

sCO2 power cycle, perhaps with first hand costs obtained from a manufacturer.

Furthermore, additional avenues for investigation include:

• Incorporating additional renewable technologies, most notably wind turbines to exploit the synergism between solar and wind. • Exploring more intelligent dispatch strategies (such as using a controller based on solar resource predictions) as compared to the fixed order dispatch assumed in this analysis. • Investigating the incorporation of newer high temperature receiver designs based on developments in research.

63

10 Reflection on professional development The professional development for engineering students is guided by Engineers Australia (EA) in the “Stage 1 competency standards for professional engineers”. By engaging in various activities over the course of this project, I have been able to develop and improve my competencies in several of the areas listed by EA. The field of renewable energy has been a strong interest of mine for a number of years. As such, I was particularly excited about my first major research experience being in a topic I am passionate about and wanted to ensure that I made the most of the opportunities for learning and professional growth. My first step in this regard was to set clear goals for myself before commencement. By doing so, I was able to gain an understanding of the areas that I would like to explore and develop knowledge in this semester.

While I have been involved in projects at university before, this is the first time I have worked on a project of this scale. This, coupled with the fact that this was the only course I have been engaged in over the semester, meant that efficient management of time and resources was important. Since the pace for this project is somewhat self-driven, I decided that a thorough timeline of project tasks would need to be created, along with clear prioritisation, and a contingency plan for potential delays. This has helped me maintain high levels of motivation, because it has provided structure and a means for assessing progress. Despite this, at times I have encountered some unexpected delays in my research. In the past I have sometimes struggled with handling the stress associated with sudden setbacks, and hence I believe that the setbacks encountered in this project are positives, as they have allowed me to foster an increased level of adaptability, and experience with adjusting the initial timeline to account for changes. This is an important learning, as it is common in industry to encounter delays and contingencies beyond control. In addition to time management, I have also aimed to follow a more rigorous approach to project management, for instance by maintaining up to date documentation in the form of a logbook and following the mitigation strategies and procedures outlined previously to minimise risks to project progress. I believe that making these practices habitual will be beneficial going forward. These learnings align with EA competencies 2.4, 3.3 and 3.5.

To engage in the research activities necessary for this project, I needed to develop an in-depth understanding of the specialist bodies of knowledge within the renewable energy field. Gaining this understanding was one of the biggest challenges initially, since I have had only limited exposure to this space in the past. However, gaining this understanding through my research and use of renewable software has been one of the most rewarding parts of this project. With renewable energy becoming increasingly prevalent, I strongly believe that I can use the knowledge and skills from this project to contribute to this space in the future. Working under the supervision and 64 guidance of Professor Hal Gurgenci has been especially valuable in this regard. The research activities themselves have also been valuable in helping me develop as a professional. For instance, while conducting the literature review I realised the importance of critical thinking and the value of being able to discern between research findings and practical applicability. In addition, by formulating my analysis methods, my ability to make use of engineering approximations has improved. The use of approximations in this project has been an eye opener for me because it has made me realise the value of this approach when exact analyses are not practical. Given that the ability to approximate complex quantities is very desirable in the engineering field, this is a skill that will be very useful in the workforce. These learnings align with EA competencies 1.1, 1.2, 1.4, 1.6, 2.2 and 2.3.

The report intensive nature of this course has also been beneficial, as it has helped me develop my written communication skills. The scale of this project has necessitated the use of professional tools such as EndNote, which I had previously used only minimally. The experience and familiarity I have gained in this area will no doubt be useful for any organised research in the future. In terms of oral communication skills, although it has been minimal, I have reached out to a number of industry contacts for information, and contacting them has provided me with experience with approaching professionals. Being able to communicate my progress and concerns over a short weekly meeting with my supervisor has also been a learning experience, as I have learnt to articulate my priorities effectively, so as to receive targeted guidance. The oral presentation, which provided an opportunity to present to a professional audience was also a very valuable experience, as it taught me to efficiently communicate research findings in a concise manner. Given that the members of the audience had differing levels of knowledge regarding this topic, I also realised the importance of making my presentation suitable for experts and novices alike by taking steps such as minimising domain specific vernacular. These experiences have contributed towards EA competencies 3.2 and 3.4.

As such, I believe that the knowledge and skills I have gained over the course of this project have helped me address my weaknesses and have strongly contributed to my professional development. I have no doubt that these enhanced competencies will serve me well in my future endeavours.

65

References ADARAMOLA, M. S., QUANSAH, D. A., AGELIN-CHAAB, M. & PAUL, S. S. 2017. Multipurpose renewable energy resources based hybrid energy system for remote community in northern Ghana. Sustainable Energy Technologies and Assessments, 22, 161-170. AECOM 2014. Australia's off-grid clean energy market, prepared by AECOM Australia Pty Ltd for the Australian Renewable Energy Agency (ARENA). AHN, Y., BAE, S. J., KIM, M., CHO, S. K., BAIK, S., LEE, J. I. & CHA, J. E. 2015. Review of supercritical CO2 power cycle technology and current status of research and development. Nuclear Engineering and Technology, 47, 647-661. ALBRIGHT, G., EDIE, J. & AL-HALLAJ, S. 2012. A comparison of lead acid to lithium-ion in stationary storage applications. AllCell Technologies LLC. AMUTHA, W. M. & RAJINI, V. 2016. Cost benefit and technical analysis of rural electrification alternatives in southern India using HOMER. Renewable and Sustainable Energy Reviews, 62, 236-246. ANACONDA 2018. Anaconda (Python distribution). ANSONG, M., MENSAH, L. D. & ADARAMOLA, M. S. 2017. Techno-economic analysis of a hybrid system to power a mine in an off-grid area in Ghana. Sustainable Energy Technologies and Assessments, 23, 48-56. ARUN, P., BANERJEE, R. & BANDYOPADHYAY, S. 2008. Optimum sizing of battery- integrated diesel generator for remote electrification through design-space approach. Energy, 33, 1155-1168. AUSTELA. 2014. The NREL System Advisor Model for Australian CSP Stakeholders [Online]. Available: www.austela.net.au/projects [Accessed 10/04/2018 2018]. AUSTRALIAN BUREAU OF STATISTICS 2017. Australian demographic statistics, June 2017. : Australian Bureau of Statistics. AUSTRALIAN ENERGY COUNCIL 2016. Solar report December 2016. Australia: Australian Energy Council. AUSTRALIAN ENERGY REGULATOR 2018. Seasonal peak demand (region). Australia. AUSTRALIAN GOVERNMENT. 2018. Energy made easy [Online]. Available: www.energymadeeasy.gov.au [Accessed 11/03/2018]. BAHADORI, A., NWAOHA, C., ZENDEHBOUDI, S. & ZAHEDI, G. 2013. An overview of renewable energy potential and utilisation in Australia. Renewable and Sustainable Energy Reviews, 21, 582-589. BAIG, M. H., SUROVTSEVA, D. & HALAWA, E. 2015. The potential of for remote mine sites in the Northern Territory, Australia. Journal of Solar Energy, 10. BECKER, M., FUNKEN, K.-H. & SCHNEIDER, G. 1992. utilization: German studies on technology and application, Berlin, Springer-Verlag Berlin Heidelberg. BELLOS, E. & TZIVANIDIS, C. 2018. A realistic approach of the maximum work extraction from solar thermal collectors. Applied System Innovation, 1. BIANCHINI, A., MAGNELLI, N., FERRARA, G., CARNEVALE, E. A. & FERRARI, L. 2015. Optimization of a PV-wind-Diesel Hybrid System for a Remote Stand-alone Application. Energy Procedia, 81, 133-145.

66

BOUSSETTA, M., ELBACHTIRI, R., KHANFARA, M. & ELHAMMOUMI, K. Performance analysis and power evaluation of hybrid off-grid system. 2016 International Renewable and Sustainable Energy Conference (IRSEC), 14-17 Nov. 2016 2016. 1130-1134. BRANCO, H., CASTRO, R. & SETAS LOPES, A. 2018. Battery energy storage systems as a way to integrate renewable energy in small isolated power systems. Energy for Sustainable Development, 43, 90-99. BRUN, K., FRIEDMAN, P. & DENNIS, R. 2017. Fundamentals and applications of supercritical carbon dioxide (sCO2) based power cycles, Duxford, Woodhead Publishing. BYRNE, L., BROWN, C., WAGNER, L. & FOSTER, J. 2015. Reviewing the Viability of Renewable Energy in Community Electrification: The Case of Remote Western Australian Communities. Munich Personal RePEc Archive. CADEX ELECTRONICS. 2017. Charging lithium ion batteries [Online]. Available: www.batteryuniversity.com/learn/article/charging_lithium_ion_batteries [Accessed 04/04/2018]. CEKIRGE, H. & ELHASSAN, A. 2015. A comparison of solar power systems (CSP): Solar tower (ST) systems versus parabolic trough (PT) systems. American Journal of Energy Engineering, 3, 29-36. CHAUDHARY, A., HUGGETT, A., YAP, W. K. & KARRI, V. 2014. Remote area hybrid solar- diesel power systems in tropical Australia. Energy Procedia, 57, 1485-1491. CHOROS, K. 2016. Maximising dispatchability of combined solar power plants. Bachelor of Engineering, University of Queensland. CHOW, T.-T. & JI, J. 2012. Environmental life-cycle analysis of hybrid solar photovoltaic/thermal systems for use in Hong Kong. International Journal of Photoenergy, 2012, 9. CLEAN ENERGY COUNCIL 2016. Clean energy Australia. Australia. CLEAN ENERGY REGULATOR. 2017. Small-scale renewable energy scheme [Online]. Australia. Available: www.cleanenergyregulator.gov.au/RET/About-the-Renewable- Energy-Target/How-the-scheme-works/Small-scale-Renewable-Energy-Scheme [Accessed 26/02/2018]. CLEMENTONI, E. M., COX, T. L. & KING, M. A. 2015. Off-nominal component performance in a supercritical carbon dioxide brayton cycle. Journal of Engineering for Gas Turbines and Power, 138, 011703-011703-8. COCCO, D., MIGLIARI, L. & PETROLLESE, M. 2016. A hybrid CSP–CPV system for improving the dispatchability of solar power plants. Energy Conversion and Management, 114, 312-323. CORGNALE, C., HARDY, B., MOTYKA, T. & ZIDAN, R. 2016. Metal hydride based thermal energy storage system requirements for high performance concentrating solar power plants. International Journal of Hydrogen Energy, 41, 20217-20230. COVENTRY, J., ARJOMANDI, M., ASSELINEAU, C.-A., CHINNICI, A., CORSI, C., DAVIS, D., KIM, J.-S., KUMAR, A., LIPIŃSKI, W., LOGIE, W., NATHAN, G., PYE, J. & SAW, W. 2017. Development of ASTRI high-temperature solar receivers. AIP Conference Proceedings, 1850, 030011. CSIRO 2017. Solar-driven supercritical CO2 Brayton cycle: Project results and lessons learnt. Australian Renewable Energy Agency. DEPARTMENT OF THE ENVIRONMENT AND ENERGY 2015. Australia's 2030 climate change target. Australian Government. 67

DIOUF, B. & PODE, R. 2015. Potential of lithium-ion batteries in renewable energy. Renewable Energy, 76, 375-380. DOWLING, A. W., ZHENG, T. & ZAVALA, V. M. 2017. Economic assessment of concentrated solar power technologies: A review. Renewable and Sustainable Energy Reviews, 72, 1019-1032. EDDHIBI, F., AMARA, M. B., BALGHOUTHI, M. & GUIZANI, A. 2015. Optical study of solar tower power plants. Journal of Physics: Conference Series, 596, 012018. ENPHASE. 2014. Guide to PVWatts derate factors for enphase systems when using pv system design tools [Online]. Available: enphase.com/sites/default/files/Enphase_PVWatts_Derate_Guide_ModSolar_06- 2014.pdf [Accessed 09/03/2018]. FADAEENEJAD, M., RADZI, M. A. M., ABKADIR, M. Z. A. & HIZAM, H. 2014. Assessment of hybrid renewable power sources for rural electrification in Malaysia. Renewable and Sustainable Energy Reviews, 30, 299-305. FAZELPOUR, F., SOLTANI, N. & ROSEN, M. A. 2016. Economic analysis of standalone hybrid energy systems for application in Tehran, Iran. International Journal of Hydrogen Energy, 41, 7732-7743. FU, R., FELDMAN, D., MARGOLIS, R., WOODHOUSE, M. & ARDANI, K. 2017. U.S. Solar cost benchmark: Q1 2017. National Renewable Energy Laboratory. GAN, L. K., SHEK, J. K. H. & MUELLER, M. A. 2015. Hybrid wind–photovoltaic–diesel–battery system sizing tool development using empirical approach, life-cycle cost and performance analysis: A case study in Scotland. Energy Conversion and Management, 106, 479-494. GLATZMAIER, G. 2011. Summary report for concentrating solar power thermal storage workshop. National Renewable Energy Laboratory. GOEL, S. & SHARMA, R. 2017. Performance evaluation of stand alone, grid connected and hybrid renewable energy systems for rural application: A comparative review. Renewable and Sustainable Energy Reviews, 78, 1378-1389. GOOGLE MAPS. 2018. Map of Australia [Online]. Available: www.google.com.au/maps/place/Australia [Accessed]. GRANTA DESIGN LIMITED 2018. CES Edupack. GREEN, A., DIEP, C., DUNN, R. & DENT, J. 2015. High capacity factor CSP-PV hybrid systems. Energy Procedia, 69, 2049-2059. GURGENCI, H. 2014. Supercritical CO2 cycles offer experience curve opportunity to CST in remote area markets. Energy Procedia, 49, 1157-1164. HARMSEN, N. 2017. Elon Musk's giant lithium ion battery completed by Tesla in SA's Mid North [Online]. Australia: ABC News. Available: www.abc.net.au/news/2017-11-23/worlds- most-powerful-lithium-ion-battery-finished-in-sa/9183868 [Accessed]. HESSE, H. C., SCHIMPE, M., KUCEVIC, D. & JOSSEN, A. 2017. Lithium-ion battery storage for the grid— A review of stationary battery storage system design tailored for applications in modern power grids. Energies, 10. HINKLEY, J. T., HAYWARD, J. A., CURTIN, B., WONHAS, A., BOYD, R., GRIMA, C., TADROS, A., HALL, R. & NAICKER, K. 2013. An analysis of the costs and opportunities for concentrating solar power in Australia. Renewable Energy, 57, 653-661. HOLL, R. J. 1978. Definition of two small central receiver systems. New Mexico: Sandia National Laboratories. 68

HOMER ENERGY. 2018. How HOMER calculates the PV array power output [Online]. Available: www.homerenergy.com/products/pro/docs/3.11/how_homer_calculates_the_pv_array_po wer_output.html [Accessed 09/03/2018 2018]. INTERNATIONAL ENERGY AGENCY 2017. Snapshot of global photovoltaic markets. Organization for Economic Cooperation and Development IPCC 2014. Climate change 2014: Mitigation of climate change. Geneva: Intergovernmental Panel on Climate Change. IRENA 2015. Battery storage for renewables: Market status and technology outlook. International Renewable Energy Agency. IRENA 2018. Renewable power generation costs in 2017. International Renewable Energy Agency. JAISWAL, A. 2017. Lithium-ion battery based renewable energy solution for off-grid electricity: A techno-economic analysis. Renewable and Sustainable Energy Reviews, 72, 922-934. JAMAL, T., SHOEB, M. A., SHAFIULLAH, G. M., CARTER, C. E. & URMEE, T. A design consideration for solar PV-diesel remote electricity network: Australia perspective. 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), Nov. 28 2016-Dec. 1 2016 2016. 821-826. JORGENSON, J., MEHOS, M. & DENHOLM, P. 2016. Comparing the net cost of CSP-TES to PV deployed with battery storage. AIP Conference Proceedings, 1734, 080003. KARUTZ, M. & HAQUE, M. H. Hybrid power generating system for off-grid communities in South Australia. 2nd IET Renewable Power Generation Conference (RPG 2013), 9-11 Sept. 2013 2013. 1-4. KISTLER, B. 1986. A user's manual for DELSOL3: A computer code for calculating the optical performance and optimal system design for solar thermal central receiver plants. New Mexico: Sandia National Laboratories. KOLHE, M. L., RANAWEERA, K. M. I. U. & GUNAWARDANA, A. G. B. S. 2015. Techno- economic sizing of off-grid hybrid renewable energy system for rural electrification in Sri Lanka. Sustainable Energy Technologies and Assessments, 11, 53-64. LAMBANI, N. E., BUQUE, C. & CHOWDHURY, S. Design of a cost effective hybrid renewable energy system for coastal and inland rural community in Africa. IEEE PES PowerAfrica, 27-30 June 2017 2017 Ghana. 451-456. LAZARD 2016. Lazard's levelised cost of energy analysis - version 10. LAZARD 2017. Lazard's levelised cost of storage analysis - version 3.0. LI, C. & YU, W. 2016. Techno-economic comparative analysis of off-grid hybrid photovoltaic/diesel/battery and photovoltaic/battery power systems for a household in Urumqi, China. Journal of Cleaner Production, 124, 258-265. LI, X., KONG, W., WANG, Z., CHANG, C. & BAI, F. 2010. Thermal model and thermodynamic performance of molten salt cavity receiver. Renewable Energy, 35, 981-988. LIU, M., STEVEN TAY, N. H., BELL, S., BELUSKO, M., JACOB, R., WILL, G., SAMAN, W. & BRUNO, F. 2016. Review on concentrating solar power plants and new developments in high temperature thermal energy storage technologies. Renewable and Sustainable Energy Reviews, 53, 1411-1432.

69

MA, Z. & TURCHI, C. S. Advanced supercritical carbon dioxide power cycle configurations for use in concentrating solar power systems. Supercritical CO2 Power Cycle Symposium, 2011 Boulder, Colorado. MAMAGHANI, A. H., ESCANDON, S. A. A., NAJAFI, B., SHIRAZI, A. & RINALDI, F. 2016. Techno-economic feasibility of photovoltaic, wind, diesel and hybrid electrification systems for off-grid rural electrification in Colombia. Renewable Energy, 97, 293-305. MANDELLI, S., BARBIERI, J., MEREU, R. & COLOMBO, E. 2016. Off-grid systems for rural electrification in developing countries: Definitions, classification and a comprehensive literature review. Renewable and Sustainable Energy Reviews, 58, 1621-1646. MATTESON, S. & WILLIAMS, E. 2015. Residual learning rates in lead-acid batteries: Effects on emerging technologies. Energy Policy, 85, 71-79. MCEVOY, A., MARKVART, T. & CASTANER, L. 2012. Practical handbook of : fundamentals and applications, Elsevier Academic Press. MCKINSEY & COMPANY. 2018. Battery technology charges ahead [Online]. Available: www.mckinsey.com/business-functions/sustainability-and-resource-productivity/our- insights/battery-technology-charges-ahead [Accessed 09/03/2018]. MEHLERI, E. D., ZERVAS, P. L., SARIMVEIS, H., PALYVOS, J. A. & MARKATOS, N. C. 2010. Determination of the optimal tilt angle and orientation for solar photovoltaic arrays. Renewable Energy, 35, 2468-2475. MEHOS, M., TURCHI, C., JORGENSON, J., DENHOLM, P., HO, C. & ARMIJO, K. 2016. On the path to SunShot: Advancing concentrating solar power technology, performance, and dispatchability. Golden: National Renewable Energy Laboratory. MEHRPOOYA, M., MOHAMMADI, M. & AHMADI, E. 2018. Techno-economic- environmental study of hybrid power supply system: A case study in Iran. Sustainable Energy Technologies and Assessments, 25, 1-10. MEYBODI, M. A., BEATH, A., GWYNN-JONES, S., VEERARAGAVAN, A., GURGENCI, H. & HOOMAN, K. Techno-economic analysis of supercritical carbon dioxide power blocks. AIP Conference Proceedings, 2017. MOHAMED, M. A., ELTAMALY, A. M. & ALOLAH, A. I. 2015. Sizing and techno-economic analysis of stand-alone hybrid photovoltaic/wind/diesel/battery power generation systems. Journal of Renewable and Sustainable Energy, 7, 063128. NETL. Overview of supercritical carbon dioxide based power cycles for stationary power generation. International seminar on organic rankine cycle power systems, 2017 Italy. National Energy Technology Laboratory. NREL. 2018a. SolarPACES - Solar power and chemical energy systems [Online]. Available: www.nrel.gov/csp/solarpaces [Accessed 23/04/2018 2018]. NREL. 2018b. System Advisor Model (SAM) [Online]. Available: sam.nrel.gov [Accessed 10/05/2018]. OGUNJUYIGBE, A. S. O., AYODELE, T. R. & AKINOLA, O. A. 2016. Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building. Applied Energy, 171, 153-171. ORHAN, T., SHAFIULLAH, G. M., STOJCEVSKI, A. & OO, A. A feasibility study on microgrid for various Islands in Australia. 2014 Australasian Universities Power Engineering Conference (AUPEC), Sept. 28 2014-Oct. 1 2014 2014. 1-8. 70

PALONE, F., PORTOGHESE, P., BUONO, L., NECCI, A., ROSATI, C. & ROSATI, D. Replacing diesel generators with hybrid renewable power plants: Giannutri Smart Island project. 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 6-9 June 2017 2017. 1-6. PAN, C. A. & DINTER, F. 2017. Combination of PV and central receiver CSP plants for base load power generation in South Africa. Solar Energy, 146, 379-388. PARKINSON, G. 2017. Redflow can’t compete with lithium batteries on price in home market. Renew Economy. PARRA, D. & PATEL, M. K. 2016. Effect of tariffs on the performance and economic benefits of PV-coupled battery systems. Applied Energy, 164, 175-187. PARRADO, C., GIRARD, A., SIMON, F. & FUENTEALBA, E. 2016. 2050 LCOE (Levelized Cost of Energy) projection for a hybrid PV (photovoltaic)-CSP (concentrated solar power) plant in the Atacama Desert, Chile. Energy, 94, 422-430. PAUL AYENG'O, S., SCHIRMER, T., KAIRIES, K.-P., AXELSEN, H. & UWE SAUER, D. 2018. Comparison of off-grid power supply systems using lead-acid and lithium-ion batteries. Solar Energy, 162, 140-152. PETROLLESE, M. & COCCO, D. 2016. Optimal design of a hybrid CSP-PV plant for achieving the full dispatchability of solar energy power plants. Solar Energy, 137, 477-489. PLATZER, W. J. 2016. Combined solar thermal and photovoltaic power plants – An approach to 24h solar electricity? AIP Conference Proceedings, 1734, 070026. PRAMANIK, S. & RAVIKRISHNA, R. V. 2017. A review of concentrated solar power hybrid technologies. Applied Thermal Engineering, 127, 602-637. RAHMAN, M. M., KHAN, M. M.-U.-H., ULLAH, M. A., ZHANG, X. & KUMAR, A. 2016. A hybrid renewable energy system for a North American off-grid community. Energy, 97, 151-160. RASHID, M., ABUJUBBEH, M. & FAHRIOGLU, M. Improving capacity factor of transmission lines by hybridizing CSP with wind. 4th International Conference on Electrical and Electronic Engineering (ICEEE), 8-10 April 2017 2017 Turkey. 1-5. REDFLOW 2015. Understanding the RedFlow battery Brisbane: Redflow. REHMAN, S. & AL-HADHRAMI, L. M. 2010. Study of a solar PV–diesel–battery hybrid power system for a remotely located population near Rafha, Saudi Arabia. Energy, 35, 4986-4995. REHMAN, S. U., REHMAN, S., QAZI, M. U., SHOAIB, M. & LASHIN, A. 2016. Feasibility study of hybrid energy system for off-grid rural electrification in southern Pakistan. Energy Exploration & Exploitation, 34, 468-482. ROHANI, G. & NOUR, M. 2014. Techno-economical analysis of stand-alone hybrid renewable power system for Ras Musherib in United Arab Emirates. Energy, 64, 828-841. ROMARE, M. & DAHLLOF, L. 2017. The life cycle energy consumption and from lithium-ion batteries. Sweden: IVL Swedish Environmental Research Institute. ROY, A. & KULKARNI, G. N. 2016. Analysis on the feasibility of a PV-diesel generator hybrid system without energy storage. Clean Technologies and Environmental Policy, 18, 2541- 2553.

71

SAVING WITH SOLAR. 2017. Tesla Powerpack Australia cost, installations [Online]. Available: www.savingwithsolar.com.au/tesla-powerpack-2-australia/ [Accessed 04/04/2018]. SEN, R. & BHATTACHARYYA, S. C. 2014. Off-grid electricity generation with renewable energy technologies in India: An application of HOMER. Renewable Energy, 62, 388-398. SHAFIULLAH, G. M. 2016. Hybrid renewable energy integration (HREI) system for subtropical climate in Central Queensland, Australia. Renewable Energy, 96, 1034-1053. SHAHAN, Z. 2015. Tesla Powerwall & Powerpacks per-kWh lifetime prices vs Aquion Energy, Eos Energy, & Imergy [Online]. Available: www.cleantechnica.com/2015/05/09/tesla- powerwall-powerblocks-per-kwh-lifetime-prices-vs-aquion-energy-eos-energy-imergy/ [Accessed 04/04/2018]. SHAHZAD, M. K., ZAHID, A., UR RASHID, T., REHAN, M. A., ALI, M. & AHMAD, M. 2017. Techno-economic feasibility analysis of a solar-biomass off grid system for the electrification of remote rural areas in Pakistan using HOMER software. Renewable Energy, 106, 264-273. SHEZAN, S. K. A., DAS, N. & MAHMUDUL, H. 2017. Techno-economic analysis of a smart- grid hybrid renewable energy system for Brisbane of Australia. Energy Procedia, 110, 340- 345. SKARSTEIN, O. & UHLEN, K. 1989. Design considerations with respect to long-term diesel saving in wind/diesel plants. Wind Engineering, 13, 72-87. SOLAR RESERVE. 2017. SolarReserve signs agreement with South Australian government [Online]. Available: www.aurorasolarthermal.com.au/2017/08/solarreserve-signs- agreement-sa-government/ [Accessed 27/02/2018]. SOULAYMAN, S. 1991. On the optimum tilt of solar absorber plates Renewable Energy, 1, 551- 554. STARKE, A. R., CARDEMIL, J. M., ESCOBAR, R. & COLLE, S. 2018. Multi-objective optimization of hybrid CSP+PV system using genetic algorithm. Energy, 147, 490-503. STARKE, A. R., CARDEMIL, J. M., ESCOBAR, R. A. & COLLE, S. 2016. Assessing the performance of hybrid CSP+PV plants in northern Chile. Solar Energy, 138, 88-97. SUNPOWER 2017. SunPower E20-435-COM commercial datasheet. SWANSON, D. 2017. Analysis and comparison of energy generation technologies in remote Australia. Bachelor and Master of Engineering, The University of Queensland. TESLA. 2015. Tesla motors earnings report: Q1 2015 conference call transcript [Online]. Available: www.thestreet.com/story/13142191/4/tesla-motors-tsla-earnings-report-q1- 2015-conference-call-transcript.html [Accessed 04/04/2018]. TESLA. 2018. Powerpack: Utility and business energy storage [Online]. Available: www.tesla.com/en_AU/powerpack [Accessed 04/04/2018]. TURCHI, C. & HEATH, G. 2013. Molten salt power tower cost model for the System Advisor Model (SAM). Colorado: National Renewable Energy Laboratory. TURCHI, C. S. Supercritical CO2 for application in concentrating solar power systems. Supercritical CO2 Power Cycle Symposium, 2009 Troy, NY. VENUS, S. A. 2016. Why do steam power plants have to be very large? . Bachelor of Engineering, The University of Queensland. VITTITOE, C. N. & BIGGS, F. Terrestrial propagation loss. International Solar Energy Society Meeting, 1978 Denver, Colorado. 72

WAGNER, M. J. 2008. Simulation and predictive performance modeling of utility-scale central receiver system power plants. Master of Science, University of Wisconsin. WAGNER, M. J. 2017. Modelling molten salt power tower systems in SAM. National Renewable Energy Laboratory. XU, C., WANG, Z., LI, X. & SUN, F. 2011. Energy and exergy analysis of solar power tower plants. Applied Thermal Engineering, 31, 3904-3913. YANG, Y., LI, G. & TANG, R. 2012. On the estimation of daily beam radiation on tilted surfaces. Energy Procedia, 16, 1570-1578. YOON, H. J., AHN, Y., LEE, J. I. & ADDAD, Y. 2012. Potential advantages of coupling supercritical CO2 Brayton cycle to water cooled small and medium size reactor. Nuclear Engineering and Design, 245, 223-232. YUSAF, T., GOH, S. & BORSERIO, J. A. 2011. Potential of renewable energy alternatives in Australia. Renewable and Sustainable Energy Reviews, 15, 2214-2221. ZAFIRAKIS, D. P. 2010. Overview of energy storage technologies for renewable energy systems. Stand-Alone and Hybrid Wind Energy Systems. Woodhead Publishing. ZHANG, H. L., BAEYENS, J., DEGRÈVE, J. & CACÈRES, G. 2013. Concentrated solar power plants: Review and design methodology. Renewable and Sustainable Energy Reviews, 22, 466-481. ZUBI, G., DUFO-LÓPEZ, R., PARDO, N. & PASAOGLU, G. 2016. Concept development and techno-economic assessment for a solar home system using lithium-ion battery for developing regions to provide electricity for lighting and electronic devices. Energy Conversion and Management, 122, 439-448.

73

Appendix A

Figure 26 Project Gantt chart 74

Appendix B The details of the receiver efficiency calculation are as follows.

The concentration ratio (C) required to achieve each operating temperature is given by Xu et al. (2011):

푇 − 푇 퐴 퐶 = 푟푒푐 푠푎푙푡 × 푚𝑖푟푟표푟 푓𝑖푒푙푑 푑 푑 푑 ̇ (7) 0 + 0 ln 0 푄푟푒푐푒𝑖푣푒푟 푑𝑖ℎ푚푠 2휇푡푢푏푒 푑𝑖

Where,

• 푇푟푒푐 is the receiver temperature, and 푇푠푎푙푡 is the average temperature of the molten salt, assuming a high temperature equal to the receiver, and a low of 290°C (Li et al., 2010).

• 푑0 and 푑𝑖 are the outer and inner diameters of the receiver tube, assuming an outer diameter of 40 mm and a thickness of 1.25 mm.

• 푄̇푟푒푐푒𝑖푣푒푟 and 퐴푚𝑖푟푟표푟 푓𝑖푒푙푑 are the incident irradiation on the receiver and the mirror field area respectively and are calculated based on the design power output.

• ℎ푚푠 and 휇푡푢푏푒 are the heat transfer coefficient of the molten salt and the conductivity of the receiver tube respectively (Li et al., 2010).

The receiver efficiency is then approximated as follows (Bellos and Tzivanidis, 2018). Note that the equation has been manipulated to scale with the area terms through the concentration ratio.

4 4 휀휎푇푎푚푏 푇푟푒푐 휂푟푒푐푒𝑖푣푒푟 = 훾훼 {1 − [( ) − 1] } (8) 퐶퐺 푇푎푚푏

Where,

• 훾 and 훼 are the intercept factor and the absorptivity of the receiver, assumed to be 0.9 and 0.95 respectively (Kistler, 1986).

• 휀 is the emissivity of the receiver. Bellos and Tzivanidis (2018) note that since this method neglects convective losses, a larger than typical value of 휀 should be used to compensate, as such, the value of 0.8 recommended by Xu et al. (2011) is used.

• 퐺 is the DNI multiplied by all the efficiency factors prior to the receiver thermal losses.

• 푇푟푒푐 and 푇푎푚푏 are the receiver, and ambient (25°C) temperatures respectively.

75

Appendix C The weather data from AUSTELA (2014) used for the three locations is presented in the form of monthly averages in the following figures.

Figure 27 Newman weather data

Figure 28 Port Augusta weather data

76

Figure 29 Halls Creek weather data

77

Appendix D The raw results returned by the Python program for the economic analysis are as follows. Note that the annual cost reported for each technology is the sum of the annualised capital costs, the O&M costs and in the case of the diesel generators also the fuel cost. The optimal sites/cases are marked in bold.

Table 27 Community economic analysis raw results

Site Temperature NPV CST Annual PV Annual DG Annual Newman 650 $38,322,188 $997,571 $89,869 $445,446 Newman 800 $41,255,394 $1,129,142 $77,030 $444,042 Newman 1000 $48,990,275 $1,421,337 $25,676 $512,596 Port Augusta 650 $87,334,973 $2,192,882 $308,123 $992,393 Port Augusta 800 $93,911,787 $2,419,223 $282,446 $1,054,801 Port Augusta 1000 $110,640,272 $2,999,953 $256,769 $1,168,887 Halls Creek 650 $22,078,542 $562,257 $128,384 $192,499 Halls Creek 800 $24,363,989 $658,846 $115,546 $200,167 Halls Creek 1000 $29,727,014 $866,594 $102,707 $219,777

Table 28 Mining economic analysis raw results

Site Temperature NPV CST Annual PV Annual DG Annual Newman 650 $321,451,057 $8,772,511 $603,408 $3,482,122 Newman 800 $335,443,084 $9,258,343 $320,962 $3,838,418 Newman 1000 $383,913,428 $11,357,090 $128,384 $3,871,061 Port Augusta 650 $373,466,156 $9,347,851 $1,283,848 $4,306,946 Port Augusta 800 $391,048,999 $9,934,363 $1,155,463 $4,552,132 Port Augusta 1000 $437,963,817 $12,071,011 $1,027,078 $4,420,462 Halls Creek 650 $296,524,122 $8,302,593 $1,540,617 $2,017,753 Halls Creek 800 $315,025,409 $9,171,102 $1,412,233 $2,017,681 Halls Creek 1000 $363,840,148 $11,357,090 $1,155,463 $2,041,051

78

Appendix E The net field capacity, which is equal to the thermal energy absorbed at the receiver (in SAM this is called the receiver thermal power), is calculated for a given design load, storage hours, storage fill time and power cycle efficiency as:

퐷푒푠𝑖푔푛 푏푎푠푒푙표푎푑 × 푆푡표푟푎푔푒 ℎ표푢푟푠 푅푒푐푒𝑖푣푒푟 푡ℎ푒푟푚푎푙 푝표푤푒푟 = (9) 퐹𝑖푙푙 푡𝑖푚푒 × 푃표푤푒푟 푐푦푐푙푒 푒푓푓𝑖푐𝑖푒푛푐푦

Alternatively, this term is defined in SAM as:

퐷푒푠𝑖푔푛 푏푎푠푒푙표푎푑 × 푆표푙푎푟 푚푢푙푡𝑖푝푙푒 푅푒푐푒𝑖푣푒푟 푡ℎ푒푟푚푎푙 푝표푤푒푟 = (10) 푃표푤푒푟 푐푦푐푙푒 푒푓푓𝑖푐𝑖푒푛푐푦

Therefore, equating (9) and (10), it is clear that the relationship between the storage hours, fill time and solar multiple is as follows.

푆푡표푟푎푔푒 ℎ표푢푟푠 푆표푙푎푟 푚푢푙푡𝑖푝푙푒 = (11) 퐹𝑖푙푙 푡𝑖푚푒

Note that the gross field capacity is calculated by dividing the net field capacity by the receiver and optical field efficiencies.

79

Appendix F The Python script used in this analysis is given below. The reader is directed to Section 4.6 for descriptions of each section of the code. The code is listed in the following order:

1. Importing necessary packages 2. Classes for implementing the PV (with battery storage) and CST plants 3. Simulation of the hybrid plant over a year 4. Functions to perform the economic analysis 5. Optimisation function 6. An example of the use of the code

""" Hybrid Plant Simulation/Optimisation Code Subject: ENGG7290 Author Name: Vishak Balaji Institution: The University of Queensland

The objective of this code is to optimise a power generation system for a given load profile.

The power generation system will incorporate a mixture of technologies: 1. Solar Thermal with thermal storage 2. Solar PV with battery storage 3. Diesel generation

To use this code, hourly data of the following will be required: 1. DNI 2. GHI 3. Electric Load """ ################################################################################ ############################### Import ######################################### import xlrd from math import * import numpy as np from timeit import default_timer as timer

#%% ################################################################################ ############################### Classes ######################################## class photovoltaic(object): """ Class for implementing PV functionality. """ def __init__(self, hours, size): 80

""" Constructor

solarthermal.__init__(float, int) """ self._storage_hours = hours #battery storage hours self._rated_size = size # kW self._storage_capacity_nominal = self.set_storage_capacity_nominal() self._available_storage = 0.0 self._total_energy_throughput = 0.0 self._energy_throughput_current = 0.0 self._fade = 100 self._excess_energy = 0.0 def set_storage_capacity_nominal(self): """ Define nominal capacity of battery storage. """ self._storage_capacity_nominal = self._rated_size*self._storage_hours return self._storage_capacity_nominal def get_storage_capacity(self): """ Reduce nominal capacity to account for capacity fade over use. """ number_cycles = self._energy_throughput_current/self._storage_capacity_nominal

if self._total_energy_throughput == 0.0: storage_capacity_actual = self.set_storage_capacity_nominal()

else: if self._fade < 70: self._fade = 100 self._energy_throughput_current = 0.0 else: self._fade = 100 - 0.02*number_cycles storage_capacity_actual = (self._fade/100)*self.set_storage_capacity_nominal()

return storage_capacity_actual

def PV_power(self, DNI, Rdash): """ Power output for a given DNI and panel tilt factor. """ derating = 0.77 81

PV_power = self._rated_size*DNI*Rdash*derating/1000 return PV_power

def get_storage_amount(self): """ Total available storage. """ return self._available_storage

def get_usable_storage(self): """ Determine usable storage based on minimum SOC. """ if self._available_storage <= 0.1*self.get_storage_capacity(): usable_storage = 0 else: usable_storage = self._available_storage - 0.1*self.get_storage_capacity() return usable_storage

def add_storage(self, amount): """ Add a given amount to the storage based on charge rate and capacity. """ available_space = self.get_storage_capacity() #total capacity amount = amount*0.88 #round-trip efficiency if amount < 0.5*available_space: if amount + self.get_storage_amount() > 0.9*self.get_storage_capacity(): self.add_excess((amount + self.get_storage_amount()) - 0.9*self.get_storage_capaci ty()) self._available_storage = 0.9*self.get_storage_capacity() else: self._available_storage += amount else: self.add_excess(amount - 0.5*available_space) usable_energy = 0.5*available_space if usable_energy + self.get_storage_amount() > 0.9*self.get_storage_capacity(): self.add_excess((usable_energy + self.get_storage_amount()) - 0.9*self.get_storage _capacity()) self._available_storage = 0.9*self.get_storage_capacity() else: self._available_storage += usable_energy

def battery_output_max(self): """ Returns the battery maximum power output in kW based on discharge rate. 82

""" if self.get_usable_storage() < 0.5*self.get_storage_capacity(): battery_output = self.get_usable_storage() else: battery_output = 0.5*self.get_storage_capacity() #accounting for discharge rate return battery_output

def subtract_storage(self, amount): """ Subtracts a defined amount from the available amount in the battery store in kWh. """ if self.get_usable_storage() >= amount: self._available_storage -= amount self._total_energy_throughput += amount self._energy_throughput_current += amount else: self._available_storage = 0.0

def add_excess(self, excess): """ Keeps track of excess energy generation. """ self._excess_energy += excess

def get_excess(self): """ Returns the total excess energy generated by the plant. """ return self._excess_energy #%% def tilt(hour, lat, long, ghi): """ Function used to calculate the optimum value of solar PV panel tilt depending on the location of the plant. Returns the solar irradiation multiplier for a solar PV panel tilted at the optimum tilt angle for the location. """ lat = radians(lat) # (ϕ) Lst = (long//15)*15 day = hour//24 n = day + 1 B = (n-1)*(360/365) E = 229.2*(0.000075+(0.001868*radians(B))-(0.032077*sin(radians(B)))- (0.014615*cos(radians(2*B)))-(0.04089*sin(radians(2*B)))) standard_time_1 = (day-(int(day)))*24 83

standard_time_2 = (day-(int(day)))*24 + 1 long_corr = 4*(Lst - abs(long)) ####Calculate solar time#### if long > Lst: solar_time_1 = ((standard_time_1*60)+ long_corr + E)/60 solar_time_2 = ((standard_time_2*60)+ long_corr + E)/60 elif long < Lst: solar_time_1 = ((standard_time_1*60)- long_corr + E)/60 solar_time_2 = ((standard_time_2*60)- long_corr + E)/60 ####Calculate declination angle (δ)#### dec = radians(23.45*sin(radians((360/365)*(284+n)))) ####Calculate hour angle (ω)#### hour_angle = radians((solar_time_1-12)*15) hour_angle_next = radians((solar_time_2-12)*15) ####Set surface azimuth angle and beta#### surf_az = radians(180) #installation will be north-facing beta = abs(lat) ####Calculate cos theta, cos theta z and Rb#### costheta = (sin(dec)*sin(lat)*cos(beta))- (sin(dec)*cos(lat)*sin(beta)*cos(surf_az))+(cos(dec)*cos(lat)*cos(beta)*cos(hour_angle))+(cos(dec) *sin(lat)*sin(beta)*cos(surf_az)*cos(hour_angle))+(cos(dec)*sin(beta)*sin(surf_az)*sin(hour_angle) ) costhetaz = (sin(dec)*sin(lat))+(cos(dec)*cos(lat)*cos(hour_angle)) Rb = (cos(lat+beta)*cos(dec)*cos(hour_angle)+(sin(lat+beta)*sin(dec)))/((cos(lat)*cos(dec)*cos (hour_angle))+(sin(lat)*sin(dec))) Rd = (1+cos(beta))/2 Rr = (1-(cos(beta)))/2 sunset_hour_angle = acos(-(sin(lat)*sin(dec))/(cos(lat)*cos(dec))) sunrise_hour_angle = -sunset_hour_angle N = (2/15)*sunset_hour_angle #sunshine hours Isc = 1367 #W/m^2 Io = ((12*3600*Isc)/pi)*(1+(0.033*cos(radians((360*n)/365))))*(cos(lat)*(cos(dec)*(sin(hour_an gle)-sin(hour_angle_next))+((pi*(hour_angle-hour_angle_next))/180)*sin(lat)*sin(dec))) kt = (ghi*3600)/Io rho = 0.2 if kt < 0.35: Id = (1 - (0.249*kt))*ghi elif 0.35 < kt < 0.75: Id = (1.557 - (1.84*kt))*ghi elif kt > 0.75: Id = 0.177 * ghi Ib = (1 - (Id/ghi))*ghi It = (Ib*Rb) + (Id*Rd) + (rho*Rr*(Ib + Id)) Rdash = (((Ib*Rb)+(Id*Rd))/(Ib + Id))+Rr return Rdash #%% 84 class solarthermal(object): """ Class for implementing solar thermal functionality. """ def __init__(self, hours, design_load, design_point_dni, temperature, fill_time): """ Constructor

solarthermal.__init__(float, int, int, int, float) """ self._temperature = temperature # celcius self._fill_time = fill_time # hours self._available_storage = 0.0 self._field_capacity = 0.0 self._plant_capacity = 0.0 self._storage_hours = hours # hours self._plant_efficiency = self.set_plant_efficiency() self._nominal_receiver_eff = self.set_receiver_efficiency() self._average_load = design_load # kW self._average_dni = design_point_dni # W/m^2 self._excess_energy = 0.0 self._storage_capacity = self.set_storage_capacity() self._field_capacity = self.get_field_capacity() self._mirror_area = self.set_mirror_area()

def set_plant_efficiency(self): """ Defines the power cycle efficency based on temperature (scale of 0 to 1) """ if self._temperature == 650: self._plant_efficiency = 0.49 elif self._temperature == 800: self._plant_efficiency = 0.54 else: self._plant_efficiency = 0.604 return self._plant_efficiency

def set_receiver_efficiency(self): """ Defines the nominal receiver efficiency based on temperature (scale of 0 to 1) Hard coded based on the outputs from an iterative solver. """ if self._temperature == 650: self._nominal_receiver_eff = 0.75 elif self._temperature == 800: self._nominal_receiver_eff = 0.72 85

else: self._nominal_receiver_eff = 0.664 return self._nominal_receiver_eff

def set_storage_capacity(self): """ Defines the amount of thermal storage capacity based on load requirement and power cycle efficiency.

""" storage_buffer = 1.2 total_storage_amount = (self._average_load*self._storage_hours*storage_buffer)/self._plant _efficiency self._storage_capacity = total_storage_amount return self._storage_capacity

def get_field_radius(self): """ Calculates the field radius. """ storage_fill_time = self._fill_time #hours irradiation_receiver = self._storage_capacity/(storage_fill_time*self._nominal_receiver_ef f) outer_radius = 2*math.sqrt(irradiation_receiver/(3.14*0.5*0.2*(self._average_dni/1000))) # this assumes a rough field efficiency of 0.5

return outer_radius

def attenuation_loss(self): """ Calculates the attenuation loss. """ tower_height = 60 heliostat_height = 10 field_radius = self.get_field_radius() net_height = tower_height - heliostat_height distance = math.sqrt(field_radius**2 + net_height**2)/1000 #hypotenuse in km transmittance = 0.99326 - 0.1046*distance + 0.017*distance**2 + 0.002845*distance**3 return transmittance

def cosine_loss(self): """ Calculates the cosine loss. """ tower_height = 60 field_radius = self.get_field_radius() 86

tower_height_nos = field_radius/tower_height cosine_loss = 0.9113 - 0.038*math.log(tower_height_nos) #equation fit to values outlined by Holl (1978). A note of caution, this relationship only holds for small plants <~20 MW size. return cosine_loss

def get_field_capacity(self): """ Returns the gross solar field capacity in kW. """ storage_fill_time = self._fill_time #hours irradiation_receiver = self._storage_capacity/(storage_fill_time*self._nominal_receiver_ef f) self._field_capacity = irradiation_receiver/(self.attenuation_loss()*self.cosine_loss()*0. 91*0.95*1) return self._field_capacity

def set_mirror_area(self): """ Returns the mirror area required to achieve the solar field capacity in m^2. """ self._mirror_area = self.get_field_capacity()/((self._average_dni/1000)) return self._mirror_area

def get_concentration_ratio(self): """ Returns the necessary concentration ratio. """ storage_fill_time = self._fill_time #hours eff = self._nominal_receiver_eff tr = self._temperature + 273 tms = (tr + 290 + 273)/2 Afield = self.set_mirror_area() d0 = 0.04 t = 0.00125 di = d0 - 2*t conductivity = 23.9 h = 2100 #by matching values with SAM term1 = d0/(di*h) term2 = d0/(2*conductivity) term3 = math.log(d0/di) del_t = tr - tms denom = term1 + (term2*term3) irradiation_receiver = self._storage_capacity*1000/(storage_fill_time*eff) concentration_ratio = del_t/((irradiation_receiver/Afield)*denom) 87

return concentration_ratio

def receiver_efficiency(self): """ Calculates receiver efficiency. """ nopt = 0.855 e = 0.8 sigma = 5.67*10**-8 tamb = 25 + 273 tr = self._temperature + 273 concentration_ratio = self.get_concentration_ratio() average_DNI = self._average_dni A = (e*sigma*(tamb**4)/(concentration_ratio*average_DNI*nopt*self.cosine_loss()*self.atten uation_loss()*0.91*0.95*1)) B = (((tr/tamb)**4)-1) receiver_efficiency = nopt*(1-(A*B)) return receiver_efficiency

def field_efficiency(self): """ Calculates overall sun to heat efficiency. """ field_efficiency = self.receiver_efficiency()*self.cosine_loss()*self.attenuation_loss()*0 .91*0.95*1 return field_efficiency

def get_plant_capacity(self): """ Returns the power cycle capacity in kW. This is redundant, since this value is set in the simulation based on peak load. However this can be made active with slight modifications to the code. """ if self._storage_capacity == 0: self._plant_capacity = 0 else: self._plant_capacity = (self.get_field_capacity()/self.set_solar_multiple())*self._pla nt_efficiency return self._plant_capacity

def get_storage_capacity(self): """ Returns the storage capacity of the solar thermal plant in kWh. """ return self._storage_capacity

88

def get_storage_amount_thermal(self): """ Returns the available amount of storage of the solar thermal plant in kWh thermal """ return self._available_storage

def get_storage_amount(self): """ Returns the current available amount of storage of the solar thermal plant in kWhe. """ kWeq_energy = self._available_storage * self._plant_efficiency return kWeq_energy

def cst_output_max(self): """ Calculates maximum deliverable electrical output. """ if self.get_storage_amount() >= self.get_plant_capacity(): output_max = self.get_plant_capacity() else: output_max = self.get_storage_amount() return output_max

def add_storage(self, DNI): """ Adds a defined amount to the available amount of storage of the solar thermal plant in kWh. """ energy = (DNI/1000)*self.field_efficiency()*self.set_mirror_area() if self.get_storage_amount_thermal() < self.get_storage_capacity(): if self.get_storage_amount() + energy > self.get_storage_capacity(): self.add_excess((energy + self.get_storage_amount_thermal()) - self.get_storage_ca pacity()) self._available_storage = self.get_storage_capacity() else: self._available_storage += energy else: self.add_excess(energy) self._available_storage = self.get_storage_capacity()

def get_energy(self, DNI): """ Returns the amount of energy captured by the solar field with a given amount of DNI. """ energy = (DNI/1000)*self.field_efficiency()*self._mirror_area 89

return energy def subtract_storage(self, amount): """ Subtracts a defined amount from the available amount of storage of the solar thermal plant. """ amount = amount / self._plant_efficiency if self._available_storage > amount: self._available_storage -= amount else: self._available_storage = 0.0 def set_solar_multiple(self): """ Returns the solar multiple of the plant (redundant). """ return 3 def add_excess(self, excess): """ Adds excess energy generated. """ self._excess_energy += excess def get_excess(self): """ Returns the total spilled energy in kWhe. """ return self._excess_energy*self._plant_efficiency def get_capital_costs(self): """ Calculate capital costs barring power cycle cost. Costs as per report. """ general_site = 20 helio_field = 120 tower = 44 thermal_storage = 30 land_use_factor = 5 indirects = 1.25*1.1 BoP = 355

if self._temperature == 650: receiver = 115 power_block = 1440 90

elif self._temperature == 800: receiver = 190 power_block = 2400 else: receiver = 417 power_block = 4306 # print("mirror area = ", self._mirror_area) print("field capacity = ", self._field_capacity) site = general_site*self._mirror_area*land_use_factor field = helio_field*self._mirror_area thermal = (tower + 0)*self._field_capacity*self.field_efficiency() receiver_cost = receiver*self._field_capacity*self.field_efficiency() store = thermal_storage*self._storage_capacity block = power_block*self.get_plant_capacity() balance = BoP*self.get_plant_capacity() capex_minus_plant = (site + field + thermal + store)*indirects return capex_minus_plant

def get_final_capex(self, cst_peak): """ Calculate capital costs including power cycle cost. """ land_use_factor = 5 indirects = 1.25*1.1 BoP = 355 cst_peak1 = cst_peak if cst_peak < 1000: cst_peak1 = 1000 #account for a minimum size of 1 MW

if self._temperature == 650:

power_block = 1440 #power cycle costs as per report elif self._temperature == 800:

power_block = 2400 else: power_block = 4306 capex_minus_plant = self.get_capital_costs() block = power_block*cst_peak1 balance = BoP*cst_peak1 total_capex = capex_minus_plant + (block+balance)*indirects print("CST adjusted peak =" , cst_peak1) return total_capex #%% ################################################################################ ############################### Simulation ##################################### 91 def sim(lfile, wfile, PV_size, PV_hours, storage_hours, temperature, storage_fill_time): """ Function used to run through one full year of load and weather data to determine the possible contributions from each technology.

Returns critical information for each technology, namely: Total contribution (kWh), Peak (kW), Excess energy generated (kWh)

sim(load file (txt - tab delimited), weather file (xlsx), PV size (kW), PV battery hours (float), CST storage hours (float), CST temperature (degrees Celcius), CST fill time (hours))

"""

PV = 0.0 PVexcess = 0.0 PVexcess_counter = 0.0 PV_peak = 0.0 CST = 0.0 CST_peak = 0.0 CST_excess = 0.0 DG = 0.0 DG_peak = 0.0 DG_hours = 0 yearly_total_load = 0.0 xl_wb = xlrd.open_workbook(wfile) # open the weather file xl_sheet = xl_wb.sheet_by_index(0) total_load = 0 total_ghi = 0 total_dni = 0 lat = xl_sheet.cell(0,5).value long = xl_sheet.cell(0,6).value with open(lfile) as foox1: for i, load in enumerate(foox1): ### Calculate averages ### total_load += float(load) total_ghi += xl_sheet.cell(i+1,3).value total_dni += xl_sheet.cell(i+1,4).value average_load = total_load/i # print(average_load) average_dni = total_dni/i #### Initialise instances of the classes #### CST_class = solarthermal(storage_hours, average_load, 950, temperature, storage_fill_time) PV_class = photovoltaic(PV_hours, PV_size) with open(lfile) as foox2: for i, current_load in enumerate(foox2): 92

current_load = float(current_load) yearly_total_load += current_load ghi = xl_sheet.cell(i+1,3).value dni = xl_sheet.cell(i+1,4).value if ghi != 0: Rdash = tilt(i, lat, long, ghi) else: Rdash = 0 #### Calculate power for a given hour #### PV_power = PV_class.PV_power(ghi, Rdash) CST_class.add_storage(dni) #### Dispatch from the technologies as per order outlined in the report #### if PV_power >= current_load: PV += current_load PVexcess = PV_power - current_load PVexcess_counter += PVexcess PV_class.add_storage(PVexcess) if current_load > PV_peak: PV_peak = current_load current_load = 0.0 elif (PV_power) + PV_class.battery_output_max() >= current_load: PV += PV_power if PV_power > PV_peak: PV_peak = PV_power current_load -= PV_power PV_class.subtract_storage(current_load) current_load = 0.0 elif (PV_power) + CST_class.cst_output_max() + PV_class.battery_output_max() >= curren t_load: PV += PV_power if PV_power > PV_peak: PV_peak = PV_power current_load -= PV_power current_load -= PV_class.battery_output_max() PV_class.subtract_storage(PV_class.battery_output_max()) CST_class.subtract_storage(current_load) CST += current_load if current_load > CST_peak: CST_peak = current_load current_load = 0.0

else: #### In the event that diesel is necessary #### PV += PV_power if PV_power > PV_peak: PV_peak = PV_power 93

current_load -= PV_power current_load -= PV_class.battery_output_max() PV_class.subtract_storage(PV_class.battery_output_max()) if CST_class.cst_output_max() > CST_peak: CST_peak = CST_class.cst_output_max() current_load -= CST_class.cst_output_max() CST += CST_class.cst_output_max() CST_class.subtract_storage(CST_class.cst_output_max())

DG += current_load DG_hours += 1 if current_load > DG_peak: DG_peak = current_load current_load = 0.0 return PV, PV_peak, CST, CST_peak, DG, DG_peak, DG_hours, PVexcess_counter, CST_class.get_ plant_capacity(), CST_class.get_excess(), CST_class.get_final_capex(CST_peak), PV_class._total_ene rgy_throughput, PV_class._storage_capacity_nominal, PV_class.get_excess(), yearly_total_load #%% ################################################################################ ############################### Functions ###################################### if __name__ == "__main__":

def dg_fuel_use(size, dg_ave): """ Function used to calculate the amount of diesel fuel being used by the diesel generator per hour. """ fuel_use = (0.2088*dg_ave)+(0.01841*size) return fuel_use

def eac(discount_rate, term, amount): """ Function used to calculate the annualised capital costs of each of the technologies.

Based on the formula: EAC = NPV/(A^t,r) where A^t,r = (1 - (1/(1+r)^t))/r """ discount_rate = discount_rate/100 Atr = (1-(1/(1+discount_rate)**term))/discount_rate eac = amount/Atr return eac #%% def LCOE_calc(params, temp): """ Function used to calculate the LCOE for a given configuration. """ # PV_size, PV_hours, CST_hours, fill_rate must be provided in a list as follows 94

PV_size = params[0] # kW PV_hours = params[1] # hours CST_hours = params[2] # hours fill_time = params[3] # hours print(params) temp = temp # Weather file and load file must be entered here wfile = 'Halls Creek.xlsx' lfile = 'Halls Creek Load 2.txt' #### Cost assumptions as per report #### diesel_fuel_cost = 0.7 DR = 7 PV_cap = 1030 PV_om = 40 DG_cap = 800 DG_replace = 800 DG_om = 30 project_life = 25.0 #Project life in years DG_life = 15000 #Diesel generator life in hours battery_cap = 576 battery_om = 15 CST_om = 50 #### Run the simulation and calculate costs accordingly #### sims = sim(lfile, wfile, PV_size, PV_hours, CST_hours, temp, fill_time) PV_excess = sims[7] CST_excess = sims[9] print("PV excess = ", PV_excess) print("CST excess = ", CST_excess) PV_cap_cost = PV_size*PV_cap PV_eac = eac(DR, project_life, PV_cap_cost) + (PV_size*PV_om) print("PV EAC = ", PV_eac) battery_rep_req = (sims[11]*project_life)/(sims[12]*0.85*1500) #total throughput divided by throughput in one purchase (0.85 is to account for linear fade to 0.7) battery_cap_cost = battery_cap*sims[12] + battery_cap*sims[12]*battery_rep_req #initial plus replacement # battery_cap_cost = battery_cap*sims[12]*battery_rep_req battery_eac = eac(DR, project_life, battery_cap_cost) + sims[12]*battery_om CST_cap_cost = sims[10] CST_eac = eac(DR, project_life, CST_cap_cost) + (sims[3]*1.05*CST_om) print("CST EAC = ", CST_eac) DG_replace_req = (sims[6]*project_life)/DG_life #Defines the number of times the diesel generator must be replaced dg_ave = sims[4]/sims[6] DG_fuel_cost = (dg_fuel_use(sims[5]*1.1, dg_ave))*sims[6]*diesel_fuel_cost print("diesel peak=", sims[5]) DG_cap_cost = sims[5]*1.1*DG_cap 95

DG_replace_cost = sims[5]*1.1*DG_replace*DG_replace_req DG_eac = eac(DR, project_life, DG_replace_cost) + DG_fuel_cost + (sims[6]*DG_om) print("DG EAC = ", DG_eac) LCOE = (PV_eac + battery_eac + CST_eac + DG_eac)/(sims[0]+sims[2]+sims[4]+sims[11]) print("LCOE = ", LCOE) energy_produced = sims[0]+sims[2]+sims[4]+sims[11] renewable_frac = (sims[0]+sims[2]+sims[11])/(sims[0]+sims[2]+sims[4]+sims[11]) CST_frac = sims[2]/energy_produced print("renewable fraction =" , renewable_frac) print("pv total energy =", sims[0]) print("cst total energy =", sims[2]) print("diesel total energy =", sims[4]) print("CST Peak =", sims[3]) # print("energy produced = ", energy_produced) # print("energy consumed = ",sims[14]) return LCOE #%% def global_minimum(temp): """ Function used to calculate the configuration that yields the global minimum LCOE for a CST temperature case using an exhaustive search. Note that prior to running this, the appropriate load and weather files must be entered within the LCOE_calc function's code. """ thermal_store_min = 0.00000000000001 # since zero will yield a math error thermal_store_max = 15 # hours PV_size_min = 0.00000000000001 PV_size_max = 20000 battery_hours_min = 0.00000000000001 battery_hours_max = 15 fill_time_min = 1 fill_time_max = 15 results_list = [] min_LCOE = 1 # granularity of search can be adjusted by modifying number of points used in each loop start = timer() for store in np.linspace(thermal_store_min, thermal_store_max, 100): for power in np.linspace(PV_size_min, PV_size_max , 100): for hours in np.linspace(battery_hours_min, battery_hours_max, 100): for fill_time in np.linspace(fill_time_min, fill_time_max, 100): params = [power, hours, store, fill_time] LCOE = LCOE_calc(params, temp) if LCOE < min_LCOE: min_LCOE = LCOE results_list = [power, hours, store, fill_time, min_LCOE] end = timer() 96

print("time taken (s) = ", end - start) print("optima [PV size, battery hours, CST hours, fill time, min LCOE] = ", results_list)

return results_list

#%%

""" The following shows an example of using the code, whereby an exhaustive search is carried out, and then the result is used as the initial guess in a minimisation function to determine the optimum. Tolerances may be modified as deemed necessary. An example of the use of LCOE_calc is also shown. """ rough_guess = global_minimum(650) from scipy import optimize result = optimize.fmin(LCOE_calc, rough_guess, xtol = 0.1, ftol=0.001, maxiter = 100000, disp =0) LCOE_calc([1000,0.0000000000000000001, 8,4], 650) # That is, 650°C, 1000 kW of PV, 0 hours battery storage, 8 hours thermal storage and 4 hours fill time

97