Electrical and Electronic Engineering - Theses
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ItemA Modelling Framework for Virtual Power Plants Under Uncertainty( 2021)The increased integration of renewable energy sources (RES) and distributed energy resources (DER) into electrical networks is causing operational challenges. The reduction in conventional generators, which would traditional provide the reliability and security services for electrical networks, means that these services must now be supplied by other resources. Simultaneously, the intermittency of RES and the lack of visibility of DER means that in some cases these services are required more frequently to maintain a reliable electrical grid. If RES and DER are aggregated and properly controlled in a virtual power plant (VPP) they have the potential to provide network services as well as increase their profitability. The operation of a VPP is a complex problem. While this problem has been examined by numerous authors, no operating framework has been previously proposed that includes consideration of: participation in multiple markets; provision of network and contractual services; modelling of network power flows and voltages; interactions between multiple energy vectors; uncertainty in operational forecasts and; tractability for short dispatch periods. These are key properties for a comprehensive framework that fully captures and unlocks the potential of a VPP. This thesis presents the design and application of a VPP operational framework that incorporates these six key properties. This optimisation-based framework is decomposed into three optimisations to integrate these properties in a tractable manner. This framework is applied to various realistic case studies to prove the efficacy of the proposed approach. The application of this framework demonstrates that the combination of scenario-based optimisation and receding horizon control used is effective at mitigating the effects of uncertainty. The inclusion of short dispatch periods is shown to be key for revenue generation in markets with short dispatch windows. In addition, the application of this framework demonstrates the ability of a VPP to participate in multiple markets and services, and that doing so is essential for maximising VPP revenue. Moreover, the integration of hydrogen resources into the electrical grid provides flexibility that can be assigned to various markets and services. Furthermore, operating in multiple markets fundamentally changes the operational strategy of hydrogen resources, and can increase the amount of hydrogen that can be profitably generated. Additionally, the convex relaxation used for the dispatch of resources is sufficiently accurate to allow a VPP to maintain a network within allowable limits whilst maintaining problem tractability. Lastly, the framework is versatile enough to be utilised by other entities (such as a distribution system operator), or for different purposed (such as techno-economic analysis for business case assessments).
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ItemDemand side management in low voltage networks with thermal storage of residential buildings( 2019)This thesis investigates the impact of the thermal inertia of residential buildings on electricity demand. The analysis demonstrates the significant potential of residential buildings in providing technical and financial flexibility to the electricity grid. This study has important implications for demand-side management in the emerging electricity networks with renewable generation. Generation and demand must be balanced at all times. In the classical power system, this balance is achieved by controlling generation. High penetration of intermittent renewable generation leads to decrease control over electricity generation. Demand-side management programs, such as residential demand response, are emerging as an attractive approach to balance demand and generation by controlling demand. The emergence of distributed energy resources and energy storage systems in residential buildings has enabled many demand-side management programs in residential buildings. While there exists a rich literature on residential demand response, most works either focus on electricity storage in batteries or thermal storage in water heaters. Not all households can afford batteries, and hot-water tanks cannot be used to shift the cooling demand of the buildings. However, all houses have thermal mass, and most have electric heating, ventilation, and air-conditioning (HVAC) systems. In this thesis, the combined effect of building thermal inertia and HVAC control on shifting peak electricity demand in low voltage network during summer is investigated. Three approaches are studied: Passive approach (external walls), Indirect approach (HVAC system control), and Direct approach (ice storage system). The analysis shows the significant potential of these approaches in shifting the peak electricity demand of the low voltage network and providing technical and financial flexibility to the electricity network.
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ItemOptimal design of low carbon residential microgrids( 2018)Internationally, the residential sector makes up 31% of global energy use [1]. In 2015, Australian households were responsible for 23.5% of Australia's total net electricity demand [2] at 456 PJ annually [3]. Without considering the potential of the Emissions Reduction Fund, coal-fired generation is projected to continue to supply the bulk of Australia’s electricity requirements to 2035 [4]. However, the residential sector has the potential to move to a low carbon future through the increased application of distributed generation and distributed storage, microgrid systems, new demand response methods, innovative passive building designs and improved energy efficiency [5]. The size of many early distributed generation, distributed storage and microgrid trials is often limited by funding, where the financial risk of deployment is minimised by keeping the trial small [6]–[8]. Through improved modelling methods, the financial risk can be reduced. Improved modelling and optimisation has the opportunity to simultaneously reduce financial risk (through higher fidelity estimates of operation and performance) and improve the overall business case (by providing guidance on the best configuration, design and structure of deployments). In this thesis, we present an integrated modelling and optimisation framework that has new capabilities to design, build and test the business case for low Carbon microgrid precincts with storage and distributed generation technologies at their core. This modelling framework analyses the impacts of low carbon infrastructure on carbon emissions, precinct demand and household demand. The modelling framework consists of four components: 1) An electricity demand model for the estimation of household and precinct load behaviour in urban precincts. 2) A standalone solar and battery model for analysing impacts on demand, costs and selecting optimal capacities. 3) A microgrid model for comparing distributed and utility storage options, and analysing impacts of installed capacity on grid demand and cost. 4) A PowerFactory microgrid network model for verifying the network voltage levels and losses of the microgrid designs. To develop the electricity demand model we draw on an extensive residential energy consumption dataset of more than 6,000 homes, we develop a demand model for the estimation of half-hourly electricity demand for individual households based on a small set of household characteristics. The Adaptive Boost Regression Tree algorithm was presented as the best approach for our application. We contribute a new modelling method that substitutes household demographic survey data for the Mosaic demographic estimate dataset and evaluate the model on three case study precincts in New South Wales, achieving an R2 value of between 0.72 and 0.86, and the Lochiel Park Precinct, achieving an R2 value of 0.81. This is an improvement in fidelity over prior pioneering works in this space. To understand the impact of standalone residential solar and battery systems on demand and cost we developed a linear programming residential hybrid solar PV/battery/grid-connected power system model. We contributed an extension to modelling techniques by considering NPV energy costs, solar lifetime, commercially available modular battery sizes and DOD limits. The model estimates an upper limit (break-even) cost for homeowners for the installation of residential distributed energy storage. Retail energy price forecasts from the Australian Energy Market Operator have been applied to determine how the economics of residential solar and battery systems are impacted due to future energy price growth. The model has been applied to the measured and simulated demands of a case study in Sydney NSW, to show that the demand model can be applied to design solar and battery systems accurately and provide confidence to the future applications of the demand model. A new comprehensive microgrid design model using mixed-integer linear programming was developed to evaluate the design and impact of microgrid precincts. The model formulation allowed individual household demands to be considered within a single optimisation problem. The formulation facilitates the modelling of load diversity, network impacts, utility storage and distributed (household) storage in a microgrid. We applied this model to identify an optimal microgrid configuration for the Lochiel Park precinct in Adelaide, South Australia. We presented how the costs of off-grid microgrids are poor, although can be improved using a higher installed solar capacity. The microgrid model is then extended through integration with the demand model. In the model integration, we first develop individual household, half-hourly, load demand simulation, using information about the precinct. Next, we provide these demand profiles to the microgrid model for microgrid design, outputting the microgrid capacity levels, resource dispatch and energy costs. This modelling process delivers a new method to design and evaluate microgrid precincts where load demand data is unknown. Australian Energy price growth forecasts have been used to present how the business case for microgrids is impacted under different price growth scenarios. To explore voltage and power flow performance of microgrid solutions, we develop an AC and DC Digsilent PowerFactory microgrid model to simulate the Lochiel Park case study solution designed by the microgrid modelling tool chain. The network model is grounded in design data provided by the South Australian electricity distribution utility, SA Power, ensuring high-fidelity estimates of power line type and length, load locations, transformer ratings and transformer locations; providing a new level of detail not seen in other comparable research. The integration of demand, microgrid and PowerFactory microgrid models provides a pathway forward for the evaluation of the technical business case for AC and DC microgrid systems for residential precincts. By way of illustration, for the case study precinct, results showed that the network losses for the DC systems are lower than the AC systems. Moreover, we show that using distributed batteries result in lower network losses than utility battery storage due to less network power flow. Despite the lower losses of the distributed storage microgrid, our results showed that the lowest cost microgrid configuration for Lochiel Park, under the current pricing mechanism and available real-world battery (energy storage) capacity levels, was an AC microgrid using utility storage. The models developed in this thesis have integrated data science, economics and electrical engineering to provide a detailed scope of the total costs, emissions reduction potential, impacts on demand and impacts on the distribution network for future low carbon precincts. The methods presented in this thesis provide a new level of utility with the potential to be applied by developers, precinct planners, and local governments for new developments, helping identify the costs of and opportunities for a low carbon energy supply future enabled by contemporary distributed energy and microgrid solutions. Our focus is predominantly on Australian energy demand and microgrid opportunities, though the techniques and methodologies are applicable more broadly, and the findings present a basis for equivalent studies in other national contexts (where tariff, regulatory, environmental and other conditions may lead to different conclusions regarding optimal design and operation of microgrid solutions).
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ItemA low-power minaturised intracranial pressure monitoring microsystem( 2017)The ultimate goal of this work is realisation of a fully implantable chronic intracranial pressure (ICP) monitoring system. Due to the required mm-scale form factor of the implantable device, the available power is scarce. This calls for investigation of new circuit and sensor integration techniques to decrease the total power consumption of the system down to a few hundreds of nano watts. So the main focus of this work is design of an ultra-low power integrated circuit (IC) for measuring ICP. Power consumption minimization of the sensing system proposed in this work paves the way for integration of an RF-power scavenger or biological fuel cells. The proposed sensing system also takes full advantage of Invensense MEMS-CMOS process to heterogeneously integrate the sensor and interface. This integration type requires no post-processing and results in sub-pF sensor-interface parasitic interconnection capacitance Cp which is an order of magnitude smaller than previously reported Cp’s.