Electrical and Electronic Engineering - Theses
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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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ItemSystem identification and control of rivers( 2016)Water resource management has immense importance in the modern world. A large amount of water is wasted due to inefficient management of rivers, lakes and other water bodies associated with them. In order to achieve improved management of water resources, control and decision support systems can be employed. To design control systems for a river, a river model is required. Traditionally, the Saint Venant equations have been used for modelling purposes. The equations describe river flows accurately, however, they are complex, non-linear and require many unknown parameters. It is therefore difficult to use them for control design purposes. On the other hand, data-based models have proven to be very useful in control design for rivers. In this thesis, different data-based modelling methods are explored, and they are applied to the data from the upper part of Murray River in Australia. For each method, the thesis analyses the ease with which available prior knowledge can be incorporated in the modelling procedure and the ability of the obtained models to describe the river well. For efficient river control, forecasts of future water demands and flows in the unregulated tributaries are required to be taken into account. A Stochastic Model Predictive Control (S-MPC) or a randomised version of it can not only accommodate such forecasts, but it can also handle physical and environmental constraints well. However, due to uncertainties in the forecasts, the feasibility of optimisation problems cannot always be guaranteed in the presence of constraints. This thesis proposes an S-MPC based river control schemes, that not only incorporate the forecasts, but also ensure feasibility of the optimisation problems. The schemes are successfully applied in simulations to the past data from the upper part of Murray River. Another important aspect of river management is to mitigate flood risks. An ideal strategy is to reduce the risk of severe floods, and at the same time not being overly cautious while performing normal river operations. This thesis uses Value-at-Risk (VaR) as a risk measure and incorporates it into the river control problem, forming a Multiple Chance-Constrained optimisation Problem (M-CCP), to be solved in an S-MPC setup. A computationally tractable Optimisation and Testing algorithm is developed to find solutions to M-CCPs, with probabilistic guarantees on the solution. The algorithm is successfully applied to the historical data from the upper part of Murray River. The simulation results show better regulation and flood avoidance.
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ItemOptimisation of energy efficiency in communication networks( 2015)The mobile data traffic is experiencing unprecedented growth due to the rapid proliferation of devices such as smart phones and tablets. Improving the efficiency of mobile networks, both in terms of traffic flow and energy consumption, is thus critical for sustaining this growing demand. While the adoption of new technologies such as small cell networks and cognitive radio reduces deployment and operational costs, challenges remain regarding how the data traffic can be efficiently processed and transported over the mobile backhaul network. The first aim of this study is to improve the energy efficiency of mobile backhaul networks, while simultaneously balancing the traffic load on its various backhaul nodes, in order to maintain required service quality. First a multi-objective optimisation problem is formulated, then a distributed algorithm is proposed to solve it. The theoretical analysis and numerical simulations demonstrate the results. It is shown that the traffic diurnal cycle poses notable challenges for operators to plan, design and operate mobile backhaul networks so as to achieve desired energy-performance tradeoffs. Continuing growth in cloud-based services and global IP traffic necessitates performance improvements in energy consumption, network delay and service availability. Data centres providing cloud services and transport networks have often multiple stakeholders, which makes it difficult to implement centralised traffic management. The second aim of this study is to apply a game-theoretic approach to data traffic management to obtain a distributed and energy-efficient solution, where each edge router is acting as a strategic player. A multi-objective optimisation problem with a-priori user-specific preferences is formulated for each player and a distributed iterative algorithm is proposed to solve the game. The existence of Nash Equilibrium (NE) of the proposed game is proven followed by the theoretical convergence analysis of the iterative algorithm. The efficiency loss between the strategic game and corresponding global optimisation method is analysed to quantify the impact of selfish behaviour on the overall system performance. Simulation results show notable challenges for operators to plan, design and operate a multimedia content network in order to optimise energy consumption, network delay and load balance over a diurnal cycle. The third aim of this study is to develop an optimisation framework for energy efficiency of optical core networks using Software Defined Networking (SDN). A general system model is proposed where switch-off/sleep mode is introduced to model the power consumption of individual network devices. A multi-objective optimisation problem is formulated by considering system power consumption, server load balance and transport network latency. To demonstrate the problem, a generic Software Defined Networking model is implemented in the Mininet platform by leveraging the OpenFlow protocol. A core network topology is studied in the Mininet framework with various parameter configurations. The simulation results show network topology, traffic diurnal cycle and user Quality of Service (QoS) requirements pose notable challenges for network plan, design and operation so as to achieve the desired energy-performance tradeoffs.