Electrical and Electronic Engineering - Theses

Permanent URI for this collection

Search Results

Now showing 1 - 1 of 1
  • Item
    Thumbnail Image
    Optimal design of low carbon residential microgrids
    Percy, Steven ( 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).