AbstractTo strike the balance between carbon emissions reduction, economic growth and energy supply security, non-conventional distributed energy resources such as solar photovoltaic are expected to dominate electricity generation in the future envisaged “smart grid”. However, the spatiotemporal variation of these smart grid technologies (SGTs) creates challenges for power system operation as there is limited knowledge regarding their impact on network reliability. Moreover, given their dependence on ambient conditions, there is a substantial risk of increased operational costs through the inefficient operation of backup conventional generation to maintain system reliability. This might defer the decarbonisation progress of several countries.
This thesis presents probabilistic time-sequential simulation techniques based on Monte Carlo methods to comprehensively assess the impact of SGTs on the reliability of power supply given the uncertainty of demand and the complexity of large networks. Accordingly, three major innovations are proposed to address these critical challenges a) the stochastic behaviour of SGTs is integrated into a reliability assessment methodology that is enhanced by the inclusion of the time-series variation of demand, electricity generation from SGTs, and the failure of network components; b) a rigorous characterisation of varying customer groups is developed by presenting the reliability performance for different load sectors (rural, suburban and urban) showing also the range of variability in terms of SGT influence; c) inspired by the model order reduction and state pruning techniques in control engineering, a novel network aggregation methodology is proposed to derive simplified grid representations that contain the most important system dynamics while minimising the error of the considered reliability metrics and being significantly faster to simulate.
The findings demonstrate that the coordinated deployment of SGTs such as demand-side response and energy storage will provide the most improvement to network reliability. The developed impact assessment methodology, which reduces network complexity through a reliability-based aggregation, will ensure that the impacts of SGTs can be analysed significantly faster while preserving accuracy. This will promote the practical use of reliability assessment for network planning and maintenance procedures that will result not only in satisfactory levels of supply continuity but also in the efficient operation of the power networks. Also, the resultant minimum targets set by national regulators to protect customers from supply outages will recognise varying customer groups and provide varying subsidies to promote uptake of relevant SGTs for the benefit of especially the worst served customers who often prefer continuous supply to the currently available outage compensation schemes.
|Date of Award||17 Feb 2021|
|Supervisor||Chenghong Gu (Supervisor) & Furong Li (Supervisor)|
- model order reduction
- monte carlo simulation
- reliability analysis
- smart grid technologies