With increasing level of distributed generation¡]DG¡^on radial feeders in electric distribution systems, it could cause over-voltages as well as under-voltages depending on several factors including DG capacity, locations, and the strategy of voltage regulation. This thesis describes the typical and proposed voltage control strategies that could allow the increase of DG interconnection capacity. By using probabilistic load flow technique, voltage regulation performance for cases with different levels of DG outputs, demands and voltage control strategies are presented. They are compared by using a voltage profile improvement index and a risk assessment technique.

Plug-in hybrid electric vehicles (PHEVs) represent a promising future direction for the personal transportation sector in terms of decreasing the reliance on fossil fuels while simultaneously decreasing emissions. Energy used for driving is fully or partially shifted to electricity leading to lower emission rates, especially in a low carbon intensive generation mixture such as that of British Columbia’s. Despite the benefits of PHEVs for vehicle owners, care will need to be taken when integrating PHEVs into existing electrical grids. For example, there is a natural coincidence between peak electricity demand and the hours during which the majority of vehicles are parked at a residence after a daily commute. This research aims to investigate the incremental impacts to distribution networks in British Columbia imposed by the charging of PHEVs. A probabilistic model based on Monte Carlo Simulations is used to investigate the impacts of uncontrolled PHEV charging on three phase networks in the BC electricity system. A model simulating daily electricity demand is used to estimate the residential and commercial demand on a network. A PHEV operator model simulates the actions of drivers throughout a typical day in order to estimate the demand for vehicle charging imposed on networks. A load flow algorithm is used to solve three phase networks for voltage, current and line losses. Representative three phase networks are investigated typical of suburban, urban and rural networks. Scenarios of increasing PHEV penetration on the network and technological advancement are considered in the absence of vehicle charging control. The results are analyzed in terms of three main categories of impacts: network demands, network voltage levels and secondary transformer overloading. In all of the networks, the PHEV charging adds a large amount of demand to the daily peak period. The increase in peak demand due to PHEV charging increases at a higher rate than the increase in energy supplied to the network as a result of vehicles charging at 240V outlets. No significant voltage drop or voltage unbalance problems occur on any of the networks investigated. Secondary transformer overloading rates are highest on the suburban network. PHEVs can also contribute to loss of transformer life specifically for transformers that are overloaded in the absence of PHEV charging. For the majority of feeders, uncontrolled PHEV charging should not pose significant problems in the near term. Recommendations are made for future studies and possible methods for mitigating the impacts.

Changes in the electric power industry have brought great challenges and uncertainties in transmission planning area. More effective planning of transmission grids with the appropriate development of advanced planning technologies is badly-needed. The aim of this research is to develop an advanced probabilistic transmission expansion planning (TEP) methodology in a continually changing market environment. The methodology should be able to strengthen and increase the robustness of existing transmission network. By using the proposed probabilistic TEP methodology, it can reduce the risks of major outages and identify weak buses in the system. The significance of this research is shown by its comprehensiveness and powerful practicability. Results from this research are able to improve the planning efficiency and reliability with consideration of financial risks in an electricity market. In order to achieve the target, this research methodologies focused on two main important issues, (1) probability based technical assessment and (2) financial investment evaluation. During the first stage study, probabilistic congestion management, probabilistic reliability evaluation and probabilistic load flow for TEP under uncertainties have been investigated and improved. The developed methodologies and indices, which truly represent the composite impact from both critical state and probability, have linked with financial terms. At financial investment evaluation part, Monte Carlo market simulation is performed to assist economic analysis. The overall planning process has been treated as a constrained multi-objective optimisation task. Comprehensive investigations are conducted on several test systems and testified by real power systems using the available reliability data and economic information from the Australian National Electricity Market (NEM). Overall, this research developed probabilistic transmission planning methodologies that can reflect modern market structures more accurately and it enable a greater utilization of current generation and transmission resources to increase potential operation efficiencies.

The penetration of photovoltaic (PV) and electric vehicles (EVs) continues to grow and is predicted to claim a vital share of the future energy mix. It poses new challenges in the built environment, as both PV systems and EVs are widely dispersed in the electricity distribution system. One of the vital tools for analyzing these challenges is load flow analysis, which provides insights on power system performance. Traditionally, for simplicity, load flow analysis utilizes deterministic approaches and neglecting  correlation between units in the system. However, the growth of distributed PV systems and EVs increases the uncertainties and correlations in the power system and, hence, probabilistic methods are more appropriate. This thesis contributes to the knowledge of how uncertainty and correlation models can improve the quality of load flow analysis for electricity distribution systems with large numbers of residential PV systems and EVs. The thesis starts with an introduction to probabilistic load flow analysis of future electricity distribution systems. Uncertainties and correlation models are explained, as well as two energy management system strategies: EV smart charging and PV curtailment. The probabilistic impact of these energy management systems in the electricity distribution system has been assessed through a comparison of allocation methods and correlation analysis of the two technologies. The results indicate that these energy management system schemes improve the electricity distribution system performance. Furthermore, an increase in correlations between nodes is also observed due to these schemes. The results also indicate that the concentrated allocation has more severe impacts, in particular at lower penetration levels. Combined PV-EV hosting capacity assessment shows that a combination of EV smart charging with PV curtailment in all buildings can further improve the voltage profile and increase the hosting capacity.  The smart charging scheme also increased the PV hosting capacity slightly. The slight correlation between PV and EV hosting capacity shows that combined hosting capacity analysis of PV systems and EVs is beneficial and is suggested to be done in one framework. Overall, this thesis concludes that an improvement of uncertainty and correlation modeling is vital in probabilistic load flow analysis of future electricity distribution systems.