The amount of distributed generation (DG) is increasing worldwide, and it is located in distribution networks close to consumers or even in the consumers¡¦ side of the meter. Therefore, the net demand to be supplied through transmission and distribution networks may decrease, allowing to postpone reinforcement of existing networks. This thesis presents a methodology for assessing the potential benefits of using non--constructional reinforcement strategies to relieve distribution network congestion and increase the utilization of the network assets. Due to the randomness of involved variables (load demand patterns, DG hourly production, DG availability, etc.), a simulation approach is used to model the uncertainties. The benefits of DG, energy storage (ES), and demand response (DR) on congestion relief and investment deferment are evaluated. The analyzed items include: the distribution network investment avoided cost, levelized annual cost, hourly overload probability, and hourly overload risk. Simulation results indicate the potential benefits of non--traditional strategies in increasing the distribution network utilization and relieving network congestion.

Recent technological developments are bringing about substantial changes that are converting traditional distribution networks into "smart" distribution networks. In particular, it is possible to observe seamless integration of Information and Communication Technologies (ICTs), including the widespread installation of automatic equipment, smart meters, etc. The increased automation facilitates active network management, interaction between market actors and demand side participation. If we also consider the increasing penetration of distributed generation, renewables and various emerging technologies such as storage and dynamic rating, it can be argued that the capacity of distribution networks should not only depend on conventional asset. In this context, taking into account uncertain load growth and ageing infrastructure, which trigger network investments, the above-mentioned advancements could alter and be used to improve the network design philosophy adopted so far. Hitherto, in fact, networks have been planned according to deterministic and conservative standards, being typically underutilised, in order for capacity to be available during emergencies. This practice could be replaced by a corrective philosophy, where existing infrastructure could be fully unlocked for normal conditions and distributed energy resources could be used for post fault capacity services. Nonetheless, to thoroughly evaluate the contribution of the resources and also to properly model emergency conditions, a probabilistic analysis should be carried out, which captures the stochasticity of some technologies, the randomness of faults and, thus, the risk profile of smart distribution networks. The research work in this thesis proposes a variety of post fault capacity services to increase distribution network utilisation but also to provide reliability support during emergency conditions. In particular, a demand response (DR) scheme is proposed where DR customers are optimally disconnected during contingencies from the operator depending on their cost of interruption. Additionally, time-limited thermal ratings have been used to increase network utilisation and support higher loading levels. Besides that, a collaborative operation of wind farms and electrical energy storage is proposed and evaluated, and their capacity contribution is calculated through the effective load carrying capability. Furthermore, the microgrid concept is examined, where multi-generation technologies collaborate to provide capacity services to internal customers but also to the remaining network. Finally, a distributed software infrastructure is examined which could be effectively used to support services in smart grids. The underlying framework for the reliability analysis is based on Sequential Monte Carlo Simulations, capturing inter-temporal constraints of the resources (payback effects, dynamic rating, DR profile, storage remaining available capacity) and the stochasticity of electrical and ICT equipment. The comprehensive distribution network reliability analysis includes network reconfiguration, restoration process, and ac power flow calculations, supporting a full risk analysis and building the risk profile for the arising smart distribution networks. Real case studies from ongoing project in England North West demonstrate the concepts and tools developed and provide noteworthy conclusions to network planners, including to inform design of DR contracts.

In this dissertation, we introduce and study robust optimization models and decomposition algorithms in order to deal with the uncertainties such as terrorist attacks, natural disasters, and uncertain demand that are becoming more and more signicant in power systems operation and planning. An optimal power grid hardening problem is presented as a defender-attacker-defender (DAD) sequential game and solved by an exact decomposition algorithm. Network topology control, which is an eective corrective measure in power systems, is then incorporated into the defender-attacker-defender model as a recourse operation for the power system operator after a terrorist attack. Computational results validate the cost-eectiveness of the novel model. In addition, a resilient distribution network planning problem (RDNP) is proposed in order to coordinate the hardening and distributed generation resource placement with the objective of minimizing the distribution system damage under uncertain natural disaster events. A multi-stage and multi-zone based uncertainty set is designed to capture the spatial and temporal dynamics of a natural disaster as an extension to the N-K worst-case network interdiction approach. Finally, a power market day-ahead generation scheduling problem, i.e., robust unit commitment (RUC) problem, that takes account of uncertain demand is analyzed. Improvements have been made in achieving a fast