Due to the increasing number of distributed renewable energy sources, the distribution grid faces new operational challenges. Information and Communication Technology (ICT) systems can resolve these challenges through grid services that use automation, monitoring, and real-time decision-making, helping maintain an acceptable operational state of the distribution grid. However, the reliance of the power system on the ICT system and vice versa in the so-called smart grid creates interdependencies between the systems, which present new pathways for failure propagation. Therefore, these interdependencies require special attention to ensure stable system operation in the face of these challenges. However, these interdependencies have not been studied extensively in the literature. This thesis investigates approaches to model, quantify and improve the performance and resilience of the smart grid infrastructure. The interdependencies are formalised as interconnectors, entities that exist in all the connected systems. These interconnectors consist of components from both systems, where the components are modelled as state variables. These state variables determine the interconnector state and the service delivered. Failures represented by a change in state variables may impact the state. These state variables are deployed in a discrete event simulation framework to determine the system performance over time. The simulation result is represented on a two-dimensional state-space diagram depicting the operational state and service delivered. This allows for the resilience analysis of a system under various scenarios. The interdependencies are further investigated by exploring the role of ICT-based grid services in power grids, whose state is defined based on ICT properties, such as latency. These properties are formalised using property graphs. The ICT properties obtained from these graphs are used to parameterise a finite state automaton model of a grid service states, which are then used to determine the state of the entire smart grid. Case studies of state estimation and adaptive protection highlight the application of this approach. The use of ICT in state estimation allows the distinction of a global and perceived view of the power grid, which influences decision-making in the face of challenges. This thesis further investigates the protection system in detail. The overcurrent protection system is adversely impacted by distributed generation, resulting in undesired phenomena such as protection blinding. This thesis characterises this phenomenon by proposing two indices that capture the protection trip time under the influence of distributed generation. These indices consider the electrical distance between faults, protection and distributed generation. These indices and simulation results identify the worst-impacted locations in the power grid in terms of protection trip time. They also identify fault locations under given assumptions that do not cause protection blinding. ICT can resolve protection blinding by adapting the sensitivity of protection relays. However, since faults must be cleared in a short timeframe, communication delays may adversely impact the fault-clearing time. A discrete event simulation model is proposed to study protection performance in distribution grids. Investigation of time distribution assumptions reveals that the lognormal distribution accurately captures the circuit breaker trip time. The impact of the distributed generation and communication delay on the protection system is determined by measuring fault-clearing times using discrete event simulation. Results show that for the system studied, protection blinding is critical for low impedance faults in grids with high fault levels, while high impedance faults are critical in grids with low fault levels. Moreover, sympathetic tripping is seen at increased distribution grid fault levels and fault impedance. Furthermore, while communication systems reduce fault clearing times, increased delays harm protection systems. Finally, communication system components like sensors can fail, preventing fault detection. This thesis proposes a genetic algorithm-based approach to optimally place redundant sensors, minimising protection blinding under communication uncertainty within a redundancy budget. Results demonstrate the algorithm's effectiveness in optimising redundant sensor locations, reducing system costs, and improving fault tolerance. For the system and scenarios investigated, an average of 60% redundant sensors are relocated, reducing the average protection trip time by 36.65% compared to a baseline approach that does not consider communication uncertainty. This encourages incorporating communication component failure considerations in power system planning.