Environmental concerns and long term energy security are the key drivers behind most current electric energy policies whose primary aim is to achieve a sustainable, reliable and affordable energy system. In a bid to achieve these aims many changes have been taking place in most power systems such as emergence of new low carbon generation technologies, structural changes of power system and introduction of competition and choice in electricity supply. As a result of these changes, the level of uncertainties is growing especially on generation side where the locations and available capacities of the future generators are not quite clear-cut. The transmission network needs to be flexibly and economically robust against all these uncertainties. The traditional operation of the network under preventive control mode is an inflexible practice which increases the total system cost. Corrective control operation strategy, however, can be alternatively used to boost the flexibility, to expedite the integration of the new generators and to decrease the overall cost. In this thesis, the main focus is on development of new techniques and methodologies that can be used for modelling and solving a transmission planning problem under the assumption that post-contingency corrective actions are plausible. Three different corrective actions, namely substation switching, demand response and generation re-dispatch are investigated in this thesis. An innovative multi-layer procedure deploying a genetic algorithm is proposed to calculate the required transmission capacity while substation switching is deployed correctively to eradicate the post-fault network violations. By using the proposed approach, a numerical study shows that the network investment reduces by 6.36% in the IEEE 24 bus test system. In another original study, generation re-dispatch corrective action is incorporated into the transmission planning problem. The ramp-rate constraints of generators are taken into account so that the network may be overloaded up to its short-term thermal rating while the generation re-dispatch action is undertaken. The results show that the required network investment for the modified IEEE 24 bus test system can be reduced by 23.8% if post-fault generation re-dispatch is deployed. Furthermore, a new recursive algorithm is proposed to study the effect of price responsive demands and peak-shifting on transmission planning. The results of a study case show that 7.8% of total investment can be deferred. In an additional study on demand response, a new probabilistic approach is introduced for transmission planning in a system where direct load curtailment can be used for either balancing mechanism or alleviating the network violations. In addition, the effect of uncertainties such as wind power fluctuation and CO2 emission price volatility are taken into account by using Monte Carlo simulation and Hypercube sampling techniques. Last but not least, a probabilistic model for dynamic thermal ratings of transmission lines is proposed, using past meteorological data. The seasonal correlations between wind power and thermal ratings are also calculated. £26.7 M is the expected annual benefit by using dynamic thermal ratings of part of National Grid's transmission network.

Power grids have rapidly evolved into Smart grids and are heavily dependent on Supervisory Control and Data Acquisition (SCADA) systems for monitoring and control. However, this evolution increases the susceptibility of the remote (VMs, VPNs) and physical interfaces (sensors, PMUs LAN, WAN, sub-stations power lines, and smart meters) to sophisticated cyberattacks. The continuous supply of power is critical to power generation plants, power grids, industrial grids, and nuclear grids; the halt to global power could have a devastating effect on the economy's critical infrastructures and human life. Machine Learning and Deep Learning-based cyberattack detection modeling have yielded promising results when combined as a Hybrid with an Intrusion Detection System (IDS) or Host Intrusion Detection Systems (HIDs). This thesis proposes two cyberattack detection techniques; one that leverages Machine Learning algorithms and the other that leverages Artificial Neural networks algorithms to classify and detect the cyberattack data held in a foundational dataset crucial to network intrusion detection modeling. This thesis aimed to analyze and evaluate the performance of a Hybrid Machine Learning (ML) and a Hybrid Deep Learning (DL) during ingress packet filtering, class classification, and anomaly detection on a Smart grid network.