Increasing Network Utilisation Using Active Measures within Transmission System Planning Studies

In recent years, increasing the share of renewables against total electricity consumption has become a key target in long-term strategies for power systems worldwide. In the island of Ireland, for example, the transmission system operators (TSOs) have launched joint policies to harvest up to 80% of annual energy consumption from renewables by 2030, based on government policies. In order to meet this target, one of the main challenges is to mitigate curtailment of renewable energy due to network congestion. Build out of transmission capacity can support this requirement, and has conventionally been achieved by constructing new lines and line uprating. However, in the last few decades, these solutions have been associated with many technical, environmental, and social issues for TSOs, forcing them to evaluate and install different asset types, based on state-of-the-art technology, such as dynamic line rating (DLR), power flow control (PFC), and energy storage systems (ESS) in order to relieve congestion and reduce renewable curtailment. The main goal of this research is performance evaluation of such active measures for increasing the utilisation of existing networks through a techno-economic framework. A particular focus is placed on DLR systems, which are later optimised in conjunction with distributed static series compensation (DSSC), and battery energy storage systems (BESS). To achieve this goal, the requirements for wide-area implementation of indirect DLR systems are examined, methodologies are developed for high-level (regional) DLR potential assessment at early-stage planning, and as a test case, potential of DLR in Ireland is evaluated based on Met Éireann reanalysis (MÉRA) data. Subsequently, using machine learning techniques, algorithms are proposed for regional clustering, for those signals later utilised in planning studies, i.e. dynamic line rating, as well as solar and wind power, and then, applied on the island of Ireland with the same reanalysis data source. An investment optimisation model is developed to investigate planning for DLR systems, distributed static series compensation, and battery energy storage systems based on mixed linear integer programming, and a two-stage stochastic formulation. Unit commitment is also incorporated, as shares of the renewables implicitly rise in the planning process and require conventional units to be de-commited. Benders decomposition is utilised to solve the resulting MILP through a distributed computational framework, developed with multi-processing, socket and object-oriented programming in Python. A methodology for applying line N-1 security for DLR systems in emergency operation is then developed, with an additional pre-optimisation stage, which can be efficiently parallelised, including an economic study on different approaches for line N-1 security provision. Iterative contingency screening is also implemented in the Benders framework. In the process of model development, different types of flexibilities for deploying DLR systems, such as applying DLR before reconductoring (staged investment) using a multi-stage representation, and concurrent investments for DLR and DSSC on the same line, as well as planning with BESS via a multi-year formulation are also studied, using the IEEE RTS 96 and 24 bus systems as test cases. Coordinated investments for DLR, DSSC, and BESS are then studied on the Irish transmission system for a future year (2028), considering line N-1 security in emergency operation, primary contingency reserve, and a range of stability-oriented constraints. Finally, operational rolling scheduling is implemented, to better assess system operation and estimate operational costs. The economic and environmental benefits of wide-area implementation of DLR systems are then investigated, taking Ireland as a test case, while utilising iterative contingency screening within a parallelised framework.