Saglam, Mustafa; (2024) Large-Scale Integration of Renewables and the Role of Interconnections in the Future Electricity System – the Case of Türkiye Mainland and Islands. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This study was designed and analyzed to provide access to a reliable and affordable electricity supply of renewable energy (RE) and interconnections in line with regional and nodal targets for the integration of RE between Türkiye islands and the mainland, and also to perform future energy forecasting with high accuracy. A combination of machine learning (ML) methods, including Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), and Multiple Linear Regression (MLR), was employed to estimate the electricity demand for mainland and Region 1 islands (Bozcaada and Gokceada), and the peak load of the mainland until 2040. The ISLA Model, a simulation model developed to analyse the future energy supply and demand scenarios, was employed to forecast sectorial and total electricity demand for Region 2 and Region 3 islands until 2040. Various independent input parameters, such as population, imports, exports, gross domestic product (GDP), car numbers, and tourist-passenger numbers, were analysed based on historical data. The performance of the methods was evaluated using statistical errors, including Determination Coefficient (R 2 ), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), and the correlation matrix was utilized to examine the relationship between variables and methods. The electricity demand forecasting results made using ML and the ISLA model were integrated into the PLEXOS, an integrated energy modelling tool, to simulate future scenarios incorporating spatial and temporal resolution modelling and RE systems deployment on the islands and mainland and the final results obtained were compared on a scenario-based basis in the Energy Trilemma Index (ETI- Security, Sustainability & Equity) and the optimum scenario was determined. Through capacity and transmission expansion optimization in different long-term (LT) scenarios, future electricity sources' mix and power generation costs were estimated at regional and national levels.

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