Enhancing the Treatment of Systems Integration in Long-term Energy Models
Abstract: Securing access to affordable energy services is of central importance to our societies. To do this sustainably, energy systems design should be – amongst other things – environmentally compliant and reconcile with the integrated management of potentially limiting resources.This work considers the role for so-called 'Smart Grids' to improve the delivery of energy services. It deals with the integration of renewable energy technologies to mitigate climate change. It further demonstrates an approach to harmonise potentially conflicting energy, water and land-use strategies. Each presents particular challenges to energy systems analysis.Computer aided models can help identify energy systems that most effectively meet the multiple demands placed on them. As models constitute a simple abstraction of reality, it is important to ensure that those dynamics that considerably impact results are suitably integrated. In its three parts, this thesis extends long-term energy system models to consider improved integration between: (A) supply and demand through Smart Grids; (B) timeframes by incorporating short-term operating constraints into long-term models; and (C) resource systems by linking multiple modelling tools.In Part A, the thesis explores the potential of Smart Grids to accelerate and improve electrification efforts in developing countries. Further, a long-term energy system model is enhanced to investigate the Smart Grid benefits associated with a closer integration of supply, storage and demand-side options. In Part B, the same model is extended to integrate flexibility requirements. The benefits of this integration are illustrated on an Irish case study on high levels of wind power penetrations. In Part C, an energy model is calibrated to consider climate change scenarios and linkages with land-use and water models. This serves to assess the implications of introducing biofuels on the small island developing state of Mauritius.The thesis demonstrates that too weak integration between models and resource systems can produce significantly diverging results. The system configurations derived may consequently generate different – and potentially erroneous – policy and investment insights.
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