Advancing energy and climate planning models [electronic resource] : optimisation methods, variable renewables, and smart grids
Keywords: Management Science and Engineering.

This dissertation aims to advance the application of mathematical modelling and computing, in particular optimisation methods, to the planning of solutions to energy and climate problems. The work first addresses two applied modelling problems relating to the electricity sector, a sector that is a major global source of greenhouse gas emissions, but also a potential provider of low carbon energy throughout the global economy. The dissertation then closes with an investigation into the appropriate formulation of the normative models used in planning, focusing on the choosing of model detail. At a high level, this work can be summarised as the development of tractable methods to incorporate necessary detail in models, followed by the introduction of a framework to understand when detail is necessary more generally. The first technical portion of this dissertation investigates how to represent intra-annual temporal variability in models of optimum electricity capacity investment. The mechanisms are shown by which inappropriate aggregation of temporal resolution can introduce substantial error into model outputs and associated economic insight, particularly in systems where variable renewable power sources are cost competitive and/or policy supported. For a sample dataset, a scenario-robust aggregation of hourly (8760) resolution is possible in the order of 10 representative hours when electricity demand is the only source of variability. The inclusion of wind and solar supply variability increases the resolution of the robust aggregation to the order of 1000. A similar scale of expansion is shown for representative days and weeks. These concepts, and underlying methods, can be applied to any such temporal dataset, providing a benchmark that any other aggregation method can aim to emulate. To the author's knowledge, this is the first time that the impact of variable renewable power sources on appropriate temporal representation has been quantified in this way. The next stage of the work considers the potential impact of emerging smart grid technologies, particularly those that enable electricity consumers to shift, automatically and optimally, their electricity demand in response to a price signal. In so doing, a model of a competitive electricity market, where consumers exhibit optimal load shifting behaviour to maximise utility and producers/suppliers maximise their profit under supply capacity constraints, is formulated and analysed. The associated computationally tractable convex optimisation formulation can be used to inform market design or policy analysis in the context of increasing availability of the smart grid technologies that enable optimal load shifting. Analytic and numeric assessment of the model allows assessment of the equilibrium value of optimal electricity load shifting, including how the value reduces as more electricity consumers adopt associated technologies. The sensitivity of the value to the flexibility of load is assessed, along with its relationship to the deployment of renewables. Additionally, a formulation of the model based on the Alternating Direction Method of Multipliers is presented. This particular optimisation method is desirable for its potential to scale to large problems. The applied modelling exercises provide examples for the final portion of the dissertation, a systematic assessment of model formulation, particularly relating to model detail. The normative models used for energy and climate planning explore long term pathways into uncharted territory. The test of predictive power used in other fields to evaluate model formulation is frequently not possible to apply in this long term context, nor necessarily makes sense in the normative context. This work introduces a conceptual framework that can potentially augment the necessary expert judgement in model formulation. It is based on the idea that some modelling decisions are testable, including the choice of model detail under certain conditions. The framework uses information theoretic principles to demonstrate the tradeoff between model detail and model accuracy for a given question, and can specifically aid with representing heterogenous spatial, temporal or population characteristics in models. This section of the dissertation represents an early attempt in a domain where limited systematic analysis has been undertaken to date.