Electrical and Electronic Engineering, Imperial College London
Doi
Abstract
Humanity has recently embarked on an ambitious journey to decarbonise the world in an effort to reverse the adverse effects of the practices deployed the past two centuries, which have led us to a serious climate change. Regulation and policy have been introduced by countries worldwide to encourage, promote and compensate these efforts, including the UK’s recent announcement pledging to be the first major economy eliminating their contribution to carbon emissions. A comprehensive framework for coordinating actions to reduce CO2 emissions is expected to be put forward addressing the challenges arising, including a significant transformation of the energy system, a restructuring of the energy market and a new way of engaging with the wider public to making what is conceptually significant, perceptually prominent.
Within this context, the concepts of smart grids and, in a smaller scale, microgrids have arisen to assist these decarbonisation efforts. Microgrids have a great potential to actively contribute to grid health status, however the current practices in network design and operation hinder the capabilities of these types of networks. In fact, they do not allow microgrids to realise their potential and enable a paradigm shift in delivering resilience and security of supply from redundancy in network assets and preventive control to a more intelligent operation at the distribution level through corrective control actions. This thesis proposes innovative design and operational models for microgrids, and particularly hybrid AC/DC microgrids, that optimise the total system cost while satisfying pre-specified resilience targets. The modelling framework introduced comprises of a tailor-made genetic algorithm (i.e. for optimal sizing) combined with a detailed AC optimal power flow (OPF) that captures the technical characteristics of both the AC and DC subgrids with an extensive set of technologies considered. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and therefore can meaningfully address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity, which is why it has been particularly developed for the needs of this thesis. An innovative Dynamic Stability Constrained OPF is proposed as an extension that incorporates differential equations, such as the swing equation, characterising the operation of power systems. This is achieved via appropriate conversion of the equations to numerically-equivalent algebraic equations. This novel aspect will enable optimal decisions to be taken considering stability properties, which are undeniably necessary in the context of energy systems with renewable penetration of above 50% and are proven to significantly impact the system cost.
Resilience is also central to this thesis, hence it is discussed and limitations of current research typically confusing resilience with reliability are identified. Subsequently, a definition to help the industry merge towards a common understanding and a way of quantifying resilience (particularly relevant to microgrids) are proposed.
As a last step, this thesis identifies the imminent digitalisation energy systems are undergoing and utilising its merits, it introduces a strategy for interaction between distribution networks (incorporating microgrids as one type of resource) and transmission systems with the focus being on exchange of voltage support services. The operational models developed in this thesis could prove to be useful towards optimising the portfolio of assets to provide the services required.Open Acces
