Maximising the benefit of distributed wind generation through intertemporal active network management

The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme representing the next stage of Smart Grid deployment in the UK? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions the thesis develops and uses tools to study the optimisation of such schemes. The tools include a Dynamic Optimal Power Flow algorithm which combines a full AC-network model with the first fully flexible model of energy storage in this context and with a detailed model for demand flexibility. The model also includes the first implementation of principles-of-access in an optimal power flow, as well as the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. Finally, the thesis goes on to apply Dynamic Optimal Power Flow to a real case study representing a deployed UK Smart Grid. The thesis reaches a number of conclusions regarding operation of energy storage and flexible demand focusing on the role of losses (both electrical and storage), and the impact on the monetary flows. In addition it quantifies the ability of flexible demand and energy storage to reduce the cost of operating a system, and how that cost reduction impact on consumer payments