Data Analytics-Based Demand Profiling and Advanced Demand Side Management for Flexible Operation of Sustainable Power Networks

This chapter introduces the two main areas of the research presented in this thesis, with some theoretical aspects. It provides an overview of the past work in these areas, with respect to both research and real-world practices. After identifying some of the gaps in previous research on the topic, the main aims and objectives are defined, followed by extracting the main contributions of this thesis.

The research reported in this thesis deals with two important and very timely aspects of the future power system operation - assessment of demand flexibility and advanced demand side management (DSM) facilitating flexible and secure operation of the power network. The thesis provides a very clear and comprehensive literature review in these two areas and states precisely the original contributions of the research. It first demonstrates the benefits of data mining for a reliable assessment of demand flexibility and its composition even with very limited observability of the end-users. It then illustrates the importance of accurate load modelling for efficient application of DSM and considers, for the first time, different criteria in designing DSM programme to achieve several objectives of the network performance simultaneously, not only demand reduction as it is the current practice. Finally, it demonstrates the importance of considering realistic assumptions when planning and estimating the success of DSM programs, as these could have a strong impact on the network performance at both transmission and distribution level. The findings of the thesis have both scientific and practical significance, and can be further used as a guidance for application in industry. The thesis also represents an excellent basis for the continuation of research in the aforementioned areas. The research results presented in the thesis have been published in 2 leading international journal papers, 13 international conference papers and embeded in 21 technical reports. Finally, taking all the above into account, including excellent technical presentation of the thesis and a successful defence of the research by the student, the examiners agreed to award the thesis as written with no corrections needed.

The rollout of smart meters (SMs) in distribution networks should enhance the observability of the demand side. In order to make this observability useful to the distribution network operator (DNO) and/or other demand response (DR) responsible parties, information about time varying demand composition and its flexibility (both in close to real time and forecast) should also be provided.

This chapter illustrates the concept of multi-objective DSM in a distribution network in support of transmission network operation. The methodology builds on the results of the methodology on Advanced Demand Profiling, detailed in the previous chapter. Information about demand composition is used to model demand at each load bus of the network, facilitating that way further studies of the effect DSM may have on network performance indicators.

This thesis has presented the results of the research performed in two main areas: decomposition of aggregated demand for demand-side flexibility assessment, and optimised DSM. The main aim of the research was to develop a methodology for multi-objective DSM in distribution network in support of transmission network operation, relying on the existence of a certain number of SMs with sub-metering technologies and application of data analytics methods, namely ANN.