Demand response (DR) is one of the integral mechanisms of today’s smart grids. It enables
consumer energy assets such as flexible loads, standby generators and storage systems to
add value to the grid by providing cost-effective flexibility. With increasing renewable
generation and impending electric vehicle deployment, there is a critical need for large
volumes of reliable and responsive flexibility through DR. This poses a new challenge for the
electricity sector.
Smart grid development has resulted in the availability of large amounts of data from
different physical segments of the grid such as generation, transmission, distribution and
consumption. For instance, smart meter data carrying valuable information is increasingly
available from the consumers. Parallel to this, the domain of data analytics and machine
learning (ML) is making immense progress. Data-driven modelling based on ML algorithms
offers new opportunities to utilise the smart grid data and address the DR challenge.
The thesis demonstrates the use of data-driven models for enhancing DR from large
consumers such as commercial and industrial (C&I) buildings. A reliable, computationally
efficient, cost-effective and deployable data-driven model is developed for large consumer
building load estimation. The selection of data pre-processing and model development
methods are guided by these design criteria. Based on this model, DR operational tasks such
as capacity scheduling, performance evaluation and reliable operation are demonstrated for
consumer energy assets such as flexible loads, standby generators and storage systems. Case
studies are designed based on the frameworks of ongoing DR programs in different
electricity markets. In these contexts, data-driven modelling shows substantial improvement
over the conventional models and promises more automation in DR operations. The thesis
also conceptualises an emissions-based DR program based on emissions intensity data and
consumer load flexibility to demonstrate the use of smart grid data in encouraging
renewable energy consumption.
Going forward, the thesis advocates data-informed thinking for utilising smart grid data
towards solving problems faced by the electricity sector
