The dynamic response of the demand is defined as the time-domain real and reactive power response to a voltage disturbance, and it represents the dynamic load characteristics. This thesis develops a methodology for probabilistic estimation and prediction of dynamic responses of the demand at bulk supply points. The main outcome of the research is being able to predict the contribution of different categories of loads to the total demand mix and their controllability without conducting detailed customer surveys or collecting smart meter data, and to predict the dynamic response of the demand without performing field tests. The prediction of the contributions of different load categories and their controllability and load characteristics in the near future (e.g., day ahead) plays an important role in system analysis and planning, especially in the short-term dispatch and control. However, the research related to this topic is missing in the publically available literature, and an approach needs to be developed to enable the prediction of the participation of different loads in total load mix, their controllability and the dynamic response of the demand. This research contributes to a number of areas, such as load forecasting, load disaggregation and load modelling. First, two load forecasting methodologies which have not been compared before are compared; and based on the results of comparison and considering the actual requirements in this research, a methodology is selected and used to predict both the real and reactive power. Second, a unique methodology for load disaggregation is developed. This methodology enables the estimation of the contributions of different load categories to the total demand mix and their controllability based on RMS measured voltage and real and reactive power. The confidence level of the estimation is also assessed. The methodology for disaggregation is integrated with the load forecasting tool to enable prediction of load compositions and dynamic responses of the demand. The prediction is validated with data collected from real UK power network. Finally, based on the prediction, an example of load shifting is used to demonstrate that different dynamic responses can be obtained based on the availability and redistribution of controllable devices and that load shifting decisions, i.e., demand side management actions, should be made based not only on the amount of demand to be shifted, but also on predicted responses before and after load shifting.

Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.

[pt] Desagregação de cargas é a tarefa de estimar o consumo individual de aparelhos elétricos a partir de medições de consumo de energia coletadas em um único ponto, em geral no quadro de distribuição do circuito. Este trabalho explora o uso de técnicas de aprendizado de máquina para esta tarefa, em uma base de dados coletada em uma fábrica de ração de aves no Brasil. É proposto um modelo combinando arquiteturas de autoencoders variacionais com as de fluxos normalizantes inversíveis. Os resultados obtidos são, de maneira geral, superiores aos melhores resultados reportados para esta base de dados até então, os superando em até 86 por cento no Erro do Sinal Agregado e em até 81 por cento no Erro de Desagregação Normalizado dependendo do equipamento desagregado. / [en] Load Disaggregation is the task of estimating appliance-level consumption from a single aggregate consumption metering point. This work explores machine learning techniques applied to an industrial load disaggregation dataset from a poultry feed factory in Brazil. It proposes a model that combines variational autoencoders with invertible normalizing flows models. The results obtained are, in general, better than the current best reported results for this dataset, outperforming them by up to 86 percent in the Signal Aggregate Error and by up to 81 percent in the Normalized Disaggregation Error.