Smart meters generate a massive volume of energy consumption data which can be analyzed to recover some interesting and beneficial information. Non-intrusive load monitoring (NILM) is one important application fostered by the mass deployment of smart meters. This paper presents a supervised event-based NILM approach for non-linear appliance activities identification. Firstly, the additive properties (stating that, when a certain amount of specific appliances’ feature is added to their belonging network, an equal amount of change in the network’s feature can be observed) of three features (harmonic feature, voltage–current trajectory feature, and active–reactive–distortion (PQD) power curve features) were investigated through experiments. The results verify the good additive property for the harmonic features and Voltage–Current (U-I) trajectory features. In contrast, PQD power curve features have a poor additive property. Secondly, based on the verified additive property of harmonic current features and the representation of waveforms, a harmonic current features based approach is proposed for NILM, which includes two main processes: event detection and event classification. For event detection, a novel model is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Compared to other event detectors, the proposed event detector not only can detect both event timestamp and two adjacent steady states but also shows high detection accuracy over public dataset with F1-score up to 98.99%. Multi-layer perceptron (MLP) classifiers are then built for multi-class event classification using the harmonic current features and are trained using the data collected from the laboratory and the public dataset. The results show that the MLP classifiers have a good performance in classifying non-linear loads. Finally, the proposed harmonic current features based approach is tested in the laboratory through experiments, in which multiple on–off events of multiple appliances occur. The research indicates that clustering-based event detection algorithms are promising for future works in event-based NILM. Harmonic current features have perfect additive property, and MLP classifier using harmonic current features can accurately identify typical non-linear and resistive loads, which could be integrated with other approaches in the future.

High penetration of Photovoltaic (PV) to the distribution network may bring under-voltage and over-voltage issues, limiting the PV hosting capacity. Air conditioners (AC) in grid-interactive buildings can support voltage regulation by manipulating flexible energy consumption. This paper developed a novel voltage control strategy to regulate the AC clusters’ on/off states for distribution network voltage regulation under high PV penetrations. The novelty lies in the distributed formulation of temperature priority-based on/off control (TPC) of AC clusters and the strategic selection and permutation of demand response technologies, including the real-time optimal demand response resources dispatch, distributed sensing of ACs based on average consensus algorithm, and the local implementation of TPC strategy and trial calculation scheme for flexibility capacity estimation. Finally, the distributed TPC is validated to be effective for system rebalancing with no comfort violations and an acceptable ON/OFF switching frequency. The theoretical and numerical analysis also proves its scalability and robustness to communication delays and link failures. It is then incorporated into a novel hierarchical control framework for smart grid voltage control in a four-bus three-phase test grid, considering the voltage sensitivities to power injections in different locations and phases.