Minimization of the total transmission loss of an interconnected AC-DC grid plays an important role for the economic operation of the AC-DC grid. Different from the conventional AC grid where the transmission loss is usually minimized by reactive power regulation, the transmission loss of a meshed AC-DC grid can be optimized by adjusting the active power exchange between the AC and DC grids. Additionally, smaller DC voltage deviation after grid disturbances is very desirable since it can bring less impact to the operations of AC-DC grid. This paper firstly presents two improved sequential power flow algorithms for modular multilevel converters (MMCs) based AC-DC grid under DC power-voltage droop control. An optimization algorithm is then proposed to minimize the total loss of the AC-DC grid and the overall DC voltage deviation after the change of operating conditions. Adaptive droop control is used in the proposed optimization algorithm in which the power references are control variables solved from the optimal AC-DC power flow. The proposed algorithm is verified in an AC-DC grid consisting of a six-terminal DC grid connected to the IEEE 39-bus AC grid.

Conventional droop control methods for power-sharing in a multi-terminal high voltage DC (MTDC) grid lead to voltage deviation from the nominal value. Moreover, the power-sharing is inaccurate in the droop-controlled MTDC system. This paper proposes a secondary controller with a distributed architecture to compensate the voltage deviation and to achieve equal power sharing automatically among the converter stations. A distributed observer is used to estimate the average voltage of the converter stations. The proposed distributed controller and observer use local measurements of output powers and voltages, and only communicate with neighboring stations. Therefore, the requirement of the global information in the centralized secondary control schemes is eliminated, which reduces the communication requirement and improves the reliability of the MTDC grid. The case studies illustrate that the proposed distributed controller regulates the stations’ average voltage and shares the power mismatch accurately. Impact of the communication time-delays and the control gain on stability of the MTDC grid are also investigated using the Lyapunov-Krasovskii LMI condition. It is shown that reducing the control gain can stabilize the MTDC grid in the case of large communication time-delays.

Home energy management requires accurate information about the appliances’ consumption pattern. This information can help consumers save energy, control their usage by shifting their usage to off-peak hours and reduce their electricity costs. Non-intrusive load monitoring (NILM) in which the power consumption profile of appliances are extracted from the aggregated signal of a household, provides this information. For the NILM problem, machine learning approaches as the training-based solutions require large training datasets for an accurate disaggregation and the optimization-based approaches employs prior information about the characteristics of appliances. This paper proposes a novel event-based optimization algorithm. In its first stage, the prior information about appliances is extracted from the events of the consumption profiles of appliances by means of clustering. Then, a new event-based down-sampling method and transition filtering are designed for decreasing the computation time of optimization. At the last stage of the proposed algorithm, post-processing considering ON duration of appliances and varying states are proposed to increase the accuracy of the power profile reconstruction. The proposed approach was successfully tested for the low-frequency dataset of a house from the REDD. Numerical results show the advantages of the proposed algorithm, marked improvement over classification-based NILM considering small training dataset and its applicability in disaggregating the power consumption measured by the smart meter.

This paper proposes a novel distributed secondary controller for droop-controlled microgrids to regulate the frequency and voltage, and autonomously share the power mismatch. The proposed scheme is entitled generalized proportional-intergal finite-time controller (GPI-FTC). The proposed GPI-FTC is synthesized based on the control Lyapunov function method and modifying the conventional PI controller by adding a consensus term to the integrand dynamic. The proposed distributed GPI-FTC provides plug-n-play capability, scalability, and fast finite-time convergence of the system states. Moreover, a reactive power sharing (Q-sharing) method is designed to improve the sharing pattern of reactive power under exact voltage regulation. Also, a distributed voltage observer is developed for average voltage regulation. Performance of the proposed GPI-FTC is validated through numerical simulations of the detailed model of the microgrid, including small signal analysis, load change, DG outage, Q-sharing, and performance comparison.