Secure Real-Time Monitoring and Management of Smart Distribution Grid using Shared Cellular Networks

The electricity production and distribution is facing two major changes. First, the production is shifting from classical energy sources such as coal and nuclear power towards renewable resources such as solar and wind. Secondly, the consumption in the low voltage grid is expected to grow significantly due to expected introduction of electrical vehicles. The first step towards more efficient operational capabilities is to introduce an observability of the distribution system and allow for leveraging the flexibility of end connection points with manageable consumption, generation and storage capabilities. Thanks to the advanced measurement devices, management framework, and secure communication infrastructure developed in the FP7 SUNSEED project, the Distribution System Operator (DSO) now has full observability of the energy flows at the medium/low voltage grid. Furthermore, the prosumers are able to participate pro-actively and coordinate with the DSO and other stakeholders in the grid. The monitoring and management functionalities have strong requirements to the communication latency, reliability and security. This paper presents novel solutions and analyses of these aspects for the SUNSEED scenario, where the smart grid ICT solutions are provided through shared cellular LTE networks

PMU-based estimation of voltage-to-power sensitivity for distribution networks considering the sparsity of Jacobian matrix

With increasing integration of various distributed energy resources, electric distribution networks are changing to an energy exchange platform. Accurate voltage-to-power sensitivities play a vital role in system operation and control. Relative to the off-line method, measurement-based sensitivity estimation avoids the errors caused by incorrect device parameters and changes in network topology. An online estimation of the voltage-to-power sensitivity based on phasor measurement units is proposed. The sparsity of the Jacobian matrix is fully used by reformulating the original least-squares estimation problem as a sparse-recovery problem via compressive sensing. To accommodate the deficiency of the existing greedy algorithm caused by the correlation of the sensing matrix, a modified sparse-recovery algorithm is proposed based on the mutual coherence of the phase angle and voltage magnitude variation vectors. The proposed method can ensure the accuracy of estimation with fewer measurements and can improve the computational efficiency. Case studies on the IEEE 33-node test feeder verify the correctness and effectiveness of the proposed method

Non-intrusive load management system for residential loads using artificial neural network based arduino microcontroller

The energy monitoring is one of the most important aspects of energy management. In fact there is a need to monitor the power consumption of a building or premises before planning technical actions to minimize the energy consumption. In traditional load monitoring method, a sensor or a group of sensors attached to every load of interest to monitor the system, which makes the system costly and complex. On the other hand, by Non-Intrusive Load Monitoring (NILM) the aggregated measurement of the building’s appliances can be used to identify and/or disaggregate the connected appliances in the building. Therefore, the method provides a simple, reliable and cost effective monitoring since it uses only one set of measuring sensors at the service entry. This thesis aims at finding a solution in the residential electrical energy management through the development of Artificial Neural Network Arduino (ANN-Arduino) NILM system for monitoring and controlling the energy consumption of the home appliances. The major goal of this research work is the development of a simplified ANN-based non-intrusive residential appliances identifier. It is a real-time ANN-Arduino NILM system for residential energy management with its performance evaluation and the calibration of the ZMPT101B voltage sensor module for accurate measurement, by using polynomial regression method. Using the sensor algorithm obtained, an error of 0.9% in the root mean square (rms) measurement of the voltage is obtained using peak-peak measurement method, in comparison to 2.5% when using instantaneous measurement method. Secondly, a residential energy consumption measurement and control system is developed using Arduino microcontroller, which accurately control the home appliances within the threshold power consumption level. The energy consumption measurement prototype has an accurate power and current measurement with error of 3.88% in current measurement when compared with the standard Fluke meter. An ANN-Arduino NILM system is also developed using steady-state signatures, which uses the feedforward ANN to identify the loads when it received the aggregated real power, rms current and power factor from the Arduino. Finally, the ANN-Arduino NILM based appliances’ management and control system is developed for keeping track of the appliances and managing their energy usage. The system accurately recognizes all the load combinations and the load controlling works within 2% time error. The overall system resulted into a new home appliances’ energy management system based on ANN-Arduino NILM that can be applied into smart electricity system at a reduced cost, reduced complexity and non-intrusively