IoE-Based Control and Monitoring of Electrical Grids

In many countries, renewable energy production already represents an important percentage of the total energy that is generated in electrical grids. In order to reach higher levels of integration, demand side management measures are yet required. In fact, different from the legacy electrical grids, where at any given instant the generation levels are adjusted to meet the demand, when using renewable energy sources, the demand must be adapted in accordance with the generation levels, since these cannot be controlled. In order to alleviate users from the burden of individual control of each appliance, energy management systems (EMSs) have to be developed to both monitor the generation and consumption patterns and to control electrical appliances. In this context, the main contribution of this chapter is to present the implementation of such an IoT-based monitoring and control system for microgrids, capable of supporting the development of an EMS.

Modeling and Validation of the Switching Techniques Applied to Back-to-Back Power Converter Connected to a DFIG-Based Wind Turbine for Harmonic Analysis

Most power quality problems for electrical grids connected to Doubly-Fed Induction Generators (DFIGs) include flicker, variations of the RMS voltage profile, and injected harmonics because of switching in power converters. These converters have different topologies with the back-to-back (B2B) topology being the most exploited in high-powered three-phase systems. Therefore, in this article a model of a DFIG connected to the B2B power converter is proposed to which different switching techniques are implemented for interharmonic propagation studies. The switching techniques that are implemented include the Sinusoidal PWM (SPWM), the third harmonic injection PWM (THIPWM), and the space vector PWM (SVPWM), to reduce the Total Harmonic Distortion (THD) index of voltage and current in both windings of the machine. MATLAB-Simulink® software is used for modeling and simulating the B2B power converter and the switching techniques. The proposed model is validated with an experimental prototype that includes a 3-kW DFIG, a 10 HP motor, a gear-box with a transmission ratio of 4.5: 1, a B2B power converter, and a three-phase transformer connecting the system to the electrical grid. Finally, it is shown that the results obtained from the experimental tests corroborate the correct operation of the proposed model.

Fault Management Architecture Based on a Digital Twin Approach

Fault management of systems is a key component in mission/operation success of each system or technology. Fault management can be implemented into various different applications, power generation, industrial processing, aviation and transportation, and electrical grids with combinations of renewable energy sources. As the complexity of the overall system design increases, reliance on just pure physics-based or pure data-based modeling is shown to be deficient in the accuracy of fault management. This work shows the potential of a combination of digital twin and a fault management algorithm. The algorithm is designed to be robust, accurate, reliable, and fast; it is based on both, physics and data-based model modeling. The algorithm compares physical and data-based approaches to provide the most reliable fault management solution, through a digital twin. The fault management algorithm is designed to use physics-based model validated on real/synthetic data (data-based model).

e-Quantum leap on a data highway: Planning for electric minibus taxis in sub-Saharan Africa's paratransit system

Minibus taxis are ubiquitous in the developing cities of the Global South. This versatile, and somewhat chaotic public transport system is now faced with the need to move to renewable energy. But the looming roll-out of electric vehicles poses a threat to the already fragile electrical grids of African cities. This chapter evaluates the energy requirements of decarbonisation and evaluates two types of data, passenger-based and vehicle-based, from research in South Africa that has modelled these taxis. Using these two data capture methods, we assess the energy requirements and charging opportunities for electric minibus paratransit in three African cities and compare the results of the two methods to assess their suitability for planning minibus taxi electrification.

RELIABILITY OF DISTRIBUTION ELECTRICAL GRIDS VOLTAGE 6-10 kV

В статье дана характеристика надежности распределительных электрических сетей. Приведена классификация и статистические данные технического состояния линий электропередачи напряжением 6-10 кВ. Проанализированы причины низкого уровня надежности сетей. The article describes the characteristics of the reliability of electrical distribution grids. The classification and statistical data of the technical condition of power transmission lines with a voltage of 6-10 kV are given. The reasons for the low level of network reliability are analyzed.

Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).

Analysis of the Response of a Static Reactive Power Compensator to Instability and Failure in Electrical grids

The reliability of electrical power systems has led to the implementation of new equipment with reliable technology to solve transient failures, in recent decades flexible AC transmission systems (FACTS) have been implemented in power grids, resulting in high levels of stability and control. One of the elements used is static VAR compensators (SVC), however there is very little information about the dynamic response of the device to network instability and electrical failures, for which Simulink analyses the response of the SVC. The device consists of a 47.1 MVar reactive power compensator and a 97.6 MVar inductive reactivator compensator, implemented in a three-phase 500 kV system. The results indicate the effectiveness of response against network instability while maintaining the stable voltage of the network, but against electrical failures the type and time of failure must be considered. In the case of phase-phase faults, the response of the SVC is limited with drops of 0.52 pu.