Smart Grids—Renewable Energy, Power Electronics, Signal Processing and Communication Systems Applications

Wireless sensors have gained popularity in smart grids scenario, and their increasing number, together with efficient data processing and decision-making, can optimize the entire operation of electricity generation and distribution. In order to meet the sensor energy demand, low-power energy harvesting systems have become a promising alternative since their principle is converting energy available in the surrounding environment into useful electrical energy to supply low-power devices. The advantage of this technique is the abundance and availability of ambient energy sources that, despite having low energy density, are sufficient to individually power each intended sensor continuously, presenting similar versatility to batteries but without constant replacement. In this context, this chapter intends to survey the leading energy harvesting techniques for wireless power sensors in a smart grid scenario, focusing on mechanical, Radio Frequency (RF), and solar sources, presenting their basic topologies, working principles, capabilities, and main characteristics.

This chapter introduces the role of hydrogen in the current energy system transition: from fossil-based to renewable and low-carbon emission sources. Although solar and wind energy are abundant renewable sources, the intermittence of electricity generation remains a challenge for security of supply and causes instabilities in the electricity grid. The integration of green hydrogen produced by water electrolysis into a smart energy system –or a smart grid–, is considered a promising solution to overcome the handicaps of the renewable electricity production and certain hard-to-decarbonize industrial sectors. The principle of water electrolysis along with the different electrolyzer technologies is also presented in the first section. In the second section, a numerical model of an industrial alkaline water electrolyzer plant is described. The different unit operators that comprise the system to produce purified hydrogen are individually introduced. The chapter concludes by showing the capabilities of an off-grid water electrolyzer system, which consists of a battery energy system and solar PV and wind power installations. Simulation of the plant demonstrates, as a proof of concept, the feasibility of the system for future integration into a smart energy system.

Electricity remains a key element for world development, and the increase in the demand for electrical energy in the industrial, commercial and residential sectors, the predicted exhaustion of fossil fuel reserves (e.g. oil, coal), the environmental risks of nuclear energy, the effects of global warming in addition to other environmental issues makes it necessary to develop alternative/renewable and non-conventional sources for the electrical energy generation. Electricity generation, based on renewable non-conventional sources, can play an important role in global energy security and contribute to the reduction of greenhouse gas emissions. The use of these renewable energies can help to reduce energy consumption based on fossil fuel, which is the biggest source of CO\(_{2}\) emissions. Some of these alternative sources, e.g. wind, and photovoltaic, suffer from seasonality and intermittency, which represents a challenge to guarantee the adequate quality and dispatchability of the source. This limitation can be reduced and/or eliminated with the use of an Energy Storage System (ESS), allowing the energy system to be managed optimally.

Due to the characteristics of combining energy generation with waste management, the use of distributed generation (DG) systems, based on biogas, has received special attention among renewable resources. On the other hand, the DG connection brings up the possibility of an equipment to keep feeding a part of the distribution grid comprising DG and local load in a condition known as unwanted islanding. Regarding this, along the years, concerns have been reported related to the safety of utilities staff, power quality issues, reclosers misoperation and DG damage risk. To prevent it, in Brazil, the utilities provide standards stating that, in case of unwanted islanding, the DG must be disconnected from the utility grid. Once the biogas based generation is usually performed applying synchronous machines as DG, its ability to keep running in islanding conditions poses special challenges to this task. In this chapter, a review over the techniques applied for islanding detection with synchronous machines is done, as well as an analysis of the Brazilian standards requirements. The working principle and setup procedure of the mandatory methods are evaluated, pointing up, new challenges over islanding detection with synchronous machines.

Distributed energy resources (DERs) have been acknowledged as strategic assets to support the continuous growth of global electricity demands. Besides, the constant growth of DER installations worldwide will significantly alter the way power systems are planned and operated, primarily due to the bidirectional power flow characteristics and the intermittent nature of DERs generation. In this context, distributed energy resources management system (DERMS) are a crucial technology to allow seamless integration, DER situational awareness, support by driving electrical market operations, and enabling grid services in the distribution network. Over the recent years, DERMS research and development has been conducted to establish not only the functional requirements for such technologies to operate but also defining open standards for data exchange and communication protocols contributing to facilitating the synergy between DERMS applications and the existing monitoring and control elements such as Energy Management Systems (EMS). As part of the efforts to establish the technology, utilities worldwide have been conducting DERMS pilot projects as a way to investigate the available benefits in real-world scenarios and possible gaps that still need to be addressed.

This chapter aims to present the optimal models of a VPP and the challenges and solutions to its implementation. First, it provides an introduction to applications of a VPP in Smart Grids and describes the definition and components of a VPP by using examples from the real world. Then, it presents the other applications of VPPs in using EVs and in power system security to cover different applications of a VPP. Next, it includes a brief review of optimization and scheduling problems faced by VPPs participating in electricity markets. Finally, it targets the application of a VPP in transportation systems, including electric vehicles. The chapter concludes with the presented contents and possible future research in this field of study by clarifying the book’s scope in smart grids and renewable energy.

A reduced number of smart meters in low-voltage (LV), especially in the extensive ones, limits the application of centralized, decentralized, or distributed voltage control in this type of network with small-scale residential photovoltaic (PV) systems. However, in local control, controllers can respond fast to distributed generation variability and are not affected by communication failures. Thus, local voltage control methods can mitigate the overvoltage using droop control curves in PV inverters, which are set offline in pre-operational studies. This chapter presents four local control methods for overvoltage mitigation in LV networks with PV generation. Three of these methods use one smart control functionality of PV inverters, while the other uses two smart control functionalities in a coordinated way. Power flow simulations are performed using data from a Brazilian real-world and extensive LV network to compare the performance of these control methods. Besides, a broader comparison is conducted by analyzing power losses in the network, the transformer power factor, and transformer loading. Finally, the above methods are evaluated considering several PV integration scenarios. This analysis can help utilities use one or more local voltage control methods to accommodate more PV systems in their LV feeders.

Wind generation has been shown to be a suitable alternative source of energy in the context of smart grids. Among the machines used in wind generation systems, the permanent magnet synchronous machine (PMSM) stands out for its high power density, high efficiency, absence of rotor windings, among others. This chapter is dedicated to the study of the variable-speed-driven three-phase surface-mounted permanent magnet synchronous machine (SMPMSM) applied to power generation. The machine model is presented in three-phase, stationary orthogonal \(\alpha \beta \) and synchronous orthogonal dq coordinate systems, as well as the waveforms of the back electromotive force (back-EMF) per phase in each coordinate system. In the case of SMPMSMs with a non-sinusoidal back-EMF waveform, the \(dq_x\) transformation is applied in order to reduce the electromagnetic torque ripple. Based on the machine model in an appropriate reference frame, it is possible to design a decoupled control of torque; as an example, the proportional-integral controller is adopted. In addition, to eliminate the need to use axis angular position sensors or to increase the redundancy, the concepts of rotor position estimation are introduced, and a strategy presented is the use of the Kalman filter together with phase-locked loops. Finally, a study using data from a real machine, employed in wind turbines, exemplifies the concepts covered in this chapter.

Household power generation is a key part of smart grids. Among the power sources, variable speed wind turbines offer good power output with reliability. This chapter investigates the use of a squirrel cage induction generator in a variable speed drive system for the aforementioned application. In this downsized scale of power, reduced-count switch converters are interesting alternatives, for the sake of cost and size reduction. Therefore, this work applies the Nine-Switch Converter (NSC) in the integration of a squirrel cage induction generator onto the grid, with use of Finite Control Set Model Predictive Control (FCS-MPC), a powerful control theory, specially for power electronics. The converter is analyzed, with focus on its available voltage vectors, leading to the development of two structures of FCS-MPC: the concentrated approach and the decoupled approach. Furthermore, the decoupled approach enables the incorporation of duty cycle optimization into FCS-MPC for the NSC, which leads to improvements on the generator torque ripple and on the grid active power ripple. Additionally, considerations are made about the use of NSC wind energy system also for grid reactive power compensation. All the considerations are accompanied by simulated results of the considered system. As a result, the NSC is a feasible alternative for variable speed drive wind energy systems in household applications.

The Doubly Fed Induction Generator (DFIG) is widely employed in wind energy and this type of source is interesting to the smart grids environment. In this way, this chapter proposes an electromagnetic analysis using the finite element method for the DFIG based Wind Energy System, during its vector controlled operation by means of a proportional-integral (PI) controller. Moreover, a new design method for the PI controller gains obtention is presented, based on the fact there is no guarantee that the DFIG will operate in the unsaturated condition. Therefore, it can compromise the performance of the power control strategy. Simulation and experimental results endorse the analyzes during DFIG normal operating conditions.

Reliable protection schemes for modern electrical energy systems play a vital role for minimizing the consequences of fault occurrences. Taking advantage of the processing and communication power of intelligent electronic devices, this chapter presents innovative solutions based on different signal processing techniques for fault detection, classification and location in power systems. The presented methods are based on Euclidean Distance and Independent Components Analysis, proving that more accurate and fast solutions can be reached when using the resources available in modern power grids, implementing smart grids concepts. The presented methods were tested against a system with real characteristics and then compared with conventional methods.

The increasing penetration of power electronics loads in smart grids inherently leads to severe concerns about power quality (PQ) disturbances. This chapter presents the principles of synthesizing control references for an active power filter (APF), which is placed in a smart grid comprising distortion loads, aiming at achieving PQ enhancement and compliance with standardized indexes. In addition, it is argued that the APF control system requires harmonic content identification to generate the targeted compensating currents. Thus, to achieve the disturbance recognition expected for synthesizing control references, harmonic analysis methods can be devised by automation tools and artificial intelligence (AI). For instance, it is demonstrated here that deep neural networks (DNN) can be used in the main pattern recognition stage, simplifying the harmonic content estimation process. The DNN-estimated harmonics are then directly used within the APF control system to compensate disturbances aiming at reducing the total harmonic distortion in a smart grid. Finally, this chapter also presents case studies using experimental load currents to depict the feasibility of the DNN-based method.

This chapter presents a study of a system for remote control of active and reactive power injected into the electrical grid based on the Short Message Service (SMS) of a mobile telephone communication network. Wireless communication is widely used in SG and SMS has become a viable option due to its simplicity and low cost. There are numerous solutions for this application regarding the power control of the inverter of a Renewable Energy System (RES), however, this chapter will focus on the use of the Finite Control Set Model Predictive Control (FCS-MPC). In this type of control, the mathematical model of the inverter connected to the electrical grid is used so that it is possible to forecast the active and reactive power. In the optimization process, the cost function selects the switch states so that the controller reaches the desired active and reactive power references. These references are then sent via SMS by the SG operator to the RES under control. The results obtained by the proposed system were validated on an experimental bench.

This chapter presents a wireless sensor network as a solution for environment monitoring in the context of smart grids in smart cities that combines the usage of two IoT solutions, one for measuring toxic gases, temperature, humidity, UV index and fire presence. and the other for monitoring the garbage collection by tracking the trucks and street sweepers carts movements to guarantee its coverage throughout the city. The two solutions shown effectiveness on obtaining this information dealing with internet connection oscillations, making the data available though a web application where the data can be exported. The tests not only proven its effectiveness as it shows a direction on a combined environmental monitoring that can address fire and environmental hazards using technology.