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This paper presents an effective resonance suppression control for grid-interactive inverters with LCL filter in weak grid conditions. The resonance suppression mechanism is based on an adaptive model predictive control (AMPC). The large parasitic impedance and low SCR of weak grid challenges the operation of grid-interactive inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. The proposed AMPC scheme autonomously alter the controller objectives for suppressing the resonance. The proposed control scheme alters the feedback currents based on the grid-condition. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with desired current as constraint in the proposed AMPC cost function. The performance of proposed controller is verified experimentally. The results demonstrate that the proposed controller features robust performance under various grid conditions, model parameter mismatch, seamless transition between controller modes of operation and feedback alteration to attain resonance suppression, eliminates required tuning efforts in comparison to classical multi-nested loop control schemes, and provides high power quality.

Here, a finite-control-set sliding-mode control (FCS-SMC) method is proposed for single-phase three-level T-type inverter-based transformerless dynamic voltage restorers (TDVRs). The inherent advantages of SMC (fast dynamic response and high robustness) are combined with FCS concept to increase the effectiveness of the method in achieving the desired control objectives. First, the use of FCS eliminates the modulator requirement, which reduces the design complexity in the practical implementation. Second, an effective method based on charging/discharging conditions of DC capacitors is proposed for balancing capacitor voltages using relevant switching state rather than combining DC voltage error with the inductor current error through a suitable weighting factor in forming the cost function. Therefore, the weighting factor necessity in the control algorithm is eliminated. Finally, the steady-state error in the compensation voltage is eliminated by the proportional-resonant (PR) controller. The studied T-type inverter-based TDVR topology offers an alternative solution to the existing DVR topologies. Experimental results are carried out to demonstrate the effectiveness of the proposed FCS-SMC method in maintaining the load voltage at the desired level despite the voltage sags, swells, and distortions occurring in the grid voltage.

This article proposes an optimal current control technique with switching event minimization for grid-interactive cascaded multilevel inverters (CMI) interfaced with battery energy storage sources. The proposed control scheme enables power-balancing functionality of battery cells, realizing optimal smart operation of CMI. Model predictive control (MPC) is known as a potential approach for multiobjective control schemes in single-loop manner for power electronics interfaces. However, MPC schemes are suffering from high computational burden that is magnified in topologies like the CMI, which have a substantial number of redundant control actions. The proposed control scheme utilizes a dynamic lookup matrix as an internal optimizer tool. The redundant switching sequences are cycled to equalize the power drawn from the independent battery energy storage sources while achieving a minimum energy control. The theoretical analysis and experimental case studies verify robustness and computational efficiency of the proposed multicriteria optimal controller with similar objective tracking when compared to finite-set MPC.

This chapter sheds light on the business model concept, its main components and how it can be used to analyze the impact of smart grid (SG) technology to create, deliver and capture a value for utility businesses. The business model concept is based on two theories of management: the resource‐based view and transaction cost economics. The traditional electricity value chain has within it the generation, transmission, distribution, retail and consumption guide from energy source to end use. Electricity markets possess retail and wholesale components. Advancing a new business model (BM) needs a deep comprehension of basic consumer needs, why competitors did not manage to meet those needs, as well as the technological and organizational trajectories. The chapter presents a review of different business models which have previously been proposed for the SG. Blockchain technology improves the market role of customers and producers. Blockchain addresses complex issues for the future digital energy system.

This chapter reviews the net zero energy buildings (nZEB) definition, design, modeling, controlling, and optimization. Occupancies energy conservation behavior and their awareness and engagement are essentials for achieving the nZEC. The chapter presents the net zero energy community (nZEC) as an extension for applying the nZEB concept. Four usually known definitions for the ZEB are as follows: net zero site energy, net zero source energy, net zero energy costs, and net zero energy emissions. Several procedures have been proposed to create energy consumption models that imitates a building for load forecasting or price saving approximations. Considering the advancement of nZEBs and the complications of community‐level energy problems, the formation of nZECs is a developing goal for local energy efficiency and sustainability. The nZEC has been presented as a solution for the mismatch between on‐site renewable energy generation and nZEB demand.

This chapter addresses the status of artificial intelligence (AI) as a central element in smart grid (SG) while focusing on the recent progress of research on machine learning techniques to pave the future work in the SG area. The SG framework provides a performance toolkit based on information and communication technologies. The SG paradigm supervises and promotes grid operations at a high level of expertise. AI systems have attributed to the realization of sustainable development goals including the vital integration of renewable energy sources (RES). The scarcity of conventional energy resources in the near future and their increasing threats to the environment extremely require the transition toward RES. The bulk penetration of RES into the electrical grid leads to unstable and volatile power generation. The chapter presents the commonly applied AI methods to the SG system and the key elements of the evaluation procedure.

This chapter provides energy systems researchers and decision makers with a good insight into the fundamental drivers of customer acceptance of the smart grid (SG) and the logical steps for their engagement to apply the SG technology and make it feasible in a timely manner. A SG is responsive to consumer, utility, and energy market needs by complete and dynamic implementation of systems and processes. Although utility‐customer relationships were one‐sided, utilities usually allocated most of their resources to energy generation, distribution, and ratemaking. The challenge of accomplishing more customer motivation with SG technology is among behavioral changes. The chapter explains challenges for SG customer engagement and policy recommendations. The main challenges entail privacy issues, radio frequency safety, and potential rate rises. Consumer engagement within the electric power industry is an evolving, ongoing process that is just on the beginning to emerge.

The structure of the smart grid (SG) can be divided into two main parts, SG infrastructure and SG applications. This chapter offers novel insights and comprehensive analysis on the cyber security of the SG to ensure that the SG is highly secure against the cyber‐attack threat for both communication and physical networks, without impacting customer confidence in the utility provider, and without substantially inconveniencing customers. The structure of the SG from the viewpoint of security can be divided into three layers; the power system layer, the information layer, and the communication layer. Smart grids are electric grids with digital information/communication capabilities. Attacks on the SG network can be divided into three categories as follows: attacks on the physical layer, data injection and data integrity attacks, and network‐based attacks. The chapter presents the challenges of building resilience control in the power system and highlights the governmental efforts needed to secure the SG.

This chapter discusses the principles of current electrical power systems. The two main characteristics of conventional electrical power systems are: centralized energy generation and unidirectional power delivery systems. The traditional power grid is a centralized control and management system that uses a supervisory control and data acquisition. The chapter investigates the implications of the transformation trend toward smart grid (SG) architecture. The SG architecture consists of three main systems: power, communication, and information. The design and analysis of future SGs require fundamental insight into the impact of power network topology and integrated network control with Big Data utilization. The SG control architecture should therefore be dynamic and multilayer to handle real‐time operation and provide tradeoff between performance and implementation. The transfer of the traditional grid to a SG requires modifications and upgrades at various levels of the electric grid. Decentralization enables active elements of the system but requires a high level of coordination.

Smart grid (SG) offers multi‐way communication among energy generation, transmission, distribution, and usage facilities. This chapter sheds light on the communication challenges and requirements for SG and describes most suitable communication architecture and technologies including wired and wireless technologies. Advanced metering infrastructure (AMI) with the employment of smart meter and communication techniques is crucial to manage the SG. AMI is used for financial benefits, enhanced services, and opportunities for taking into account environmental concerns. The chapter summarizes the challenges and characteristics of communication needed for SG applications. SG communications have certain conditions from technical and economic standpoints, such as quality of services, scalability, flexibility, interoperability, and security. The communication infrastructure in SG should support its expected functionalities and meet its performance conditions. Wired and wireless communication technologies are usually implemented in the SG. Choosing a communication technology is predicated upon many aspects, for instance, users or devices, network architecture, data type, environmental conditions, and price.

The smart grid (SG) allows integration of renewable energy sources, distributed generation (DG) and storage systems. This chapter builds on the concepts of data management and analytics in SG to build the foundation needed for data analytics to transform Big Data for high‐value action. Big data sources in SG generally fall into two main categories; electric utility data sources and supplementary data sources. The Big Data system will store, process, and mine information in an efficient manner to enhance different SG services. The chapter presents a list of the most common big data tools utilized by successful analytics developers to store, manage, and analyze big data. Big Data needs additional convincing methods to handle the massive amount of information in a limited time span. Any data exchange in SG must be effectively protected through specific “Privacy Concerns” which have potential privacy impacts of SG and smart meter systems.

This article presents a hierarchical finite-set model predictive control (MPC) scheme to enable autonomous operation and self-balancing cascaded multilevel inverter. The proposed approach is an alternative to MPC scheme based on a generic cost function, which in some applications is ill fit or challenging to design. The proposed controller has a hierarchical framework to eliminate the overall cost function optimization and associated weight factor design stage of the control objectives. The control formulation approach allows for multiobjective optimization with a cost-tolerance framework. The concept is well suited to simplify the control design stage of cascaded H-bridge inverters at the grid-edge with advanced functionality. The control scheme achieves active and reactive power control with switching event reduction while equalizing power draw from the independent voltage sources. The latter of these objectives is made possible by the proposed hierarchical approach to the control objective tracking. The control is modularized for each phase, making the system robust to unbalanced grid conditions. The concept is explained in depth in simulation, and then tested experimentally on hardware.

This article presents a stability analysis of photovoltaic (PV) clusters forming a dc microgrid with a distributed power reserve capability. The PV clusters feature flexible PV power generation under fluctuating ambient conditions via the distributed power reserve control. The PV set-points are adjusted based on grid operator command that is accomplished in a decentralized manner. Stability analysis of the PV clusters has been carried out to determine the safe operation regions of the PV units with respect to operation set-point adjustments. The proposed stability analysis approach considers all the characteristics of the PV subsystems. The stability analysis demonstrates that the PV cluster architecture is stable as far as the dc bus stray resistance has a finite value. Also, the grid-interfacing converter must operate in input current control mode (CCM) instead of input voltage control mode (VCM) for better stability margin. Several case studies are emulated on an IEEE 5-Bus system using real-time controller hardware in the loop (C-HIL) to verify the stability and the theoretical expectations of the proposed system.

Phase-shifted pulsewidth modulation (PS-PWM) is a well-known switching technique for quasi-Z-source (qZS)-based cascaded multilevel inverters (qZS-CMI). PS-PWM ensures equal power distribution (among operating modules) and equal switchings in all the semiconductors of the every given module; however, this technique suffers from higher number of switchings. On the other hand, level-shifted pulsewidth modulation (LS-PWM) ensures optimal switching but suffers from uneven distribution of power among the all the operating modules and also all the semiconductors of the same module of qZSI. This article proposes a novel switching technique which combines the advantages of both PS-PWM and LS-PWM, such as equal power distribution (among operating modules) and optimal switching sequence along with equal switch utilization. The proposed method is phase opposed disposed PS-PWM. This article details the switching technique along with switching loss analysis and compares it with the PS-PWM technique to show its superiority. Simulation results are carried out to study the cascaded multilevel inverter (CMLI) performance. Experimental prototype is developed to validate the performance of the proposed modulation. 10.13039/100008982-Qatar National Research Fund (Grant Number: NPRP X-033-2-007) Scopus

This article proposes a Lyapunov-based model predictive control (MPC) design for a dual output multilevel rectifier. The investigated topology, a seven-level packed U-cell (PUC7) converter, is selected based on its high reliability, compactness, and low cost. The proposed controller has the following advantages over the conventional MPC controllers: First, no gain tuning is required; second, easy implementation; and third, reduced number of sensors (the load currents are estimated using the mathematical model of the PUC7 rectifier). Simulation and experimental results are provided to show the high dynamic performance and effectiveness of the Lyapunov-based MPC controller in tracking the output voltage references under grid change and parameters mismatch conditions.

An efficient and cost-effective power converter is a pre-requisite for the modern power applications. With the evolvement of matured medium power self-commutated switching devices, multilevel inverters (MLIs) are emerged as a promising solution for high-power medium-voltage applications. Though, MLIs are performing a promising role in industrial applications, their high device count, size, cost and control complexities have restricted their market penetration. To address the disadvantages of MLIs, researchers are continuously contributing to new generation topologies under the name of reduced switch count (RSC) MLIs. From the past decade, numerous RSC-MLIs topologies have been reported for various applications. Therefore, this paper presents a comprehensive review and classification of RSC-MLI topologies, in terms of their structure, features, limitations, suitability and selection for specific applications.

Accurate modeling of partial discharge (PD) within gas-insulated switchgear (GIS) has become of paramount significance to improve the utilization of ultrahigh frequency (UHF) sensors within such capital assets. Providing better insight on how electromagnetic (EM) waves propagate within GIS systems provides valuable information that can be used to determine the optimal allocation of UHF sensors. Thus far, most existing models are not considering the full complexity of the GIS systems due to the many associated challenges and the high computational demand of such system modeling. In this work, a GIS system is modeled using the Maxwell solver of COMSOL multiphysics. CIGRE sensitivity verification recommendations (CSVR) are used to inject UHF signals using an internal sensor. Then, two external sensors, placed on the outer belt of dielectric spacers, are used to capture the radiated EM waves. The electric field distribution at selected frequencies is presented and discussed under two operating conditions for disconnecting switches. The modeled GIS contains many barriers, including two bends, multiple sudden changes in the outer to inner diameters ratios, six dielectric spacers, and two disconnecting switches. Time-domain simulation results are also presented to provide an insight into the attenuation properties of EM waves due to the different barriers. Finally, a comparison between simulated and measured results has been carried out to verify the modeling accuracy. The results show that GIS systems form complex structures for the EM waves, and fully understanding the wave propagation can be tedious.

This article presents an autonomous model predictive controlled smart photovoltaic (PV) inverter with proactive grid fault-ride through capability. The proposed smart inverter control features decoupled active and reactive power. It can seamlessly switch between the anticipated modes of operation based on grid condition or grid operator command. The smart inverter autonomously adjusts its active and reactive power set-points according to the grid condition, it operates in maximum power point tracking (MPPT) and low voltage ride through (LVRT) modes in normal grid and faulty grid condition modes, respectively. The proposed novel autonomous model predictive control (AMPC) scheme is leveraged to enhance the operation of the smart inverter. The AMPC includes online weight factor auto-tuning and control objective normalization to eliminate the required trial-and-error weight factor design stage in conventional model predictive control. This feature is particularly beneficial to the dual-mode smart inverter operation. The performance of the proposed grid-tied smart inverter based on the AMPC is verified experimentally. The results demonstrate that the proposed AMPC-based smart inverter features robust grid fault detection, autonomous adjustment of active and reactive power set-points, seamless transition between modes of operation, and elimination of controller tuning effort.

Gas-insulated switchgears (GIS) have become essential parts of electrical power substations due to the associated merits of these capital assets. Although such resilient devices can rarely suffer from failure, partial discharge (PD) is responsible for around 85% of their recorded collapses. Ultra-high frequency (UHF) techniques have been widely used in the detection and localization of PD for a long time because of their immunity to noise and high sensitivity. Understanding electromagnetic (EM) wave behavior in GIS systems is significant for improving the utilization of UHF sensors in PD detection and for the optimal allocation of UHF antennas inside GIS systems. Thus, this paper is devoted to building a detailed 3D finite element (FE) model based on UHF detection techniques to understand the propagation behavior of EM waves inside GIS. A disk-type UHF sensor is used for acquiring EM waves inside the GIS. The sensitivity of the sensor has been obtained using a gigahertz transverse-electromagnetic (GTEM) test cell. The proposed model investigates the impact of multiple disconnecting parts including L-structure, relative angle between PD source and sensors, and disconnecting switches on the propagation of electromagnetic waves based on step 1 of the CIGRE recommendations. To validate the modeled GIS, a simple L-structured model is initially built, and a comparative analysis has been conducted between the built model and the experimental and analytical results from the literature.