Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting.

The inappropriate mechanism designs for demand response (DR) in the community of microgirds (CoMGs) may cause massive problems, such as increase of consumers’ costs, rebound peaks, and thereby lack of optimality in the network. In this article, a bilevel energy management system (EMS) is proposed to tackle the challenges associated with DR programs for CoMGs. The current structure successfully models users’ behavior and dissatisfaction in the first level of optimization to develop best DR program for each of them. Moreover, in the second level, power system constraints are taken into account to prevent voltage and current deviation from their statutory limits. Each user is assumed to be part of a microgrid (MG) whose operation is controlled and optimized through its local EMS in the first level. On the other hand, the overall operation of all MGs is delegated to the whole system operator, which acts as the central EMS (CEMS) in the second level. An iterative transactive energy management method is proposed by CEMS to fairly limit the excess power of the MGs one day ahead for voltage and current regulation. The obtained results indicate the effectiveness of the proposed structure in preventing discomfort issues, voltage deviation and creation of the rebound peaks in the system.

Electrical power systems are the backbone of industry, society and current civilisation. Power engineers have accumulated the necessary skills and procedures, to enable them to operate the system in the most reliable way possible. The main principles of system operation have not changed much, despite the rapid developments in power electronics, computing, information and communication technologies. This paper surveys existing power networks and their limits in meeting modern developments and environmental concerns. There is an evaluation of the impacts of deployment of new low carbon technologies such as distributed generation and electric vehicles on existing networks, together with the main drivers for change and features of the new concept of the ‘smart grid’. The paper also examines the challenges and benefits that the ‘smart grid’ will bring, and describes the enabling technologies.

The paper investigates the harmonic interaction between the AC and DC sides of a 3-level inverter used as a static synchronous VAr compensator (STATCOM). It is illustrated that due to the 3rd harmonic voltage across each DC side capacitor, the conventional selective harmonic elimination modulation (SHEM) may not actually eliminate the target harmonics when the system is on load. A new modulation method, which dynamically adjusts the switching angles according to the operating point, is proposed to overcome this difficulty. The proposed method is verified using simulation and experimental test.

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

Power quality (PQ) is becoming increasingly important owing to the increasing use of power electronic devices, coupled with the increasing penetration of loads, which are sensitive to voltage disturbances. As a result of the problems caused by the confluence of these two trends, there is an increasing need for PQ to be monitored in order to diagnose its nature and locate the source of the disturbance, allowing remedial measures to be taken. While automated systems for diagnosis of PQ events have been developed, identifying the location of the source of a disturbance is a problem, which has not been fully addressed to date; in particular the question of locating a non-stationary disturbance. In this study, a novel approach to identify the location of the source of a PQ disturbance is described, using a form of artificial neural network known as a self-organising map. The proposed technique is verified via simulation of the IEEE 14-bus model in PSCAD and an experimental system based on the IEEE 6-bus model. This approach provides a mean of locating the source of PQ events, including transient disturbances.