Advances in Operation, Optimization, and Control of Smart Grids

1. Introduction

Power systems are equipped with a set of equipment for the generation, transmission, and distribution of electrical energy to consumption centers in a continuous manner and with quality indices. Power systems have evolved over the years through the inclusion of new generation sources such as wind and photovoltaic generation; new forms of energy transmission such as direct current transmission; and the growing demand for electrical energy in consumption centers [1], including the development of electric vehicles [2]. Although this evolution is carried out to ensure the continuous supply of energy to consumption centers, challenges have emerged that can compromise the safe and stable operation of power systems [1,3]. Thus, there is a need to develop new tools that can benefit the operation of power systems in the most diverse fields.

Over the years, new technologies have been developed and incorporated into power systems, such as Synchronized Phasor Measurement Systems equipped with Phasor Measurement Units (PMUs) installed on transmission and distribution system buses, which measure three-phase voltages and three-phase currents with high sampling rates and time synchronization [4]. Many applications of PMU data have been proposed and continue to be proposed with the aim of improving the operation of power systems [5]. Power systems equipped with sensors such as PMUs are usually called smart grids.

There are different challenges in the operation of smart grids that have aroused interest in the scientific community. Many recent applications in power system monitoring have been proposed, such as monitoring low-frequency oscillations [6,7], load margin [8], transient stability [9], and faults in transmission and distribution systems [10]. In terms of smart grid optimization, there is a greater field of interest for researchers. There is research, for example, that aims to minimize costs [11], consumption interruptions [12], and power losses [13] and research, for example, that aims to maximize reliability rates [14]. Smart grid control also presents interesting and necessary fields of research to control, for example, voltage [15], the damping rates of oscillation modes [16], generation redispatch [17], and the operation of an energy storage system [18].

Therefore, this Special Issue aimed to collect the most recent advances related to methods for the operation, optimization, and control of smart grids. This Special Issue managed to collect seven scientific contributions: six research articles and one review article. Section 2 presents a brief description of the main contributions of each article in this Special Issue, and Section 3 presents the conclusions to this editorial.

2. An Overview of Published Articles

The authors of Contribution 1 propose an energy management method to be applied in distribution networks composed of high renewable energy sources to interface multiple microgrids with Soft Open Points. Furthermore, system uncertainties are considered using a two-point estimation method. The control objectives of this energy management method are to reduce voltage deviations from a nominal value and to control both reactive and active power flows. Thus, the method proposed by the authors provides the benefits of proper operation of a distribution system.

The author of Contribution 2 presents a new machine learning technique based on neural networks to determine the load margin of power systems. This new technique applies physical knowledge related to the system’s power flow during the neural network training process. The load margin is an important index applied in the operation centers to assess how far the system is from voltage instability. In addition, this method uses three-phase voltage data collected using Phasor Measurement Units (PMUs) from selected buses of the system to speed up the calculation of this load margin.

The authors of Contribution 3 propose a new method for the transient stability assessment of power systems when a set of contingencies occur. Furthermore, this proposed method based on Adaptive step-size Differential Transformation (AsDTM) method is capable of contingency screening and ranking, which will be beneficial for necessary control decision making. The authors demonstrate from the achieved results that the method is fast enough for real-time dynamic security assessment. Thus, the method proposed by the authors provides great benefits for the transient stability assessment of modern power systems.

The authors of Contribution 4 aim to solve the optimal phase-balancing problem in distribution systems operating with phase asymmetry by proposing two different mixed-integer nonlinear programming formulations. The optimization problems studied by the authors aim to reduce system voltage deviations and to reduce power losses. Several analyses were conducted and discussed with the proposed method and other approaches already consolidated in the literature. The results show a set of benefits of the different proposed methods for optimal operation of modern distribution systems.

The review paper in Contribution 5 presents a comprehensive review of electrical microgrids, especially with a high integration of renewable energy sources. The review was conducted considering a hybrid microgrid with advantages of both AC and DC systems. Different optimization techniques are presented and discussed, pointing out the main challenges of operating a microgrid and the main objectives that are optimized. There is a brief description of the impacts of energy variability and system costs on different microgrid configurations.

The authors of Contribution 6 proposed a new load-forecasting method to be applied in microgrids. The method uses three tools: (i) visibility graph transformations, (ii) temporal decay adjustments, and (iii) superposed random walk approach. Case studies were performed at a regional airport, and existing methods in the literature were compared with the proposed method. The obtained results show reliable load forecasting indices based on the proposed method. Thus, the method of these authors is useful for system load forecasting and if there is a need for control measures for proper operation of power systems.

The authors of Contribution 7 propose a promising method for fault diagnosis in distribution systems. The method is the development of a new machine learning technique. The case studies were performed with real measurements of a distribution system with real fault cases. The results show the ability of the method to correctly identify different types of faults. Thus, this method is beneficial in power systems for fault diagnosis and subsequent protection measures.

3. Conclusions

Smart grids have provided remarkable benefits to the power system. However, challenges still persist, and the scientific community is focused on solving them. This Special Issue presents papers with scientific contributions on advances in the operation, optimization, and control of smart grids. Further details on these contributions can be obtained by reading the original papers.

Future research in the operation, optimization, and control of smart grids is vast. Possible research lines include (i) the development of control strategies to deal with cyberattacks on data transmission from PMUs, (ii) the development of economic models to reduce generation costs and technical losses, (iii) the development of operating strategies for power systems with intermittent power generation, and (iv) the development and application of machine learning techniques for monitoring and controlling smart grids. It should be noted that many of the existing tools can be improved to meet a higher level of performance requirements for power systems.