State estimation (SE) is a vital monitoring tool for active distribution networks (ADNs). Conventional cubature Kalman filter (CKF)-based ADN-SE delivers deteriorated estimates due to fast state variations caused by intermittency in injections from distributed energy resources (DERs). With the nature of DER outputs being unknown, the process noise covariance ($\mathbf {Q}$) adaptation is essential, and Q-scaled adaptive CKF (QS-CKF) is initially proposed for forecasting aided ADN-SE to deliver improved accuracy. Thereafter, performance assessment with unknown non-Gaussian measurement noise statistics using QS-CKF indicates an inferior accuracy. To overcome this limitation, a Mahalanobis distance (MD)-based outlier detection and robustification of QS-CKF (RQS-CKF) is proposed, where the outliers are detected via MD criterion, and robustness is achieved by measurement noise covariance ($\mathbf {R}$) inflation. The scaling factor for inflating$\mathbf {R}$remains unknown initially and is determined via Newton’s method, which is also numerically stable for different non-Gaussian noise distributions. Testing these estimators on IEEE benchmark systems reveals that RQS-CKF delivers fast and superior estimates, thereby ensuring adaptability and robustness.

Power distribution systems require three-phase state estimation due to their inherently unbalanced nature. Identifying a balanced reference bus is essential for conducting three-phase state estimation. However, such a reference bus is rarely available in distribution systems. Existing distribution system state estimation (DSSE) methodologies often randomly select a bus for this purpose, leading to biased results, while those based on a single angular reference face convergence issues. To address these challenges, this article introduces a novel method designed to mitigate the limitations associated with the reference bus in DSSE. This method is executed in two stages: first, it obtains the exact phase angles of the reference bus using local measurements available at that bus; second, it utilizes these precise phase angles to estimate the states of the distribution system. The efficacy of the proposed method is demonstrated through validation on the IEEE 13-node feeder, IEEE 37-node feeder, and IEEE 123-node feeder.

Processing of power system data containing outliers and noise is important for state estimation. This article aims to improve the quality of data to the state estimator. It addresses noises in the data, viz., normally distributed noise and bias. Along with this, it also handles the outliers, missing data, and time stamping error. In the first stage, outliers, missing data, and time stamping error are handled. In the second stage, data from the first stage pass through the proposed weighted deep neural network that makes use of measurement variance information to reduce noises and bias present in the data. The data after noise reduction are utilized by the state estimation program to find the system states. The proposed method is tested on the IEEE 13-node test feeder.

The protection of multiple interconnected microgrids is a challenging task because of changes in the topology of the system. A microgrid can operate in an islanded mode or get connected to another autonomous microgrid or grid-connected microgrid or directly to the utility grid. The short-circuit currents can change widely because of the connection status of a microgrid in the system of multiple or networked microgrids. In this article, a novel setting groups based scheme is presented for the protection of networked microgrids using directional overcurrent relays. The developed scheme can provide adequate protection to all microgrids under all possible interconnection among the microgrids and utility grid. A vector is generated for each possible interconnection of microgrids in the system to categorize them into four groups usingk-means clustering. The optimum settings of relays considering all operational aspects of each group are calculated using a nonlinear programming based algorithm. Based on the topological interconnections of microgrids in the system, one of the setting groups can be enabled using a low-bandwidth communication link. The proposed protection approach has been validated on a benchmark test system for networked microgrids. The suitability and the effectiveness of the developed settings of the relays have been analyzed adequately.

The increasing trend of renewable energy sources integration in the distribution systems may cause voltage fluctuations and voltage limit violations. The smart inverters with reactive power capability and active power curtailment (APC) control can solve the grid voltage issues. In this article, three-phase optimal power flow (TPOPF) is applied to dispatch the APC and reactive power injection (RPI) of inverters. The TPOPF takes smart meter measurements as inputs. The nonproportional APC and proportional APC are examined. In this article, a fair incentive scheme for APC by the inverters is proposed, appropriate for both utility and renewable energy producers. Similarly, unequal RPI and equal RPI by the inverters to maintain the system voltage are studied. The RPI incentive scheme is analyzed by accounting for the costs incurred in overrating of the inverters, power losses, and life reduction due to reactive power supplied by the inverters. The effective location of inverters for APC and RPI in the distribution network is explored. The incentive schemes are implemented in 27-node distribution system and IEEE European low-voltage distribution system.

A battery energy storage system (BESS) can play a critical role in regulating system frequency and voltage in an islanded microgrid. A$\mu$-synthesis-based robust control has been proposed for dc link voltage regulation of BESS for achieving frequency regulation and voltage quality enhancement of islanded microgrid. Variation in the operating condition of ac microgrid affects the operating condition of the BESS’ converter. This controller synthesis accounts for such uncertain variations as parametric uncertainty. The stability and performance of the proposed controller can be guaranteed for bounded parametric variations. The bounds on parameters are selected based on practical limitations of BESS. In this article, the proposed controller's performance is tested on an islanded CIGRE TF C6:04:02 benchmark low voltage ac microgrid system. The importance of dc link voltage regulation is analyzed based on performance comparison with a benchmark controller. The controller performance is also validated using a real-time Typhoon HIL emulator.

The coordinated operation of multiple microgrids (MGs) enables high penetration of locally available distributed energy resources. It enhances the reliability and resiliency of the power network and reduces the cost of energy. Although networked MGs have attracted significant research interests, validation of various studies is difficult because there is no benchmark test system available for such systems. A benchmark test system can be used to validate static and dynamic studies related to the networking of multiple MGs, such as optimal power flow, energy management, control, stability, and protection. To fill in this research gap, a benchmark test system for networked MGs is proposed in this article, where four independent MGs are interconnected and coordinated. Required data, such as line parameters, load data, and power generating sources, have been prepared for each MG considered in the system. To provide a general test platform, typical datasets are made as close to practical MGs as possible. Parameters used to evaluate reliability indices and resiliency measures of the system are given for the entire test system. Future potential studies, which can be tested on the proposed benchmark test system, are discussed.

This article proposes a network reconfiguration methodology using repository-based constrained nondominated sorting genetic algorithm with preference order ranking for an unbalanced distribution system. The algorithm can accommodate the variable nature of load demand and distributed generator output. A mathematical multiobjective model is formulated to obtain the optimal topology for a whole day considering minimization of daily energy loss, energy not supplied, and cumulative current unbalance factor under the constraint of minimum switching action. A wavelet transform-based ARIMA model is used for wind speed forecasting and is proposed for solar irradiance and load forecasting as well. Hourly network reconfiguration (NR) is also performed, and a comparison is performed between hourly and whole-day NR. The proposed approach has been implemented on IEEE 34-bus and IEEE 123-bus systems to evaluate the effectiveness of the developed methodologies.

The operation of multiple microgrids (MGs) in coordination with distribution system enables high penetration of locally available distributed energy resources (DERs). This approach enhances the reliability and resiliency of the power supply significantly. Also, the overall cost of energy gets reduced because of the integration of cost-free power from photovoltaic panels and wind turbines. The most effective utilization of DERs can be achieved through networked MGs. However, the implementation of the concepts of networked MGs requires extensive research. This paper presents a comprehensive literature review of the most important research works on networked MGs. Major benefits and challenges related to this new and highly exploring area have been analyzed. Also, some of the most important research areas related to networked MGs have been highlighted and discussed as the future perspectives.