Constrained by low capacity and volatility, the rapid growth of distributed energy resources are obviously slowdown resulting in consumption difficulty and investment obstacle. As an effective integration and management technology, virtual power plant (VPP) becomes a suitable cornerstone of renewable energy future development. Based on current scientific research, this study intends to provide a detailed review of VPP from an internal perspective (e.g. energy resources’ integration and operation) to the external aspect including participation in electricity market. In accordance with market diversity, different bidding strategy optimisation problems of VPP are formulated and their corresponding mathematical solution methods are literately reviewed. To extract characteristics of VPP, a comparison between energy management techniques (e.g. VPP, microgrid, active distribution network, and load aggregator) is conducted, where advantages and defects of VPP are also concluded. Meanwhile, realistic deployments of VPP in European electricity markets are elaborated to verify its feasibility and applicability. To better accommodate future development of renewable energy, a flexible structure and effective management mechanism of VPP should be built leading to its technical innovation and management reform. Possessed with the threefold development orientation, VPP will undoubtedly improve the utilisation and management of renewable energy sources as a coordinated and operational entity.

Sustainable electrification calls the need for more efficient protection schemes. Synchronous-based distributed generations (SBDGs) are more susceptible to power grid faults owing to their low inertia time constants. Accordingly, fault elimination faster than the associated critical clearing time (CCT) is required to prevent unintentional disconnection of SBDGs, which inevitably makes the coordination of directional over-current relays (DOCRs) complicated than before, especially in meshed structure distribution networks (DNs). This study proposes communication assisted dual-setting DOCRs as effective solutions for providing fast-response protection in order to meet transient stability constraints of SBDGs in meshed active DNs. Dual-setting DOCRs are capable of operating in both forward and reverse directions with individual settings for each direction and thus providing flexibility in the coordination process. In this regard, time domain simulation as a reliable method is conducted to calculate CCTs. Afterwards, coupled with selectivity constraints associated with dual-setting DOCRs, transient stability constraints are all together accommodated in the optimisation process. Moreover, dual-setting DOCRs are allowed to follow user-defined time-inverse characteristics which help to alleviate clearing time of faults. The approach reveals a non-linear programming model which is tackled by seeker algorithm. Detailed numerical studies are carried out to validate the performance of the proposed approach.

The enduring life span of the machine insulation will be decided based on degradation level in motor and generator stator windings. The non-destructive diagnostic tools like dielectric loss and capacitance test and partial discharge (PD) analysis, recognized to access the deterioration in the insulation system of rotating machines. The experiments reveal various characteristic parameters such as leakage current, dielectric dissipation factor, the capacitance value, and PD magnitude. The integrity of the rotating machine can be find out by analyzing these parameters. This research study shows the hybrid method for prediction of insulation condition in the stator winding by utilizing the artificial neural network (ANN) with gravitational search algorithm in comparison with ANN and ANN–genetic algorithm. The advent of expert systems ensures the quality assurance and service life assessment of the high-voltage assets. It offers a predictive maintenance solution to personnel dealt with power utilities thereby increasing the uptime, reliability, and productivity, which in turn reducing the operating costs, downtime and unplanned outages. For testing and predicting the insulation status, several 11 kV machines are considered. The predicted results using hybrid techniques extend a close agreement with reference to the data obtained from the experiments performed. The proposed method indicate the competent and trustworthy, by the presented test results.

Ever increasing use of renewable energies beside other uncertain parameters in power systems makes it necessary to evaluate power system issues, probabilistically. One of the important studies in power systems operating is optimal power flow (OPF), which should be considered as probabilistic OPF (POPF). However, the Monte Carlo simulation (MCS) method can be efficiently used for handling any type of uncertainty but this method suffers from large calculation requirements and cannot be implemented in various studies. Especially, in evolutionary based optimization problems the use of MCS method is completely restricted. Data clustering is an alternative method which keeps the accuracy of the MCS method and requires very less calculation burden. In this study, data clustering is used for probabilistic assessment of power system in POPF problem. The proposed method is very fast, accurate and can easily handle any type of correlation between stochastic input variables. The correlated input variables are generated by the Cholesky decomposition method. The Genetic Algorithm (GA) is used as optimization tool. In order to demonstrate and validate the performance of the proposed method, IEEE 30- and 118-bus standard test systems are studied and the results are compared with a modified version of the two-point estimate method.

We present a new noise-resilient secondary control scheme for voltage and frequency synchronisation of an autonomous microgrid (MG). The communication network is an integral part of distributed secondary control structure. The communication links among the distributed generator units are assumed to be ideal in nature. However, the communication links are subjected to uncertain noises, which can significantly affect the synchronisation performance of MG control. We consider the information received over the communication link is corrupted with generalised Gaussian noise. Further, the complete non-linear model of the MG system has been considered for designing a robust distributed control scheme augmented with an auxiliary corrective control input to counterbalance the impact of noises. The performance of the proposed control scheme is evaluated by pursuing simulation of a MG test system in MATLAB/SimPoweSystem environment under load perturbation, changes in communication topology, communication link delays, data dropout, and variations in noise parameter. The results of a comparative assessment of the proposed control scheme with two approaches first a neighbourhood tracking error-based control scheme and second a noise-resilient control scheme are presented to illustrate the effectiveness of the proposed robust noise-resilient control scheme in voltage and frequency synchronisation and accurate active power-sharing.