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Microgrids (MGs) use renewable sources to meet the growing demand for energy with increasing consumer needs and technological advancement. They operate independently as small-scale energy networks using distributed energy resources. However, the intermittent nature of renewable energy sources and poor power quality are essential operational problems that must be mitigated to improve the MG's performance. To address these challenges, researchers have introduced heuristic optimization mechanisms for MGs. However, local minima and the inability to find a global minimum in heuristic methods create errors in non-linear and nonconvex optimization, posing challenges in dealing with several operational aspects of MG such as energy management optimization, cost-effective dispatch, dependability, storage sizing, cyber-attack minimization, and grid integration. These challenges affect MG's performance by adding complexity to the management of storage capacity, cost minimization, reliability assurance, and balance of renewable sources, which accelerates the need for meta-heuristic optimization algorithms (MHOAs). This paper presents a state-of-the-art review of MHOAs and their role in improving the operational performance of MGs. Firstly, the fundamentals of MG optimization are discussed to explore the scopes, requisites, and opportunities of MHOAs in MG networks. Secondly, several MHOAs in the MG domain are described, and their recent trends in MG's techno-economic analysis, load forecasting, resiliency improvement, control operation, fault diagnosis, and energy management are summarized. The summary reveals that nearly 25% of the research in these areas utilizes the particle swarm optimization method, while the genetic and grey wolf algorithms are utilized by nearly 10% and 5% of the works studied in this paper, respectively, for optimizing the MG's performance. This result summarizes that MHOA presents a system-agnostic optimization approach, offering a new avenue for enhancing the effectiveness of future MGs. Finally, we highlight some challenges that emerge during the integration of MHOAs into MGs, potentially motivating researchers to conduct further studies in this area. • Studying the fundamentals of microgrid optimization. • Investigating the scope of microgrid optimization using metaheuristic techniques. • Summarizing the recent trends of meta-heuristic optimization approaches in microgrid optimization. • Exploring the challenges and future research directions in microgrid optimization area. [ABSTRACT FROM AUTHOR]

• Proposed an intelligent approach for transmission line fault analysis based on capsule network. • An unsupervised model is developed which requires no manual labeling of the data. • The performance against line parameter variation and high impedance faults is assessed. • Noises are considered to justify the robustness of the proposed approach. • The performance is compared with the baseline capsule network and existing approaches. Detection and classification of transmission line (TL) faults are key factors for the fault root cause analysis and rapid restoration of the power network. Deep learning can extract representative features automatically from big data, thus becoming an important breakthrough in fault analysis. Existing deep learning-based fault diagnosis models rely on a large number of data obtained from a variety of fault conditions to be generalized. Indeed, in the TL domain, it is difficult to define and, therefore to collect the fault incepted features of all possible fault scenarios. This paper proposes an unsupervised framework for fault detection and classification (FDC) of TL based on a capsule network (CN). Instead of using the baseline CN, an extension to this with a sparse filtering technique is adopted in this work. The capsule network with sparse filtering (CNSF) voluntarily learns the expensive fault features and significantly improves the model performance without involving a large number of data. The proposed scheme receives 1 / 2 cycle post-fault three-phase signals and encodes them into a single image that is defined as the input for the proposed CNSF model. The effectiveness of the proposed CNSF model is corroborated by four different TL topology confirming the model's adaptability to a topology change in response to intended control action or the switching actions due to cascading faults. Further assessment of the model's performance against noise, high impedance faults (HIF), and line parameter variations are also carried out in order to confirm the high reliability of the proposed model. In addition, a rigorous comparative study is conducted to guarantee the state-of-the-art performance of the proposed model. [ABSTRACT FROM AUTHOR]

• Microgrid is a small grid that uses renewable energy sources to generate power. • Performance of microgrid may deteriorate due to the uncertainty in load dynamics. • A controller is proposed to control the voltage, current and power of a microgrid. • The controller is designed for both single and three phase islanded microgrid. • Performance of the controller is measured and compared against various loads. Future grids will consist of many small and large microgrids that will require complex control of grid interaction as well as islanding operation. This paper addresses the control of islanded microgrids under dynamic system conditions. In this paper, a robust model reference modified adaptive proportional integral derivative (PID) controller is designed for voltage and current control augmentation of islanded microgrid against various load dynamics. The variation of load dynamics may cause the unsafe operation of the microgrid. The objective of the designed controller is to confirm the safe operation against voltage and current control under different load dynamics. The controller is designed based on selecting the desired reference model and controlling parameters. The effectiveness of the proposed controller is evaluated against the presence of load dynamics, harmonic sources, asynchronous machines, nonlinear and unknown loads. The results show that the proposed controller is capable of providing high tracking performance and safe operation for both the single and three phase islanded microgrid system. [ABSTRACT FROM AUTHOR]

Modernization in the area of smart energy equipment, are forming the perception of an interlinked energy network in subsequent times. This energy network is contemplated to govern real-time energy consumption and optimization in the future. Traversing a prolonged period of development, the energy industry has reached the landmark of Virtual Power Plant (VPP) and still going onward to this newfangled energy network, also can be called the next generation VPP. This paper demonstrates the challenges and opportunities in executing the transpiring aspects of the next generation VPP. Based on essential components and requisites, detailed descriptions are depicted from different technological viewpoints such as electrical and communication, computing technologies, energy management, economic profit, geographical factors, and adoption of emerging technologies. We also present the recent updates of existing VPP projects. Finally, considerations towards implementing the next generation VPP are marked out, along with necessary frameworks, regulations, and policies. This paper can provide prudent support for researchers and practitioners in the field of VPP. [Display omitted] • Review of development aspects in virtual power plants (VPP). • Critical requirements and necessary components for VPPs are presented. • Emerging technologies that can be integrated with next generation VPP are discussed. • Challenges and opportunities in the road of developing the next generation VPP are characterized. • Next generation VPP's framework, policies, and regulations are outlined. [ABSTRACT FROM AUTHOR]