Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

Offshore Wind Power Systems (OWPS) offer great energy and environmental advantages, but also pose significant Operation and Maintenance (O&M) challenges. In this survey, we analyze these challenges and propose some optimization strategies and technologies for OWPS comprehensively. The existing literature review mainly focuses on a certain field of offshore wind power O&M, but lacks a comprehensive introduction to offshore wind power. We consider the energy efficiency, reliability, safety, and economy of OWPS from various aspects, such as offshore wind and wave energy utilization, offshore wind turbine components, and wind power operation parameters, and compare them with onshore wind power systems. We suggest that OWPS can benefit from advanced design optimization, digital twin, monitoring and forecasting, fault diagnosis, and other technologies to enhance their O&M performance. This paper aims to provide theoretical guidance and practical reference for the technological innovation and sustainable development of OWPS.

In this paper, we present a flexible, breathable and optically transparent metasurface with ultra-wideband absorption. The designed double layer of indium tin oxide (ITO) films with specific carved structure realizes absorption and electromagnetic (EM) isolation in dual-polarization, as well as good air permeability. Under the illumination of x- and y-polarization incidence, the metasurface has low reflectivity and transmission from about 2 to 18 GHz. By employing ITO film based on polyethylene terephthalate (PET), the presented metasurface also processes the excellent flexibility and optically transparency, which can be utilized for wearable device application. In addition, the dual-layer design enables mechanically-reconfigurable property of the metasurface. The transmission and reflection coefficients in two polarizations show distinct difference when arranging the different relevant positions of two layers of the metasurface. A sample with 14*14 elements is designed, fabricated and measured, showing good agreement with the simulation results. We envision this work has various potentials in the wearable costume which demands both EM absorption and isolation.

A high-gain and tri-band terahertz microstrip antenna with a photonic band gap (PBG) substrate is presented in this paper for terahertz communications. Polyimide dielectric columns are inserted into the silicon substrate to form the PBG substrate to improve the gain of the antenna. The PBG substrate and polyimide substrate constituted a multilayer substrate structure and enabled the multi-band operation of the antenna. The PBG substrate antenna achieves gains of 6.28 dB, 4.84 dB, and 7.66 dB at resonant frequencies of 0.360 THz, 0.580 THz, and 0.692 THz, respectively, outperforming the homogeneous substrate THz microstrip antenna (H antenna) by 1.18 dB, 1.74 dB, and 1.82 dB, respectively. The radiation efficiencies at three operating bands are over 93%, 92%, and 88%, respectively, which are slightly higher than that of the H antenna and greater than that of the standard multi-band antenna.