Deep Reinforcement Learning for Distribution Network Operation and Electricity Market

The conventional distribution network and electricity market operation have become challenging under complicated network operating conditions, due to emerging distributed electricity generations, coupled energy networks, and new market behaviours. These challenges include increasing dynamics and stochastics, and vast problem dimensions such as control points, measurements, and multiple objectives, etc. Previously the optimization models were often formulated as conventional programming problems and then solved mathematically, which could now become highly time-consuming or sometimes infeasible. On the other hand, with the recent advancement of artificial intelligence technologies, deep reinforcement learning (DRL) algorithms have demonstrated their excellent performances in various control and optimization fields. This indicates a potential alternative to address these challenges. In this thesis, DRL-based solutions for distribution network operation and electricity market have been investigated and proposed. Firstly, a DRL-based methodology is proposed for Volt/Var Control (VVC) optimization in a large distribution network, to effectively control bus voltages and reduce network power losses. Further, this thesis proposes a multi-agent (MA)DRL-based methodology under a complex regional coordinated VVC framework, and it can address spatial and temporal uncertainties. The DRL algorithm is also improved to adapt to the applications. Then, an integrated energy and heating systems (IEHS) optimization problem is solved by a MADRL-based methodology, where conventionally this could only be solved by simplifications or iterations. Beyond the applications in distribution network operation, a new electricity market service pricing method based on a DRL algorithm is also proposed. This DRL-based method has demonstrated good performance in this virtual storage rental service pricing problem, whereas this bi-level problem could hardly be solved directly due to a non-convex and non-continuous lower-level problem. These proposed methods have demonstrated advantageous performances under comprehensive case studies, and numerical simulation results have validated the effectiveness and high efficiency under different sophisticated operation conditions, solution robustness against temporal and spatial uncertainties, and optimality under large problem dimensions

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV

Techno-economic feasibility assessment of grid-defection

The interest in off-grid solar PV system has grown significantly in recent years. The price of suitable batteries started to decline following the price trajectory of solar PV, the curtailment of solar feed-in-tariff in many countries and the increasing electricity price. These are some important factors for grid defection trends. In this paper, we analyse the reliability and levelised cost of electricity (LCOE) of residential off-grid customers' PV-battery systems in order to carry out a techno-economic feasibility study of grid-defection. In more detail, this study demonstrates how off-grid customers can trade-off reliability for LCOE of their PV-battery systems, taking into consideration the priority/user-preference of different load types in a household. Consequently, we propose a novel decision making tool to improve the performance of a dynamic home energy management system (HEMS). The HEMS is set up as a mixed-integer linear optimization (MILP) problem which is solved using a rolling-horizon approach for weekly and yearly simulations. Weekly simulations were first performed in order to investigate the optimal range of PV and battery size combinations based on reliability and LCOE, using a week with the lowest solar PV output and the highest demand. Given the optimal PV-battery size combinations, yearly simulations were performed to assess the reliability and LCOE of PV-battery systems of randomly selected residential households in the Ausgrid Solar Home Electricity Data. Simulation results over the critical time period of the year indicate that an off-grid PV-battery system is relatively feasible for customers who are willing to compromize lower reliability in exchange for a lower cost. The result however indicates that the cost is still higher compared with a grid-connected system

The Stray Grains from Fragments in the Rejoined Platforms of Ni-Based Single-Crystal Superalloy

Nickel-based single crystal superalloy is the most important material for blade preparation. However, some solidification defects inevitably occur during the process of preparing single-crystal blades through directional solidification. In this study, in order to study the origin of misorientation defects during solidification, a model with rejoined platforms was designed according to the geometry of single-crystal guide vanes. Electron Back-Scattering Diffraction (EBSD) was used to quantify the orientation deviation of the dendrites and identify the solidification defects in the rejoined platforms. The results showed that stray grain defects appeared in the platforms and their misorientation changed gradually, not abruptly. Combined with the simulation results, it was proposed that the stray grains formed as the result of the dendrites fragment, which was induced by solute enrichment in the mushy zone during solidification. Meanwhile, it was accompanied by a obvious dendritic deformation, which was caused by solidification shrinkage stress. This suggested that the fragmentation was induced by multiple factors, among which, the concave interface shape provided favorable conditions for solute enrichment, and the dynamic variability in the local thermal gradient and fluctuations of the solidification rate might play catalytic roles

A comprehensive study on source terms in irradiated graphite spheres of HTR-10

With previously developed experimental methods which include the preparation and measurement process for the graphite sample, two new irradiated graphite spheres with surface γ dose rates of 51.00 μSv/h and 0.14 μSv/h from the reactor core of the 10 MW high temperature gas-cooled reactor (HTR-10) have been investigated experimentally. The total β counting rate, the β spectra and the γ spectra for each graphite sample of irradiated graphite spheres were recorded with a total α/β counting measuring apparatus, a liquid scintillation counter and a high-purity germanium detector connected to a multichannel analyzer, respectively. Combined with previous experimental data of two irradiated graphite spheres with surface γ dose rates of 25.10 μSv/h and 1.17 μSv/h, the types of key nuclides in the irradiated graphite sphere of HTR-10 were determined, which were H-3, C-14, Co-60, Cs-137, Eu-152 and Eu-154. The distributions for each nuclide in four irradiated graphite spheres were compared. The generation mechanisms of H-3, C-14, Co-60, Cs-137, Eu-152 and Eu-154 in the irradiated graphite sphere of HTR-10 were discussed and analyzed. Based on all the experimental data regarding impurities and uranium contamination in the matrix graphite of HTR-10 available, a sensitivity analysis was performed to explain the effect of impurities and uranium contamination on the specific activity of key nuclides in the graphite sphere. The influence of the neutron flux and the dwell time in the core on the specific activity of key nuclides was also considered. The differences of experimental specific activities among these irradiated graphite spheres were compared and explained. Current comprehensive studies on irradiated graphite spheres of HTR-10 can provide valuable information for the source term analysis, waste minimization and radiation protection of high temperature gas-cooled reactors (HTGRs)

Towards a Green and Self-Powered Internet of Things Using Piezoelectric Energy Harvesting

Internet of things (IoT) is a revolutionizing technology which aims to create an ecosystem of connected objects and embedded devices and provide ubiquitous connectivity between trillions of not only smart devices but also simple sensors and actuators. Although recent advancements in miniaturization of devices with higher computational capabilities and ultra-low power communication technologies have enabled the vast deployment of sensors and actuators everywhere, such an evolution calls for fundamental changes in hardware design, software, network architecture, data analytic, data storage and power sources. A large portion of IoT devices cannot be powered by batteries only anymore, as they will be installed in hard to reach areas and regular battery replacement and maintenance are infeasible. A viable solution is to scavenge and harvest energy from environment and then provide enough energy to the devices to perform their operations. This will significantly increase the device life time and eliminate the need for the battery as an energy source. This survey aims at providing a comprehensive study on energy harvesting techniques as alternative and promising solutions to power IoT devices. We present the main design challenges of IoT devices in terms of energy and power and provide design considerations for a successful implementations of self-powered IoT devices. We then specifically focus on piezoelectric energy harvesting and RF energy harvesting as most promising solutions to power IoT devices and present the main challenges and research directions. We also shed light on the security challenges of energy harvesting enabled IoT systems and green big data.Comment: The paper has been submitted to IEEE Acces