Emmanuel Attah Marfo

Efficient reduction of power losses in modern systems can be achieved by selectively connecting tie lines and disconnecting radial branches in distribution networks with scattered energy generation sources. The influence of load power is pivotal in this optimization process. Consequently, it becomes crucial to accurately model the demand side, portraying the behavior of electricity consumers more effectively. Given that loads are not constant and exhibit variations with changes in voltage magnitude, contingent on the consumer type, it becomes imperative to represent each load through its load model's constant-impedance, constant-power, and constant-current components. Establishing the relationship between these components and load types is essential for proposing more flexible reconfiguration models. Therefore, this study employs mathematical formulations to express the correlation of load components with consumer types, aiming to provide a more flexible reconfiguration formulation compared to conventional models. The results obtained from well-known distribution systems demonstrate that the proposed framework is sufficiently efficient, offering optimal solutions within a shorter computing time compared to other existing reconfiguration models.

It is critically important to plan properly for integrating and deploying unmanned aerial vehicles (UAVs) in the bridge inspection process, there is a need for tools to support implementation and decision-making regarding the use of UAVs at specific structures. In this study, a resource estimation tool that can be used to estimate the resources required for UAV-assisted bridge inspections is developed. The tool can aid inspectors in determining the estimated flight time and resources required for using a specific UAV and operator during the inspection of a specific bridge. The tool supports the development of optimal flight paths based on the structural geometry and positioning of structural elements of a bridge, establishes a range of recommended flight speeds for conducting reliable UAV-assisted bridge inspections based on the skill level(s) of the pilot(s) who were involved in conducting inspections. The developed tool also establishes a recommended range of wind speed and the corresponding standoff clearance information for safely conducting UAV-assisted bridge inspections. The tool also provides an estimated number of batteries required to allow the estimated required flight time. In this paper, the development of the tool is described, and the advantages of the tool are illustrated by its application in a case study involving a 10-span steel continuous multi-beam bridge with a reinforced concrete deck. The tool is developed as a spreadsheet and is publicly available through a GitHub page, accessible at https://github.com/ACCESSLab/Resource-Assessment-Tool-for-Effective-UAV-Assisted-Bridge-Inspection .

The reconfiguration of distribution networks results in modifications to the voltage magnitudes of buses, subsequently influencing the power demands of connected loads. This variable load demand plays a pivotal role in determining the optimal system topology. While a few studies have recognized this crucial aspect, they often present non-linear models or simplified formulations. Non-linear models, though solvable with commercial solvers, demand extensive computational time, particularly when accommodating the time variability of loads in the formulation. Alternatively, solving these models may require meta-heuristic methods, introducing the challenge of obtaining precise solutions. In contrast, existing linear models have been subject to approximation through piecewise linearization, introducing a level of uncertainty. Achieving accurate solutions for reconfiguration problems within a short computational timeframe is imperative for practical operational applications. As a result, present study addresses this challenge by introducing four effective reconfiguration methods that explicitly consider the voltage dependence of time-varying loads. These techniques leverage linear solvers, enabling the computation of precise solutions within a condensed computational timeframe. By adopting these proposed methods, the study aims to enhance the efficiency and accuracy of solving distribution network reconfiguration problems, thereby facilitating practical applications in real-time operational scenarios. The linear mathematical models introduced in the current study can find exact solutions in a short computational time, unlike meta-heuristic algorithms and non-linear formulations. Additionally, the proposed time and voltage variability formulation causes the presented reconfiguration frameworks to solve the problem in very low computational time compared to models with exact load variations inclusion.

Enhancing the efficiency of distribution systems can be effectively achieved through hybrid reconfiguration and the coordinated operation of distributed generators (DGs). This integrated approach proves to be highly effective in minimizing power losses within distribution networks, with a particular emphasis on the substantial impact of load power and grid voltage on distribution losses and the output of DGs. Load and DG powers are dynamic, varying with fluctuations in voltage levels. Furthermore, the correlation between power demand and renewable sources generation with voltage is contingent upon the specific type of load and DG. Nevertheless, scant attention has been given to these crucial factors in the research pertaining to network reconfiguration and the planning of DG. A limited number of papers have taken into account the type and voltage dependency of DG or load within their respective formulations. However, the reconfiguration models they proposed introduce significant nonlinearity without considering the simultaneous effects of grid voltage on load and DG power, necessitating computation through nonlinear solvers or metaheuristic algorithms. Alternatively, the implementation may require intensive linearization techniques for compatibility with linear solvers. Nonlinear solvers demand extensive computational time, while metaheuristic algorithms cannot ensure the attainment of optimal solutions. Hence, it is crucial to accurately model load behavior when undertaking the reconfiguration of active distribution systems. This paper presents a streamlined reconfiguration model, designed to be easily implemented with conventional optimization tools while ensuring accuracy in identifying appropriate solutions for the reconfiguration problem.