Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

Ali, Aamir; Abbas, Ghulam; Keerio, Muhammad Usman; Mirsaeidi, Sohrab; Alshahr, Shahr; Alshahir, Ahmed

doi:10.1109/access.2023.3242546

Cited by 19 publications

(15 citation statements)

References 56 publications

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“…Meanwhile, nl shows the total number of branches, and (s, r) is the particular branch between bus s and r. V m and V k are the bus voltages and G sr is the branch conductance connected between bus s and r and θ sr is the branch voltage angle difference between bus angles θ s and θ s . And α is the Power loss co-efficient equal to 80.49 $/MWh [64]. Nodal voltage magnitude is an important indicator to evaluate system security and power quality (PQ).…”

Section: A Objective Functionsmentioning

confidence: 99%

Dynamic Multi-Objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary Algorithm

Ali,

Liu,

Ali

et al. 2024

IEEE Access

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This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems' sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm's superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more

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Section: A Objective Functionsmentioning

confidence: 99%

Dynamic Multi-Objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary Algorithm

Ali,

Liu,

Ali

et al. 2024

IEEE Access

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“…One of the notable improvements in the YOLO V8 model is its superior throughput compared to other YOLO algorithms, which are trained at 640 image resolution (Hussain, 2023). Despite having similar parameters, YOLO V8 demonstrates higher throughput, as shown in Figures 3A, B (Hussain, 2023;Ali et al, 2023b). This is due to its efficient design, which enables it to process large amounts of data quickly and accurately.…”

Section: Yolo V8 Model Descriptionmentioning

confidence: 99%

“…This is due to its efficient design, which enables it to process large amounts of data quickly and accurately. A further detailed comparison is explained below in Table 1 (Hussain, 2023;Ali et al, 2023b).…”

Section: Yolo V8 Model Descriptionmentioning

confidence: 99%

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Khan,

Raza,

Abbas

et al. 2024

Front. Built Environ.

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Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.

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“…Several MOEAs have been applied to solve Multi-objective OPF (MOOPF) problem reviewed in (Niu et al, 2014;Skolfield and Escobedo, 2022) considering conventional thermal generators; these includes: enhanced GA (EGA) (Kumari and Maheswarapu, 2010), shuffle frog leaping algorithm (SFLA) (Niknam et al, 2011), quasioppositional teaching learning based optimization (QOTLBO) (Mandal and Kumar Roy, 2014), modified imperialist competitive algorithm (MICA) (Ghasemi et al, 2014), Multi-objective DE (MDE) (Shaheen et al, 2016), multi-objective modified ICA (MOMICA) (Ali et al, 2023b), modified TLBO (MTLBO) (Shabanpour-Haghighi et al, 2014), modified gaussian barebones ICA (MGBICA) (Ghasemi et al, 2015), non-dominated sorting gravitational search algorithm (NSGSA) (Bhowmik and Chakraborty, 2015), improved strength Pareto evolutionary algorithm 2 (I-SPEA2) (Yuan et al, 2017), multi-objective evolutionary algorithm based decomposition (MOEA-D) (Zhang et al, 2016), enhanced self-adaptive differential evolution (ESDE-MC) (Pulluri et al, 2017), novel quasi-oppositional modified Jaya algorithm (QOMJaya) (Ali et al, 2023c), multi-objective dimensionbased firefly algorithm (MODFA) (Chen et al, 2018b), semidefinite programming (SDP) (Abbas et al, 2022), improved normalized normal constraint (INNC) (Rahmani and Amjady, 2018), multiobjective firefly algorithm with a constraints-prior paretodomination (MOFA-CPD) (Chen et al, 2018c), novel hybrid bat algorithm with constrained pareto fuzzy dominant (NHBA-CPFD) (Habib et al, 2022), modified pigeon-inspired optimization algorithm (MIPO) (Chen et al, 2020), and interior search algorithm (ISA) (Chandrasekaran, 2020). In these papers, the integration of renewable energy sources was not considered.…”

Section: Introductionmentioning

confidence: 99%

“…Several single objective EAs considering uncertainties in wind generation were implemented to solve the OPF problem that includes; self-adaptive evolutionary programming (SAEP) (Shi et al, 2012), Gbest-guided ABC (GABC) (Roy and Jadhav, 2015) modified bacteria foraging algorithm (MBFO) (Panda et al, 2014;Ali et al, 2023d). Whereas these papers did not employ MOEAs and do not consider the impacts of FACTS devices.…”

Section: Introductionmentioning

confidence: 99%

Optimal site and size of FACTS devices with the integration of uncertain wind generation on a solution of stochastic multi-objective optimal power flow problem

Hafeez,

Ali,

Keerio

et al. 2023

Front. Energy Res.

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To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.

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Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

Cited by 19 publications

References 56 publications

Dynamic Multi-Objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary Algorithm

Dynamic Multi-Objective Optimization of Grid-Connected Distributed Resources Along With Battery Energy Storage Management via Improved Bidirectional Coevolutionary Algorithm

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Optimal site and size of FACTS devices with the integration of uncertain wind generation on a solution of stochastic multi-objective optimal power flow problem

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