The escalating installation of distributed generation (DG) within active distribution networks (ADNs) diminishes the reliance on fossil fuels, yet it intensifies the disparity between demand and generation across vari...
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The escalating installation of distributed generation (DG) within active distribution networks (ADNs) diminishes the reliance on fossil fuels, yet it intensifies the disparity between demand and generation across various regions. Moreover, due to the intermittent and stochastic characteristics, DG also introduces uncertain forecasting errors, which further increase difficulties for power dispatch. To overcome these challenges, an emerging flexible interconnection device, soft open point (SOP), is introduced. A distributionally robust chance-constrained optimization (DRCCO) model is also proposed to effectively exploit the benefits of SOPs in ADNs under uncertainties. Compared with conventional robust, stochastic and chance-constrained models, the DRCCO model can better balance reliability and economic profits without the exact distribution of uncertainties. More-over, unlike most published works that employ two individual chance constraints to approximate the upper and lower bound constraints (e.g, bus voltage and branch current limitations), joint two-sided chance constraints are introduced and exactly reformulated into conic forms to avoid redundant conservativeness. Based on numerical experiments, we validate that SOPs' employment can significantly enhance the energy efficiency of ADNs by alleviating DG curtailment and load shedding problems. Simulation results also confirm that the proposed joint two-sided DRCCO method can achieve good balance between economic efficiency and reliability while reducing the conservativeness of conventional DRCCO methods.
Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering appli*** present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorith...
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Metaheuristics are commonly used in various fields,including real-life problem-solving and engineering appli*** present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm(ACSA).The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this *** work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions,identified as classical benchmark *** method was subsequently examined by application to 12 CEC 2022 benchmark problems of different ***,the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches,including evolutionary,human,physics,and ***,a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing *** results show that the ACSA strategy can quickly reach the global optimum,avoid getting trapped in local optima,and effectively maintain a balance between exploration and *** outperformed 42 algorithms statistically,according to post-hoc *** also outperformed 9 algorithms *** study concludes that ACSA offers competitive solutions in comparison to popüler methods.
Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwr...
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Coping with noise in computing is an important problem to consider in large systems. With applications in fault tolerance (Hastad et al., 1987;Pease et al., 1980;Pippenger et al., 1991), noisy sorting (Shah and Wainwright, 2018;Agarwal et al., 2017;Falahatgar et al., 2017;Heckel et al., 2019;Wang et al., 2024a;Gu and Xu, 2023;Kunisky et al., 2024), noisy searching (Berlekamp, 1964;Horstein, 1963;Burnashev and Zigangirov, 1974;Pelc, 1989;Karp and Kleinberg, 2007), among many others, the goal is to devise algorithms with the minimum number of queries that are robust enough to detect and correct the errors that can happen during the computation. In this work, we consider the noisy computing of the threshold-k function. For n Boolean variables x = (x1, ..., xn) ∈ {0, 1}n, the threshold-k function THk(·) computes whether the number of 1's in x is at least k or not, i.e., (Equation presented) The noisy queries correspond to noisy readings of the bits, where at each time step, the agent queries one of the bits, and with probability p, the wrong value of the bit is returned. It is assumed that the constant p ∈ (0, 1/2) is known to the agent. Our goal is to characterize the optimal query complexity for computing the THk function with error probability at most δ. This model for noisy computation of the THk function has been studied by Feige et al. (1994), where the order of the optimal query complexity is established;however, the exact tight characterization of the optimal number of queries is still open. In this paper, our main contribution is tightening this gap by providing new upper and lower bounds for the computation of the THk function, which simultaneously improve the existing upper and lower bounds. The main result of this paper can be stated as follows: for any 1 ≤ k ≤ n, there exists an algorithm that computes the THk function with an error probability at most δ = o(1), and the algorithm uses at most (Equation presented) queries in expectation. Here we define m (Eq
Wireless communication has grown tremendously in recent years, impacting nearly every feature of our lives. The increased exigency for wireless broadband services leads to a huge demand for dynamic spectrum access, su...
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Different malfunctions may arise during the operation of power equipment, impacting the quality and dependability of the power supply. Conventional monitoring techniques face challenges, prompting the introduction of ...
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Part of individuals infected by SARS-CoV-2 experiences long-lasting effects that include a wide range of symptoms, with fatigue being one of the most common complaints. Self-reported fatigue can have different manifes...
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To completely eliminate the time delays caused by phasor data compressions for real-time synchrophasor applications,a real-time synchrophasor data compression(RSDC)is proposed in this *** two-way rotation characterist...
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To completely eliminate the time delays caused by phasor data compressions for real-time synchrophasor applications,a real-time synchrophasor data compression(RSDC)is proposed in this *** two-way rotation characteristic and elliptical trajectory of dynamic synchrophasors are introduced first to enhance the compressions along with a fast solving method for elliptical trajectory fitting *** RSDC for phasor data compression and reconstruction is then proposed by combining the interpolation and extrapolation *** proposed RSDC is verified by both the actual phasor measurement data recorded in a two-phase short-circuit incident and a subsynchronous oscillation incident,and the synthetic dynamic *** is also compared with two previous real-time phasor data compression techniques,i.e.,phasor swing door trending(PSDT)and exception and swing door trending(SDT)data compression(ESDC).The verification results demonstrate that RSDC can achieve significantly higher compression ratios for offline applications with the interpolation and the zero-delay phasor data compression with the extrapolation for real-time applications simultaneously.
The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical ***,existing road infrastructure confronts challenges due to p...
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The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical ***,existing road infrastructure confronts challenges due to prolonged use and insufficient *** research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance,while neglecting road construction information,leading to potential time and energy inefficiencies in real-world scenarios involving infrastructure *** address this issue,a digital twin-embedded multi-objective autonomous vehicle navigation is proposed under the condition of infrastructure *** authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extrac-tion and modelling of the autonomous vehicle ***,a wavelet neural network is developed to predict real-time traffic flow,considering its inherent ***,a multi-objective brainstorm optimisation(BSO)-based method for path planning is introduced,which optimises total time-cost and energy consumption objective *** ensure optimal trajectory planning during infrastructure con-struction,the algorithm incorporates a real-time updated digital twin throughout autonomous vehicle *** effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road *** results highlight the improved performance and reli-ability of the autonomous vehicle system when equipped with the authors’approach,demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.
Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most ...
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Since Leonardo da Vinci’s creation of a self-propelled cart in the 1500s (Palmer. in Significant figures in world history p. 75--7, 2018), the evolution of Autonomous Vehicles (AVs) has aimed to revolutionize transpo...
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