342 research outputs found
K-Means and Alternative Clustering Methods in Modern Power Systems
As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies
Novel supervisory management scheme of hybrid sun empowered grid-assisted microgrid for rapid electric vehicles charging area
The spread of electric vehicles (EV) contributes substantial stress to the present overloaded utility grid which creates new chaos for the distribution network. To relieve the grid from congestion, this paper deeply focused on the control and operation of a charging station for a PV/Battery powered workplace charging facility. This control was tested by simulating the fast charging station when connected to specified EVs and under variant solar irradiance conditions, parity states and seasonal weather. The efficacy of the proposed algorithm and experimental results are validated through simulation in Simulink/Matlab. The results showed that the electric station operated smoothly and seamlessly, which confirms the feasibility of using this supervisory strategy. The optimum cost is calculated using heuristic algorithms in compliance with the meta-heuristic barebones Harris hawk algorithm. In order to long run of charging station the sizing components of the EV station is done by meta-heuristic barebones Harris hawk optimization with profit of USD 0.0083/kWh and it is also validated by swarm based memetic grasshopper optimization algorithm (GOA) and canonical particle swarm optimization (PSO)
Smart electric vehicle charging strategy in direct current microgrid
This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for
integrating network loads, EV charging/discharging and dispatchable generators (DGs) using
droop control within DCMG. A novel two-stage optimization framework is deployed, which
optimizes power flow in the network using droop control within DCMG and solves charging
tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest
path problem considering system losses and battery degradation from the distribution system
operator (DSO) and electric vehicles aggregator (EVA) respectively.
Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic
behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and
energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters.
Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability
distribution for those load profiles and further tests show the scheme is suitable for
decentralized computing of its low burn-in request, fast convergent and good parallel acceleration
performance.
Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic
distribution model into the optimization framework, which becomes the first stage of
the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed
where the previous deterministic model is deployed in the second stage which stage one and
stage two are combined as a chance-constrained problem in stage three and solved as a random
walk problem.
Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained
show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary
services. Meanwhile, both system loss and battery degradation from DSO and EVA can be
minimized.Open Acces
Dynamic priority-based efficient resource allocation and computing framework for vehicular multimedia cloud computing
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In intelligent transportation system, smart vehicles are equipped with a variety of sensing devices those offer various multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and many entertainment and comfort-related applications. These smart vehicles produce a massive amount of multimedia related data that required fast and real-time processing which cannot be fully handled by the standalone onboard computing devices due to their limited computational power and storage capacities. Therefore, handling such multimedia applications and services demanded changes in the underlaying networking and computing models. Recently, the integration of vehicles with cloud computing is emerged as a challenging computing paradigm. However, there are certain challenges related to multimedia contents processing, (i.e., resource cost, fast service response time, and quality of experience) that severely affect the performance of vehicular communication. Thus, in this paper, we propose an efficient resource allocation and computation framework for vehicular multimedia cloud computing to overcome the aforementioned challenges. The performance of the proposed scheme is evaluated in terms of quality of experience, service response time, and resource cost by using the Cloudsim simulator
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