Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey

Poyrazoglu, Gokturk

doi:10.3390/en14041014

Cited by 16 publications

(4 citation statements)

References 43 publications

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“…Similarly, in [217], this algorithm is utilized to categorize all participating companies in the market based on their similarities, facilitating strategic involvement in competitive electricity markets. References [218], [219],and [220] employs a K-means clustering model to segment similar price zones into clusters, aiming to improve energy price prediction accuracy by creating separated models for each cluster. Likewise, In the study [221], the authors applied the K-means algorithm to group electric vehicles (EVs) based on their travel behavior patterns.…”

Section: E Electricity Market 1) Power Marketsmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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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.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: E Electricity Market 1) Power Marketsmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…To improve and develop models based on ecological and social challenges, some suggestions have been presented by Borowski [2] to improve zonal and nodal models that may hold promise as new models for the functioning of the electricity market in Europe in the near future. Poyrazoglu [3] applied three clustering methods to pricing zone detection for an electricity market in a Turkey case study. However, a more accurate estimation of equipment costs is essential to power plants exergo-economic analysis, as those costs have direct and influential impacts on final economic results.…”

Section: Introductionmentioning

confidence: 99%

Development of Cost Correlations for the Economic Assessment of Power Plant Equipment

et al. 2021

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A comprehensive cost correlation analysis was conducted based on available cost correlations, and new equipment cost correlation models were proposed based on QUE$TOR modeling. Cost correlations for various types of equipment such as pumps, compressors, heat exchangers, air coolers, and pressure vessels were generated on the basis of extracted cost data. The models were derived on the basis of robust multivariable regression with the aim of minimizing the residuals by using the genetic algorithm. The proposed compressor models for both centrifugal and reciprocating types showed that the Turton cost estimation for carbon steel compressor and Matche’s and Mhhe’s data were compatible with the generated model. According to the results, the cost trend in the Turton correlation for carbon steel had a somewhat lower estimation than these correlations. Further, the cost trend of the Turton correlation for carbon steel pressure vessels was close to the presented model trend for both bullet and sphere types. The Turton cost trend for U-tube shell-and-tube heat exchangers with carbon steel shell and stainless steel tube was close to the proposed heat exchanger model. Furthermore, the Turton cost trend for the flat-plate heat exchanger using carbon steel was similar to the proposed model with a slight difference.

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“…The optimal design of the distribution network must be dealt with care considering the extensive network layouts, system losses, and frequent characteristic interruptions [1]. Energy markets around the world are based on two pricing models; zonal and nodal, to deal with congestion due to limited transmission capacity [2,3]. However, the nodal model takes into account the actual state of the power system in a transparent manner to allocate the future distributed generation [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks

et al. 2021

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Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits.

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Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey

Cited by 16 publications

References 43 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Development of Cost Correlations for the Economic Assessment of Power Plant Equipment

Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks

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