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Keywords = deregulated electricity market

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19 pages, 4542 KiB  
Article
Forecasting Volatility of the Nordic Electricity Market an Application of the MSGARCH
by Muhammad Naeem, Hothefa Shaker Jassim, Kashif Saleem and Maham Fatima
Risks 2025, 13(3), 58; https://doi.org/10.3390/risks13030058 - 19 Mar 2025
Viewed by 743
Abstract
This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these [...] Read more.
This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these prices. Using in-sample criteria, we find that regime-switching models have lower AIC (Akaike information criterion) than single-regime GARCH models. In addition, out-of-sample forecasts indicate that regime-switching GARCH models have superior Value-at-Risk (VaR) prediction ability relative to single-regime models, which is directly pertinent to risk management. These findings highlight the importance of incorporating regime shifts into volatility models for accurately assessing and mitigating risks associated with electricity price fluctuations in deregulated markets. Full article
(This article belongs to the Special Issue Modern Statistical and Machine Learning Techniques for Financial Data)
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30 pages, 3983 KiB  
Review
A Review of System Strength and Inertia in Renewable-Energy-Dominated Grids: Challenges, Sustainability, and Solutions
by Paul Moore, Oyeniyi Akeem Alimi and Ahmed Abu-Siada
Challenges 2025, 16(1), 12; https://doi.org/10.3390/challe16010012 - 10 Feb 2025
Cited by 3 | Viewed by 6794
Abstract
The global shift towards renewable energy sources (RESs) presents significant challenges to power grid stability, particularly in grids with a high penetration of inverter-based resources (IBRs). The shift to RESs is critical to improve planetary health; however, grids must remain reliable and affordable [...] Read more.
The global shift towards renewable energy sources (RESs) presents significant challenges to power grid stability, particularly in grids with a high penetration of inverter-based resources (IBRs). The shift to RESs is critical to improve planetary health; however, grids must remain reliable and affordable throughout the transition to ensure economies can thrive and critical infrastructure remains secure. Towards that goal, this review introduces the issues of declining system strength and inertia in such grids, illustrated by case studies of curtailment measures employed by system operators in the deregulated electricity markets of Australia, Ireland, and Texas. In these high-IBR markets, curtailment has become essential to maintain system security. This paper presents the current mitigation strategies used by system operators and discusses their limitations. In addition, the paper presents a comprehensive review and analysis of current research on system strength and inertia estimation techniques, grid modelling approaches, and advanced inverter control, with a particular focus on virtual inertia. Future research directions and recommendations are outlined based on the identified gaps. These recommendations are intended to minimise system operator intervention and RES curtailment while maintaining reliable and affordable grid operation. The insights presented in this paper provide a framework to guide system operators, researchers, and policymakers toward enhancing grid stability while targeting 100% RES. Full article
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26 pages, 850 KiB  
Article
Forecasting Half-Hourly Electricity Prices Using a Mixed-Frequency Structural VAR Framework
by Gaurav Kapoor, Nuttanan Wichitaksorn, Mengheng Li and Wenjun Zhang
Econometrics 2025, 13(1), 2; https://doi.org/10.3390/econometrics13010002 - 8 Jan 2025
Cited by 1 | Viewed by 1325
Abstract
Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this [...] Read more.
Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this paper, we employ a mixed-frequency vector autoregression (MF-VAR) framework where we propose a VAR specification to the reverse unrestricted mixed-data sampling (RU-MIDAS) model, called RU-MIDAS-VAR, to provide point forecasts of half-hourly electricity prices using several weather variables and electricity demand. A key focus of this study is the use of variational Bayes as an estimation technique and its comparison with other well-known Bayesian estimation methods. We separate forecasts for peak and off-peak periods in a day since we are primarily concerned with forecasts for peak periods. Our forecasts, which include peak and off-peak data, show that weather variables and demand as regressors can replicate some key characteristics of electricity prices. We also find the MF-VAR and RU-MIDAS-VAR models achieve similar forecast results. Using the LASSO, adaptive LASSO, and random subspace regression as dimension-reduction and variable selection methods helps to improve forecasts where random subspace methods perform well for large parameter sets while the LASSO significantly improves our forecasting results in all scenarios. Full article
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24 pages, 5740 KiB  
Article
Advanced Optimal System for Electricity Price Forecasting Based on Hybrid Techniques
by Hua Luo and Yuanyuan Shao
Energies 2024, 17(19), 4833; https://doi.org/10.3390/en17194833 - 26 Sep 2024
Viewed by 1392
Abstract
In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics [...] Read more.
In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model’s ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid)
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19 pages, 2823 KiB  
Article
Multi-Period Optimal Transmission Switching with Voltage Stability and Security Constraints by the Minimum Number of Actions
by Mei Zhang, Lei Wang, Jiantao Liu, Xiaofan Deng and Ke Wu
Sustainability 2024, 16(18), 8272; https://doi.org/10.3390/su16188272 - 23 Sep 2024
Viewed by 1243
Abstract
Due to the ever-growing load demand and the deregulation of the electricity market, power systems often run near the stability boundaries, which deteriorates system voltage stability and raises voltage issues for the stable operations of power systems. Transmission switching (TS) has been applied [...] Read more.
Due to the ever-growing load demand and the deregulation of the electricity market, power systems often run near the stability boundaries, which deteriorates system voltage stability and raises voltage issues for the stable operations of power systems. Transmission switching (TS) has been applied to improve economic benefits and security operations for many applications. In this paper, a multi-period voltage stability-constrained problem (MP-VSTS) is established, intending to improve voltage security and the stability of a power system. Considering the online application of transmission switching, the minimum number of switching actions is taken as the objective function of the proposed MP-VSTS problem, which extends the TS application for real industries. The proposed model provides the switching lines for the upcoming period and the state of power systems for several successive periods. To overcome the solving difficulties of the proposed model, a two-stage approach is presented, which balances speed and accuracy. Numerical studies on the IEEE 118- and 662-bus power systems have demonstrated the proposed approach’s performance. Full article
(This article belongs to the Section Sustainable Engineering and Science)
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27 pages, 4090 KiB  
Article
An Effective Strategy for Achieving Economic Reliability by Optimal Coordination of Hybrid Thermal–Wind–EV System in a Deregulated System
by Ravindranadh Chowdary Vankina, Sadhan Gope, Subhojit Dawn, Ahmed Al Mansur and Taha Selim Ustun
World Electr. Veh. J. 2024, 15(7), 289; https://doi.org/10.3390/wevj15070289 - 28 Jun 2024
Cited by 5 | Viewed by 1084
Abstract
This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level [...] Read more.
This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (EEV,max, EEV,opt, EEV,low, and EEV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP). Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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29 pages, 2164 KiB  
Article
Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting
by Vasileios Laitsos, Georgios Vontzos, Apostolos Tsiovoulos, Dimitrios Bargiotas and Lefteri H. Tsoukalas
Electronics 2024, 13(10), 1996; https://doi.org/10.3390/electronics13101996 - 20 May 2024
Cited by 3 | Viewed by 1772
Abstract
Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial [...] Read more.
Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve. Full article
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2 pages, 144 KiB  
Correction
Correction: Pourdaryaei et al. Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market. Energies 2021, 14, 6104
by Alireza Pourdaryaei, Mohammad Mohammadi, Mazaher Karimi, Hazlie Mokhlis, Hazlee A. Illias, Seyed Hamidreza Aghay Kaboli and Shameem Ahmad
Energies 2024, 17(10), 2266; https://doi.org/10.3390/en17102266 - 8 May 2024
Viewed by 788
Abstract
There was an error in the original publication [...] Full article
19 pages, 598 KiB  
Article
Interdependent Expansion Planning for Resilient Electricity and Natural Gas Networks
by Weiqi Pan, Yang Li, Zishan Guo and Yuanshi Zhang
Processes 2024, 12(4), 775; https://doi.org/10.3390/pr12040775 - 12 Apr 2024
Cited by 3 | Viewed by 1297
Abstract
This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy [...] Read more.
This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy system planning strategy that can enhance the systems’ ability to respond to such events. Our strategy unfolds in two stages. Initially, we devise expansion strategies for the interdependent networks through a detailed tri-level planning model, including transmission, generation, and market dynamics within a deregulated electricity market setting, formulated as a mixed-integer linear programming (MILP) problem. Subsequently, we assess the impact of extreme events through worst-case scenarios, applying previously determined network configurations. Finally, the integrated expansion planning strategies are evaluated using real-world test systems. Full article
(This article belongs to the Special Issue Process Design and Modeling of Low-Carbon Energy Systems)
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33 pages, 5934 KiB  
Article
Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks
by Rabea Jamil Mahfoud, Nizar Faisal Alkayem, Emmanuel Fernandez-Rodriguez, Yuan Zheng, Yonghui Sun, Shida Zhang and Yuquan Zhang
Mathematics 2024, 12(2), 300; https://doi.org/10.3390/math12020300 - 17 Jan 2024
Cited by 4 | Viewed by 1337
Abstract
Distribution companies (DISCOs) aim to maximize their annual profits by performing the optimal planning of distributed generators (DGs) or energy storage systems (ESSs) in the deregulated electricity markets. Some previous studies have focused on the simultaneous planning of DGs and ESSs for DISCO [...] Read more.
Distribution companies (DISCOs) aim to maximize their annual profits by performing the optimal planning of distributed generators (DGs) or energy storage systems (ESSs) in the deregulated electricity markets. Some previous studies have focused on the simultaneous planning of DGs and ESSs for DISCO profit maximization but have rarely considered the reactive powers of DGs and ESSs. In addition, the optimization methods used for solving this problem are either traditional or outdated, which may not yield superior results. To address these issues, this paper simultaneously performs the optimal planning of DGs and ESSs in distribution networks for DISCO profit maximization. The utilized model not only takes into account the revenues of trading active and reactive powers but also addresses the active and reactive powers of DGs and ESSs. To solve the optimization problem, a new hybrid evolutionary algorithm (EA) called the oppositional social engineering differential evolution with Lévy flights (OSEDE/LFs) is proposed. The OSEDE/LFs is applied to optimize the planning model using the 30-Bus and IEEE 69-Bus networks as test systems. The results of the two case studies are compared with several other EAs. The results confirm the significance of the planning model in achieving higher profits and demonstrate the effectiveness of the proposed approach when compared with other EAs. Full article
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22 pages, 2672 KiB  
Article
Strategic Guidelines for Battery Energy Storage System Deployment: Regulatory Framework, Incentives, and Market Planning
by Roberto Dias Filho, Amanda C. M. Monteiro, Tatiane Costa, Andrea Vasconcelos, Ana Clara Rode and Manoel Marinho
Energies 2023, 16(21), 7272; https://doi.org/10.3390/en16217272 - 26 Oct 2023
Cited by 9 | Viewed by 4951
Abstract
This research addresses strategic recommendations regarding the applications of battery energy storage systems (BESS) in the context of the deregulated electricity market. The main emphasis is on regulatory dimensions, incentive mechanisms, and the provision of marketable storage services. The study’s findings demonstrate that [...] Read more.
This research addresses strategic recommendations regarding the applications of battery energy storage systems (BESS) in the context of the deregulated electricity market. The main emphasis is on regulatory dimensions, incentive mechanisms, and the provision of marketable storage services. The study’s findings demonstrate that battery energy storage systems (BESS) have distinct characteristics that challenge their conventional classification as a load or generator within power systems. The study additionally emphasizes the insights, lessons learned, and good practices gained from early adopter countries in implementing energy storage systems (ESS). These insights include the importance of establishing a precise definition of ESS, promoting collaborative engagement with relevant stakeholders, and developing a series of incentive strategies. The results show that nations that pioneered BESS’s application in their electricity matrices have effectively promoted storage services in deregulated markets, employing storage assets for various purposes such as peak reduction, frequency regulation, renewable energy support, and energy arbitrage applications. These applications underline the potential of BESS to increase grid stability and minimize exposure to risk and volatility in the revenues of storage agents in deregulated markets. Full article
(This article belongs to the Section D: Energy Storage and Application)
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23 pages, 1261 KiB  
Article
Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique
by Hasnain Iftikhar, Josue E. Turpo-Chaparro, Paulo Canas Rodrigues and Javier Linkolk López-Gonzales
Energies 2023, 16(18), 6669; https://doi.org/10.3390/en16186669 - 18 Sep 2023
Cited by 25 | Viewed by 3445
Abstract
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this [...] Read more.
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges. Full article
(This article belongs to the Special Issue Advanced Optimization and Forecasting Methods in Power Engineering)
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12 pages, 2359 KiB  
Article
Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application
by Salahuddin Khan
Sustainability 2023, 15(16), 12311; https://doi.org/10.3390/su151612311 - 12 Aug 2023
Cited by 8 | Viewed by 4505
Abstract
Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, [...] Read more.
Electrical load forecasting plays a crucial role in planning and operating power plants for utility factories, as well as for policymakers seeking to devise reliable and efficient energy infrastructure. Load forecasting can be categorized into three types: long-term, mid-term, and short-term. Various models, including artificial intelligence and conventional and mixed models, can be used for short-term load forecasting. Electricity load forecasting is particularly important in countries with restructured electricity markets. The accuracy of short-term load forecasting is crucial for the efficient management of electric systems. Precise forecasting offers advantages for future projects and economic activities of power system operators. In this study, a novel integrated model for short-term load forecasting has been developed, which combines the wavelet transform decomposition (WTD) model, a radial basis function network, and the Thermal Exchange Optimization (TEO) algorithm. The performance of this model was evaluated in two diverse deregulated power markets: the Pennsylvania-New Jersey-Maryland electricity market and the Spanish electricity market. The obtained results are compared with various acceptable standard forecasting models. Full article
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23 pages, 4171 KiB  
Review
Revisiting Market Power in the Polish Power System
by Przemysław Kaszyński, Aleksandra Komorowska and Jacek Kamiński
Energies 2023, 16(13), 4856; https://doi.org/10.3390/en16134856 - 21 Jun 2023
Cited by 3 | Viewed by 1663
Abstract
The consequences of the liberalisation of electricity markets have been widely discussed in the literature emphasising the successes or failures of privatisation and deregulation. While most developed power systems have undergone a form of economic transformation, they still require to be monitored and [...] Read more.
The consequences of the liberalisation of electricity markets have been widely discussed in the literature emphasising the successes or failures of privatisation and deregulation. While most developed power systems have undergone a form of economic transformation, they still require to be monitored and analysed to assess market power. The Polish power system is an example wherein the potential of market power examined fifteen years ago was summarised as significant. Since then, the transformation process and changes in the ownership structure have taken place. This study focuses on the assessment of the potential of market power in the Polish electricity market. For this purpose, statistics on power companies were collected and processed. Then, structural and behavioural measures were applied, including concentration ratios, the entropy coefficient, the Gini coefficient, the Herfindahl–Hirschman Index (HHI), the Residual Supply Index (RSI), and the Lerner Index. The results reveal that, despite a dynamic increase in renewable capacity, market concentration has increased in recent years, achieving an HHI of 2020.9 in 2021. An increase in the Lerner Index of lignite and hard coal-fired units is also observed, indicating high mark-ups by the key market players. Based on quantitative analysis, policy recommendations are outlined to reduce the negative impact of market power on consumers. Full article
(This article belongs to the Special Issue Prospects and Challenges of Energy Transition)
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24 pages, 3773 KiB  
Article
Optimal Congestion Management with FACTS Devices for Optimal Power Dispatch in the Deregulated Electricity Market
by Abhilipsa Sahoo, Prakash Kumar Hota, Preeti Ranjan Sahu, Faisal Alsaif, Sager Alsulamy and Taha Selim Ustun
Axioms 2023, 12(7), 614; https://doi.org/10.3390/axioms12070614 - 21 Jun 2023
Cited by 14 | Viewed by 2043
Abstract
A deregulated electricity market provides open access to all market players. In an open-access power market, the system operator is responsible for ensuring that all contracted power is dispatched. However, if this results in line flows that exceed their acceptable range, then it [...] Read more.
A deregulated electricity market provides open access to all market players. In an open-access power market, the system operator is responsible for ensuring that all contracted power is dispatched. However, if this results in line flows that exceed their acceptable range, then it could threaten the system’s security. Therefore, the system operator checks for congestion as the line flow exceeds its limit. For congestion management, the system operator applies different curtailment strategies to limit the requested transaction. Therefore, in this work, an optimal power dispatch model has been presented in order to reduce the curtailment of requested power. A modified moth flame optimization technique has been implemented to frame this OPD model. The impact of congestion management on power dispatch has been analyzed, considering bilateral and multilateral dispatch in an electricity market. In addition, the effect of FACTS devices on reducing congestion and curtailing power is studied. Verification studies showed that the proposed solution reduces congestion costs by 27.14% and 29.4% in 14- and 30-bus systems, respectively. It has been verified that the MMFO approach with the FACTS device improves transaction deviations and ensures that the deregulated system provides secure energy with less cost reflected on the customers. Full article
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