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Optimization problems can be found in many studies, especially in engineering sectors. Some of them are single-objective problems while others are multi-objective ones. Meanwhile, metaheuristics are popular methods to be used to handle various optimization problems. This paper introduces a new metaheuristic namely narrowing step optimization (NSO). Its core idea is utilizing iteration to control the maximum step size which is used in its three guided searches. The maximum step size declines during iteration. In these three guided searches, the unit move toward the optimal solution in the first search, a randomly picked better unit in the second search, and a randomly picked unit or the middle between two randomly picked units in the third search. Then, NSO is challenged to solve both constrained and unconstrained problems. The 23 standard functions are picked as the unconstrained problems while the multi-objective economic emission dispatch (EED) problem and economic load dispatch (ELD) problem are picked as the constrained ones. There are four power demands employed in each economic dispatch (ED) problem. In both assessments, NSO is benchmarked with recent stochastic optimizers including total interaction algorithm (TIA), preschool education optimization algorithm (PEOA), carpet waver optimization (CWO), sculptor optimization algorithm (SOA), and wombat optimization algorithm (WOA). The result shows that NSO is competitive in handling both problems. Moreover, NSO is successful in catching the global optimal solution of six functions. The result exhibits the fierce competition in ED problem. [ABSTRACT FROM AUTHOR]

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers' food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment. [ABSTRACT FROM AUTHOR]

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7. [ABSTRACT FROM AUTHOR]

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs. [ABSTRACT FROM AUTHOR]

Featured Application: This paper describes an application that is currently used in the national railway transportation system to measure zigzag length. Currently, measurements are performed in the laboratory based on images recorded of the contact point between the pantograph and the contact wire based on the geographical position of the present location. A prospective future application provides a real-time approach for measuring zigzag length by employing pattern recognition in images. Recent years have seen outstanding developments in research and technology, highlighting the importance of railway transportation, especially the implementation of high-speed trains, which is becoming more and more challenging. This facilitates extensive research into the science and technology of the electrical interaction between the components of pantograph–catenary systems (PCSs). Problems regarding the PCS can result in infrastructure incidents, potentially stopping train operations. A common cause of failure in electrified railway PCS is a contact wire's zigzag length that exceeds the prescribed technical limit, which can be caused by missing droppers or faults in the mounting mechanism. This work proposes a video camera-based monitoring technique for zigzag geometry measurement that additionally employs a Global Positioning System (GPS) sensor to detect the current geographical position of the point of zigzag length measurement. There are two proposed techniques for measuring the length of the zigzag based on image processing. In the first technique, using previously recorded data, the images are analyzed in the laboratory, and in the second, the images are analyzed in real time. Based on the results, we suggest a model and prediction of zigzag length employing hybrid deep neural networks. [ABSTRACT FROM AUTHOR]

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement. [ABSTRACT FROM AUTHOR]

Power system engineers' intention is to produce power, transport it, and finally distribute it to customers under safe and reliable operating conditions to provide continuous and stable electrical energy. However, this goal is often hindered by unexpected faults that can lead to system breakdown. As a result, power system modeling is an effective technique which helps the analysis of power systems. Besides this technique, mathematical methods provide comprehensible information about the system's state. Furthermore, various studies have employed different techniques to detect electrical faults. In this paper, electrical faults are selected using a risk and impact approach, and fault characteristics are found using the Short-time Fourier transform. The case study is the Djibouti power network, and it is modeled with all real parameters using MATLAB-SIMULINK software. Following that, several fault scenarios were run, and the analysis was conducted using the proposed mathematical method. Ultimately, the simulation results indicate that the most critical faults are single-line-to-ground and double-line-to-ground. Hence, the extraction of signal features for these fault types is carried out. These faults have a high short circuit current, which can cause damage to the electrical network, and while clearing the fault, the oscillatory transient state appears with low-frequency components. These frequency components significantly affect power quality, thereby reducing the system's performance. [ABSTRACT FROM AUTHOR]

In pursuit of the "double carbon" objectives, converting high-carbon thermal power plants into carbon capture power plants is recognized as an effective measure to mitigate carbon emissions. In order to improve the economy of the system, an electric hydrogen generation unit is introduced, and an economic dispatch strategy that considers the combination of a flexible carbon capture power plant and electric hydrogen generation is proposed. Firstly, the low-carbon principle of the flexible carbon capture plant is introduced, and secondly, the joint operation structure of the flexible carbon capture plant, pumped storage, and hydrogen generation is constructed in order to fully exploit the synergistic operation potential of the hydrogen and carbon capture plants. On this basis, an economic dispatch model with the objective of the lowest combined system cost of the sum of system operation cost, system carbon trading cost, hydrogen production revenue, and wind and light abandonment cost is established, fuzzy parameters are used to characterize the uncertainty levels of wind and light, fuzzy opportunity constraints are established, and the model is solved using an improved sparrow search algorithm. The results of the example show that the proposed model reduces the comprehensive cost by 20.9% and carbon emissions by 61.77% compared with the existing model, which has significant low-carbon economic benefits. [ABSTRACT FROM AUTHOR]

With the continuous increase in the penetration rate of renewable energy, the randomness and flexibility demand in the power system continues to increase. The main grid side of the power system vigorously develops pumped hydro storage (PHS) resources. However, the current PHS station scheduling method of a fixed time period and fixed power has lost a certain flexibility supply. In this paper, an optimal dispatching model of multi-pumped hydro storage stations is proposed to supply flexibility for different regions of the state grid in east China. Firstly, the credible predictable power (CPP) of renewable energy is calculated and the definition of flexibility demand of a power system is given. The calculation model for flexibility demand is established. Secondly, considering the regional allocation constraint in the state grid in east China, a non-centralized model of multi-PHS within the dispatch scope is established. In the model, the constraints of storage capacity of different hydropower conversion coefficients of each PHS station is considered. The flexibility supply model of PHS stations to each region of the state grid in east China is established to realize reasonable flexibility allocation. Then, by combining the PHS station models and the flexibility demand calculation model, the optimal dispatching model for the flexibility supply of multi-PHS stations is established. Finally, based on the network dispatching example, the effectiveness and superiority of the proposed strategy are verified by a case study. [ABSTRACT FROM AUTHOR]

Modern power systems are high-dimensional, strongly coupled nonlinear systems with complex and diverse dynamic characteristics. The polynomial model of the power system is a key focus in stability research. Therefore, this paper presents a study on the approximate transient stability solution targeting the fault process in power systems. Firstly, based on the inherent sinusoidal coupling characteristics of the power system swing equations, a generalized polynomial matrix description in perturbation form is presented using the Taylor expansion formula. Secondly, considering the staged characteristics of the stability process in power systems, the approximate solutions of the polynomial model during and after the fault are provided using coordinate transformation and regular perturbation techniques. Then, the structural characteristics of the approximate solutions are analyzed, revealing the mathematical basis of the stable motion patterns of the power grid, and a monotonicity rule of the system's power angle oscillation amplitude is discovered. Finally, the effectiveness of the proposed methods and analyses is further validated by numerical examples of the IEEE 3-machine 9-bus system and IEEE 10-machine 39-bus system. [ABSTRACT FROM AUTHOR]

This article is concerned with the stability issue of PI-based load frequency control (LFC) systems with time-varying delays and load interference. Firstly, a novel augmented Lyapunov–Krasovskii functional (LKF) is developed, in which the single integral term includes the augmented s-dependent integral term of delay-partition. For the purpose of coordinating with the constructed LKF effectively, a generalised free-matrix-based integral inequality and quadratic inequality are utilised to estimate the functional derivatives accurately, so that the stability criterion of less conservative is obtained. Finally, the relationship between the PI gains and the delay margins is presented, and the effects of time delays and PI gains on the system dynamic performance are discussed simultaneously by simulation. The simulation results verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy in power system continues to increase, that power system will face the risk of a multi-time-scale supply and demand imbalance. The rational planning of energy storage facilities can achieve a dynamic time–delay balance between power system supply and demand. Based on this, and in order to realize the location and capacity optimization determination of multiple types of energy storage in power system, this paper proposes a collaborative optimization planning framework for multiple types of energy storage. The proposed planning framework is modelled as a two-stage MILP model based on scenarios via the stochastic optimization method. In the first stage, investment decisions are made for two types of energy storage: battery energy storage (short term) and hydrogen energy storage (long term). In the second stage, power system operation simulation is conducted based on typical scenarios. Finally, the progressive hedging (PH) algorithm is applied to realize the efficient solving of the proposed model. A modified IEEE 39-bus test system is used to verify the validity of the proposed multiple types of energy storage collaborative optimization planning model and PH algorithm. [ABSTRACT FROM AUTHOR]

Digital twin technology is an important tool for the digitalization of the power industry. A digital twin is a concept that allows for the creation of virtual copies of real objects that can be used for technical state analysis, predictive analysis, and optimization of the operation of power systems and their components. Digital twins are used to address different issues, including the management of equipment reliability and efficiency, integration of renewable energy sources, and increased flexibility and adaptability of power grids. Digital twins can be developed with the use of specialized software solutions for designing, prototyping, developing, deploying, and supporting. The existing diversity of software requires systematization for a well-informed choice of digital twin's development tool. It is necessary to take into account the technical characteristics of power systems and their elements (equipment of power plants, substations and power grids of power systems, mini- and microgrids). The reviews are dedicated to tools for creating digital twins in the power industry. The usage of Digital Twin Definition Language for the description data of electromagnetic, thermal, and hydrodynamic models of a power transformer is presented. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we propose a CNN-GAN-based real-time processing technique for filtering images of underwater cables used in power systems. This addresses the excessive interference impurities that are frequently observed in images captured by remotely operated vehicles (ROVs). The process begins with the input of the original image into the convolutional neural network (CNN). Subsequently, the training outcomes, which serve as input parameters for the generative adversarial network (GAN), facilitate the filtering process. The system also calculates both the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), performing model updates via backward propagation. This technique utilizes deep learning technologies to achieve rapid, real-time filtering of underwater cable images. The experimental results reveal that the loss function of the CNN reaches 0.16 with an accuracy of 97.5%, while the loss function of the adversarial GAN network approaches 0.05. Compared with traditional methods such as DDN, JORDOR, RESCAN, and PRENet, the proposed CNN-GAN algorithm exhibits superior performance, as evidenced by the higher PSNR and SSIM values. Specifically, for clear water images, the PSNR reaches 29.86 dB and the SSIM is 0.9045. For severely polluted images, the PSNR is 28.67 dB and the SSIM is 0.8965, while for unevenly illuminated images, the PSNR and SSIM values are 24.37 dB and 0.88, respectively. These enhancements significantly benefit the monitoring and maintenance of power systems. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid optimization algorithm known as particle swarm optimization and grey wolf optimizer (PSO-GWO) based proportional integral derivative (PID) controller is suggested for automatic voltage regulator (AVR) system terminal tracking problem. The main objective of the suggested approach is to reduce crucial performance factors such as rise time, settling time, peak overshoot and peak time of the voltage of the power system in order to improve the AVR system's transient response. This analysis was compared to results obtained from existing heuristic algorithmbased approaches found in the literature, proving the improved PID controller's enhanced performance obtained through the suggested approach. Furthermore, the performance of the tuned controller with respect to disturbance rejection and its robustness to parametric uncertainties were evaluated separately and compared with existing control approaches. According to the obtained comparison results and from all simulations, using MATLAB-Simulink tool, it has been noted that the PID controller optimized using PSO-GWO algorithm has superior control performance compared to PID controllers tuned by ABC, DE, BBO and PSO algorithms. The main conclusion of the presented study highlights that the recommended strategy can be effectively implemented to improve the performance of the AVR system. [ABSTRACT FROM AUTHOR]

In power system, stability analysis becomes important to identify the level of stability and security of electrical power systems. This article proposes a flexible alternating current systems-unified power flow compensator (FACTS-UPQC) compensator installed in the high-voltage network to ensure stability of voltage and frequency in the power grid facing voltage dips, over-voltage and short-circuit faults. Thus, an artificial intelligence algorithm based on fuzzy logic method is implemented to have the appropriate values of FACTS-UPQC conditioner. The voltage stability improvement is demonstrated by the variation margin of amplitude and phase angle. Frequency stability aims to obtain a frequency within a minimal variation. A 14-bus test electrical system is modeled to implement the advanced control strategy. MATLAB/Simulink software is used to prove the functionality of the method in improving the stability of power system. The simulation results showed a reduction of harmonic distortion rate (HDR) and a minimization of the voltage variation range for the implemented fuzzy logic system compared with the literature. [ABSTRACT FROM AUTHOR]

This paper proposes an estimator-based single phase robustness variable structure load frequency controller (SPRVSLFC) for the multi-region interconnected power systems (MRIPS) with communication delays. The key attainments of this research consist of two missions: i) a global stability of the power systems is guaranteed by removing the reaching phase in traditional variable structure control (TVSC) technique; and ii) a novel output feedback load frequency controller is established based on the estimator tool and output information only. Initially, a single-phase switching function is constructed to disregard the reaching phase in TVSC. Then, an unmeasurable state variable of the MRIPS is estimated by using the proposed estimator tool. Next, a new SPRVSLFC for the MRIPS is suggested based on the support of the estimator tool and output data only. Furthermore, a sufficient constraint is constructed by retaining the linear matrix inequality (LMI) procedure for ensuring the robust stability of motion dynamics in sliding mode. Finally, the performance of interconnected power plant under changed multi-constraints is imitated with the novel control technique to validate the practicability of the plant. [ABSTRACT FROM AUTHOR]

Occurrences of disturbance affect the rotor angle operation of a synchronous generator in the generation system of a power system. The disturbance will disrupt the synchronous generator's rotor oscillation and result in rotor angle instability, which will degrade the power system's performance. This paper aims to develop a Lyapunov-based rotor angle deviation regulator for the nonlinear swing equation of a synchronous generator. The proposed regulator is expected to assure asymptotic stability of the rotor angle and robustness to uncertainty. Backstepping and Lyapunov redesign techniques are employed in developing the regulator. To validate the effectiveness and robustness of the regulator, a simulation in MATLAB/Simulink is carried out. The simulation result shows that the asymptotic stability and robustness of the regulator are guaranteed regardless of the disturbance. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544). [ABSTRACT FROM AUTHOR]

The integration of stochastic and intermittent distributed PVs brings great challenges for power system operation. Precise net load forecasting performs a critical factor in dependable operation and dispensing. An approach to probabilistic net load prediction is introduced for sparse variant Gaussian process based algorithms. The forecasting of the net load is transferred to a regression problem and solved by the sparse variational Gaussian process (SVPG) method to provide uncertainty quantification results. The proposed method can capture the uncertainties caused by the customer and PVs and provide effective inductive reasoning. The results obtained using real-world data show that the proposed method outperforms other best-of-breed algorithms. [ABSTRACT FROM AUTHOR]

Importing substantial amount of green hydrogen from countries like South Africa, which have abundant solar and wind potentials to replace fossil fuels, has attracted interest in developed regions. This study analyses South African strategies for improving and decarbonizing the power sector while also producing hydrogen for export. These strategies include the Integrated Resource Plan, the Transmission Development Plan, Just Energy Transition and Hydrogen Society Roadmap for grid connected hydrogen production in 2030. Results based on an hourly resolution optimisation in Plexos indicate that annual grid-connected hydrogen production of 500 kt can lead to a 20–25% increase in the cost of electricity in scenarios with lower renewable energy penetration due to South African emission constraints by 2030. While the price of electricity is still in acceptable range, and the price of hydrogen can be competitive on the international market (2–3 USD/kgH 2 for production), the emission factor of this hydrogen is higher than the one of grey hydrogen, ranging from 13 to 24 kgCO2/kgh 2. When attempting to reach emission factors based on EU directives, the three policy roadmaps become unfeasible and free capacity expansion results in significant sixteen-fold increase of wind and seven-fold increase in solar installations compared to 2023 levels by 2030 in South Africa. [Display omitted] • We analyse the impact of hydrogen production on the power system using PLEXOS. • We evaluate South Africa's 2030 strategies to provide insights for policymakers. • We show that 500 kt annual hydrogen demand increase emission factors and costs. • 2030 power system plans cannot produce green hydrogen consistent with EU standards. • To meet EU H2 standards, additional 56 GW wind and 43 GW solar capacity are needed. [ABSTRACT FROM AUTHOR]

Renewable energy sources, such as wind, tide, solar cells, etc, are the primary research areas that deliver enormous amounts of energy for our daily usage and minimize the dependency upon fossil fuel. Paralley, harnessing ambient energy from our surroundings must be prioritized for small powered systems. Nanogenerators, which use waste energy to generate electricity, are based on such concepts. We refer to these nanogenerators as energy harvesters. The purpose of energy harvesters is not to outcompete traditional renewable energy sources. It aims to reduce reliance on primary energy sources and enhance decentralized energy production. Energy storage is another area that needs to be explored for quickly storing the generated energy. Supercapacitor is a familiar device with a unique quick charging and discharging feature. Encouraging advancements in energy storage and harvesting technologies directly supports the efficient and comprehensive use of sustainable energy. Yet, self-optimization from independent energy harvesting and storage devices is challenging to overcome. It includes instability, insufficient energy output, and reliance on an external power source, preventing their direct application and future development. Coincidentally, integrating energy harvesters and storage devices can address these challenges, which demand their inherent action. This review intends to offer a complete overview of supercapacitor-based integrated energy harvester and storage systems and identify opportunities and directions for future research in this subject. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach to design a robust estimator that is able to keep its consistency in system state estimation when system process model mismatch occurs. To successfully develop such an estimator, not only the estimation strategy proposed but also the designer's knowledge and experience about the system behavior are crucial and determining. To assess the performance of the resultant estimator, its performance is compared with that of three well‐known estimators, that is, the unscented Kalman filter, the cubature Kalman filter, and the extended Kalman filter on the IEEE 5‐generator 14‐bus system. The results indicate that the proposed method has led to an estimator outperforming its rivals under the presence of model errors. [ABSTRACT FROM AUTHOR]

Green hydrogen or renewable hydrogen is promising cause it has no carbon emissions in production process, which is water electrolysis with electricity from renewables. Although grids can ensure stable and enough electricity supply, it is difficult to determine whether the grid electricity for hydrogen production is from renewable sources. This article uses EU rules to identify green hydrogen produced by grid electricity and analyse the production. European grids are classified according to the calculated indexes regarding renewables penetration and electricity carbon emission intensity. The negative correlation between green hydrogen production and electricity prices in different time scales and geographical distribution is concluded. Using imported electricity can improve the potential and diversity of green hydrogen production. Nordic countries and Albania have the most potential of green hydrogen production using grid electricity. The results can be a reference for the electricity market on flexibility supply and the future green hydrogen market development. • Nordic countries and Albania have the most green hydrogen production by grid power. • Other bidding zones with lower electricity prices have more green hydrogen production. • Green hydrogen production is negative correlated with hourly electricity prices. • Using imported electricity can improve the production potential and diversity. • Green hydrogen production by grid power can provide flexibility to electricity market. [ABSTRACT FROM AUTHOR]

The article discusses advancements in smart energy systems aimed at reducing carbon emissions and improving energy efficiency through innovative planning, operation, and equipment. Topics discussed include distribution network and grid planning, energy storage and control strategies, and renewable energy integration and forecasting.

Virtual power plants (VPPs) have gained significant attention in recent years as a promising solution for optimizing the operation of power systems. VPPs allow for the integration and coordination of Distributed Energy Resources (DERs), such as renewable energy sources, to provide grid support and stabilize the power supply. This paper presents a VPP that utilizes a three-phase power flow analysis for its modeling. This approach allows for an accurate representation of the power flow in the VPP, considering the dynamic nature of the power system. In addition to the three-phase power flow analysis, the authors propose a tabu continuous ant colony search (TCACS) to optimize the operation of the VPP, which combines tabu search and ant colony optimization to find near-optimal solutions for complex optimization problems. It uses a tabu list to guide the search towards promising solutions while avoiding getting stuck in local minima. The performance of the proposed VPP has been evaluated through simulation studies, and the results show that it is effective at minimizing power loss and improving the stability of the power system. The proposed VPP can be a valuable tool for utilities to manage their power generation and distribution, particularly in the context of increasing renewable energy integration. [ABSTRACT FROM AUTHOR]

Surge arresters are essential protective devices in power systems that redirect excess voltage and prevent damage to sensitive equipment. Extensive research has been conducted in electro-thermal modeling for surge arresters to better understand and mitigate faults and accidents. These efforts have largely focused on FEM and heat transfer modeling techniques to analyze temperature distribution, electric field effects, thermal-mechanical stress, burning point analysis, puncture risks, and thermal runaway behavior. This study also presents frequency and time series analysis of surge arresters during short circuits. By integrating these two analyses, engineers can enhance their understanding of surge arrester behavior during short circuits, refine design strategies to address issues like resonance and harmonics, and ensure reliable system protection against overvoltage occurrences. [ABSTRACT FROM AUTHOR]

In this paper a novel method is presented to analyze the dynamical reliability of the stochastic power systems with discrete random variability. It is inevitable for the power systems to suffer from external stochastic disturbance. At the same time, the components' failure will bring abrupt changes in their substructures, which can be considered as the internal stochastic disturbance. It is demonstrated that the components' failure performs random jumpy factors switching between a finite number of modes. This salient feature allows us to identify this type of dynamic behavior as the response of the hybrid power systems undergoing Markovian jumps. Utilizing a two-step approximate technique, the considered multi-DOF hybrid system can be reduced to a one-dimensional averaged Itô equation of the form of the system's total energy. The approximate analytical solution of the associated back Kolmogorov equation of the system's energy is derived to predict the dynamical reliability of the original hybrid systems. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

Abstract A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement.

Defect ratings and analysis and processing of different devices and equipment in the power system are often affected by the subjectivity of operation and maintenance personnel, resulting in different severity ratings for the same defect text description. Differences in expertise also lead to differences in diagnostic analysis and different diagnostic efficiency. In order to improve the accuracy and efficiency of defect diagnosis, a defect text rating classification method based on graph neural network and a large model intelligent diagnosis and analysis assistant are proposed. Firstly, a professional dictionary is constructed to normalize the text description using natural language processing algorithms. Secondly, the semantic representation of defective text is optimized by statistical methods. Then, graph attention neural network and robustly optimized BERT approach (RoBERTa) are integrated to accurately rate and classify defective text. Finally, low-rank adaptation (LoRA) fine-tuning training based on the large language model Qwen1.5-14B-Chat is performed to obtain the large model Qwen-ElecDiag for power equipment diagnosis, which is combined with retrieval enhancement to generate the assistant for defect diagnosis of technology development equipment. In addition, the collation provides the instruction dataset for fine-tuning the power equipment diagnosis macromodel. Comparative experimental results show that the proposed graph neural network-based defect rating classification method improves nearly 8 percentage points in accuracy over the optimal baseline model BERT; the diagnostic assistant??s power knowledge as well as defect diagnostic capability is improved. By improving the accuracy of defect ratings and providing comprehensive specialized diagnostic suggestions, it not only improves the intelligent level of power equipment O&M, but also provides new solutions for intelligent O&M in other vertical fields.

[Introduction] In the face of energy scarcity and carbon reduction imperatives, the transition to clean energy centered on electricity is crucial. To achieve this, power system development must prioritize stability, automation and intelligence. Artificial intelligence (AI) technology is a key means to realize this goal. [Method] This paper introduced the carbon emissions of domestic and foreign power systems at first, and pointed out the difficulties and feasible solutions for low-carbon development of the power system in China on this basis. Then, the application and prospect of AI technology in power system development were discussed. [Result] In response to the transition to low-carbon and low-energy power systems, a series of low-carbon development routes, such as integration of capital and resources in the thermal power industry, low-carbon transition (such as the application of carbon capture technology), and promoting the consumption of and smooth replacement by clean energy, were proposed in this paper. It was also noted that AI technology will play a crucial role in automating, intelligentizing and optimizing power systems, with wide-ranging applications in power dispatching, relay protection, power equipment management, power system stability evaluation, and decision-making processes. [Conclusion] As third-generation AI technology continues to evolve and fourth-generation AI technology emerges, the application of AI technology in the power system will become increasingly widespread.

Abstract Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654 ). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544 ).

Objective Hydrogen energy becomes one of the solutions for energy transition in rail transit due to its characteristics such as abundant reserves, ecological friendliness, and efficient transition. At the initial stage of hydrogen energy application in rail transit vehicles, it is necessary to analyze the existing research status and put forward application suggestion. Method The application background and characteristics of hydrogen energy are described, and the application status of hydrogen energy in rail transit vehicles at home and abroad is introduced. On this basis, the key technical challenges of hydrogen energy application in the field of rail transit are extracted, and their research status is analyzed. Then, according to the application and research status, the urgent problems to be solved and overcome in the application of hydrogen energy in the field of rail transit are summarized, and relevant suggestions are put forward. Result & Conclusion Driven by both the application promotion and the technological innovation, hydrogen energy has a broad application prospect in the field of rail transit. The industrial policy, standard system and application technology should be continuously promoted for the further application of hydrogen energy in rail transit field, helping energy transition in rail transit.

ObjectivesTo enhance the intelligent management of substation equipment maintenance, timely identify and mitigate the risks of failures caused by device overheating, and ensure the safe and stable operation of the power grid, the temperature situation awareness and auxiliary decision-making scheme of substation equipment were proposed.MethodsThe research was carried out from four aspects: the perception layer, the understanding layer, the prediction layer, and the auxiliary decision-making layer. In the perception layer, the K-nearest neighbor (KNN) classification algorithm was used to analyze the correlation of multi-class temperature data. In the understanding layer, a BP neural network was employed to construct a historical data transmission model to address missing historical data issues. In the prediction layer, a temperature prediction model combining autoregressive integrated moving average (ARIMA) and support vector machine (SVM) was designed to handle nonlinear data and noise. In the auxiliary decision-making layer, the grey relational analysis was applied to analyze the relationship between equipment temperature changes and fault risks.ResultsThe verification results of numerical examples based on the proposed scheme show that the scheme realizes the effective perception of the future temperature variation trends of the equipment and provides a basis for the identification of equipment defects.ConclusionsThrough multi-dimensional and deep-level temperature data analysis, the proposed scheme reveals the potential correlation between equipment temperature and fault risk, realizes the prediction of the operational trend of substation equipment, and provides a reference for the optimization of operational mode and the formulation of equipment maintenance plan.

Energy transition is crucial for realizing sustainable development and environmental friendliness; it is also vital for maintaining national energy security and promoting social equity of China. Therefore, studying the potential risks and their coping strategies regarding energy transition has practical and strategic significance. This study analyzes the essential requirements of China’s modernization for energy transition, identifies the risks during energy transiton based on the essential requirements, and proposes the coping strategies and safeguard mechanisms. The results of the study reveal that China’s modernization puts forward three essential requirements for energy transition, namely energy security, environmental friendliness, and social equity. In terms of energy security, China’s energy transition faces security risks regarding the coal industry, oil and gas supply, and new power system operation; in terms of environmental friendliness, it faces constraints from low-carbon technologies and the supply risk of key metals and minerals for new energy; it also faces the risk of social inequity and certain financial risks. Furthermore, strategies are proposed to coping with the risks regarding energy security, environmental friendliness, social equity, and finance; safeguard mechanisms for each type of coping strategies are established from two perspectives: short-term prioritized implementation and long-term sustainable enforcement.

Abstract Transformer is a well-known power system apparatus utilized in conjunction with solid insulations such as paper and press board, as well as liquid insulations like mineral oil, a petroleum-based fluid. Despite the notable drawbacks associated with mineral oil, such as limited resources for future generations and its non-eco-friendly nature, its usage remains ubiquitous. There is a growing imperative to explore alternative fluids that surpass mineral oil in terms of environmental impact and performance. Amidst the global shift towards green energy, this study focuses on vegetable seed oils such as corn oil, soybean oil, mustard oil, and rice bran oil as potential substitutes. The research evaluates these oils based on key transformer properties including breakdown voltage, water content, interfacial tension, viscosity, acidity, flash point, and fire point. Interestingly, rice bran oil and soybean oil exhibit promising characteristics that suggest they could effectively replace petroleum-based fluids in transformers. Furthermore, the study extends to blending mineral oil with vegetable seed oils in various compositions, incorporating natural and synthetic antioxidant additives ranging from 0 to 1%. Comparative analyses between samples with and without additives reveal that the inclusion of 1% propyl gallate yields outstanding performance improvements. For instance, a blend comprising 25 ml of mineral oil and 25 ml of soybean oil, supplemented with 1% propyl gallate, demonstrates 90% higher effectiveness compared to other blends and additives tested. Moreover, the research employs statistical regression analysis to establish relationships between different parameter variables, providing deeper insights into the performance and compatibility of these blended oils in transformer applications. This comprehensive investigation underscores the potential of vegetable seed oils as viable alternatives to mineral oil, contributing to the advancement of eco-friendly solutions in power systems.

Abstract A self-adaptive energy storage coordination control strategy based on virtual synchronous machine technology was studied and designed to address the oscillation problem caused by new energy units. By simulating the characteristics of synchronous generators, the inertia level of the new energy power system was enhanced, and frequency stability optimization was achieved. This strategy is integrated with the frequency response model of the new energy power system to improve the system's frequency regulation capability and achieve more stable and efficient operation. From the results, the damping of the system increased, the oscillation frequency decreased after a duration of about 15 s, and the system stability improved by 76.09%. The proposed strategy based on virtual synchronous generator adaptive energy storage coordination control strategy was improved by 83.25%. In addition, the proposed strategy has improved stability indicators and system completion efficiency by 40.57% and 22.21% respectively, both of which are better than the comparative strategies. As a result, this strategy significantly enhances the frequency regulation capability of the system, which has a positive effect on achieving efficient operation of the new energy power system and maintaining the stability of the power system.

In order to better detect and identify the running state of the wind turbine equipment and its generator in the power system wind farm, and then improve the operation reliability of the wind turbine in the power system wind farm, the monitoring method of power system state operation based on Supervisory Control and Data Acquisition (SCADA) model was studied. The SCADA model pre installs different types of sensors on the units of the power system to collect, record and store the power data of the power system. The empirical wavelet transform method is selected to extract the signal characteristics from the state operation signals collected by the SCADA model. ReliefF algorithm is used to select features related to equipment status monitoring from the extracted status operation signal features. Fuzzy C-means clustering algorithm is used to analyze the correlation of feature selection results and identify power system operating conditions. According to the condition identification results of power system, support vector regression prediction algorithm is selected to output the monitoring results of power system state operation. The experimental results show that the monitoring time of the generator and other equipment is less than 100 ms, which provides a strong guarantee for the reliable operation of the power system. This result not only validates the validity of the monitoring and data acquisition model proposed in this paper, but also provides a new idea and method for the intelligent operation and safety management of power system.

The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development. In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area.

Power transformers are critical to electrical power systems but are prone to failures due to factors such as heat, electricity, chemical reactions, mechanical stress, and adverse environmental conditions. Moni-toring the insulating oil effectively is key to preventing these failures. A major challenge in this process is determining the optimal weights for the oil quality index, which lacks a standardized benchmark and often relies on subjective expert assessments. To reduce expert bias and subjectivity, this research utilizes a genetic algorithm to optimize the weightings for five essential parameters: color, water content, break-down voltage (BDV), interfacial tension (IFT), and acidity. The algorithm operates through three stages: crossover, mutation, and selection, and analyzes data from 504 oil tests across various transformers. The mean absolute percentage error (MAPE) is used as the fitness value to assess the algorithm's effective-ness. The optimization process determined the best conditions as 132 iterations, a population size of 180, a crossover rate of 0.2, and a mutation rate of 0.8. These parameters achieved an average MAPE of 1.799% over ten trials, indicating high accuracy. This research not only optimizes the weighting of the oil quality index but also significantly reduces the need for expert input and subjective judgments in trans-former maintenance. The findings are expected to improve the efficiency and reliability of power trans-formers, thereby minimizing failures and associated economic costs.

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning. [ABSTRACT FROM AUTHOR]

In this study, a three-dimensional (3D) performance function is established to evaluate 3D seismic resilience, and a multiobjective optimization scheduling model based on node importance and repair order evaluation indicators is developed. On the basis of sequential coding, considering the number of repair teams, the concept of a "repair team vector" is proposed, and an improved genetic algorithm based on the Pareto dominance relationship and an elite file strategy is introduced to screen for the optimal postearthquake repair scheduling scheme. The results demonstrate that the developed optimization scheduling model yields improved computational accuracy and speed in the case evaluations. [ABSTRACT FROM AUTHOR]

In Indonesia, the power generation sector is the primary source of carbon emissions, largely due to the heavy reliance on coal-fired power plants, which account for 60% of electricity production. Reducing these emissions is essential to achieve national clean energy transition goals. However, achieving this initiative requires careful consideration, especially regarding the complex interactions among multiple stakeholders in the Indonesian electricity market. The electricity market in Indonesia is characterized by its non-competitive and heavily regulated structure. This market condition often requires the PLN, as the system operator, to address multi-objective and multi-constraint problems, necessitating optimization in the generation dispatch scheduling scheme to ensure a secure, economical, and low-carbon power system operation. This research introduces a multiparadigm approach for GS optimization in a regulated electricity market to support the transition to clean energy. The multiparadigm integrates multi-agent system and system dynamic paradigms to model, simulate, and quantitatively analyze the complex interactions among multiple stakeholders in the Indonesian regulated electricity market. The research was implemented on the Java–Madura–Bali power system using AnyLogic 8 University Researcher Software. The simulation results demonstrate that the carbon policy scheme reduces the system's carbon emissions while increasing the system's cost of electricity. A linear regression for sensitivity analysis was conducted to determine the relationship between carbon policies and the system's cost of electricity. This research offers valuable insights for policymakers to develop an optimal, acceptable, and reasonable power system operation scheme for all stakeholders in the Indonesian electricity market. [ABSTRACT FROM AUTHOR]

As power big data plays an increasingly important role in the operation, maintenance, and management of power systems, complex and covert false data attacks pose a serious threat to the safe and stable operation of the power system. This article first explores the characteristics of new power systems, and the challenges posed by false data attacks. The application of big data technology in power production optimization, energy consumption analysis, and user service improvement is then investigated. The article classifies typical attacks against the four stages of power big data systems in detail and analyzes the characteristics of the attack types. It comprehensively summarizes the attack detection technologies used in the four key stages of power big data, including state estimation, machine learning, and data-driven attack detection methods in the data collection stage; clock synchronization monitoring and defense strategies in the data transmission stage; data processing and analysis, data integrity verification and protection measures of blockchain technology in the third stage; and traffic supervision, statistics and elastic computing measures in the control and response stage. Finally, the limitations of attack detection mechanisms are proposed and discussed from three dimensions: research problems, existing solutions, and future research directions. It aims to provide useful references and inspiration for researchers in power big data security to promote technological progress in the safe and stable operation of power systems. [ABSTRACT FROM AUTHOR]

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process, but, on the other hand, the electrical system has to face the problem of unbalances. Renewable energies sources are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real-time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work, we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part, we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part, we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced. [ABSTRACT FROM AUTHOR]