Search Results (27)

This article investigates the zonotope-based state estimation for boost converter system with Markov jump process. DC-DC boost converters are pivotal in modern power electronics, enabling renewable energy integration, electric vehicle charging, and microgrid operations by elevating low input voltages from sources like photovoltaics to stable high outputs. However, their nonlinear dynamics and sensitivity to uncertainties/disturbances degrade control precision, driving research into robust state estimation. To address these challenges, the boost converter is modeled as a Markov jump system to characterize stochastic switching, with time delays, disturbances, and noises integrated for a generalized discrete-time model. An adaptive event-triggered mechanism is adopted to administrate the data transmission to conserve communication resources. A zonotopic set-membership estimation design is proposed, which involves designing an observer for the augmented system to ensure $H_{\infty }$ performance and developing an algorithm to construct zonotopes that enclose all system states. Finally, numerical simulations are performed to verify the effectiveness of the proposed approach. Full article

Electric arc furnace technology is a key factor in the sustainable transformation of the iron and steel industry. This study compares two discrete-time multi-objective optimization models—integer and mixed-integer linear programming—that integrate unit commitment with economic and environmental dispatch. After evaluating both approaches, the integer linear programming model is used, due to its reasonable calculation time, to assess demand-side management potentials under real-world processes and day-ahead market conditions. The model is applied to various scenarios with differing energy price dynamics, CO₂ pricing, EAF utilization levels, and weighting of the objective functions. Results indicate cost savings of up to 6.95% and CO₂ emission reductions of up to 10.86%, though these are subject to a non-linear trade-off between economic and environmental goals. Due to process constraints and market structures, EAFs’ flexibility in energy carrier use (switch between electricity and natural gas) is limited to 3.07%. Additionally, lower furnace utilization does not necessarily increase flexibility, as downstream process requirements restrict scheduling options. The study underscores the importance of green electrification, with up to 36% CO₂ savings when using 100% renewable electricity. Overall, unlocking industrial flexibility requires technical solutions, supportive market incentives, and regulatory frameworks for effective industrial decarbonization. Full article

This paper proposes the integration and operation of lithium-ion battery energy storage systems (ESS) in active distribution networks with high penetration of distributed generation based on renewable energy. The goal is to minimize total system costs, including energy purchasing at the substation node, as well as ESS integration, maintenance, and replacement costs over a 20-year planning horizon. The proposed master–slave methodology uses the Salp Swarm Optimization Algorithm to determine ESS location, technology, and daily operation schemes, combined with a successive approximation power flow to compute the objective function value and enforce constraints. This approach employs a discrete–continuous encoding, reducing processing times and increasing the likelihood of finding the global optimum. Validated on a 33-node test system adapted to Medellín, Colombia, the methodology outperformed five metaheuristic algorithms, achieving the highest annual savings (USD 16,605.77), the lowest average cost (USD 2,964,139.99), and the fastest processing time (345.71 s). The results demonstrate that the proposed methodology enables network operators to reduce distribution network costs effectively, offering high repeatability, solution quality, and computational efficiency. Full article

Currently, Industry 4.0 creates new opportunities for analyzing data on production processes and extracting knowledge from them. With the Internet of Things, data is continuously collected from machine sensors to analyze machine health. Thanks to artificial intelligence methods and discrete simulation, it is possible to process data and dynamically adjust the operating conditions of the production line to the expected time of failure-free operation of the machine or reliable work of an employee. Recently, machine learning techniques have been used to automatically adapt the production line to changes in a given production environment. The paper presents various methods of modeling actions, i.e., forecasting the failure-free operation time of a machine or the error-free working time of an employee. The possible actions the agent can perform, the possible prediction techniques that can be selected are presented. The time between failures is described by a log-normal distribution. The asymmetric lognormal distribution is much more flexible for practical modeling compared to the “perfectly” symmetric normal distribution. In practice, the asymmetric lognormal distribution, strongly shifted to the left, can be used to describe the decreasing time between failures due to human error, as well as the time between failures of a machine in the third phase of its life cycle, which decreases as the machine ages and its components wear out. The parameters of the distribution are estimated using the maximum-likelihood approach, theempirical moments approach, the renewal-theory approach, the empirical distribution function and the method based on coefficient of variation. Numerical examples of predicting failure-free operation times described by the log-normal distribution are presented. The results are compared assuming that failure-free times are described by exponential, normal and Weibull distributions. The results are also compared with an example of the simplest learning method. Full article

The growing demand for electricity, integration of renewable energy sources, and recent advances in power electronics have driven the development of HVDC systems. Multi-terminal HVDC (MTDC) grids, enabled by Voltage Source Converters (VSCs), provide increased operational flexibility, including the ability to reverse power flow and independently control both active and reactive power. However, fault propagation in DC grids occurs more rapidly, potentially leading to significant damage within milliseconds. Unlike AC systems, HVDC systems lack natural zero-crossing points, making fault isolation more complex. This paper presents the implementation of a wavelet-based protection algorithm to detect faults in a four-terminal VSC-HVDC grid, modelled in MATLAB and SIMULINK. The study considers several fault scenarios, including two internal DC pole-to-ground faults, an external DC fault in the load branch, and an external AC fault outside the protected area. The discrete wavelet transform, using Symlet decomposition, is applied to classify faults based on the wavelet entropy and sharp voltage and current signal variations. The algorithm processes the decomposition coefficients to differentiate between internal and external faults, triggering appropriate relay actions. Key factors influencing the algorithm’s performance include system complexity, fault location, and threshold settings. The suggested algorithm’s reliability and suitability are demonstrated by the real-time implementation. The results confirmed the precise fault detection, with fault currents aligning with the values in offline models. The internal faults exhibit more entropy than external faults. Results demonstrate the algorithm’s effectiveness in detecting faults rapidly and accurately. These outcomes confirm the algorithm’s suitability for a real-time environment. Full article

Forecasting of time series data presents some challenges because the data’s nature is complex and therefore difficult to accurately forecast. This study presents the design and development of a novel forecasting system that integrates efficient data processing techniques with advanced machine learning algorithms to improve time series forecasting across the sustainability domain. Specifically, this study focuses on solar irradiation forecasting in Riyadh, Saudi Arabia. Efficient and accurate forecasts of solar irradiation are important for optimizing power production and its smooth integration into the utility grid. This advancement supports Saudi Arabia in Vision 2030, which aims to generate and utilize renewable energy sources to drive sustainable development. Therefore, the proposed forecasting system has been developed to the parameters characteristic of the Riyadh region environment, including high solar intensity, dust storms, and unpredictable weather conditions. After the cleaning and filtering process, the filtered dataset was pre-processed using the standardization method. Then, the Discrete Wavelet Transform (DWT) technique has been applied to extract the features of the pre-processed data. Next, the extracted features of the solar dataset have been split into three subsets: train, test, and forecast. Finally, two different machine learning techniques have been utilized for the forecasting process: Support Vector Machine (SVM) and Gaussian Process (GP) techniques. The proposed forecasting system has been evaluated across different time horizons: one-day, five-day, ten-day, and fifteen-day ahead. Comprehensive evaluation metrics were calculated including accuracy, stability, and generalizability measures. The study outcomes present the proposed forecasting system which provides a more robust and adaptable solution for time-series long-term forecasting and complex patterns of solar irradiation in Riyadh, Saudi Arabia. Full article

Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade. Full article

This paper designs a multi-variable hybrid islanding-detection method (HIDM) using signal-processing techniques. The signals of current captured on a test system where the renewable energy (RE) penetration level is between 50% and 100% are processed by the application of the Stockwell transform (ST) to compute the Stockwell islanding-detection factor (SIDF) and the co-variance islanding-detection factor (CIDF). The signals of current are processed by the application of the Hilbert transform (HT), and the Hilbert islanding-detection factor (HIDF) is computed. The signals of current are also processed by the application of the Alienation Coefficient (ALC), and the Alienation Islanding Detection Factor (AIDF) is computed. A hybrid islanding-detection indicator (HIDI) is derived by multiplying the SIDF, CIDF, AIDF, and an islanding weight factor (IWF) element by element. Two thresholds, designated as the hybrid islanding-detection indicator threshold (HIDIT) and the hybrid islanding-detection indicator fault threshold (HIDIFT), are selected to detect events of islanding and also to discriminate such events from fault events and operational events. The HIDM is effectively tested using an IEEE-13 bus power network, where solar generation plants (SGPs) and wind generation plants (WGPs) are integrated. The HIDM effectively identified and discriminated against events such as islanding, faults, and operational. The HIDM is also effective at identifying islanding events on a real-time distribution feeder. The HIDM is also effective at detecting islanding events in the scenario of a 20 dB signal-to-noise ratio (SNR). It is established that the HIDM has a small non-detection zone (NDZ). The effectiveness of the HIDM is better relative to the islanding-detection method (IDM) supported by the discrete wavelet transform (DWT), an IDM using a hybridization of the slantlet transform, and the Ridgelet probabilistic neural network (RPNN). An IDM using wavelet transform multi-resolution (WT-MRA)-based image data and an IDM based on the use of a deep neural network (DNN) were used. The study was performed using the MATLAB software (2017a) and validated in real-time using the data collected from a practical distribution power system network. Full article

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities. Full article

Producers are encouraged to reduce their energy consumption of manufacturing systems by applying less-energy-intensive modern technologies and advanced machine tools and operating methods at the system level. In the paper, organizational and analytical solutions are combined to model the sustainable production system. Managers can study the behavior of a production system organized using energy-saving rules by changing key parameters of the input model (arrival intensity, bottleneck service rate, buffer size, setup and shutdown time) to analyze the queue size of the production system and therefore performance. A discrete-time queueing model of a single-bottleneck production line with a finite input buffer capacity is proposed. Jobs occur according to a binomial process and are processed individually, one by one, according to the natural FIFO service discipline, with a general discrete-type cumulative distribution function. The total number of jobs present in the system is bounded by a non-random fixed value N. Every time the system becomes empty, an energy-saving mechanism is started: the processing machine (server) is turned off during a geometrically distributed shutdown time. Similarly, the first job arriving into the empty system initializes a geometrically distributed setup time. Identifying renewal moments in the evolution of the model, a system of difference equations is built for the transient queue-size distribution conditioned by the state of the system at the opening. The solution is obtained explicitly in terms of probability-generating functions. In addition, the Drum-Buffer-Rope concept is proposed to reduce the energy consumption of the production line. The throughput of the production system is maximized by adjusting the time between the order arrivals and the size of the input buffer to the capacity of the bottleneck. Turning off a machine under certain conditions and slowing down non-critical machines are strategies to reduce energy consumption. A detailed illustrating numerical and simulation study of the considered model is attached as well, in which the sensitivity of the queue-size behavior to changes of the key input model parameters is investigated. Full article

A review of quality control for large irradiance datasets is applied as a case study for the Southern African Universities Radiometric Network (SAURAN) database. The quality control procedure is automated and applied to 24 stations from the database with a total of 848,189 hourly datapoints. From this, the individual station’s data quality is also analysed. The assessment validates the automated methodology without the need for a user-based review of the data. The SAURAN database can play a significant role in advancing solar and wind energy; however, the number of offline stations hinders this process. Data scarcity remains an obstacle to these goals, and therefore, recommendations are provided to address this. Recommendations regarding each site’s usability in time-series and discrete applications are made, which provides an overall indication of the SAURAN database’s irradiance measurement quality. Of the 24 measuring stations assessed, eight are recommended, 11 are recommended with cautious use, and five are recommended with extremely cautious use. These recommendations are based on multiple factors, such as whether a dataset has more than one full year of data or is missing minimal datapoints. Further, a study of the irradiance correlation between the stations was conducted. The results indicated groupings of different stations that showed highly correlated irradiance measurements and similar weather patterns. This is useful if a proposed renewable energy power plant, such as PV, falls within a cluster where the data from the SAURAN database can be used as a substitute if no data is available. SAURAN presents an opportunity for Southern Africa to increase its research outputs in solar and wind energy and lessen its dependency on fossil fuel-based energy production. Full article

Improvement of the principles of the implementation of a hybrid solar-wind system equipped with a battery for self-consumption of a local object, with the control of power consumed from the grid, is considered. The aim is to increase the degree of energy use from renewable energy sources for consumption while limiting the degree of battery discharge, taking into account deviations in the load schedule and generation of energy sources relative to the calculated (forecast) values. The possibility of compensating for deviations in the load schedule and renewable energy sources generation relative to the calculated (forecast) values is shown when electricity consumption decreases and the degree of energy use increases. Compliance of the schedule of the battery state of charge with the calculated schedule is achieved by correcting the consumption of active power according to the deviation of the state of charge with a given discreteness of time. The algorithm of the control was improved by taking into account the measured value of the load power with an increase in the degree of energy use. Also, the use of correction allows you to limit the depth of discharge of the battery at the accepted value. A mathematical 24 h model of energy processes was developed, taking into account the error in estimating the state of charge. The results of the modeling using archival data on renewable sources generation confirm that the proposed solutions are effective. For the considered application with average monthly generation in February, the correction allows reducing electricity consumption by 16–21% and payment costs at three tariffs by 24–27%. Full article

The Western Syncline hosts reduced-facies, or Kupferschiefer-type, sedimentary rock-hosted stratiform Cu deposits (SSC) in the lowermost meters of the Nonesuch Formation, which is part of a thick section of clastic sedimentary rocks that comprise the upper fill of the Mesoproterozoic Midcontinent Rift of North America. Located in the Porcupine Mountains Cu district in Upper Peninsula, Michigan, these blind deposits were discovered in 1956, but are not yet developed, although recent renewed interest may result in near-term production. The deposits are distinguished by their relatively undeformed nature and lack of superposed hydrothermal events. Prior to lithification, chalcocite mineralization replaced diagenetic pyrite within two discrete tabular, albeit discontinuous, potential orebodies referred to as the lower Cu-bearing sequence (LCBS) and the upper Cu-bearing sequence (UCBS). The Top Cu Zone transgresses lithologic boundaries, suggesting that a limited volume of Cu-bearing fluids moved vertically upwards through the unlithified stratigraphy, since reductant pyritic rocks above this zone are essentially barren of Cu. The total Cu inventory that has a reasonable expectation of economic extraction is 3678 M lbs. of Cu with 15.3 M oz. of byproduct Ag. When a cutoff grade of 0.9% Cu over a minimum thickness of 2 m is applied to justify an underground room-and-pillar mine, the LCBS and UCBS are not continuous over the Western Syncline. Sedimentology is the first-order control of potential ore and its continuity; dark-gray shales and siltstones deposited under low-energy, anoxic conditions are preferred host rocks, whose thickness must be >2 m to be potential ore since host-rock thickness determines economic viability of extraction. Furthermore, stratigraphy influences the time constraints on mineralization as the lithification process impedes vertical permeability and thus the flow of Cu-bearing fluids upward through the unlithified section. Syn-sedimentary tectonic movements, likely along pre-existing buried faults, are a third-order control as the thickness of host rocks is enhanced under such conditions. Therefore, an understanding of the depositional and tectonic history throughout the Western Syncline is fundamental to understanding the limits of possible economic exploitation and to optimizing ore extraction. Full article

In a recent work we introduced a semi-Markovian discrete-time generalization of the telegraph process. We referred to this random walk as the ‘squirrel random walk’ (SRW). The SRW is a discrete-time random walk on the one-dimensional infinite lattice where the step direction is reversed at arrival times of a discrete-time renewal process and remains unchanged at uneventful time instants. We first recall general notions of the SRW. The main subject of the paper is the study of the SRW where the step direction switches at the arrival times of a generalization of the Sibuya discrete-time renewal process (GSP) which only recently appeared in the literature. The waiting time density of the GSP, the ‘generalized Sibuya distribution’ (GSD), is such that the moments are finite up to a certain order $r\le m-1$ ($m\ge 1$) and diverging for orders $r\ge m$ capturing all behaviors from broad to narrow and containing the standard Sibuya distribution as a special case ($m=1$). We also derive some new representations for the generating functions related to the GSD. We show that the generalized Sibuya SRW exhibits several regimes of anomalous diffusion depending on the lowest order m of diverging GSD moment. The generalized Sibuya SRW opens various new directions in anomalous physics. Full article

Due to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC electrical network. This will reduce the operation costs and cover the investment and maintenance cost related to the new technologies (PV distributed generators), thus satisfying all technical and operative constraints of the distribution grid. It is important to propose solution methodologies that require short processing times, with the aim of exploring a large number of scenarios while planning energy projects that are to be presented in public and private contracts, as well as offering solutions to technical problems of electrical distribution companies within short periods of time. Based on these needs, this paper proposes the implementation of a Discrete–Continuous Parallel version of the Particle Swarm Optimization algorithm (DCPPSO) to solve the problem regarding the integration of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grids, with the purpose of reducing the annual costs related to energy purchasing as well as the investment and maintenance cost associated with PV sources in a scenario of variable power demand and generation. In order to evaluate the effectiveness, repeatability, and robustness of the proposed methodology, four comparison methods were employed, i.e., a commercial software and three discrete–continuous methodologies, as well as two test systems of 33 and 69 buses. In analyzing the results obtained in terms of solution quality, it was possible to identify that the DCPPSO proposed obtained the best performance in relation to the comparison methods used, with excellent results in relation to the processing times and standard deviation. The main contribution of the proposed methodology is the implementation of a discrete–continuous codification with a parallel processing tool for the evaluation of the fitness function. The results obtained and the reports in the literature for alternating current networks demonstrate that the DCPPSO is the optimization methodology with the best performance in solving the problem of the optimal integration of PV sources in economic terms and for any kind of electrical system and size. Full article