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The Volterra integral-functional series is the classic approach for nonlinear black box dynamical system modeling. It is widely employed in many domains including radiophysics, aerodynamics, electronic and electrical engineering and many others. Identifying the time-varying functional parameters, also known as Volterra kernels, poses a difficulty due to the curse of dimensionality. This refers to the exponential growth in the number of model parameters as the complexity of the input-output response increases. The least squares method (LSM) is widely acknowledged as the standard approach for tackling the issue of identifying parameters. Unfortunately, the LSM suffers with many drawbacks such as the sensitivity to outliers causing biased estimation, multicollinearity, overfitting and inefficiency with large datasets. This paper presents an alternative approach based on direct estimation of the Volterra kernels using the collocation method. Two model examples are studied. It is found that the collocation method presents a promising alternative for optimization, surpassing the traditional least squares method when it comes to the Volterra kernels identification including the case when input and output signals suffer from considerable measurement errors. [ABSTRACT FROM AUTHOR]

In this study we consider linear and nonlinear Volterra integral equations (VIEs) of the second kind with discontinuous kernel. A novel iterative method using floating point arithmetic (FPA) is presented to solve the problem. Also a convergence theorem and error analysis of the method are presented. The main novelty of this study is to validate the results using the CESTAC (Controle et Estimation Stochastique des Arrondis de Calculs) method which is based on stochastic arithmetic (SA). Moreover instead of applying usual mathematical softwares we use the CADNA (Control of Accuracy and Debugging for Numerical Applications) library which must be done on Linux operating system using C, C++ or Fortran codes. Applying the method and the library we will be able to find the optimal results such as optimal error, optimal iteration of the method and optimal approximation. Proving the main theorem of the CESTAC method we show the equality between the number of common significant digits (NCSDs) of two successive iterations with the exact and approximate solutions. Thus it can help us to apply a new termination criterion instead of absolute error to show the accuracy and efficiency of the method. [ABSTRACT FROM AUTHOR]

The objective of this paper was to present a new inverse problem statement and numerical method for the Volterra integral equations with piecewise continuous kernels. For such Volterra integral equations of the first kind, it is assumed that kernel discontinuity curves are the desired ones, but the rest of the information is known. The resulting integral equation is nonlinear with respect to discontinuity curves which correspond to integration bounds. A direct method of discretization with a posteriori verification of calculations is proposed. The family of quadrature rules is employed for approximation purposes. It is shown that the arithmetic complexity of the proposed numerical method is O (N 3) . The method has first-order convergence. A generalization of the method is also proposed for the case of an arbitrary number of discontinuity curves. The illustrative examples are included to demonstrate the efficiency and accuracy of proposed solver. [ABSTRACT FROM AUTHOR]

Experiments with such an improved topological DC bus system in the MATLAB/Simulink environment demonstrated the efficiency of the proposed MPC-based control strategy. The concept of cyber-physical systems (CPSs) in electrical, civil and mechanical engineering is closely related to Smart Grids and Smart Cities, based on advanced computing technologies used for monitoring, control and communication. A number of scientists [[4]] also note the close connection of the concept of "digital energy" with other concepts and approaches, such as the Internet of energy, transactive energy, energy cloud, etc. The "double carbon" policy will further promote the energy internet, energy-to-gas conversion, hydrogen production, combined cooling, electric vehicles, heating and power generation, etc. [Extracted from the article]

The Cauchy problem for a nonlinear system of differential equations with a Stieltjes integral (loads) of the desired solution is considered. The equation contains bifurcation parameters where the system has a trivial solution for any values. The necessary and sufficient conditions are derived for those parameter values (bifurcation points) in the neighborhood of which the Cauchy problem has a non-trivial real solution. The constructive method is proposed for the solution of real solutions in the neighborhood of those points. The method uses successive approximations and builds asymptotics of the solution. The theoretical results are illustrated by example. The Cauchy problem with loads and bifurcation parameters has not been studied before. [ABSTRACT FROM AUTHOR]

Multi-terminal hybrid AC/DC power systems have found a wide application in different areas, such as wind and solar energy, microgrids, shipboard power systems (SPS), etc. To ensure reliability and survivability of the SPS, the emergency control is necessary to be implemented, which is widely utilized in conventional AC networks and hybrid AC/DC systems. This paper proposes a concept of reconfiguration theory based on a centralized emergency control algorithm of the SPS with a complex structure, which has high requirements for reliability and survivability. The proposed method complements the well-proven local control approaches, paying particular attention to the SPS reconfiguration. A special emphasis is placed on improving the SPS reliability by combining the AC nodes, as well as on increasing the reconfiguration procedure efficiency, the latter being achieved by not taking into account the special constraint on the DC circuit breakers states. The algorithm carries out a preventive calculation of control actions using the genetic algorithm optimization procedure. The case studies were performed by using the modified DC zonal electrical distribution system (DC-ZEDS) model, and the results verify the feasibility and effectiveness of the proposed method and model. [ABSTRACT FROM AUTHOR]

An inverse problem in spectroscopy is considered. The objective is to restore the discrete spectrum from observed spectrum data, taking into account the spectrometer's line spread function. The problem is reduced to solution of a system of linear-nonlinear equations (SLNE) with respect to intensities and frequencies of the discrete spectral lines. The SLNE is linear with respect to lines' intensities and nonlinear with respect to the lines' frequencies. The integral approximation algorithm is proposed for the solution of this SLNE. The algorithm combines solution of linear integral equations with solution of a system of linear algebraic equations and avoids nonlinear equations. Numerical examples of the application of the technique, both to synthetic and experimental spectra, demonstrate the efficacy of the proposed approach in enabling an effective enhancement of the spectrometer's resolution. [ABSTRACT FROM AUTHOR]

We propose the generalized quadrature methods for numerical solution of singular integral equation of Abel type. We overcome the singularity using the analytic computation of the singular integral. The problem of solution of singular integral equation is reduced to nonsingular system of linear algebraic equations without shift meshes techniques employment. We also propose generalized quadrature method for solution of Abel equation using the singular integral. Relaxed errors bounds are derived. In order to improve the accuracy we use Tikhonov regularization method. We demonstrate the efficiency of proposed techniques on infrared tomography problem. Numerical experiments show that it makes sense to apply regularization in case of highly noisy (about 10%) sources only. That is due to the known fact that Volterra equations of the first kind enjoy selfregularization property. [ABSTRACT FROM AUTHOR]

The problem of diagnosing the actual depth of cut at cylindrical grinding is considered. A mathematical model of the behavior of the grinding wheel and the workpiece during processing is worked out. According to this model it is produced a synthesis the model of the Luenberger observer with the Kalman filter to control the process of external cylindrical grinding. The developed approach is to improve the accuracy of control and the related with them computational procedures of assessment and management. [ABSTRACT FROM AUTHOR]

This special issue collects innovative contributions addressing the top challenges in integral equations, integro-differential equations, multi dimensional problems, and ill-posed and singular problems with modern applications. Linear and nonlinear integral equations of the first and second kinds have many applications in engineering and real life problems. Brief Overview of the Contributions Ibrahimov and Imanova in "Multistep Methods of the Hybrid Type and Their Application to Solve the Second Kind Volterra Integral Equation" [[1]] have focused on solving the integral equations with variable boundaries. Noeiaghdam et al. have focused on "Error Estimation of the Homotopy Perturbation Method to Solve Second Kind Volterra Integral Equations with Piecewise Smooth Kernels: Application of the CADNA Library" in [[9]]. [Extracted from the article]

The aim of this paper is to present a new method and the tool to validate the numerical results of the Volterra integral equation with discontinuous kernels in linear and non-linear forms obtained from the Adomian decomposition method. Because of disadvantages of the traditional absolute error to show the accuracy of the mathematical methods which is based on the floating point arithmetic, we apply the stochastic arithmetic and new condition to study the efficiency of the method which is based on two successive approximations. Thus the CESTAC method (Controle et Estimation Stochastique des Arrondis de Calculs) and the CADNA (Control of Accuracy and Debugging for Numerical Applications) library are employed. Finding the optimal iteration of the method, optimal approximation and the optimal error are some of advantages of the stochastic arithmetic, the CESTAC method and the CADNA library in comparison with the floating point arithmetic and usual packages. The theorems are proved to show the convergence analysis of the Adomian decomposition method for solving the mentioned problem. Also, the main theorem of the CESTAC method is presented which shows the equality between the number of common significant digits between exact and approximate solutions and two successive approximations.This makes in possible to apply the new termination criterion instead of absolute error. Several examples in both linear and nonlinear cases are solved and the numerical results for the stochastic arithmetic and the floating-point arithmetic are compared to demonstrate the accuracy of the novel method. [ABSTRACT FROM AUTHOR]

While ML algorithms have improved conventional ES optimization models, new models using ML techniques have become increasingly important [[55]]. These ML algorithms that have focused on the optimization of ESs aim to accelerate the conventional ES optimization models, decentralize the centralized optimization models, or speed them up by approximating the iteration processes and parameters related to optimization. This editorial overviews the contents of the Special Issue "Machine Learning for Energy Systems 2021" and review the trends in machine learning (ML) techniques for energy system (ES) optimization. In other words, a few ML algorithms have improved the optimization of current ES optimization models rather than been used in the development of new models. [Extracted from the article]

The aim of this study is to present a novel method to find the optimal solution of the reverse osmosis (RO) system. We apply the Sinc integration rule with single exponential (SE) and double exponential (DE) decays to find the approximate solution of the RO. Moreover, we introduce the stochastic arithmetic (SA), the CESTAC method (Controle et Estimation Stochastique des Arrondis de Calculs) and the CADNA (Control of Accuracy and Debugging for Numerical Applications) library instead of the mathematical methods based on the floating point arithmetic (FPA). Applying this technique, we would be able to find the optimal approximation, the optimal error and the optimal iteration of the method. The main theorems are proved to support the method analytically. Based on these theorems, we can apply a new stopping condition in the numerical procedure instead of the traditional absolute error. These theorems show that the number of common significant digits (NCSDs) of exact and approximate solutions are almost equal to the NCSDs of two successive approximations. The numerical results are obtained for both SE and DE Sinc integration rules based on the FPA and the SA. Moreover, the number of iterations for various ε are computed in the FPA. Clearly, the DE case is more accurate and faster than the SE for finding the optimal approximation, the optimal error and the optimal iteration of the RO system. [ABSTRACT FROM AUTHOR]

The objective of this editorial is to overview the content of the special issue "Machine Learning for Energy Systems". This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems' operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems' risk assessment, battery's degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling. [ABSTRACT FROM AUTHOR]

The evolutionary integral dynamical models of storage systems are addressed. Such models are based on systems of weakly regular nonlinear Volterra integral equations with piecewise smooth kernels. These equations can have non-unique solutions that depend on free parameters. The objective of this paper was two-fold. First, the iterative numerical method based on the modified Newton–Kantorovich iterative process is proposed for a solution of the nonlinear systems of such weakly regular Volterra equations. Second, the proposed numerical method was tested both on synthetic examples and real world problems related to the dynamic analysis of microgrids with energy storage systems. [ABSTRACT FROM AUTHOR]

The necessary and sufficient conditions of existence of the nonlinear operator equations' branches of solutions in the neighbourhood of branching points are derived. The approach is based on the reduction of the nonlinear operator equations to finite-dimensional problems. Methods of nonlinear functional analysis, integral equations, spectral theory based on index of Kronecker-Poincaré, Morse-Conley index, power geometry and other methods are employed. Proposed methodology enables justification of the theorems on existence of bifurcation points and bifurcation sets in the nonstandard models. Formulated theorems are constructive. For a certain smoothness of the nonlinear operator, the asymptotic behaviour of the solutions is analysed in the neighbourhood of the branch points and uniformly converging iterative schemes with a choice of the uniformization parameter enables the comprehensive analysis of the problems details. General theorems and effectiveness of the proposed methods are illustrated on the nonlinear integral equations. [ABSTRACT FROM AUTHOR]

Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal's banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the valuable information at different stages of AC/DC hybrid power systems projects development with stand-alone hybrid solar-wind power generation systems. [ABSTRACT FROM AUTHOR]

This research focuses on exploring various techniques and models for simulating the random behavior of energy facilities or systems. These simulations are essential in identifying the likelihood of component failures within the studied facilities. By assessing the potential consequences of emergency scenarios, this analysis serves as a fundamental aspect of synthesizing and analyzing reliability in the cyber-physical system. Ultimately, the study aims to enhance the management and control of reliability and safety for these facilities. In this study, a unified heating source is considered as an energy facility (as part of district heating systems), for example, a combined heat and power plant. However, the developed methods and models have sufficient universality for their adaptation to other energy facilities without significant changes. The research methodology is based on the use of Markov random processes and laws of the probability theory. The basic model of the energy facilities is formulated for the conditions of the simplest events flow with appropriate assumptions and constraints, in particular, ordinary events and independence of events (failures and restorations). To take into account the non-ordinary events (failures) and dependences between some failures, corresponding modifications of the basic model are proposed. A computational experiment was carried out using the developed models, and graphical interpretations of the results are presented. The obtained results allow us to formulate some preliminary conclusions about the range of influence of the simulated factors on the reliability analysis of studied facilities and to outline conditions and areas of their admissible application. [ABSTRACT FROM AUTHOR]

Over the past few years, there have been significant advancements in Microgrid (MG) systems, particularly in the field of power electronics. These advancements aim to address the needs of the grid and loads, while integrating low-voltage, non-linear, and highly sensitive power sources, such as solar PV modules, batteries, and supercapacitors. It is crucial to select the appropriate converter configuration and power converters in MG systems, as they greatly impact their optimal performance. To achieve the best results, numerous architectures and converter configurations have been suggested for integrating different energy sources. As a result, a considerable number of research articles have been published, necessitating a thorough review. This article continues studies of Part I and presents a comprehensive overview of various architectures based on the arrangement of different sources and provides a detailed analysis and discussion of these architectures. This article covers thirty-three different categories of DC-DC converters, both isolated and non-isolated. These converters are divided into subcategories, such as conventional type, switched-capacitor type, soft-switching type, multi-phase type, and multilevel type. The article also evaluates the suitability of these topologies based on factors such as high conversion gain, power decoupling, efficiency, isolation, power handling capabilities, and compact design. The critical examination and comparative study presented in this work can be valuable for industry professionals and academics in selecting the most suitable architectural and power converter topologies for optimal performance. [ABSTRACT FROM AUTHOR]

Numeric methods for solution of the weakly regular linear and nonlinear evolutionary (Volterra) integral equations of the first kind are proposed. The kernels of such equations have jump discontinuities along the continuous curves (endogenous delays) which start at the origin. In order to linearize these equations the modified Newton–Kantorovich iterative process is employed. Two direct quadrature methods based on the piecewise constant and piecewise linear approximation of the exact solution are proposed for linear solutions. The accuracy of proposed numerical methods is O ( 1 / N ) and O ( 1 / N 2 ) respectively. A certain iterative numerical scheme enjoying the regularization properties is suggested. Furthermore, generalized numerical method for nonlinear equations is adduced. The midpoint quadrature rule in all the cases is employed. In conclusion several numerical examples are applied in order to demonstrate the efficiency of proposed numerical methods. [ABSTRACT FROM AUTHOR]

In the case of microgrid (MG) systems, the choice of the right configuration plays a vital role to meet grid/load necessities when integrating low voltage, non-linear and highly sensitive (to environmental conditions) power sources such as solar PV modules, batteries and supercapacitors (SCs), etc. In the case of MG systems, the choice of the right configuration and the appropriate type of power converters in any application can have a significant impact on the optimum performance. Numerous architectures have been proposed for the integration of various energy sources to achieve optimum performance. A large number of research articles have been published in these areas. In this article, the detailed organization of various architectures based on the arrangement of various sources and detailed analyses is presented along with a discussion on those architectures. Moreover, the suitability of all the reviewed architectures based on driving factors such as (a) high conversion gain, (b) good power decoupling, (c) high efficiency, (d) isolation, (e) power-handling capabilities and (f) compact design is presented in the discussions section. The critical examination and comparative study presented in this work can assist both industry personnel and academicians in selecting the best architectural and power converter topologies required for optimum performance. [ABSTRACT FROM AUTHOR]

The energy communities based on the integration of microgrids make it possible to gain economic, environmental, technical, and social benefits. The paper aims to propose a unified multi-criteria approach covering both the planning stage and the stage of managing the energy community, in the context of various interests of its participants. Planning stage should take into account the long-term goals of the community and possible changes in external conditions. Therefore, we suggest an approach relying on the multi-attribute value theory considering the uncertainty of decision makers' preferences. Interval estimators used to express preferences enable a choice of community configuration with robust performance under changing conditions within some limits. In the operation stage, the new multi-criteria model of an intelligent "energy community operator" is proposed. It is based on bi-level programming and reinforcement learning, implementing the structure of a fair local market for sustainable development of the community. To optimize the operation of individual microgrids within the community, the multi-objective Monte-Carlo Tree Search (MCTS) algorithm is used, which helps to improve the convergence in the Stackelberg game. The multi-criteria version of the MCTS algorithm allows implementing an adaptive local automation model to solve a multi-objective lower-level problem: minimize operating costs, risk of power shortage, and CO2 emissions; smooth load peaks, and optimize power exchange between microgrids. At the top level, a management strategy that will be beneficial to all members of the community is chosen to guarantee their long-term aggregation. The effectiveness of the proposed approach is demonstrated by the example of an energy community created for three remote villages located on the coast of the Sea of Japan. The natural and climatic conditions of the area allow the efficient use of wind, solar, and biomass resources. Building the community involves the consideration of three scenarios, in which priority is given to economic efficiency, environmental efficiency, or balanced development. • The practical multi-criteria approach for development a sustainable energy community. • Energy community sizing under uncertainty of decision makers' preferences. • Bi-level programming for a "fair" community operator model. • Monte-Carlo Tree Search algorithm for multi-criteria community management. [ABSTRACT FROM AUTHOR]

In this research, an efficient scheme is presented to validate the numerical results and solve the second kind integral equations (IEs). For this reason the homotopy perturbation method (HPM) is illustrated and the stochastic arithmetic is applied to implement the CESTAC 1 1 Controle et Estimation Stochastique des Arrondis de Calculs. method for solving IEs. The accuracy of method is shown by proving a main theorem. Also, the CADNA 2 2 Control of Accuracy and Debugging for Numerical Applications. library is used instead of other usual softwares. Applying the mentioned method, the optimal approximation, iteration, validation of results and any numerical instability can be found whereas the floating-point arithmetic (FPA) has not these properties. Some examples are solved to determine the significance of applying the SA in place of the FPA. [ABSTRACT FROM AUTHOR]

The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin. [ABSTRACT FROM AUTHOR]

The efficient construction and employment of block operators are vital for contemporary computing, playing an essential role in various applications. In this paper, we prove a generalisation of the Frobenius formula in the setting of the theory of block operators on normed spaces. A system of linear equations with the block operator acting in Banach spaces is considered. Existence theorems are proved, and asymptotic approximations of solutions in regular and irregular cases are constructed. In the latter case, the solution is constructed in the form of a Laurent series. The theoretical approach is illustrated with an example, the construction of solutions for a block equation leading to a method of solving some linear integrodifferential system. [ABSTRACT FROM AUTHOR]

Energy communities is a new, but already successful prosumer model of the local energy systems' construction. It is based on distributed energy sources and the electricity consumers' flexibility who are the members of the community. In search of the most effective ways to interact within themselves and with the external energy system, local energy communities become platforms for exciting experiments in the field of new energy practices including local markets for flexibility, building cooperative microgrids, achieving energy autonomy, and many others. This work aims to present a unified approach to building and optimally managing the community microgrids with an internal market, given the social, environmental, and economic benefits of a particular location of such a community. A new modeling framework is introduced, based on bilevel programming and reinforcement learning, for structuring and solving the internal local market of a community microgrids, composed of entities that may exchange energy and services among themselves. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator (Community EMS). We strengthen the traditional bilevel problem statement by the local energy management system (Local EMS) introduction based on Monte-Carlo tree search algorithm. Our approach makes it possible to enable interaction of the local control systems for microgrids with the community microgrid operator as part bilevel programming problem solution. Numerical results obtained on the real test case of the microgrid community for the settlements located in the Transbaikal National Park (Russia), which include various renewable energy sources (wind, solar power, biomass gasifiers) and storage devices, show reduction of the LCOE index from 20% to 40% and improving the quality of electricity supply to the analyzed settlements. • Methodology for economically optimised design a community microgrids based on RESs. • The concept of building green microgrids communities integrated with biomass gasifiers. • The concept of a fair local electricity market using bi-level optimization. [ABSTRACT FROM AUTHOR]

This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported. [ABSTRACT FROM AUTHOR]

The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive progress has been made in the gasification technologies development during the last decade. This paper deals with the control and optimization problems for an isolated microgrid combining the renewable energy sources (solar energy and biomass gasification) with a diesel power plant. The control problem of an isolated microgrid is formulated as a Markov decision process and we studied how reinforcement learning can be employed to address this problem to minimize the total system cost. The most economic microgrid configuration was found, and it uses biomass gasification units with an internal combustion engine operating both in single-fuel mode (producer gas) and in dual-fuel mode (diesel fuel and producer gas). [ABSTRACT FROM AUTHOR]

The difficulties in implementing the model predictive control (MPC) in interior permanent-magnet synchronous motors (IPMSMs) consist of the nonlinear behavior of IPMSMs and the computational effort required by MPC. This paper presents an IPMSM controller design method for electric vehicles based on explicit MPC (EMPC), which uses a different linearization method. The proposed controller combines the speed and current controllers and replaces the traditional cascade structure. First, the nonlinear terms in the system model are added into the control input as voltage compensation to obtain a simple linear model. Next, the proposed controller based on MPC is designed, which considers the effects of load torque and uses an increment model. Furthermore, the controller applies both current and voltage constraints. The EMPC method based on a binary search is used to accelerate the solution of the optimization problem. Finally, the simulation results show the validity and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a controllably inductive power filtering (CIPF) method to improve the power quality of a shipboard power system (SPS). The main circuit topology for implementing the CIPF method in the SPS is presented, which contains a rectifier transformer integrated with linear filtering reactors, fully tuned branches, and a voltage-source inverter. The characteristic parameters of the rectifier transformer integrated with linear filtering reactors are investigated in detail by theoretical analysis. Furthermore, a mathematical model and an equivalent model are established to reveal the mechanism of harmonic suppression and the advantage of damping harmonic resonance of SPS. Moreover, a coordinated control strategy is designed based on the theoretical analysis. Finally, a detailed case study validates the proposed CIPF method and indicates that this method is suitable for SPS, because it can decrease the installed space and effectively prevent harmonic components from flowing into the generator side (power source), thus resulting in suppressing the vibration and noise pollution of the rectifier transformer. Besides, this method can effectively dampen harmonic resonance caused by the varied short-circuit capacity or the harmonic voltage in SPS. [ABSTRACT FROM AUTHOR]

• A power interval prediction model is established based on LSTM and LUBE to quantify the uncertainty of wind power. • The energy storage management is transformed into Markov decision process and solved by deep reinforcement learning. • According to the real-time state, the proposed strategy can make the charge/discharge schedule automatically. Wind power generation combined with energy storage is able to maintain energy balance and realize stable operation. This article proposes a data-driven energy storage management strategy considering the prediction intervals of wind power. Firstly, a power interval prediction model is established based on long-short term memory and lower and upper bound estimation (LUBE) to quantify the uncertainty of wind power, which solves the issue that traditional LUBE cannot adopt gradient descent method. Secondly, the energy storage management is transformed into Markov decision process and solved by deep reinforcement learning. The state space, action space and reward function of the interaction between agent and environment are established, and the value function is approximated through the deep Q network. Then, according to the real-time state, such as wind power, power prediction intervals, local load, dynamic electricity price and state of charge, the proposed strategy can make the charge/discharge schedule automatically. Finally, the effectiveness and superiority of the proposed energy storage management strategy are verified based on real wind farm dataset. The proportion of wrong decisions is zero, and daily transaction cost and wear cost of energy storage management system decrease significantly. [ABSTRACT FROM AUTHOR]

The short-term forecasting of wind power, photovoltaic (PV) generation and loads is important for the secure and economical dispatching of smart community with smart grid. Considering the smart community has plenty of distributed generations, here, a concept of net load is defined as the active power difference between renewable generations (wind and PV power) and loads. Then, a combined forecasting approach, which enables to build a real-time forecasting model with parameters self-adjustment, is proposed for the forecasting of the net load in smart community. Compared with the traditional forecasting methods such as support vector machine (SVM), the proposed approach can wavily optimize the parameters of the forecasting model. Besides, an optimal method named Grid-GA searching is developed to reduce the computation time during the forecasting. Therefore, it can improve the forecasting accuracy even if there is a great of uncertainty component in wind power, PV generation and loads. Detailed case studies give a contrastive analysis of the traditional and the proposed forecasting approach. The results show that the proposed approach has the capability of self-adaption on the fluctuations of wind and PV power, and can effectively improve the forecasting accuracy and efficiency. [ABSTRACT FROM AUTHOR]

To overcome the limitation of the existing topologies and control strategies of SST, a modular multilevel converter type solid state transformer (MMC-SST) with internal model control method is proposed in this paper. First, the structure and operating characteristics of MMC-SST are analyzed, and accordingly its mathematical model of input- and output-stage under synchronous rotating coordinate is established. Secondly, a new kind of dual-loop control structure combine with the internal model control (IMC) current inner loop and proportional integral (PI) voltage outer loop is developed, according to the characteristics of IMC. Lastly, a simulation model is established and the typical operating cases, i.e., the grid side voltage variation, load change, grid and load side power factor deviation, are simulated. Results show that the internal model control based MMC-SST can run stable under the required power factor, and the good performances such as fast voltage and current response speed, strong anti-disturb ability to load, and enhanced robustness on system operation are demonstrated. [ABSTRACT FROM AUTHOR]

Complicated construction of Lyapunov–Krasovskii functional (LKF) makes the quadratic correlation terms of time-delay appear in its derivative. This paper proposes two relaxed negative-determination lemmas to deduce the negative definite condition of a quadratic function with respect to a time-varying delay, which contain some popularly lemmas as their special cases. Combining a novel augmented LKF and generalized free-matrix-based integral inequality (GFMBII), the developed lemmas are respectively applied to derive the stability criteria for nominal and uncertain systems. Three numerical examples are given to prove the potential gain of the two lemmas and the superiority of the criteria over the previous work. [ABSTRACT FROM AUTHOR]

• The MPSR-SVR model is proposed to predict the electrical, heating, cooling and gas load of the IES. • The coupling characteristics among multiple loads of the IES are analysed. • The Pearson correlation coefficient is used to describe the correlation between multiple loads and environmental factors. • The C-C algorithm is used to reconstruct the phase space to fully explore the evolution law of time series. In order to alleviate the energy crisis and improve the energy utilization rate, the integrated energy system (IES) has become an important way of energy utilization. IES integrates electricity, natural gas, heating and cooling energy supply. Accurate energy load forecasting is essential, which has a significant impact on the economic scheduling and optimal operation of the IES. Herein, a combined model prediction method of multivariate phase space reconstruction (MPSR) and support vector regression (SVR) is proposed in this paper. First, a quantitative analysis of the coupling relationship between different integrated energy subsystems is conducted, and Pearson correlation analysis theory is used to analyse the historical time series of electrical, cooling, heat, gas loads and environmental factors one by one, then the input variables of the combined forecasting model are obtained. After that, the multivariate phase space is reconstructed by the C-C method, and the SVR model is used to predict electricity, cooling, heating and gas loads. Final, the model is validated by the actual data of the IES in Arizona State University, the results of three cases show the efficiency and high accuracy of the proposed forecasting method that considers the coupling relationship between multi-energy loads of IES. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of quasilinear third-order differential equations with a delay argument. We establish some conditions of such certain third-order quasi-linear neutral differential equation as oscillatory or almost oscillatory. Those criteria improve, complement and simplify a number of existing results in the literature. Some examples are given to illustrate the importance of our results. [ABSTRACT FROM AUTHOR]

This study is connected with the nonoscillatory and oscillatory behaviour to the solutions of nonlinear neutral impulsive systems with forcing term which is studied for various ranges of of the neutral coefficient. Furthermore, sufficient conditions are obtained for the existence of positive bounded solutions of the impulsive system. The mentioned example shows the feasibility and efficiency of the main results. [ABSTRACT FROM AUTHOR]

At present, the entire world is moving towards digitalization, including in the electric power industry. Digitalization is in its heyday and a lot of articles and reports are devoted to this topic. At the same time, the least digitalized of the electrical networks are distribution networks that account for a very large share in electric power systems. The article proposes a methodology for creating a flexible distribution network based on the use of digital technology. Additionally, we elaborate a methodology with the identification and collection of the necessary information to create digital networks, develop ways to adapt the required equipment, and suggest methods of recognition of some short circuits. Furthermore, we address the issue of reliability of the information obtained from digital devices, develop a technique for arranging the devices to cover the entire network as required to improve the power system protection of electrical power distribution networks. The above measures make it possible to ensure the flexibility of the active distribution network, as well as to adjust the parameters of the actuation of power system protection depending on changes in external conditions and in the event of emergencies. We propose a technique for controlling the distribution network, based on the factoring-in of the type of damage during an emergency in real time, as well as a technique for arranging the measuring devices and the creation of an information and communication network. We provide recommendations for the design and operation of electric power distribution networks with digital network control technology. [ABSTRACT FROM AUTHOR]

We prove the logarithmic convergence rate of the families of usual and modified iterative Runge-Kutta methods for nonlinear ill-posed problems between Hilbert spaces under the logarithmic source condition, and numerically verify the obtained results. The iterative regularization is terminated by the a posteriori discrepancy principle. [ABSTRACT FROM AUTHOR]

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications. [ABSTRACT FROM AUTHOR]

An integro-differential equation describes the non-equilibrium thermal response of glass-forming substances with a dynamic (time-dependent) heat capacity to fast thermal perturbations. We found that this heat transfer problem could be solved analytically for a heat source with an arbitrary time dependence and different geometries. The method can be used to analyze the response to local thermal perturbations in glass-forming materials, as well as temperature fluctuations during subcritical crystal nucleation and decay. The results obtained can be useful for applications and a better understanding of the thermal properties of glass-forming materials, polymers, and nanocomposites. [ABSTRACT FROM AUTHOR]

A high proportion of renewable energy connected to the power grid has caused power quality problems. Voltage-sensitive loads are extremely susceptible to voltage fluctuations, causing power system safety issues and economic losses. Considering the uncertainty factor and the time-varying characteristic, a linearized random ZIP model (constant impedance (Z), constant current (I), and constant power (P)) with time-varying characteristics was proposed. In order to improve the voltage quality of the voltage-sensitive loads in the day-here stage in an active distribution network (ADN), a linearized two-stage active and reactive power coordinated stochastic optimization model was established. The day-ahead active and reactive power coordination optimization was to smooth the large voltage fluctuation and develop a reserve plan to eliminate the unbalanced power caused by the prediction error in the day-here optimization. In the day-here real-time redispatch, the voltage was further improved by the continuous reactive power compensation device. Finally, the simulation results on the IEEE-33 bus system showed that the control strategy could better eliminate the unbalanced power caused by the prediction error and obviously improve the voltage of sensitive loads in the real-time stage on the premise of maintaining economic optimality. [ABSTRACT FROM AUTHOR]

A high proportion of renewable energy connected to the power grid has caused power quality problems. Voltage-sensitive loads are extremely susceptible to voltage fluctuations, causing power system safety issues and economic losses. Considering the uncertainty factor and the time-varying characteristic, a linearized random ZIP model (constant impedance (Z), constant current (I), and constant power (P)) with time-varying characteristics was proposed. In order to improve the voltage quality of the voltage-sensitive loads in the day-here stage in an active distribution network (ADN), a linearized two-stage active and reactive power coordinated stochastic optimization model was established. The day-ahead active and reactive power coordination optimization was to smooth the large voltage fluctuation and develop a reserve plan to eliminate the unbalanced power caused by the prediction error in the day-here optimization. In the day-here real-time redispatch, the voltage was further improved by the continuous reactive power compensation device. Finally, the simulation results on the IEEE-33 bus system showed that the control strategy could better eliminate the unbalanced power caused by the prediction error and obviously improve the voltage of sensitive loads in the real-time stage on the premise of maintaining economic optimality. [ABSTRACT FROM AUTHOR]

This paper studies the second kind linear Volterra integral equations (IEs) with a discontinuous kernel obtained from the load leveling and energy system problems. For solving this problem, we propose the homotopy perturbation method (HPM). We then discuss the convergence theorem and the error analysis of the formulation to validate the accuracy of the obtained solutions. In this study, the Controle et Estimation Stochastique des Arrondis de Calculs method (CESTAC) and the Control of Accuracy and Debugging for Numerical Applications (CADNA) library are used to control the rounding error estimation. We also take advantage of the discrete stochastic arithmetic (DSA) to find the optimal iteration, optimal error and optimal approximation of the HPM. The comparative graphs between exact and approximate solutions show the accuracy and efficiency of the method. [ABSTRACT FROM AUTHOR]

The reliability analysis method and risk assessment model for the traditional single network no longer meet the requirements of the risk analysis of coupled systems. This paper establishes a risk assessment system of electric-gas integrated energy system (EGIES) considering the risk security of components. According to the mathematical model of each component, the EGIES steady state analysis model considering the operation constraints is established to analyze the operation status of each component. Then the EGIES component accident set is established to simulate the accident consequences caused by the failure of each component to EGIES. Furthermore, EGIES risk assessment system is constructed to identify the vulnerability of EGIES components. Finally, the risk assessment of IEEE14-NG15 system is carried out. The simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to ensure the information security, most of the important information including the data of advanced metering infrastructure (AMI) in the energy internet is currently transmitted and exchanged through the intranet or the carrier communication. The former increases the cost of network construction, and the latter is susceptible to interference and attacks in the process of information dissemination. The blockchain is an emerging decentralized architecture and distributed computing paradigm. Under the premise that these nodes do not need mutual trust, the blockchain can implement trusted peer-to-peer communication for protecting the important information by adopting distributed consensus mechanisms, encryption algorithms, point-to-point transmission and smart contracts. In response to the above issues, this paper firstly analyzes the information security problems existing in the energy internet from the four perspectives of system control layer, device access, market transaction and user privacy. Then blockchain technology is introduced, and its working principles and technical characteristics are analyzed. Based on the technical characteristics, we propose the multilevel and multichain information transmission model for the weak centralization of scheduling and the decentralization of transaction. Furthermore, we discuss that the information transmission model helps solve some of the information security issues from the four perspectives of system control, device access, market transaction and user privacy. Application examples are used to illustrate the technical features that benefited from the blockchain for the information security of the energy internet. [ABSTRACT FROM AUTHOR]