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The aim of this study, is to present the fractional model of energy supply-demand system (ES-DS) based on the Caputo-Fabrizio derivative. For the first time, the existence and uniqueness of solution of the fractional model of ES-DS are proved and t is the main novelty of this paper. Also, we know that the obtained results from mathematical models with fractional order are more accurate than usual models. This model is based on four important functions, energy resources demand (ERD) ei, energy resource supply (ERS) S2, energy resource import (ESI) S3 and renewable energy resources (RER) e4. Also, applying the obtained numerical results, we can forecast the rate of these functions for spacial interval of time. [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]

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]

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]

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]

• 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]