Melentiev Energy Systems Institute

Air pollution forecasting can provide reliable information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short-term forecasting model based on deep learning is proposed for PM2.5 (particulate matter with an aerodynamic diameter less than or equal to $2.5~\mu \text{m}$ ) concentration, and the convolutional-based bidirectional gated recurrent unit (CBGRU) method is presented, which combines 1D convnets (convolutional neural networks) and bidirectional GRU (gated recurrent unit) neural networks. The case is carried out by using the Beijing PM2.5 data set in UCI Machine Learning Repository. Comparing the prediction results with the traditional ones, it is proved that the error of the CBGRU model is lower and the prediction performance is better.

The increasing penetration of various distributed and renewable energy resources at the consumption premises, along with the advanced metering, control and communication technologies, promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids (MMGs). The networked MMG system is an interconnected cluster of distributed generators, energy storage as well as controllable loads in a distribution system. And its operation complexity can be decomposed to decrease the burdens of communication and control with a decentralized framework. Consequently, the multi-microgrid energy management system (MMGEMS) plays a significant role in improving energy efficiency, power quality and reliability of distribution systems, especially in enhancing system resiliency during contingencies. A comprehensive overview on typical functionalities and architectures of MMGEMS is illustrated. Then, the emerging communication technologies for information monitoring and interaction among MMG clusters are surveyed. Furthermore, various energy scheduling and control strategies of MMGs for interactive energy trading, multi-energy management, and resilient operations are thoroughly analyzed and investigated. Lastly, some challenges with great importance in the future research are presented.

Tens and hundreds of thousands of disturbances occur annually in modern power systems. The overwhelming majority of them are eliminated by relay protection devices and other automatic systems and by the actions of the dispatching personnel. A small fraction of the emergencies (tens of cases in such large power interconnections as those in the United States and Canada, Europe, and the United Power System (UPS) of Russia) result in significant system failures, sometimes of a cascading nature. They are consequences of unusual primary disturbances, failures of automatic emergency control systems, protection device malfunctions, and errors by personnel, but do not cause extreme consequences for the power system and the consumers. Of these, only some rare failures-blackouts-become catastrophes with severe long-term consequences for the national economies and population. Recent blackouts in North America, Europe, Russia, and other countries require specialists once again to pay closer attention to the blackout phenomenon. It is often believed that the philosophy of preventing blackouts should be based on dispatching personnel training, wide-area system visibility,and better computer models for the analysis of the stability and security of power systems. The authors of this paper also think that in emergency situations of a cascading nature, automatic emergency control systems should play a major role. A confirmation for this statement is the fact that from 1975 to 2005 there were no blackouts in the UPS of Russia (where automatic emergency control systems are widely used). At the same time, the Moscow blackout demonstrated that the growing problems in the Russia's UPS (such as aging equipment and load growth) made it also vulnerable to major blackouts. This stresses again that the electrical power industry faces common global problems and that a global effort, cooperation, and exchange of the best practices are needed to prevent blackouts. This paper describes the Russian

Under the pressure of climate change, the demands for alternative green hydrogen (H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) production methods have been on the rise to conform to the global trend of transition to a H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> society. This article proposes a multirenewable-to-hydrogen production method to enhance the green H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> production efficiency for renewable-dominated hydrogen fueling stations (HFSs). In this method, the aqueous electrolysis of native biomass can be powered by wind and solar generations based on electrochemical effects, and both electrolysis current and temperature are taken into account for facilitating on-site H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> production and reducing the electricity consumption. Moreover, a capsule network based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> demand forecasting model is formulated to estimate the gas load for HFS by extracting the underlying spatial features and temporal dependencies of traffic flows in the transportation network. Furthermore, a hierarchical coordinated control strategy is developed to suppress high fluctuations in electrolysis current caused by volatility of wind and solar outputs based on model predictive control framework. Comparative studies validate the superior performance of the proposed methodology over the power-to-gas scheme on electrolysis efficiency and economic benefits.

This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids. In this framework, the interconnected microgrids not only fulfil the multienergy demands of with local hybrid biogas-solar-wind renewables, but also proactively trade their available multienergy and communication resources with each other for delivering secured and high quality of services. The multimicrogrid multienergy and communication trading is an intractable optimization problem because of their inherent strong couplings of multiple resources and independent decision-makings. The original problem is thus formulated as a Nash bargaining problem and further decomposed into the subsequent social multiresource allocation subproblem and payoff allocation subproblem. Furthermore, fully-distributed alternating direction method of multipliers approaches with only limited trading information shared are developed to co-optimize the communication and energy flows while taking into account the local resource-autonomy of heterogeneous microgrids. The proposed methodology is implemented and benchmarked on a three-microgrid system over a 24-h scheduling periods. Numerical results show the superiority of the proposed scheme in system operational economy and resource utilization, and also demonstrate the effectiveness of the proposed distributed approach.

This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.

This paper proposes an optimal day-ahead optimization schedule for gas-electric integrated energy system (IES) considering the bi-directional energy flow. The hourly topology of electric power system (EPS), natural gas system (NGS), energy hubs (EH) integrated power to gas (P2G) unit, are modeled to minimize the day-ahead operation cost of IES. Then, a second-order cone programming (SOCP) method is utilized to solve the optimization problem, which is actually a mixed integer nonconvex and nonlinear programming issue. Besides, cutting planes are added to ensure the exactness of the global optimal solution. Finally, simulation results demonstrate that the proposed optimization schedule can provide a safe, effective and economical day-ahead scheduling scheme for gas-electric IES.

Energy storage systems will play a key role in the power system of the 21st century considering the large penetrations of variable renewable energy, growth in transport electrification, and decentralization of heating loads. Therefore, reliable real-time methods to optimize energy storage, demand response, and generation are vital for power system operations. This article presents a concise review of battery energy storage and an example of battery modeling for renewable energy applications and details an adaptive approach to solve this load leveling problem with storage. A dynamic evolutionary model based on the first-kind Volterra integral equation is used in both cases. A direct regularized numerical method is employed to find the least-cost dispatch of the battery in terms of the integral equation solution. Validation on real data shows that the proposed evolutionary Volterra model effectively generalizes a conventional discrete integral model taking into account both the state of health and the availability of generation/storage.

The paper deals with the main prospects and challenges of radical transformations of electric power systems (EPSs) with changes in their structure and properties conditioned by wide use of innovative energy-related technologies and digitalization and intellectualization of system operation and control. Structural trends of EPS development are the focus of the analysis. Consideration is given to changes in EPS properties driven by the use of new technologies, to the problems of system flexibility and to its enhancement. EPS “resiliency” and “survivability” notions are subjected to comparison. The main factors favoring the formation of future EPSs to cyber-physical systems are discussed. Objective trends of EPS control and protection system development are under consideration.

In order to reduce dependence on nonrenewable resources and environmental damage, alternative renewable feedstocks are needed to replace fossil fuels to produce benzene, toluene, and xylene (BTX). Biomass has been considered an ideal feedstock because of its abundance, renewability, and carbon neutrality. Catalytic fast pyrolysis (CFP) is a potential method for the conversion of biomass to BTX. This paper summarizes the mechanisms and pathways of biomass to BTX via CFP and attempts to provide an overview of the factors affecting BTX production from biomass in terms of feedstocks, catalysts, process configurations, and pyrolysis conditions. On the basis of understanding these factors, potential optimization methods to enhance the yield and selectivity of BTX from biomass CFP are proposed.

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.

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.

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.

Recent blackouts in the USA, Europe and Russian Federation have clearly demonstrated that secure operation of large interconnected power systems cannot be achieved without full understanding of the system behavior during abnormal and emergency conditions. Current practice of managing separate parts of the system without knowledge of the `full picture' will lead to even greater blackouts. This paper proposes a novel approach to the system monitoring and control with the goal of identification of potential voltage instability problems before they lead to major blackouts. The proposed approach is based on detecting alarm states using self-organized Kohonen neural networks, and activating a multi-agent control system to take necessary preventive actions. The Kohonen network is trained off-line and then applied on-line to predict possible emergencies. The intelligent system was realized in STATISTICA 8.0 and tested on the modified 42-bus IEEE power system. Results are presented and discussed.

Modern electricity grids continue to be vulnerable to large-scale blackouts. As all states leading to large-scale blackouts are unique, there is no algorithm to identify pre-emergency states. Moreover, numerical conventional methods are computationally expensive, which makes it difficult to use for the on-line security assessment. Machine learning techniques with their pattern recognition, learning capabilities and high speed of identifying the potential security boundaries can offer an alternative approach. The purpose of this paper is not to suggest that one particular kind of machine learning technique for security assessment would be more appropriate than others. We start from the premise that almost every method may be useful within some restricted context. Based on this idea, we developed an automated multi-model approach for on-line security assessment. The proposed method allows us to automatically test the different state-of-art techniques in order to find both the best algorithm and its top performance tuning for particular analyzed power system. A case study using the IEEE RTC-96 system demonstrates the effectiveness of the proposed approach.

Integrated multi-energy systems give good possibilities to have high effectiveness of energy supply to consumers. The concept of energy hub is developed for modeling and simulation of integrated multi-carrier systems. Based on previous investigations, in this paper the authors develop the simulation model of energy hub. Basic principles of designing simulation energy hub model are discussed. Realization of simulation model using Matlab/Simulink is suggested. Simulation results for integrated electricity and heat demand systems are explained as the possibilities demonstration of simulation energy hub model.

The paper presents a control system based on the multi-agent technique. The control system coordinates different discrete and continuous control devices during the post-disturbance period in order to prevent voltage collapse of the whole system. The model of the test power system was simulated by Matlab/PSAT software. Multi-agent system (MAS) has been implemented in Java language using JADE (Java agent development framework) package. The efficiency of the proposed technique has been proved by numerical simulations. The proposed MAS software allows the use of complex Matlab/PSAT routines as well as the modeling of complex behavior of the agents.

Results on experimental investigation of the dynamics of boiling-up at stepwise heat release on a horizontally oriented cylindrical surface in a large volume of freon-21 are presented. Experimental data on the propagation velocity, structure, and other local characteristics of development of self-sustained evaporation fronts at different temperature differences of boiling-up in saturated liquid were obtained. New experimental results on the dynamics of vapor phase incipience and evolution on the surface of a vertical heat releasing tube and on the dynamics of changing the heater temperature and pressure in a flow of liquid (water, ethanol) subcooled to saturation temperature in the channel under nonstationary heat release conditions are represented. It was revealed that the dependence of the expectation time of intense bubble growth on the water motion velocity is nonmonotonic.

The paper is of tutorial nature, covering a wide range of the spectral and singular analyses of the electric power systems' (EPS) nodal admittance matrix and Jacobian matrix. Results of the analyses are applied to detect sensors and weak places in EPS and to visualize them. This allows for the solution to many important problems related to power system operation like estimation of network reinforcement and determination of sites for power quality metering. In this paper the theoretical background of the approach and its above mentioned applications are presented and discussed in the scope of their usefulness for the power system operation, planning and control.

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 it 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) 1, energy resource supply (ERS) 2, energy resource import (ESI) 3 and renewable energy resources (RER) 4. Also, applying the obtained numerical results, we can forecast the rate of these functions for spacial interval of time.