Electric Energy Systems Research Articles

Special Issue on Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Nowadays, energy is becoming more electrical in each field of engineering application, thus power converters have assumed an increasingly relevant role for electrical machines, renewable energy and transportation systems [...]

Optimized operation of large scale battery systems

Abstract In the decentralized renewable driven electric energy system, economically viable battery systems become increasingly important for providing grid-related services. End of 2016, STEAG has successfully started the commercial operation of six 15 MW large scale battery systems which have been incorporated in STEAG’s primary control pool. During the commissioning phase, extensive effort has been spent in optimizing the operational efficiency of these systems with promising results. However, the operation experience has shown that there is still significant potential for improving the system behavior as well as reducing the aging of the battery cells. By analyzing historical data of the charging power associated with the state of charge management, opportunities for significantly reducing the operational costs have been identified. By means of more involved model-based control strategies, which adequately consider the specific characteristics of the battery system, and by using mathematical optimization and artificial intelligence, adapting the state of charge management strategy to new applications, these additional cost savings can be obtained. Apart from giving insights into the operational experience with large scale battery systems, the contribution of this paper lies in proposing strategies for reducing the operational costs of the battery system using classical approaches as well as mathematical optimization and neural networks. These approaches will be illustrated by simulation results.

EV-Based reconfigurable smart grid management using support vector regression learning technique machine learning

Renewable microgrid (RMG) is a new solution to improve the safety, reliability, quality, and performance of energy in electrical systems. By implementing different renewable energy sources, like photovoltaic panels and wind turbines, RMGs can reduce greenhouse gas emissions and enhance proficiency. The paper suggests a RMG energy management method based on machine learning, which considers a reconfigurable frame according to the remote link switching and segmentation. The proposed scheme investigates advanced support vector machines to model and estimates the charging demand (ChD) of hybrid electric vehicles (HEVs). To reduce the load impact of HEV on the network, 2 various cases have been implemented; one is coordinated, and the other is smartly charged. Because of the complex construction of the problem statement, an improved optimization technique according to the gray wolf is proposed. The simulation outcomes of the IEEE microgrid (MG) trial system depict that it is of adequate and efficient quality in both cases. Based on the result of the forecast for the total charge demand of HEVs, the average absolute percentage error is 0.978 that is so small. Furthermore, the outcomes are shown that, compared to the coordination scheme, the total operating cost of the MG in smart load is reduced by 2.5%.

Scenarios for future power system development in Finland

This paper demonstrates how various part-solutions can be combined in different scenarios for a more climate-neutral electric energy system. The case study is the Finnish electric energy system. Four scenarios are discussed. A base scenario for 2030 consists of already agreed future investments in new energy production facilities by 2022 supplemented by additional consumption and a moderate increase in renewable energy generation. Alongside the base scenario 2030, two scenarios with more ambitious renewable energy targets and flexibility issues are examined. The fourth scenario is for year 2050 analyzing the operational properties of the system with high share of variable renewable generation, flexible loads, and high-capacity energy storages. These scenarios help to assess how, for example, investing in wind or solar production, heat pumps on a large scale or the battery storage of electric vehicles influences the other components of the system and the implications for emissions.

The Study of Synchronous Generators' Voltage and Frequency Group Prognostic Controllers of a Small-scale HPP

The application of a large number of distributed generation plants, built on the basis of synchronous generators in Electric Energy System (EES), requires solving the problem of their centralized control, adjustment of voltage and frequency local controllers, which entails taking into account a large number of interrelated system parameters. These problems can be solved using prognostic control algorithms. The purpose of the study was to determine the effect of the proposed group prognostic voltage and frequency controllers for small-scale Hydroelectric Power Plant (HPP) on various operating modes of the EES. The studies were conducted in the MATLAB environment using the Simulink and SimPowerSystems simulation packages. The results of computer simulation indicate that the use of group prognostic controllers significantly reduces overshoot, oscillability index, transient time and voltage dips, and frequency in the normal and emergency conditions. The proposed methods of the group prognostic controller formation and tuning allow to improve quality indices of small-scale HPP voltage and frequency control, while retaining the former settings of synchronous generators controllers.

Guest Editorial: Special Section on Demand Response Applications of Cloud Computing Technologies

The papers in this special section focus on demand response applications in cloud computing technologie.s The use of distributed energy resources for self-generation and self-consumption along with Information and Communications Technologies and the Internet of Things is rapidly increasing the ability of the consumers and prosumers to actively engage with the electric energy system. Sustained consumer and prosumer engagement in demand response programs has been identified as a key factor in future electric energy systems, especially with a high penetration of renewable energy sources. This engagement has allowed demand-side resources to play a larger role in energy and reserve markets, whether by generating, storing or participating in demand response programs through increased flexibility, towards the consumer-driven energy transition. However, in real life, there is still a long way to go until demand response solutions take off and become entirely integrated into the daily life of the consumers, thus utilizing their full potential. Stronger engagement of consumers and prosumers is needed, as well as more flexibility services for system operation, benefiting Smart Grid developments.

Сценарії прогнозування адекватності розвитку генеруючих потужностей і механізми підтримки розвитку електроенергетики ЄС

A generalized interpretation of the adequacy of generating capacities is presented. Methods for assessing the adequacy of generating capacities correspondent to the EU regulations and standards are examined. The peculiarities of forecasting the adequacy of the development of generating capacities at different time intervals (long-term, medium-term and short-term) according to the methodological approach of ENTSO-E are covered. The main scenarios for forecasting the adequacy of the development of generating capacities at medium-term and long-term time intervals are provided. Attention is focused on the key stages of building the market-based stochastic models for forecasting the supply and demand of electric energy in accordance with the methodological approach of ENTSO-E to predict the adequacy of the development of generating capacities: forecasting the hourly load in the electric energy system; determination of probabilistic characteristics of power generation based on the climate database; assessment of the flexibility of the electric power system; forecasting the development of transboundary flows of electric energy; evaluation of criteria for the adequacy of the development of generating capacities. The methodological aspects of forecasting and assessing the adequacy of the development of generating capacities for each of the stages are presented. To support the adequate development of the European electric energy industry, the relevant market supporting mechanisms are introduced, namely the capacity market and the renewable electric energy market. The main types of mechanisms for the functioning of the capacity market in the EU are provided. Schemes of government support for the development of renewable electric energy in the EU are considered

Toward a “Shinayaka (Flexible and Resilient)” Electric Power and Energy System

1．はじめに

Preface

Welcome to the EPES21--the International Conference on Frontiers of Electrical Power & Energy Systems 2021 which is organized by Beijing Institute of Technology (Zhuhai) and supported by Zhejiang University on December 18-19, 2021, in Zhuhai, China. An ideal place to gather new findings and ideas, discuss across the industrial community. EPES 2021 highlights the latest advances, innovations, and applications in the field of electrical and electronic engineering, energy and Power system and automation and interdisciplinary engineering applications.This proceeding comprises selected papers of EPES 2021, 67 papers from 165 contributions. The topics discussed revolve around the both theoretical and practical knowledge and understanding primarily in the areas of electrical engineering, electronic engineering, energy and power system, automation, transdisciplinary engineering and technology. The contributions were selected by means of a rigorous international peer-review process, present a wealth of exciting ideas that will open novel research directions and foster multidisciplinary collaboration among different specialists.We seek this conference as a to equip our participants to work closely with industry, and to promote innovation and entrepreneurship in our research and education and to promote the engineering profession and to provide leadership within engineering and scientific activities both nationally and internationally.List of General Chairs, General Co-Chair, Publication Editors, Prgram Chair, Advise Chair, Local Chairs, International Scientific Committee Members are available in this pdf.

Adaptive Robust Expansion Planning of Interdependent Electricity, Gas, and Heat Energy Systems

Adaptive Robust Expansion Planning of Interdependent Electricity, Gas, and Heat Energy Systems

Residential Demand Response-Based Load-Shifting Scheme to Increase Hosting Capacity in Distribution System

Increasing the use of solar photovoltaic (PV) generation in order to decarbonize the electric energy system results in many challenges. Overvoltage is one of the most common problems in distribution systems with high penetration of solar PV. Utilizing demand-side resources such as residential demand response (RDR) have the potential to alleviate this problem. To increase the solar PV hosting capacity, we propose an RDR based load-shifting scheme that utilizes the interaction between the distribution system operator (DSO) and demand-side resources. We first model a customer utility that consists of the cost of purchasing power, revenue from the subsidy, and discomfort due to load shifting. When an overvoltage problem is expected, DSO issues a local subsidy, and customers in the distribution system move their load in response. An optimization framework that minimizes the additional cost due to the subsidy while keeping the voltages in a prescribed range is proposed. Because of the non-linearity of the power flow analysis, we propose a sub-optimal algorithm to obtain a subsidy, prove the performance gap between the optimal subsidy and the subsidy obtained by the algorithm. A case study shows that the proposed RDR scheme increases the hosting capacity to almost its theoretical limit at a lower cost than the curtailment method.

Energy Storage Sizing and Probabilistic Reliability Assessment for Power Systems Based on Composite Demand

This paper presents an energy storage system (ESS) sizing model and reliability assessment framework to quantify reliability improvements due to ESS of electric energy systems with high penetration of renewables. The model formulation takes into account: (a) variable generation (VG) forced outage rates (FORs), (b) reserve and demand requirements, and (c) conventional generation (CG) operational constraints and practices. Correlation coefficients among wind speed, solar radiation, and demand are computed using the least square estimation method (LSE). Depending on the correlation, probabilistic models for the composite demand are computed and integrated into the ESS sizing model. Subsequently, the ESS optimal sizes are computed along with the ESS optimal charging and discharging schedules. Then, the reliability is quantified using the probabilistic production costing (PPC) method. A case study is presented in which one VG was approximated as independent variable because of its low correlation with demand and other VG. The case study compares the error when treating the VG as independent.

A new hybrid boost converter with P & O MPPT for high gain enhancement of solar PV system

Day-by-day the percentage penetration levels of green energy systems are increasing. The potentiality and the Technological advancements of fuel cell system and solar photo-voltaic systems to improve the economic condition, to increasing the generating capacity of electrical energy system and to reduce the percentage of greenhouse gas emission of electrical energy systems are increasing. The most cost-effective way to utilize the potentiality of the green energy systems is combined operation of nearby existing sources conventional or green energy systems. The efficient way to change the voltage levels of these systems to the higher levels and connect to the grid or the application by using an efficient buck-boost converters. With the main objective of the work is to build a green energy system consisting of a solar photo-voltaic system with a boost converter. Further the work is focused to build a new hybrid boost converter for high gain system and its equivalent system with a perturb and observe algorithm based maximum power point tracking control mechanism to extract maximum power from the solar PV system. The simulation studies have been carried out to show the effectiveness of a new hybrid boost converter to obtain the high gain system using MATLAB/Simulink.

Optimal Scheduling of Electrical Energy Systems Using a Fluid Dynamic Analogy

The electricity-gas transformation problem and related intrinsic mechanisms are considered. First, existing schemes for the optimization of electricity-gas integrated energy systems are analyzed through consideration of the relevant literature, and an Electricity Hub (EH) for electricity-gas coupling is proposed. Then, the distribution mechanism in the circuit of the considered electricity-gas integrated system is analyzed. Afterward, a mathematical model for the natural gas pipeline is elaborated according to the power relationship, a node power flow calculation method, and security requirements. Next, the coupling relationship between them is implemented, and dedicated simulations are carried out. Through experimental data, it is found that after 79 data iterations, the optimization results of power generation and gas purchase cost in the new system converge to $54,936 in total, which is consistent with the data obtained by an existing centralized optimization scheme. However, the new proposed optimization scheme is found to be more flexible and convenient.

Rüzgâr Enerjisi Dönüşüm Sistemlerinde bir DC-DC Güç Dönüştürücüsünün Denetimli Öğrenme Destekli Kontrolü

Over the last decades, to adopt high penetration of renewable energy sources (RESs) in electrical energy systems, distributed energy resources (DERs) have become prominent. Due to easy attainability status of small wind turbines (WTs), wind energy conversion systems (WECSs) are feasible applications for small customers, especially in windy areas. The next decade is likely to witness a considerable rise in DERs. In this context, WECSs are preferred broadly, thus harvesting wind energy into electrical energy effectively is a substantial issue. WTs can be got involved in the grid-connected or autonomous mode with a variety of topologies. In this paper, we examine to control of DC-DC boost converter of a WECS with the help of artificial intelligence (AI)-aided PI controller based on supervised learning method. Regarding the proposed method, artificial neural networks (ANNs) as a subset of AI are utilized. To test and ensure the applicability of the proposed control method, a small WECS with a permanent magnet synchronous generator (PMSG) connected a DC bus was implemented in MATLAB/Simulink environment. The proposed ANN scheme has reached a high accuracy rate with an overall mean squared error (MSE) equal to 7.4e-08. The results present that dynamic response and less complexity with a high accuracy rate have been obtained under study. The main target of this study is to reduce the number of sensors in the control layer. Thus, a cost-effective and more reliable structure is obtained with fewer sensor requirements.

Modeling and simulation of fault propagation of combined heat and electricity energy systems under extreme cold disaster based on Markov process

Extremely cold weather is the most common disaster faced by combined heat and electricity energy systems during winter days. Assessing the damage caused by the disaster to the system, and preventing disasters all need to simulate the operating state of the system. This paper proposes a cascading fault simulation model suitable for combined heat and electricity energy systems during extreme cold disasters. A multi-time scale Markov process is used in this model to describe the propagation process of cascading faults caused by disasters. Moreover, the impact of extreme cold on the equipment performance is added to the Markov state. By this model, cascading fault events and the fault probability of the system under a given disaster scene can be obtained. Test results show the effectiveness of the proposed model.

Cyber-attack simulation against the electrical power and energy system (EPES) part of SDN microSENSE project

Cyber-attack simulation against the electrical power and energy system (EPES) part of SDN microSENSE project

Frequency-Locked Loops in Electrical Power and Energy Systems: Equivalent or Different to Phase-Locked Loops?

In the electrical power and energy (EPE) area, the traditional concept in designing the synchronization system of power converters is using the phase-locked loop (PLL) concept. An alternative way is using the frequency-locked loop (FLL) concept, which is also known as the adaptive notch filter, adaptive noise cancelation technique, gradient descent estimator, adaptive observer, D-estimation method, and transformation angle detector, among others names. Along with the increased popularity and application of FLLs in EPE systems, the misconception about this design concept is growing. To be more exact, most EPE research communities introduce FLLs as synchronization systems of different properties compared to PLLs. This article aims to explain the main reasons behind this misconception and contribute to dispelling it.

Water Activity Control in PEMFC Electrical Energy System

This work is designed to study the energy management of a Proton Exchange Membrane Fuel Cell (PEMFC), which is based on the activity and management of water and its effect on the electrolyte membrane (FC) as a function of the influx of hydrogen and oxygen, by taking into account the influence of humidification on the implementation of this electrical system to avoid drying and flooding that can cause deterioration of the FC. The cell voltage and system efficiency are also influenced by current density and operating temperature, and simulation results on Matlab / simulink are discussed.

Hydrogen: Targeting $1/kg in 1 Decade

The societal energy system is evolving rapidly as the impacts of our existing energy system are better appreciated and technological advances have dramatically decreased the cost of renewable resources. Simultaneously, the importance of energy transfer across timeframes and the difficulty of decarbonizing the industrial loads and transportation demands using electricity are being recognized. At the center of this need is achieving low-cost clean hydrogen. The U.S. Department of Energy launched the Hydrogen Energy EarthShot which seeks to reduce the cost of clean hydrogen to $1 per 1 kilogram in one decade. Reaching the $1/kg goal is dependent on the evolution of the electrical energy system to increase renewable energy deployment and low electricity costs; advances in electrolysis technology and manufacturing readiness; and infrastructure build-out and hydrogen market establishment that results in the ability to make, move, store, and use clean hydrogen economically while spurring job creation.