Performance Analysis of Energy Storage in Smart Microgrid Based on Historical Data of Individual Battery Temperature and Voltage Changes

In this work, a historical data based battery management system (BMS) was successfully developed and implemented using an embedded system for condition monitoring of a battery energy storage system in a smart microgrid. The performance was assessed for 28 days of operating time with a one-minute sampling time. The historical data showed that the maximum temperature increment and the maximum temperature difference between the batteries were 4.5 °C and 2.8 °C. One of the batteries had a high voltage rate of change, i.e. above 3.0 V/min, and its temperature rate of change was very sensitive, even at low voltage rate of changes. This phenomenon tends to indicate problems that may deplete the battery energy storage system's total capacity. The primary findings of this study are that the voltage and temperature rates of change of individual batteries in real operating conditions can be used to diagnose and foresee imminent failure, and in the event of a failure occurring the root cause of the problem can be found by using the historical data based BMS. To ensure further safety and reliability of acceptable practical operating conditions, rate of change limits are proposed based on battery characteristics for temperatures below 0.5 °C/min and voltages below 3.0 V/min

A Novel Generic Battery Modeling Approach for Power System Simulation Applications

Very large capacity energy storage systems are required in power systems for utility shaping when renewable energy systems like wind farms are not supporting sufficient generation. Energy storage systems are indispensible during evacuation problem in existing grid structure. For addressing power quality aspects also, quick responsive energy storage systems are requisite. In these contexts, before practical implementation of energy storage systems, for a particular or combined application, its characteristics are to be simulated in power system environment to suit for the specific application. In this perspective, the various battery modeling are briefed and a novel generic battery modeling approach which will be useful in power system simulation application is presented in this paper. The contribution through this work is, real time physical parameters of battery are incorporated in look-up table. Those values are read during simulation to compute standard electrode potential of battery. As future scope of work, real-time interfacing of physical parameters of battery can be implemented during simulation. Vanadium redox flow battery and lithium-ion battery are simulated using the generic battery modeling approach and their results presented, comparing their suitability for utility shaping, power quality enhancement aspects and distributed grid technology application

Control of an Autonomous Hybrid Microgrid as Energy Source for a Small Rural Village

Nowadays, the exhaustion of electricity power in rural areas is becoming an important issue for many African Nations. Moreover, challenges include the high cost of extending the power grid to these locations, the economic health of the utilities and lack of revenue in impoverished villages. Numerous new initiatives are being implemented in the countries some of them co-financed by international organizations. In this paper, the hybrid microgrid is carried out as a feasible solution for a small rural village. A model of hybrid microgrid consisting of combination of photovoltaic (PV) panels and battery energy storage (BES) and a control system for managing the components of entire system to feed the village as local load is proposed. The control system must avoid the interruptions of power delivered to the consumers (village) and, therefore, good quality and reliability of system is required. The PI controllers are used to regulate the voltage and current using three-phase dq transformation, while the parameters are determined using Ziegler-Nichols tuning method. The effectiveness of the proposed method is verified by simulation results given by the Matlab/SimPowerSystems environment. 

Research trends on microgrid systems: a bibliometric network analysis

The numeral of academic publications in the microgrid system field has rapidly grown. A microgrid system is a group of interconnected distributed generation, loads, and energy storage operating as a single controllable entity. Many published articles recently focused on distributed generation, system control, system stability, power quality, architectures, and broader focus areas. This work analyzes microgrid: alternating current (AC), direct current (DC), and hybrid AC/DC microgrid systems with bibliometric network analysis through descriptive analysis, authors analysis, sources analysis, words analysis, and evolutionary path based on the Scopus database between 2010 and 2021. The finding helps find out the top authors and most impact sources, most relevant and frequently used in the research title, abstract, and keyword, graphically mapping the research evolved and identifying trend topic

Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model

Battery energy storage systems (BESS) play a pivotal role in future power systems as they contribute to achiev-ing the net-zero carbon emission objectives. The BESS systems, predominantly employing lithium-ion batteries, have been exten-sively deployed. The degradation of these batteries significantly affects system efficiency. Deep neural networks can accurately quantify the battery degradation, however, the model complexity hinders their applications in energy scheduling for various power systems at different scales. To address this issue, this paper pre-sents a novel approach, introducing a linearized sparse neural network-based battery degradation model (SNNBD), specifically tailored to quantify battery degradation based on the scheduled BESS daily operational profiles. By leveraging sparse neural networks, this approach achieves accurate degradation predic-tion while substantially reducing the complexity associated with a dense neural network model. The computational burden of inte-grating battery degradation into day-ahead energy scheduling is thus substantially alleviated. Case studies, conducted on both microgrids and bulk power grids, demonstrated the efficiency and suitability of the proposed SNNBD-integrated scheduling model that can effectively address battery degradation concerns while optimizing day-ahead energy scheduling operations

Development of a digital twin for real-time simulation of a combustion engine-based power plant with battery storage and grid coupling

Coordinated control of combustion engine-based power plants with battery storage is the next big thing for optimising renewable energy. Digital twins can enable such sophisticated control but currently are too simplistic for the required insight. This study explores the feasibility of a fully physics-based combustion engine model in real-time co-simulation with an electrical power plant model, including battery storage. A detailed, crank-angle resolved, one-dimensional model of a large-bore stationary engine is reduced to a fast-running model (FRM). This engine digital twin is coupled with a complete power plant control model, developed in Simulink. Real-time functions are tested on a dedicated rapid-prototyping system using a target computer. Measurement data from the corresponding power plant infrastructure provide validation for the digital twin. The model-in-the-loop simulations show real-time results from both the standalone combustion and electric submodels mostly within 5% of measured values. The model coupling for fully predictive simulation was tested on a desktop computer, showing expected functionality and validity within 4% and 8% of the respective measured generator and converter outputs. However, execution time of the FRM needs reducing when moving to final hardware-in-the-loop implementation of a complete power plant model.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

P2PEdge : A Decentralised, Scalable P2P Architecture for Energy Trading in Real-Time

Author Contributions: Conceptualization, J.K., D.H.-S., R.N.A., B.S. and K.M.; Formal analysis, J.K., D.H.-S. and B.S.; Investigation, J.K.; Methodology, J.K.; Project administration, K.M.; Supervision, K.M. and D.H.-S.; Validation, J.K. and D.H.-S.; Visualization, J.K.; Writing—original draft, J.K.; Writing—review & editing, J.K., K.M., D.H.-S., R.N.A. and B.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding.Peer reviewedPublisher PD

Fotovoltaik enerji üretim tesisleri için batarya yönetim sistemi tasarımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Fotovoltaik enerji sistemleri güneş enerjisini elektrik enerjisine çeviren sistemlerdir. Güneş enerjisinin yenilenebilir olması ve çevreye zararlı olmaması fosil yakıtlardan elde edilen enerjiye alternatif olarak gösterilmesinin nedenleri arasında yer almaktadır. Fotovoltaik enerjiden daha iyi faydalanmak için enerji depolama sistemleri kullanılabilir. Günümüzde elektrik enerjisinin depolanması için genelde bataryalar kullanılmaktadır. Batarya yönetim sistemleri bataryalar için şarj durumu, gerilim, sıcaklık vb. değerlerin izlenmesini sağlar. Bu çalışmada fotovoltaik bir sistem ve bir kurşun asit batarya modellenmiştir. Oluşturulan sistem için bir batarya yönetim sistemi tasarlanmıştır. Batarya yönetim sisteminin tasarımı için açık devre gerilimi yöntemiyle birlikte en küçük kareler yöntemi de kullanılmıştır. Elde edilen sonuçlar bir tabloda gösterilmiştir ve bağıl hata değeri hesaplanarak sistemin güvenilirliği test edilmiştir. Anahtar Kelimeler: Batarya, fotovoltaik sistemler, batarya yönetimiPhotovoltaic energy systems are systems that convert solar energy to electrical energy. Being renewable and less harmful to the environment is the reason solar energy is an alternative for fossil fuel energy. Energy storage systems can be used for better use of photovoltaic energy. Batteries are usually utilized for storing energy. Battery management systems provide monitoring parameters like state of charge,voltage and temperature etc. In this study, a photovoltaic system and a lead acid battery is modelled. A battery management system is designed for the system modelled in Simulink. Open circuit voltage and least squares method are combined together for designing the battery management systems and relative error is also calculated to test the reliability of the system. Keywords: Battery, photovoltaic systems, battery managemen

Operasi Optimum Stand-Alone Microgrid Menggunakan Metode Cuckoo Search Optimization Dengan Mempertimbangkan Karakteristrik Umur Baterai

Microgrid adalah suatu sistem kelistrikan yang terdiri dari lebih dari satu generator energi terbarukan. Sumber energi listrik yang digunakan untuk mensuplai sistem microgrid didapatkan dari sumber energi terbarukan dan dari sumber energi yang tersimpan pada baterai. Pada penelitian ini sistem microgrid yang digunakan adalah Stand Alone yang tidak terhubung dengan sistem jaringan pembangkit Negara atau Perusahaan listrik Negara, menggunakan Diesel Engine (DE), Wind Turbine (WT) dan Photovoltaic (PV), sebagai pembangkit energi listrik pada sistem. Baterai digunakan sebagai media pemasok cadangan energi untuk mempertahankan utilitas dari sistem Stand Alone Microgrid. Saat Distributed Generator tidak dapat memberikan energi yang diharapkan, sistem akan menggunakan DE dan baterai sebagai energi cadangan. Penyimpanan daya pada sistem akan diatur dengan mempertimbangkan energy management system (EMS) untuk membuat sistem microgrid bekerja secara optimal. State of charge (SOC) akan diperhatikan dalam sistem operasi microgrid, saat kondisi charging dan discharging sehingga life time dari baterai dapat dimaksimalkan. Operasi optimum pada sistem ini ditujukan untuk meminimalkan biaya pembangkitan serta memaksimalkan umur baterai. Metode operasi optimum sistem microgrid tersebut menggunakan metode Cuckoo Search Optimization (CSO) yang nantinya akan dibandingkan dengan metode lainya. Dari hasil simulasi diketahui bahwa metode CSO memiliki biaya pembangkitan yang lebih rendah jika dibandingkan dengan PSO meskipun tidak terlalu siknifikan, yaitu $853.0304 Untuk metode CSO dan$ 792.0302 untuk metode PSO pada studi kasus I. untuk metode CSO didapatkan total life loss cost $7.72 dan untuk metode PSO sebesar sebesar$ 15.51 pada studi kasus I. ======================================================================================================================== Microgrid is an electrical system that consist of more than one generator of renewable energy. Source of electrical energy obtained from the grid system, renewable energy sources and renewable energy storage. In this st udy microgrid system used Stand Alone are not connected to the utility grid system, using a Diesel Engine, Wind Turbine and Photovoltaic, as electrical energy generation on the system. Batteries are used as a backup suppliers of energy to maintain the utility of the stand alone microgrid system. When Distributed Generator can not deliver the expected energy, the sy stem will use the Diesel Engine and battery as an energy reserve. Power storage system will be set taking into account the Energy Management System (EMS) to create a microgrid system to work optimally. State of charge (SOC) will be considered in the opera ting system microgrid, when conditions c harge or discharging, battery life time can be maximized. The optimum operation of this system is to minimize the generation cost and maximize battery life. The method of operation optimum microgrid system using Cuck oo Search Optimization (CSO) method has been compared by Particle Swarm Optimization (PSO) . In study case I, the best generation cost result of CSO method is $853.0304 , and$ 792.0302 result of PSO method , on study case I. Result of battery total life los s cost using CSO obtained $7.72 and$ 15.51 on study case 1