Browsing by Author "Onen, Ahmet"

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Citation - WoS: 53
Citation - Scopus: 64
Neuro-Fuzzy Model Predictive Energy Management for Grid Connected Microgrids
(MDPI, 2020) Ulutas, Ahsen; Altas, Ismail Hakki; Onen, Ahmet; Ustun, Taha Selim
With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.
Citation - WoS: 15
Citation - Scopus: 18
Local Steady-State and Quasi Steady-State Impact Studies of High Photovoltaic Generation Penetration in Power Distribution Circuits
(Pergamon-Elsevier Science Ltd, 2015) Jung, Jaesung; Onen, Ahmet; Russell, Kevin; Broadwater, Robert P.
Both steady-state and quasi steady-state impact studies in high Photovoltaic (PV) penetration distribution circuits are presented. The steady-state analysis evaluates impacts on the distribution circuit by comparing conditions before and after extreme changes in PV generation at three extreme circuit conditions, maximum load, maximum PV generation, and when the difference between the PV generation and the circuit load is a maximum. The quasi steady-state study consists of a series of steady-state impact studies performed at evenly spaced time Points for evaluating the spectrum of impacts between the extreme impacts. Results addressing the impacts of cloud cover and various power factor control strategies are presented. PV penetration levels are limited and depend upon PV generation control strategies. The steady state and quasi steady-state impact studies provide information that is helpful in evaluating the effect of PV generation on distribution circuits, including circuit problems that result from the PV generation. Published by Elsevier Ltd.
Citation - WoS: 6
Citation - Scopus: 6
Economic Evaluation of Distribution System Smart Grid Investments
(Taylor & Francis inc, 2015) Onen, Ahmet; Cheng, Danling; Broadwater, Robert P.; Scirbona, Charlie; Cocks, George; Hamilton, Stephanie; Roark, Jeffrey
This article investigates the economic benefits of smart grid automation investments. A system consisting of 7 substations and 14 feeders is used in the evaluation. Here benefits that can be quantified in terms of dollar savings are considered, termed "hard dollar" benefits. Smart grid investment evaluations to be considered include investments in improved efficiency, more cost effective use of existing system capacity with automated switches, and coordinated control of capacitor banks and voltage regulators. These smart grid evaluations are sequentially ordered, resulting in a series of incremental hard dollar benefits. Hard dollar benefits come from improved efficiency, delaying large capital equipment investments, shortened storm restoration times, and reduced customer energy use. Analyses used in the evaluation involve hourly power flow analysis over multiple years and Monte Carlo simulations of switching operations during storms using a reconfiguration for a restoration algorithm. The economic analysis uses the time-varying value of the locational marginal price. Algorithms used include reconfiguration for restoration involving either manual or automated switches and coordinated control involving two modes of control. Field validations of phase balancing and capacitor design results are presented. The evaluation shows that investments in automation can improve performance while simultaneously lowering costs.
Citation - WoS: 1
Citation - Scopus: 1
PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm
(Frontiers Media S.A., 2021) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.
Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.
Citation - WoS: 3
Citation - Scopus: 8
Cloud Induced PV Impact on Voltage Profiles for Real Microgrids
(Institute of Electrical and Electronics Engineers Inc., 2018) Kocer, Mustafa Cagatay; Yoldaş, Yeliz; Gören, Selçuk; Onen, Ahmet; Alan, İrfan; Al-Agtash, Salem Y.; Tzovaras, Dimitrios K.
Integration of renewable energy sources (RESs) into power systems has been a popular topic for a long time. Due to government policies and incentives, it will be more popular in the future since it is a free and environment-friendly nature. Besides its advantages, photovoltaic (PV) generation causes some serious problems to the grid. Since PV generation directly depends on the solar irradiance, cloud movements can cause sudden changes on the output of PV power and this results in some power issues in the system such as voltage violations, reverse power flow, voltage fluctuations. These types of issues complicate to maintain voltage within compulsory levels at customer sides. Thus, cloud-induced transients in PV power are seen as a potential handicap for the future expansion of renewable energy resources. This study investigates effects of instantaneous changes in PV power on the customer side voltage levels. Daily PV power output and voltage profiles were simulated using a real-world microgrid design that will be implemented in the Malta College of Arts Science and Technology (MCAST) Campus. © 2023 Elsevier B.V., All rights reserved.
Citation - WoS: 15
Citation - Scopus: 22
Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure
(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Al-Abri, Tariq; Onen, Ahmet; Al-Abri, Rashid; Hossen, Abdulnasir; Al-Hinai, Amer; Jung, Jaesung; Ustun, Taha Selim
With the rapid transformation of the energy sector towards modern power systems represented by smart grids (SGs), microgrids (MG), and distributed generation, blockchain (BC) technology has shown the capability for solving security, privacy, and reliability challenges that hinder progress. Currently, the energy structure is forming a decentralized system that prioritizes customer satisfaction. BC technology undertakes power network stockholders in a secure energy market, transparent transactions, and fair competition and offers promising energy solutions. This paper is a comprehensive review of energy applications using BC integration. Firstly, we introduce the drivers of BC leverage that make it a potentially important component of the power network. Following that, we provide background information on BC and its application in areas other than the energy sector. Subsequently, we discuss studies and sort potential energy applications from various recent papers and surveys that have already adopted BC technology in the energy sector. Then, we summarize the pricing infrastructure for applying BC in the energy sector and identify the requirements to build it. Finally, energy security and privacy challenges based on BC are highlighted, along with potential drawbacks and concerns related to the pricing infrastructure.
Editorial Market-Based Distributed Energy Resources Operation for Future Power Systems
(Frontiers Media S.A., 2022) Onen, Ahmet; Jung, Jaesung; Guerrero, Josep M. M.; Lee, Chul-Ho; Hossain, Md Alamgir
Citation - WoS: 110
Citation - Scopus: 166
Peer-to-Peer Energy Trading in Virtual Power Plant Based on Blockchain Smart Contracts
(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Seven, Serkan; Yao, Gang; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.
A novel Peer-to-peer (P2P) energy trading scheme for a Virtual Power Plant (VPP) is proposed by using Smart Contracts on Ethereum Blockchain Platform. The P2P energy trading is the recent trend the power society is keen to adopt carrying out several trial projects as it eases to generate and share the renewable energy sources in a distributed manner inside local community. Blockchain and smart contracts are the up-and-coming phenomena in the scene of the information technology used to be considered as the cutting-edge research topics in power systems. Earlier works on P2P energy trading including and excluding blockchain technology were focused mainly on the optimization algorithm, Information and Communication Technology, and Internet of Things. Therefore, the financial aspects of P2P trading in a VPP framework is focused and in that regard a P2P energy trading mechanism and bidding platform are developed. The proposed scheme is based on public blockchain network and auction is operated by smart contract addressing both cost and security concerns. The smart contract implementation and execution in a VPP framework including bidding, withdrawal, and control modules developments are the salient feature of this work. The proposed architecture is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Ropsten Test Network.
Citation - WoS: 16
Citation - Scopus: 21
Provision of Ancillary Services by a Smart Microgrid: An OPF Approach
(Institute of Electrical and Electronics Engineers Inc., 2018) Martinez-Ramos, J. L.; Marcolini, Alejandro Marano; García-López, Francisco De Paula; Almagro-Yravedra, Fernando; Onen, Ahmet; Yoldaş, Yeliz; Fragale, Nunziatina
Ancillary services are all services required by the transmission (TSO) or distribution system operator (DSO) to maintain the integrity and stability of the transmission or distribution system as well as the power quality. Ancillary services that can be provided by a microgrid in grid-connected operation are frequency control support, voltage control support, congestion management, reduction of grid losses, and improvement of power quality. This paper presents the optimization problems used in the 3DMicroGrid project to determine the set-points of the different resources present in the microgrid to provide ancillary services to the power system in grid-connected operation: Frequency control, voltage control and load curtailment. Results of the optimization of the pilot microgrid used in 3DMicroGrid are presented. © 2018 Elsevier B.V., All rights reserved.
Citation - WoS: 4
Citation - Scopus: 4
A Reinforcement Learning-Based Demand Response Strategy Designed From the Aggregator's Perspective
(Frontiers Media S.A., 2022) Oh, Seongmun; Jung, Jaesung; Onen, Ahmet; Lee, Chul-Ho
The demand response (DR) program is a promising way to increase the ability to balance both supply and demand, optimizing the economic efficiency of the overall system. This study focuses on the DR participation strategy in terms of aggregators who offer appropriate DR programs to customers with flexible loads. DR aggregators engage in the electricity market according to customer behavior and must make decisions that increase the profits of both DR aggregators and customers. Customers use the DR program model, which sends its demand reduction capabilities to a DR aggregator that bids aggregate demand reduction to the electricity market. DR aggregators not only determine the optimal rate of incentives to present to the customers but can also serve customers and formulate an optimal energy storage system (ESS) operation to reduce their demands. This study formalized the problem as a Markov decision process (MDP) and used the reinforcement learning (RL) framework. In the RL framework, the DR aggregator and each customer are allocated to each agent, and the agents interact with the environment and are trained to make an optimal decision. The proposed method was validated using actual industrial and commercial customer demand profiles and market price profiles in South Korea. Simulation results demonstrated that the proposed method could optimize decisions from the perspective of the DR aggregator.
Citation - Scopus: 6
Distribution Automation Effects on Reliability During Major Contingencies
(IEEE Computer Society help@computer.org, 2018) Yoldaş, Yeliz; Onen, Ahmet; Alan, İrfan; Broadwater, Robert P.
Distribution automation affects reliability by providing faster restoration ability. In this study, the effect of distribution automation on radial distribution circuits during substation failures at peak load is investigated. The ultimate goal is to compare circuit automation to manual operation, where the comparison evaluates planning criteria reliability for customer interruption hours. The results show that distribution automation can improve reliability measurements such as SAIDI, SAIFI and CAIDI. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 5
Modeling and Real Time Digital Simulation of Microgrids for Campuses Malta and Jordan Based on Multiple Distributed Energy Resources
(Institute of Advanced Engineering and Science, 2020) Khiat, Sidahmed; Chaker, Abdelkader El Kader; Zacharia, Lazaros; Onen, Ahmet
This paper presents the modeling and real-time digital simulation of two microgrids: The malta college of arts, science and technology (MCAST) and the German jordan university (GJU). The aim is to provide an overview of future microgrid situation and capabilities with the benefits of integrating renewable energy sources (RES), such as photovoltaic panels, diesel generators and energy storage systems for projects on both campuses. The significance of this work starts with the fact that real measurements were used from the two pilots, obtained by measuring the real physical systems. These measures were used to plan different solutions regarding RES and energy storage system (ESS) topologies and sizes. Also, the demand curves for the real microgrids of MCAST and GJU have been parameterized, which may serve as a test bed for other studies in this area. Based on actual data collected from the two pilots, a real-time digital simulation is performed using an RT-LAB platform. The results obtained by this tool allow the microgrid manager to have a very accurate vision of the facility operation, in terms of power flow and default responses. Several scenarios are studied, extracting valuable insight for implementing both projects in the future. Eventually, the proposed models would be a blueprint for training and research purposes in the microgrid field. © 2020 Elsevier B.V., All rights reserved.
Citation - WoS: 63
Citation - Scopus: 82
Rooftop Solar PV Penetration Impacts on Distribution Network and Further Growth Factors-A Comprehensive Review
(MDPI, 2021) Uzum, Busra; Onen, Ahmet; Hasanien, Hany M.; Muyeen, S. M.
In order to meet the electricity needs of domestic or commercial buildings, solar energy is more attractive than other renewable energy sources in terms of its simplicity of installation, less dependence on the field and its economy. It is possible to extract solar energy from photovoltaic (PV) including rooftop, ground-mounted, and building integrated PV systems. Interest in rooftop PV system applications has increased in recent years due to simple installation and not occupying an external area. However, the negative effects of increased PV penetration on the distribution system are troublesome. The power loss, reverse power flow (RPF), voltage fluctuations, voltage unbalance, are causing voltage quality problems in the power network. On the other hand, variations in system frequency, power factor, and harmonics are affecting the power quality. The excessive PV penetration also the root cause of voltage stability and has an adverse effect on protection system. The aim of this article is to extensively examines the impacts of rooftop PV on distribution network and evaluate possible solution methods in terms of the voltage quality, power quality, system protection and system stability. Moreover, it is to present a comparison of the advantages/disadvantages of the solution methods discussed, and an examination of the solution methods in which artificial intelligence, deep learning and machine learning based optimization and techniques are discussed with common methods.
Citation - WoS: 12
Citation - Scopus: 19
Artificial Neural Networks Based Harmonics Estimation for Real University Microgrids Using Hourly Solar Irradiation and Temperature Data
(Elsevier, 2023) Yarar, Nurcan; Yagci, Mustafa; Bahceci, Serkan; Onen, Ahmet; Ustun, Taha Selim
The need for renewable energy is increasing day by day due to different factors such as increasing energy demand, environmental considerations as well as the will to decrease the share of fossil fuel-based generation. Due to their relative low-cost and ease of installation, PV systems are leading the way for renewable energy deployments around the globe. However, there are meticulous studies that need to be undertaken for realization of such projects. Studying local weather and load patterns for proper panel sizing or considering grid components to determine cable and transformer sizing can be named as some examples for pre-installation studies. In addition to these, post-installation impact studies, e.g. accurate harmonic analysis contribution, is more important to ensure safe and secure operation of the overall system. These steps need to be taken for all PV installation projects. The aim of this study is to show a step-by-step analysis of the effect of a real PV system on the network and to improve the prediction and give a new perspective to the harmonic estimation by using the hourly temperature and radiation data together. At the first phase of the study, a detail real-time 250 kW PV system was modeled for real university campus, and then harmonic estimation based on hourly solar irradiation and hourly temperature was performed with artificial neural networks (ANN) and nonlinear autoregressive exogenous (NARX). The accuracy of the prediction made with ANN was 0.98, and the accuracy of the prediction made with NARX was 0.96.Researchers in PV sizing and control field as well as engineers in power quality area would find these findings beneficial and useful. Use of ANNs and NARX for such analysis indicates the trend in this field that can be targeted by new research projects.
Citation - WoS: 32
Citation - Scopus: 43
Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
(MDPI, 2021) Unsal, Derya Betul; Ustun, Taha Selim; Hussain, S. M. Suhail; Onen, Ahmet
Integration of information technologies with power systems has unlocked unprecedented opportunities in optimization and control fields. Increased data collection and monitoring enable control systems to have a better understanding of the pseudo-real-time condition of power systems. In this fashion, more accurate and effective decisions can be made. This is the key towards mitigating negative impacts of novel technologies such as renewables and electric vehicles and increasing their share in the overall generation portfolio. However, such extensive information exchange has created cybersecurity vulnerabilities in power systems that were not encountered before. It is imperative that these vulnerabilities are understood well, and proper mitigation techniques are implemented. This paper presents an extensive study of cybersecurity concerns in Smart grids in line with latest developments. Relevant standardization and mitigation efforts are discussed in detail and then the classification of different cyber-attacks in smart grid domain with special focus on false data injection (FDI) attack, due to its high impact on different operations. Different uses of this attack as well as developed detection models and methods are analysed. Finally, impacts on smart grid operation and current challenges are presented for future research directions.
Citation - WoS: 23
Citation - Scopus: 29
Optimal Control of Microgrids With Multi-Stage Mixed-Integer Nonlinear Programming Guided Q-Learning Algorithm
(State Grid Electric Power Research inst, 2020) Yoldas, Yeliz; Goren, Selcuk; Onen, Ahmet
This paper proposes an energy management system (EMS) for the real-time operation of a pilot stochastic and dynamic microgrid on a university campus in Malta consisting of a diesel generator, photovoltaic panels, and batteries. The objective is to minimize the total daily operation costs, which include the degradation cost of batteries, the cost of energy bought from the main grid, the fuel cost of the diesel generator, and the emission cost. The optimization problem is modeled as a finite Markov decision process (MDP) by combining network and technical constraints, and Q-learning algorithm is adopted to solve the sequential decision subproblems. The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming (MINLP) problem into a series of single-stage problems so that each subproblem can be solved by using Bellman's equation. To prove the effectiveness of the proposed algorithm, three case studies are taken into consideration: (1) minimizing the daily energy cost; (2) minimizing the emission cost; (3) minimizing the daily energy cost and emission cost simultaneously. Moreover, each case is operated under different battery operation conditions to investigate the battery lifetime. Finally, performance comparisons are carried out with a conventional Q-learning algorithm.
Citation - WoS: 6
Citation - Scopus: 9
Optimization of Multiple Battery Swapping Stations With Mobile Support for Ancillary Services
(Frontiers Media S.A., 2022) Kocer, Mustafa Cagatay; Onen, Ahmet; Ustun, Taha Selim; Albayrak, Sahin
The recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging-discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging-discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging-discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.
Citation - Scopus: 1
Is the Smart Grid a Good Investment
(Institute of Electrical and Electronics Engineers Inc., 2015) Onen, Ahmet; Broadwater, Robert P.
Electric distribution design and operational goals include meeting customer reliability requirements at the lowest cost. Smart Grid investments have the potential for helping meet these goals, and this paper presents a series of analyses that evaluate the incremental economic benefits of smart grid automation investments. Smart Grid investments provide a number of benefits to customers. Here only benefits that can be objectively quantified in terms of economic savings are considered. Smart Grid automation investments in this work include investments in feeder efficiency, automated switches, and coordinated control of capacitor banks, voltage regulators and load tab changers. Benefits that come from these investments are improved efficiency, reduced demand, shortened storm restoration time, and improved performance during reconfiguration events. The analyses used in the evaluation are very detailed, involving hourly, quasi-steady state power flow analysis over a ten year period for calculating energy consumption and costs, and Monte Carlo simulations for six different storm types. The evaluation shows that similar to other industries, an investment in automation can be justified in terms of hard dollars. © 2017 Elsevier B.V., All rights reserved.
Citation - WoS: 13
Citation - Scopus: 15
Dynamic Rolling Horizon Control Approach for a University Campus
(Elsevier, 2022) Yoldas, Yeliz; Goren, Selcuk; Onen, Ahmet; Ustun, Taha Selim
An energy management system based on the rolling horizon control approach has been proposed for the grid-connected dynamic and stochastic microgrid of a university campus in Malta. The aims of the study are to minimize the fuel cost of the diesel generator, minimize the cost of power transfer between the main grid and the micro grid, and minimize the cost of deterioration of the battery to be able to provide optimum economic operation. Since uncertainty in renewable energy sources and load is inevitable, rolling horizon control in the stochastic framework is used to manage uncertainties in the energy management system problem. Both the deterministic and stochastic processes were studied to approve the effectiveness of the algorithm. Also, the results are compared with the Myopic and Mixed Integer Linear Programming algorithms. The results show that the life span of the battery and the associated economic savings are correlated with the SOC values. (c) 2021 The Author(s). Published by Elsevier Ltd.
Citation - WoS: 9
Citation - Scopus: 10
Configurable, Hierarchical, Model-Based, Scheduling Control With Photovoltaic Generators in Power Distribution Circuits
(Pergamon-Elsevier Science Ltd, 2015) Jung, Jaesung; Onen, Ahmet; Russell, Kevin; Broadwater, Robert P.; Steffel, Steve; Dinkel, Alex
Existing distribution systems and their associated controls have been around for decades. Most distribution circuits have capacity to accommodate some level of PV generation, but the question is how much can they handle without creating problems. This paper proposes a Configurable, Hierarchical, Model-based, Scheduling Control (CHMSC) of automated utility control devices and photovoltaic (PV) generators. In the study here the automated control devices are assumed to be owned by the utility and the PV generators and PV generator controls by another party. The CHMSC, which exists in a hierarchical control architecture that is failure tolerant, strives to maintain the voltage level that existed before introducing the PV into the circuit while minimizing the circuit loss and reducing the motion of the automated control devices. This is accomplished using prioritized objectives. The CHMSC sends control signals to the local controllers of the automated control devices and PV controllers. To evaluate the performance of the CHMSC, increasing PV levels of adoption are analyzed in a model of an actual circuit that has significant existing PV penetration and automated voltage control devices. The CHMSC control performance is compared with that of existing, local control. Simulation results presented demonstrate that the CHMSC algorithm results in better voltage control, lower losses, and reduced automated control device motion, especially as the penetration level of PV increases. Published by Elsevier Ltd.