WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 23Citation - Scopus: 29Optimal 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, AhmetThis 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.Article Citation - WoS: 15Citation - Scopus: 18Local Steady-State and Quasi Steady-State Impact Studies of High Photovoltaic Generation Penetration in Power Distribution Circuits(Pergamon-Elsevier Science Ltd, 2015-03) 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.Article Citation - WoS: 12Citation - Scopus: 13Implementation of Cost Benefit Analysis of Vehicle to Grid Coupled Real Micro-Grid by Considering Battery Energy Wear: Practical Study Case(Sage Publications Ltd, 2020-10-07) Koubaa, Rayhane; Yoldas, Yeliz; Goren, Selcuk; Krichen, Lotfi; Onen, AhmetThe proposed research represents a spin-off of the Malta College of Arts, Science and Technology (MCAST) Micro-Grid (MG) project. Particularly, economic impact of Electric Vehicles (EV) integration into the MG is investigated in this paper. The MCAST MG consists of photovoltaic generation unit, a diesel generator and a battery storage system. In this paper, a Vehicle-to grid (V2G) concept is considered where utilities can profit from controlled energy trading operations according to EVs availability. EVs are categorized under different profiles considering energy and time availability of owners typical work hours. V2G energy cost is estimated based on battery energy wear due V2G extra cycling and refunded to EVs owners. As most of developed V2G studies don't consider real world input data or/and EV battery aging cost in system modeling and evaluation, the present paper presents a reliable study as it considers a real life MG with in field measurement input data and appropriate battery degradation model. The adopted model represents a linear approximation with a minimum error value to make a suitable tradeoff of computational complexity and accuracy of obtained results. Economic assessment of the system according to the proposed energy management is performed, where results indicate that the V2G system assisted the MG operation during high electricity price period and achieved economic profit to EVs owners. According to numerical results, V2G energy trading achieved 29.90 EUR of gross selling revenues with only 4.46 EUR as battery degradation cost which makes a 16.41% average cost reduction of daily MG operation cost.Article Citation - WoS: 376Citation - Scopus: 481Enhancing Smart Grid With Microgrids: Challenges and Opportunities(Pergamon-Elsevier Science Ltd, 2017-05) Yoldas, Yeliz; Onen, Ahmet; Muyeen, S. M.; Vasilakos, Athanasios V.; Alan, IrfanThe modern electric power systems are going through a revolutionary change because of increasing demand of electric power worldwide, developing political pressure and public awareness of reducing carbon emission, incorporating large scale renewable power penetration, and blending information and communication technologies with power system operation. These issues initiated in establishing microgrid concept which has gone through major development and changes in last decade, and recently got a boost in its growth after being blessed by smart grid technologies. The objective of this paper is to presents a detailed technical overview of microgrid and smart grid in light of present development and future trend. First, it discusses microgrid architecture and functions. Then, smart features are added to the microgrid to demonstrate the recent architecture of smart grid. Finally, existing technical challenges, communication features, policies and regulation, etc. are discussed from where the future smart grid architecture can be visualized.Article Citation - WoS: 10Citation - Scopus: 14Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-Synchronous Oscillation Modes Using PMU Data(State Grid Electric Power Research inst, 2024) Philip, Joice G.; Jung, Jaesung; Onen, AhmetThis paper proposes an empirical wavelet transform (EWT) based method for identification and analysis of sub-synchronous oscillation (SSO) modes in the power system using phasor measurement unit (PMU) data. The phasors from PMUs are preprocessed to check for the presence of oscillations. If the presence is established, the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm. The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China. Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.Article Citation - WoS: 101Citation - Scopus: 115Economic Optimal Operation of Community Energy Storage Systems in Competitive Energy Markets(Elsevier Sci Ltd, 2014-12) Arghandeh, Reza; Woyak, Jeremy; Onen, Ahmet; Jung, Jaesung; Broadwater, Robert P.Distributed, controllable energy storage devices offer several benefits to electric power system operation. Three such benefits include reducing peak load, providing standby power, and enhancing power quality. These benefits, however, are only realized during peak load or during an outage, events that are infrequent. This paper presents a means of realizing additional benefits by taking advantage of the fluctuating costs of energy in competitive energy markets. An algorithm for optimal charge/discharge scheduling of Community Energy Storage (CES) devices as well as an analysis of several of the key drivers of the optimization are discussed. (C) 2014 Elsevier Ltd. All rights reserved.Article Citation - WoS: 9Citation - Scopus: 10Configurable, Hierarchical, Model-Based, Scheduling Control With Photovoltaic Generators in Power Distribution Circuits(Pergamon-Elsevier Science Ltd, 2015-04) Jung, Jaesung; Onen, Ahmet; Russell, Kevin; Broadwater, Robert P.; Steffel, Steve; Dinkel, AlexExisting 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.Article Citation - WoS: 12Citation - Scopus: 23Artificial Neural Networks Based Harmonics Estimation for Real University Microgrids Using Hourly Solar Irradiation and Temperature Data(Elsevier, 2023-03) Yarar, Nurcan; Yagci, Mustafa; Bahceci, Serkan; Onen, Ahmet; Ustun, Taha SelimThe 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.Article Citation - WoS: 16Citation - Scopus: 23Adaptive Fault Detection Scheme Using an Optimized Self-Healing Ensemble Machine Learning Algorithm(China Electric Power Research inst, 2021) Yavuz, Levent; Soran, Ahmet; Onen, Ahmet; Li, Xiangjun; Muyeen, S. M.This paper proposes a new cost-efficient, adaptive, and self-healing algorithm in real time that detects faults in a short period with high accuracy, even in the situations when it is difficult to detect. Rather than using traditional machine learning (ML) algorithms or hybrid signal processing techniques, a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms. In the proposed method, the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization (PSO) weights. For this purpose, power system failures are simulated by using the PSCAD-Python co-simulation. One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information. Therefore, the proposed technique will be able to work on different systems, topologies, or data collections. The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.