Profile URL: https://hdl.handle.net/20.500.12573/5656
Main Affiliation:01. Abdullah Gül University
Status: Former Staff
Name Variants:
Onen, Ahmet Onen, A.
45 results
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Article Citation - WoS: 9Citation - Scopus: 13Network Cost Allocation Methods for Pay-As Peer-to-Peer Energy Trading: A Comparison(Elsevier, 2022-11) Noorfatima, N.; Choi, Y.; Onen, A.; Jung, J.In pay-as-bid peer-to-peer (P2P) energy trading, various types of prosumers and consumers can participate, regardless of their offers. Thus, various types of participants impact the network differently. However, very few pay-as-bid P2P energy trading studies have specifically discussed appropriate compensation for network usage, although the market is implemented in existing utility-owned grids. Therefore, to improve the performance of pay-as-bid P2P energy trading, it is important to determine the appropriate compensation to utilities for network usage. This study aims to obtain an appropriate network cost allocation method for pay-as-bid P2P energy trading. Hence, the authors present a review of pay-as-bid P2P market mechanisms and various network cost allocation (NCA) methods. Additionally, a comprehensive evaluation framework is proposed to determine the most appropriate NCA method for the pay-as-bid P2P energy trading system. A comparison was made between various NCA methods to investigate the outcomes of the implementation of different NCA methods to various market conditions. The study constructs a case study based on the operator-oriented P2P model to represent the pay-as-bid P2P energy trading system. The simulation of pay-as-bid P2P energy trading with large participant number is applied in the IEEE 69-bus distribution system. The study concluded that applying the appropriate NCA method would improve the performance of pay-as-bid P2P energy trading operation.Article Citation - WoS: 17Citation - Scopus: 24Review 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 SelimWith 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 Editorial Market-Based Distributed Energy Resources Operation for Future Power Systems(Frontiers Media S.A., 2022-12-13) Onen, Ahmet; Jung, Jaesung; Guerrero, Josep M. M.; Lee, Chul-Ho; Hossain, Md AlamgirArticle Citation - WoS: 127Citation - Scopus: 184Peer-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.Article Citation - WoS: 70Citation - Scopus: 89Rooftop Solar PV Penetration Impacts on Distribution Network and Further Growth Factors-A Comprehensive Review(MDPI, 2020-12-31) 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.Doctoral Thesis Akıllı Mikro-Şebekelerde Kontrol Stratejilerinin Geliştirilmesi(Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü, 2021) Yoldaş, Yeliz; Yoldaş, Yeliz; Önen, AhmetThis thesis concerns the transformation of aged power systems to modern power systems that include microgrids with renewable energy sources and energy storage systems. The integration of renewable energy sources brings excellent opportunities to provide better reliability and efficiency. However, the uncertainty and intermittent nature of renewable energy sources may potentially degrade the stability and quality of the electrical grid. Therefore, the aim of this dissertation is to maintain the supply-demand balance in microgrids while minimizing the cost in real time operation. A microgrid energy management system that can optimize the dispatch of the controllable distributed energy resources in grid-connected mode of a pilot microgrid on a university campus in Malta was developed to achieve this goal. Three different methods were used in this study: mixed integer linear programming (MILP), MILP based rolling horizon control and Q-learning, Designing intelligent method for the real-time energy management of the stochastic and dynamic microgrid is the primary goal of this research. Moreover, the detailed mathematical models of the network model and of the technical model are considered for the economic and environmental operation of the microgrid system to solve the optimization problem under more real-world conditions. 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 a 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 using Bellman's equation. To prove the effectiveness of the proposed algorithm, three different case studies are taken into consideration. A predictive control framework is also proposed to provide optimal operation with minimum cost. This method allows the consideration of operational cost values, demand with uncertainty, generation units' profiles with uncertainty, and constraints related to the network model and technical model. The stochastic and deterministic cases are conducted to validate the efficiency of the approach.Doctoral Thesis Görüntü Tanıma Tabanlı Gökyüzü Kamerası Entegrasyonunu Kullanarak Sezgisel Vektörize Öğrenme Yöntemine Dayalı PV Tahmini(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yavuz, Levent; Önen, AhmetIn order to ensure the continuity of demand and production balance, the use of renewable energy resources (RES) by countries will increase in the near future. Solar power generation is important for the integration of renewable energy into the power grid, but it can cause problems in power systems due to the uncertain and intermittent nature of solar power. Deep learning methods provide promising results in solar energy prediction, but the performance of these models depends on the initial weights assigned to the network. In this thesis, a novel weight initialization method, the Heuristic Vectorised Learning method, which takes into account certain characteristics of solar generation data has been proposed. This method aims to achieve better accuracy in solar forecasting by combining a statistical approach with a method based on deep learning. The method was compared with other commonly used methods such as Xavier, LeCun, He and Random, and it was seen that the proposed method performed better. Overall, the proposed weight initialization method provides significant benefits for solar forecasting applications in the context of renewable energy integration into the power grid. So, to reach higher accuracy, monitoring the sky is the best option for intra-day forecasts. Therefore, a hybrid model was created for photovoltaic generation prediction by using it together with environmental sensor data. The proposed method and panel shading model achieve higher accuracy values at the Abdullah Gül University campus in Kayseri. The proposed system provides a reliable PV energy forecast for the intraday energy markets.Conference Object Citation - WoS: 3Citation - Scopus: 9Cloud Induced PV Impact on Voltage Profiles for Real Microgrids(Institute of Electrical and Electronics Engineers Inc., 2018-09) Kocer, Mustafa Cagatay; Yoldaş, Yeliz; Gören, Selçuk; Onen, Ahmet; Alan, İrfan; Al-Agtash, Salem Y.; Tzovaras, Dimitrios K.; Borg, NicholasIntegration 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.Conference Object Citation - WoS: 17Citation - Scopus: 23Provision of Ancillary Services by a Smart Microgrid: An OPF Approach(Institute of Electrical and Electronics Engineers Inc., 2018-09) Martinez-Ramos, J. L.; Marcolini, Alejandro Marano; García-López, Francisco De Paula; Almagro-Yravedra, Fernando; Onen, Ahmet; Yoldaş, Yeliz; Fragale, Nunziatina; Marano-Marcolini, AlejandroAncillary 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.Article Citation - WoS: 6Citation - Scopus: 7A Reinforcement Learning-Based Demand Response Strategy Designed From the Aggregator's Perspective(Frontiers Media S.A., 2022-09-15) Oh, Seongmun; Jung, Jaesung; Onen, Ahmet; Lee, Chul-HoThe 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.