Browsing by Author "Lee, Chul-Ho"

Now showing 1 - 2 of 2

Editorial Market-based distributed energy resources operation for future power systems
(FRONTIERS MEDIA SA, 2022) Onen, Ahmet; Jung, Jaesung; Guerrero, Josep M. M; Lee, Chul-Ho; Hossain, Md Alamgir; 0000-0001-7086-5112; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Önen, Ahmet
One of the biggest challenges in the current power system operation is caused by the large scale integration of distributed energy resources (DERs) that have high volatility generations (Uzum et al., 2021). Communication and control technologies are significantly improved to provide direct interaction between agents and customers, such as in peer-to-peer frameworks. In addition, the recent developments in monitoring, sensor networks, and advanced metering infrastructure (AMI) greatly enhance the variety, volume, and speed of measurement data in electricity transmission and distribution networks. By harnessing these technologies, the application of big data, artificial intelligence, and machine learning methods can be implemented to overcome the challenges from massive DERs integration in power systems. However, these technologies require a large amount of capital to operate, which can lead to financial loss if used without an appropriate strategy. In this context, the topics of interest of this Research Topic address market-based DER operations, regulation, and decision-making, and analyze the impact of market-based DER operation on power systems.
A reinforcement learning-based demand response strategy designed from the Aggregator's perspective
(FRONTIERS MEDIA SA, 2022) Oh, Seongmun; Jung, Jaesung; Onen, Ahmet; Lee, Chul-Ho; 0000-0001-7086-5112; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Önen, Ahmet
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.