Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

Lee, Seongwoo; Seon, Joonho; Kyeong, Chanuk; Kim, Soohyun; Sun, Young Ghyu; Kim, Jin‐Young

doi:10.3390/en14175515

Cited by 10 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approaches for energy trading have been researched, including auction processes [86], game theory [87], blockchain [88], single-agent methods [89], and multi-agent methods [90]. In auction processes, ML algorithms have been employed to predict energy production [86].…”

Section: Ml-based Etrmsmentioning

confidence: 99%

Recent Trends and Issues of Energy Management Systems Using Machine Learning

Lee,

Seon,

Hwang

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

show abstract

Section: Ml-based Etrmsmentioning

confidence: 99%

Recent Trends and Issues of Energy Management Systems Using Machine Learning

Lee,

Seon,

Hwang

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, Liu et al ( 2021) introduced a quarter-hourly dynamic pricing strategy, leveraging the DDPG algorithm, to address the discretization issue encountered in traditional time-division pricing models. Lee et al (2021) present an innovative energy trading system among Microgrids (MGs), incorporating a DDQN algorithm and a double Kelly strategy. Although these techniques have explored the dynamic interaction of numerous agents, the optimality search process relies on cooperation and communication among individual agents, which is inconsistent with competitive market bidding in the absence of knowledge about other rivals.…”

Section: Current Research On Bidding Strategy Algorithms Of Multi-age...mentioning

confidence: 99%

A mini-review on trading mechanisms of emerging joint energy markets with high shares of renewables

Gao,

Yang,

Zhu

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

The intermittence and unpredictability of large-scale renewable integration poses significant challenges to the operation of the electricity market. New paradigms of the joint electricity spot market (EM) and ancillary service market (ASM) incorporating frequency regulation (FR) and flexible ramping product (FRP) are considered as potential solutions, addressing the challenge of limited compatibility in the electricity market with the widespread integration of renewable sources. This work focuses on three critical technical obstacles: optimizing the joint market mechanisms, constructing bidding models, and exploring algorithmic solutions. This paper presents a brief review of recent research on bidding mechanisms, models, and strategies for the electricity joint market with high-penetration renewable integration. Furthermore, challenges and future research prospects of these issues are also discussed.

show abstract

“…In a different instance, [22] reduced the operational cost by 20.75% using RL. More recently, Proximal Policy Optimization (PPO) has emerged as a powerful RL algorithms and was utilized in [23] and [24] to optimize energy cost in a microgrid with promising results. However, load forecasting was not included.…”

Section: Of 16mentioning

confidence: 99%

Energy Management System for an Industrial Microgrid using Optimization Algorithms based Reinforcement Learning Technique

Upadhyay,

Ahmed,

Mihet-Popa

2024

Preprint

View full text Add to dashboard Cite

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront and the research interest in microgrids which rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper proposes an optimization algorithm based on reinforcement learning in an industrial microgrid that is capable of trading energy with the main grid, providing significant cost savings. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared to logic-based optimization and 86% savings compared to not using any optimization. These findings contribute to digitalization and decarbonization of energy technology and support fundamental goals and policies of the European Green Deal.

show abstract

Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

Cited by 10 publications

References 28 publications

Recent Trends and Issues of Energy Management Systems Using Machine Learning

Recent Trends and Issues of Energy Management Systems Using Machine Learning

A mini-review on trading mechanisms of emerging joint energy markets with high shares of renewables

Energy Management System for an Industrial Microgrid using Optimization Algorithms based Reinforcement Learning Technique

Contact Info

Product

Resources

About