相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Green & Sustainable Science & Technology
Mohammad Mehdi Hosseini et al.
Summary: This paper proposes a model that combines deep reinforcement learning and mathematical optimization to operate distributed energy storage systems in distribution grids. By utilizing the fast response capability of deep reinforcement learning and keeping network constraints in check with mathematical optimization, the paper addresses the issues caused by load and renewable generation uncertainties.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Green & Sustainable Science & Technology
Mohammad Mehdi Hosseini et al.
Summary: This paper proposes a model that combines deep reinforcement learning (DRL) and mathematical optimization for the operation of energy storage systems (ESS) in distribution grids. The model utilizes the fast response capability of DRL while maintaining network constraints, enabling efficient operation of ESS under uncertain load and renewable generation conditions.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Engineering, Electrical & Electronic
Matthias Noebels et al.
Summary: Power outages caused by cascading failures triggered by extreme weather events are posing a growing risk to modern societies, leading to a need for greater power network resilience. Machine learning is utilized for real-time decision-making on preventative actions to reduce the risk of cascading failures, using training data from Monte Carlo simulations. The proposed ML-based decision-making process shows promise in efficiently preventing the uncontrolled spread of cascading failures.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2022)
Article
Engineering, Electrical & Electronic
Zhuoli Zhao et al.
Summary: This paper proposes a distributed robust model predictive control (DRMPC)-based energy management strategy for islanded multi-microgrids to address the issues caused by uncertain renewable energy output in microgrid systems. This strategy combines the advantages of robust optimization and model predictive control, and forms a dynamic energy trading market to enhance the overall economy of the multi-microgrid system.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Automation & Control Systems
Pengfei Zhao et al.
Summary: This article discusses the cyber resilience issues of a multivector energy distribution system (MEDS) caused by false data injection (FDI) and renewable resource uncertainty. A novel two-stage distributionally robust optimization (DRO) approach is proposed, which provides more economic operation schemes compared to robust optimization. The Wasserstein metric-based ambiguity set enables additional flexibility to hedge against renewable uncertainty, enhancing the system's resilience against severe cyber attacks.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Mohammad Mehdi Hosseini et al.
Summary: This article introduces an intelligent resilience controller (IRC) developed using deep reinforcement learning, which generates real-time operation decisions to dispatch distributed generation and energy storage units for power restoration after sudden outages. The proposed model successfully learns the failure development pattern of uncertain high-impact events and performs well in a simulated hurricane scenario with reduced operation costs and minimal running time.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Thermodynamics
Guozhou Zhang et al.
Summary: The energy management of hybrid energy systems is crucial for addressing the reliance on fossil fuels and freshwater shortage in remote arid regions. By incorporating information entropy theory and deep reinforcement learning algorithm, the study successfully overcomes the challenges posed by system uncertainties and achieves optimal control strategies. Simulation results demonstrate that a well-trained agent can provide superior control policies and reduce costs by up to 14.17% compared to other methods.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Energy & Fuels
Nariman L. Dehghani et al.
Summary: This paper develops a planning framework using Deep Reinforcement Learning to enhance the long-term resilience of power distribution systems. The framework quantifies the impact of multiple stochastic events on system life and outperforms traditional strategies.
Article
Energy & Fuels
Taha Abdelhalim Nakabi et al.
Summary: This study investigates the performance of various deep reinforcement learning algorithms in enhancing the energy management system of a microgrid. A novel microgrid model is proposed and seven algorithms are implemented and compared. The results show significant differences in convergence to optimal policies among the algorithms, with the modified asynchronous advantage actor-critic algorithm achieving the highest model performance and convergence to near-optimal policies.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2021)
Article
Thermodynamics
Giuseppe Pinto et al.
Summary: The study explores the use of Deep Reinforcement Learning for coordinated energy management in a cluster of buildings to achieve higher energy flexibility, leading to reduced overall costs and peak demand.
Article
Engineering, Electrical & Electronic
Juan Carlos Bedoya et al.
Summary: The paper proposes a Reinforcement Learning model based on Monte Carlo Tree Search, which efficiently restores a distribution system and provides a robust decision-making tool for asynchronous and partial information scenarios. The results demonstrate the effectiveness and scalability of the proposed method on IEEE 13-bus test feeder and IEEE 8500-node distribution test feeder.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Qiuling Yang et al.
IEEE TRANSACTIONS ON SMART GRID
(2020)
Article
Computer Science, Information Systems
Huanhuan Nie et al.
Article
Engineering, Electrical & Electronic
Eduardo Alejandro Martinez Cesena et al.
IEEE TRANSACTIONS ON SMART GRID
(2019)
Article
Engineering, Electrical & Electronic
Elena Mocanu et al.
IEEE TRANSACTIONS ON SMART GRID
(2019)
Article
Energy & Fuels
Brida V. Mbuwir et al.
Article
Energy & Fuels
Manuel Jesus Vasallo et al.
Article
Computer Science, Artificial Intelligence
Ganesh Kumar Venayagamoorthy et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2016)
Article
Engineering, Electrical & Electronic
Vassilis Kekatos et al.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2015)
Article
Engineering, Electrical & Electronic
Katayoun Rahbar et al.
IEEE TRANSACTIONS ON SMART GRID
(2015)
Article
Engineering, Electrical & Electronic
Rabih A. Jabr et al.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2012)
