PowerGridworld

Biagioni, David; Zhang, Xiangyu; Wald, Dylan; Vaidhynathan, Deepthi; Chintala, Rohit; King, Jennifer; Zamzam, Ahmed S.

doi:10.1145/3538637.3539616

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…However, due to the inherent uncertainty and randomness in its production capacity, there exist potential safety hazards in connecting it to the electrical grid. According to the research data of Biagion et al [35], the photovoltaic power generation during a day mainly exhibits a parabolic shape, similar to the curve of solar radiation energy. The normalized daily photovoltaic power generation data is shown in Figure 2.…”

Section: Pv Systemmentioning

confidence: 99%

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Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

Yao

Zhang

et al. 2023

Sustainability

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Due to the uncertainty and randomness of clean energy, microgrid operation is often prone to instability, which requires the implementation of a robust and adaptive optimization scheduling method. In this paper, a model-based reinforcement learning algorithm is applied to the optimal scheduling problem of microgrids. During the training process, the current learned networks are used to assist Monte Carlo Tree Search (MCTS) in completing game history accumulation, and updating the learning network parameters to obtain optimal microgrid scheduling strategies and a simulated environmental dynamics model. We establish a microgrid environment simulator that includes Heating Ventilation Air Conditioning (HVAC) systems, Photovoltaic (PV) systems, and Energy Storage (ES) systems for simulation. The simulation results show that the operation of microgrids in both islanded and connected modes does not affect the training effectiveness of the algorithm. After 200 training steps, the algorithm can avoid the punishment of exceeding the red line of the bus voltage, and after 800 training steps, the training result converges and the loss values of the value and reward network converge to 0, showing good effectiveness. This proves that the algorithm proposed in this paper can be applied to the optimization scheduling problem of microgrids.

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Section: Pv Systemmentioning

confidence: 99%

“…Inspired by the microgrid model proposed by Biagion et al [35], known as the Power Grid World, we have established a new microgrid model that incorporates HVAC, PV, and ES. The microgrid structure is presented in Figure 1.…”

Section: Microgrid Scheduling Modelmentioning

confidence: 99%

Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

Yao

Zhang

et al. 2023

Sustainability

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“…Multi agent reinforcement learning (MARL), with its capability to process large-scale multidimensional data and make timely decisions, offers a promising approach to managing these emergencies (Busoniu et al, 2008;Chu et al, 2020). MARL involves deploying multiple RL agents across the power system (Biagioni et al, 2022). Each agent focuses on a specific area or component of the system, reducing the complexity of the problem space it needs to manage.…”

Section: Multi-agent Reinforcement Learning In Emergency Controlmentioning

confidence: 99%

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Porawagamage,

Dharmapala,

Chaves

et al. 2024

Front. Smart Grids

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Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.

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“…This approach approximates the optimal attack migration strategy by determining the required number of migrations, leading to improved power system security. The authors of Biagioni et al (2022) introduce a flexible modular extension framework that serves as a simulation environment and experimental platform for various agent algorithms in power systems. They validate the framework's performance using the multi-agent deep deterministic policy gradient algorithm, addressing a gap in power system agent training.…”

Section: Related Workmentioning

confidence: 99%

Intelligent grid load forecasting based on BERT network model in low-carbon economy

Tao

et al. 2023

Front. Energy Res.

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In recent years, the reduction of high carbon emissions has become a paramount objective for industries worldwide. In response, enterprises and industries are actively pursuing low-carbon transformations. Within this context, power systems have a pivotal role, as they are the primary drivers of national development. Efficient energy scheduling and utilization have therefore become critical concerns. The convergence of smart grid technology and artificial intelligence has propelled transformer load forecasting to the forefront of enterprise power demand management. Traditional forecasting methods relying on regression analysis and support vector machines are ill-equipped to handle the growing complexity and diversity of load forecasting requirements. This paper presents a BERT-based power load forecasting method that leverages natural language processing and image processing techniques to enhance the accuracy and efficiency of transformer load forecasting in smart grids. The proposed approach involves using BERT for data preprocessing, analysis, and feature extraction on long-term historical load data from power grid transformers. Multiple rounds of training and fine-tuning are then conducted on the BERT architecture using the preprocessed training datasets. Finally, the trained BERT model is used to predict the transformer load, and the predicted results are compared with those obtained based on long short-term memory (LSTM) and actual composite values. The experimental results show that compared with LSTM method, the BERT-based model has higher short-term power load prediction accuracy and feature extraction capability. Moreover, the proposed scheme enables high levels of accuracy, thereby providing valuable support for resource management in power dispatching departments and offering theoretical guidance for carbon reduction initiatives.

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PowerGridworld

Cited by 12 publications

References 17 publications

Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Intelligent grid load forecasting based on BERT network model in low-carbon economy

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