Nastaran Gholizadeh
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Nastaran Gholizadeh has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Petr Musilek
University of Alberta
7 shared publications - Nazli Kazemi
Department of Electrical Engineering, Poly-Grames Research Center, Polytechnique Montral, Montreal, QC, Canada
2 shared publications Petr Musilek
1 shared publication- Syed Muhammad Ahsan
Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
1 shared publication
Latest Publications
- Open Access
- 13 June 2025
- Published by Elsevier in Renewable Energy
- Vol. 254
Multi-agent systems in networked microgrids: Reinforcement learning and strategic pricing mechanisms
- 13 June 2025
- Published by Elsevier in Renewable Energy
- Vol. 254
Open AccessMulti-agent systems in networked microgrids: Reinforcement learning and strategic pricing mechanisms
- Open Access
A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling
Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.
Open AccessA Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling
Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.
- Open Access
- 3 June 2024
- Published by Elsevier in Data in Brief
- Vol. 55, 110587
Daily electric vehicle charging dataset for training reinforcement learning algorithms
- 3 June 2024
- Published by Elsevier in Data in Brief
- Vol. 55, 110587
Open AccessDaily electric vehicle charging dataset for training reinforcement learning algorithms
- Open Access
- 27 May 2024
- Published by Elsevier in Energy Reports
- Vol. 11, 5703-5715
Explainable reinforcement learning for distribution network reconfiguration
- 27 May 2024
- Published by Elsevier in Energy Reports
- Vol. 11, 5703-5715
Open AccessExplainable reinforcement learning for distribution network reconfiguration
- Open Access
- 8 February 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 11, 13714-13723
A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling
Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33- and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.
- 8 February 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 11, 13714-13723
Open AccessA Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling
Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33- and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.
- Open Access
Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning
Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations.Open AccessSelective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning
Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations. - 1 March 2022
- Published by Elsevier in Internet of Things
- Vol. 17
Federated learning with hyperparameter-based clustering for electrical load forecasting
- 1 March 2022
- Published by Elsevier in Internet of Things
- Vol. 17
Federated learning with hyperparameter-based clustering for electrical load forecasting
- Open Access
Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges
In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.
Open AccessDistributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges
In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.