@article{joshi_chow_2024, title={Hierarchical Distributed Consensus Based Networked Microgrid Energy Management For Disaster Relief}, ISBN={["979-8-3503-6087-5"]}, ISSN={["2156-2318"]}, url={http://dx.doi.org/10.1109/iciea61579.2024.10665212}, DOI={10.1109/iciea61579.2024.10665212}, journal={2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024}, author={Joshi, Aditya and Chow, Mo-Yuen}, year={2024} }
 @inproceedings{joshi_capezza_chow_2024, title={Strategic Leader Selection and Cluster Formation in Hierarchical Networked Microgrids}, url={http://dx.doi.org/10.1109/pesgm51994.2024.10688456}, DOI={10.1109/pesgm51994.2024.10688456}, booktitle={2024 IEEE Power & Energy Society General Meeting (PESGM)}, author={Joshi, Aditya and Capezza, Skieler and Chow, Mo-Yuen}, year={2024}, month={Jul} }
 @inproceedings{capezza_joshi_chow_2023, title={Hierarchical Distributed Consensus Based Economic Dispatch of Distributed Energy Resources (DERs) for Networked Microgrids}, url={http://dx.doi.org/10.1109/ieses53571.2023.10253706}, DOI={10.1109/ieses53571.2023.10253706}, abstractNote={Rapid inclusion of distributed energy resources (DERs) has led to the need for a reliable energy management system (EMS) capable of managing these resources effectively at a large scale. This paper proposes a novel hierarchical distributed consensus-based approach for solving the economic dispatch problem in networked microgrids (NMGs). The proposed approach arranges the network in hierarchical structure, thereby reducing the communication requirement between the distributed controllers. Simulation results are presented to demonstrate the effectiveness of the proposed approach. The simulation involves comparing the proposed approach with distributed consensus approach for different network topologies.}, booktitle={2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)}, author={Capezza, Skieler and Joshi, Aditya and Chow, Mo-Yuen}, year={2023}, month={Jul} }
 @article{joshi_capezza_alhaji_chow_2023, title={Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems}, volume={10}, ISSN={2329-9266 2329-9274}, url={http://dx.doi.org/10.1109/JAS.2023.123657}, DOI={10.1109/JAS.2023.123657}, abstractNote={In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS's potential in real-world applications.}, number={7}, journal={IEEE/CAA Journal of Automatica Sinica}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Joshi, Aditya and Capezza, Skieler and Alhaji, Ahmad and Chow, Mo-Yuen}, year={2023}, month={Jul}, pages={1513–1529} }
 @inproceedings{capezza_joshi_chow_2023, title={Weighted Hierarchical Consensus based Economic Dispatch Utilizing Cluster Size Estimation for Networked Microgrids}, url={http://dx.doi.org/10.1109/iecon51785.2023.10312209}, DOI={10.1109/iecon51785.2023.10312209}, abstractNote={The fast adoption of distributed energy resources (DERs) to meet the ever-growing energy demand has created a need for an efficient and reliable energy management system (EMS). This paper proposes a weighted consensus based method for solving the economic dispatch problem in hierarchical distributed networked microgrid system. The combination of cluster size estimation and weighted consensus enable us to obtain an optimally operating energy management framework for our system. The proposed approach is validated through extensive simulations, demonstrating its effectiveness over existing methods in terms of convergence time and system performance optimization in networked microgrids.}, booktitle={IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society}, author={Capezza, Skieler and Joshi, Aditya and Chow, Mo–Yuen}, year={2023}, month={Oct} }