Gund Postdoctoral Fellow, College of Engineering and Mathematical Sciences, Department of Electrical and Biomedical Engineering

Shitikantha Dash is an incoming Gund Postdoctoral Fellow at the University of Vermont (UVM). Before joining UVM, he was a research associate at the Indian Institute of Technology (IIT) Ropar, Rupnagar, India. He received his doctoral degree from IIT Ropar in 2022 and bachelor's and master's degrees in Electrical and Electronics Engineering and Power System Engineering from the Biju Patnaik University of Technology, Rourkela, India, in 2013 and 2016, respectively. His primary research interests can broadly be categorised into twofold, viz., 1) Energy Monitoring (low-cost and convenient solutions for appliance-wise energy consumption monitoring in residences) and 2) Energy Management (energy management strategies for tariff and state-regulation sensitive residential grids).

In his tenure at IIT Ropar, he addressed the problems of Indian residential communities subjected to inclining block rate (IBR) tariffs by reducing monthly energy bills and maintaining the supply-demand mismatch using an integer programming-based bilayer clustered-priority-driven decision mechanism. Residential energy storage systems and incentive models played a vital role in grid stability and handling uncertainties. Later for a futuristic scenario – an environment with local energy market provisions – he developed an ADMM-based decentralised energy management model that solves the OPF efficiently and quickly for a resource-scarce small residential community with less aggregation flexibility.

Publications

1. S. Dash, R. Sodhi and B. Sodhi, "A Novel Instrumentation Approach for Clustered Appliance Load Monitoring," in IEEE Transactions on Power Delivery, vol. 34, no. 6, pp. 2257-2259, Dec. 2019, doi: 10.1109/TPWRD.2019.2917585.

2. S. Dash, R. Sodhi and B. Sodhi, "An Appliance Load Disaggregation Scheme Using Automatic State Detection Enabled Enhanced Integer Programming," in IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 1176-1185, Feb. 2020, doi: 10.1109/TII.2020.2975810.

3. S. Dash, R. Sodhi and B. Sodhi, "A Bi-Layer Clustered Priority Driven Energy Management Model for Inclining Block Rate Tariff Environment," in IEEE Transactions on Industrial Informatics, Sep. 2021, doi: 10.1109/TII.2021.3114361.

4. S. Dash, K. Gandhi and R. Sodhi, "An Automatic State Detection Algorithm for Non-intrusive Load Monitoring," 2019 IEEE 16th India Council International Conference (INDICON), 2019, pp. 1-4, doi: 10.1109/INDICON47234.2019.9030372.

5. S. Dash, R. Sodhi and B. Sodhi, "A Serverless Cloud Computing Framework for Real-Time Appliance-Usage Recommendation," 2020 21st National Power Systems Conference (NPSC), 2020, pp. 1-6, doi: 10.1109/NPSC49263.2020.9331847.

Areas of Expertise and/or Research

Non-intrusive load monitoring, serverless cloud computing, local energey markets, power system optimization, energy management

Education

  • PhD in Electrical Engineering, Indian Institute of Technology
  • Masters in Technology, Power System Engineering, Center for Advanced Post Graduate Studies, BPUT
  • Bachelor in Technology, Electrical and Electronics Engineering, Biju Patnaik University of Technology

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