The goal of this project is to incentivize and aggregate the demand flexibility of residential customers by synergistically combining methods from economic sciences and power systems engineering.
The recent developments on the massive integration of edge-control devices and smart meters are leveraged to enable communication and control between residential customers and the grid.
- Estimate customer’s willingness-to-pay for electricity at the granularity of individual households using deep machine learning to discern electricity use associated with various hidden household states in conjunction with recently developed choice-experiment type estimation.
- Translate each customer’s willingness-to-pay to bilateral contracts in the form of load reduction or short-term load curtailment.
- Develop strategies for a profit-seeking aggregator to bid in the wholesale market while simultaneously maximizing the current and future value of demand response contracts and ensuring the satisfaction of critical operating constraints for the power distribution systems.
Students working on the project:
Link to project materials
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Project Publications
Journal Articles:
- M. Ostadijafari, Rahul Ranjan Jha and A.Dubey, “Bidding Demand Response into Wholesale Market Considering Distribution System Constraints and Economic Market Parameters” Submitted to IEEE Transactions on Industry Applications.
Conference Articles:
- M. Ostadijafari, Rahul Ranjan Jha and A.Dubey, “Aggregation and Bidding of Residential Demand Response into Wholesale Market”, 2020 IEEE Texas Power and Energy Conference (TPEC), 2020, pp. 1-6.