Robust Distribution System Expansion Planning Incorporating Thermostatically-Controlled-Load Demand Response Resource

Smart meter data provides rich information on customers' energy consumption behaviors, which can be a valuable resource for evaluating customers' demand response (DR) potential and thus can inform the distribution system expansion planning (DSEP). This paper presents a novel DSEP framework incorporating a data-driven model for a particular type of incentive-based DR called the thermostatically-controlled-load DR. The heterogeneity in individual customers' DR potential is considered in the DSEP by leveraging the healing, ventilation, and air conditioning (HVAC) load information extracted from smart meter data. The relationship between the DR incentive and the customer participation rate is also considered so that incentives can be optimally designed for customers with different DR potentials in a differentiated way. The overall DSEP problem is formulated into a robust optimization framework to address the uncertainties from the load demands, renewable energy generations, and DR resources. Case studies show that the proposed DSEP model can substantially reduce the total expansion cost over conventional planning paradigms, demonstrating the positive role of the proposed data-driven DR model in DSEP.

Automated summary

This paper proposes a data-driven framework for distribution system expansion planning (DSEP) by utilizing smart meter data to evaluate customers' demand response (DR) potential and optimize incentive schemes. The model takes into consideration the heterogeneity of individual customers' potential and the relationship between incentives and participation rates. Case studies demonstrate that the proposed model can significantly reduce expansion costs.