Optimal Demand Response Scheduling With Real-Time Thermal Ratings of Overhead Lines for Improved Network Reliability

This paper proposes a probabilistic framework for optimal demand response scheduling in the day-ahead planning of transmission networks. Optimal load reduction plans are determined from network security requirements, physical characteristics of various customer types, and by recognizing two types of reductions, voluntary and involuntary. Ranking of both load reduction categories is based on their values and expected outage durations, while sizing takes into account the inherent probabilistic components. The optimal schedule of load recovery is then found by optimizing the customers' position in the joint energy and reserve market, while considering several operational and demand response constraints. The developed methodology is incorporated in the sequential Monte Carlo simulation procedure and tested on several IEEE networks. Here, the overhead lines are modeled with the aid of either static-seasonal or real-time thermal ratings. Wind generating units are also connected to the network in order to model wind uncertainty. The results show that the proposed demand response scheduling improves both reliability and economic indices, particularly when emergency energy prices drive the load recovery.