(531c) Integrated Load Shifting and Curtailment for Demand Response of Central Chilled Water Plants

The participation of industrial electricity users in Demand Response (DR) programs has increased in the past decade (FERC, 2019), motivated by the necessity of ensuring the electricity grid’s generation-load balance and transmission reliability. Programs that promote load shifting and curtailment and the dispatching of distributed generation assets have become common products for independent system operators. While load shifting as a response to price signals has been widely studied in the past, the provision of load curtailment services from the viewpoint of the user started to gain attention in the last decade. Previous studies in this area considered processes such as cement milling (Vujanic et al., 2012), aluminum smelters (Zhang and Hug, 2015) and air separation plants (Zhang et al., 2015). In these works, there is a considerable variation in the load curtailment program (e.g., how far in advance the bids are placed and how far into the future they are implemented) and industrial process addressed, the combination of which results in different problem setups and solution approaches.

In this work, we study how load curtailment bids can be optimized and implemented along with load shifting and analyze its closed-loop performance under forecast uncertainty, specifically for large-scale central chilled water plants with Thermal Energy Storage (TES) used for District Cooling. First, an optimization framework is proposed that integrates the two problems of real-time load shifting (considering hourly day-ahead prices) and day-ahead load curtailment bidding and implementation. We perform a cost-benefit analysis that demonstrates the potential benefits of providing load curtailment under varying incentive profiles. Lastly, we analyze the effect of forecast uncertainty on the closed-loop performance and identify opportunities for integrating uncertainty into the optimization problem. The case-studies are performed with real data from the central chilled water plant located on the campus of the University of California, Davis.

References

FERC, 2019. 2019 Assessment of Demand Response and Advanced Metering, Technical Report.

R. Vujanic, S. Mariéthos, P. Goulart, M. Morari, 2012. Robust integer optimization and scheduling problems for large electricity consumers. Proceedings of the 2012 American Control Conference, 3108–13.

Q. Zhang, I. E. Grossmann, C. F. Heuberger, A. Sundaramoorthy, J. M. Pinto, 2015. Air separation with cryogenic energy storage: optimal scheduling considering electric energy and reserve markets. AIChE Journal 61 (5), 1547–58.

X. Zhang, G. Hug, 2015. Bidding Strategy in Energy and Spinning Reserve Markets for Aluminum Smelters Demand Response. In: Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE PowerEnergy Society, 1–5.