An Overview of Optimal Control for Central Cooling Plants with Ice Thermal Energy Storage

Author:

Henze Gregor P.1

Affiliation:

1. Architectural Engineering, University of Nebraska-Lincoln, 1110 S. 67th Street, Peter Kiewit Institute 203D, Omaha, NE 68182-0681

Abstract

This paper surveys past and current research of optimal control for central chilled water plants with ice thermal energy storage. The motivation for thermal energy storage in commercial buildings is provided and common operating strategies for ice storage including their implementation are presented. The concept of optimality serves as the basis for introducing the various approaches to optimal control of thermal energy storage. Optimal strategies minimizing either energy or demand costs, near-optimal rule based control minimizing total cost, comfort-based energy optimal control, and combined optimal sizing and energy cost optimal control are discussed and contrasted with mathematically non-optimal, but heuristically improved operating strategies. Fully optimal control based on perfect knowledge is introduced and subsequent developments of predictive optimal control subject to uncertain weather, load, and utility rate information illustrated. In addition, recent investigations of adaptive optimal reinforcement learning based control are presented.

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference23 articles.

1. ASHRAE, 1999, Handbook of HVAC Applications, pp. 40.25–40.28.

2. Rawlings, L. K. , 1985, “Ice Storage System Optimization and Control Strategies,” ASHRAE Trans., 91/1b, pp. 12–22.

3. Tamblyn, R. T. , 1990, “Optimizing Storage Savings,” Heating/Piping/Air-Conditioning, 62(8), pp. 43–46.

4. Bahnfleth, W., and Joyce, W., 1994, “Energy Use in a District Cooling System With Stratified Chilled Water Storage,” ASHRAE Trans., 100(1), pp. 1767–1778.

5. Dodier, R. H., and Henze, G. P., 1996, “Statistical Analysis of Neural Networks as Applied to Building Energy Prediction,” Proc. of ASME International Solar Energy Conf., ASME, New York, NY.

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