Kirk H. Drees

Development and Evaluation of a Rule-Based Control Strategy for Ice Storage Systems

This paper describes the development and evaluation of a near-optimal control strategy for ice storage systems. The strategy is based upon simple heuristics that were developed from daily and monthly simulations of cooling systems with internal melt, area-constrained ice storage tanks. Dynamic programming was used to obtain the optimal control trajectories which minimized an integrated energy and demand cost function for both the daily and monthly simulations. In addition to leading to simple heuristics, the monthly optimal control results were used as benchmarks to evaluate the performance of both conventional and the new control strategy. For a range of partial-storage systems, load profiles, and utility rate structures, the monthly electrical costs for the rule-based control strategy were, on average, within about 3% of the optimal costs. In contrast, the monthly electrical costs associated with the most common conventional control strategy, chiller-priority control, were as much as 20% greater than op...

Modeling of Area-Constrained Ice Storage Tanks

This paper describes the development and validation of a model based on physical parameters that can predict the thermal performance of area constrained ice-storage tanks. The model, which builds on the work of Jekel et al. (1993), was used to study the effect of assumptions and operating conditions on tank performance during charging and discharging. In particular, it was found that the contents of the tank can be treated as a lumped system with the heat-transfer rate modeled using the product of an overall conductance and log-mean temperature difference. Consistent with the results of Jekel, heat-transfer effectiveness was found to be highly coupled to flow rate and insensitive to the inlet temperature of the fluid used to charge and discharge the tank. Results of the model were compared with experimental data obtained from a well-instrumented thermal storage system at Purdue University. The average difference between the predicted and experimental heat-transfer rates during charging was about 6%, while...

A Stochastic Model Predictive Control Framework for Stationary Battery Systems

A stochastic model predictive control (MPC) framework is presented to determine real-time commitments in energy and frequency regulation markets for a stationary battery while simultaneously mitigating long-term demand charges for an attached load. The control problem is multi-scale in nature and poses challenges on computational tractability of the stochastic program and of forecasting and uncertainty quantification (UQ) procedures. The framework deals with tractability of the stochastic program by using a discounting factor for long-term demand charges, while a Ledoit–Wolf covariance estimator is used to overcome UQ tractability issues. The performance of stochastic MPC is benchmarked against that of perfect information MPC and deterministic MPC for different prediction horizon lengths and demand charge discounting strategies. A case study using real load data for a typical university campus and price and regulation data from PJM is considered. It is found that stochastic MPC can recover 83% of the ideal value of the battery, which is defined as the expected savings obtained by operating the battery under perfect information MPC. In contrast, deterministic MPC can only recover 73% of this ideal value. It is also found that operating the battery under stochastic MPC improves the battery payback period by 12.1%, while operating it under perfect information improves it by 27.9%.