Control and Optimization of Thermal Energy Storage Systems with Real Time Pricing Electricity Rates

Recent data demonstrates that the growth in buildings energy consumption comes predominantly from electricity [1]. Electricity’s share of primary energy use in buildings increased from 56% in 1980 to 72% in 2005. Also, HVAC energy consumption is usually heaviest when electricity prices are at their highest (in the afternoons and early evenings). To address this issue, many have suggested the use of Thermal Energy Storage (TES), [2-4], which can reduce costs if energy is purchased and stored at low price periods and then recovered during periods of high demand and price. In addition to reducing energy costs, TES systems will have a great impact on our Nation’s power infrastructure by leveling power demand and thus reduce the occurrence of low efficiency power generation. In addition as wind and solar based sources are added to the grid, the non-dispatchable nature of these sources will exacerbate the discrepancy between generated power and consumer demand, and further increase the occurrence of low efficiency generation. By assessing the optimal design of system and operation strategies for TES systems, the viability of non-dispatchable renewable energy sources will be enhanced.

The control scheme proposed in this study is grounded on the notion of back-off control. Process constraints represent maximum and minimum specifications with respect to indoor air temperature and equipment capabilities. Also, the influence of external disturbances (i.e. changes in electricity prices and outdoor air temperature) will cause the system to operate, not at a single point, but within an Expected Dynamic Operating Region (EDOR). Thus, the challenge is to select a Backed-off Operating Point (BOP) that is economically close to the Optimal Steady-State Operating Point (OSSOP). The present work will also extend the back-off control approach to include the notion of market responsive control. The controller will reduce the operation costs by optimizing equipment usage, i.e. utilizing active thermal energy storage to draw extra electricity when prices are low and thus reduce electricity usage during peak price periods.

[1] EIA. "Annual Energy Outlook 2009." US Department of Energy, posted at http://www.eia.doe.gov/oiaf/aeo/index.html (2009).

[2] Meckeler, G. “Cold Air Distribution Options with Ice Storage.” ASHRAE Trans, 95, pt. 2, (1989).

[3] Potter, R.A., D.P. Weitzel, D.J. King, and D.D. Boettner. “ASHRAE RP-766: study of operational experience with thermal storage systems.” ASHRAE Trans., 101, no. 2 (1995): 549 – 557.

 [4] Henze G.P., and M. Krarti. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory: Faculty Publications University of Nebraska (2003).