This research addresses the importance of proper scheduling control of equipment in Combined Heat and Power (CHP) systems for commercial/institutional buildings. These building CHP (BCHP) plants which involve multiple prime movers, chillers and boilers require more careful and sophisticated equipment scheduling and control methods as compared to those in industrial CHP, due to the large variability in thermal and electric loads as well as the equipment scheduling issue. Equipment scheduling involves determining which of the numerous equipment combinations to operate, i.e., is concerned with starting or stopping prime movers, boilers and chillers. The second and lower level type of supervisory control is called continuous control which involves determining the optimal values of the control parameters (such as loading fractions of prime movers, boilers and chillers) under a specific combination of equipment schedule. Most of the work to date in the HVAC&R literature was concerned with multiple electric and hybrid chillers and cooling plants. Further, these studies focused on the lower level problem of continuous control since the studies were concerned with simpler systems where the number of possible equipment combinations is relatively few.

One needs to differentiate between two terms: optimal and near-optimal, which are used differently by different professionals. One manner of differentiating these is to view the latter as a simplification of the former in terms of the modeling equations describing the performance of the various equipment, the methods of framing and solving the optimization function, and whether the problem is treated as a static or a dynamic problem (i.e., treating the problem on an hourly basis or over a planning horizon which could be several hours in a day or a whole month as well). Another viewpoint is to consider near-optimal as synonymous with simplified and heuristic strategies which are close to the optimum one but are much simpler to implement in actual practice. Optimal control from the equipment scheduling viewpoint differs from that of continuous equipment control. In this research, near-optimal scheduling control has been defined differently. From a practical operational viewpoint, BCHP operators are averse to switching equipment on and off over the planning horizon, and they would prefer to select a particular set of BCHP equipment to startup at the beginning of the planning horizon and keep this set operational till the end with, however, the ability to control the individual already operating equipment each hour in an optimal manner. Such an operational strategy has been referred to as near-optimal in this research as against one where the equipment scheduling can be changed in a quasi-static manner at the beginning of each hourly time step and controlled optimally. Thus, there are as many near-optimal solutions as there are feasible combinations during the selected day (that is those which would allow the building loads to be met during each hour of the planning horizon). This type of near-optimal operation and control will result in a higher operational cost. A quantity called CPR (cost penalty ratio) has been defined as the ratio of the near-optimal to the optimal solution, and it is the variation and magnitude of this quantity with building type, location, day of the year, and price signal which has been the primary focus of this research. A secondary objective has been to identify preliminary heuristic guidelines for cost-effective operation of such BCHP plants.

The research project involved two phases. The first involved the generation of necessary data for certain characteristic building types with rationally designed and sized BCHP equipment. This entailed specifying the detailed scope of the research including selection of representative building types, and geographic climates; performing careful design and sizing of the BCHP systems and equipment; and using a detailed simulation program to generate hourly loads. Seven buildings have been selected: three large buildings under real-time electrical pricing (RTP) (hospital, school, and hotel) and four buildings (two large and two small) under time-of-use (TOU) rates. Subsequently, a certain number of days in the year over which to perform the optimization study were identified for each building.

The second phase involved performing the parametric simulations and studying the magnitude and variability of the CPR values across the seven building scenarios selected and distilling the results. Typically such buildings have 1-2 prime movers, two boilers, two vapor compression chillers and one absorption chiller. Not all combinations are feasible solutions. The number of feasible combinations for the seven scenarios and for the selected days was found to be between 10-30 for the large buildings- a large choice. The analysis revealed that there is large variation in the CPR values between feasible solutions. Further, the median values of CPR change from scenario to scenario and from day to day, and are generally large. For the three RTP cases, it was found that median CPR values were from 1.10 (i.e., 10% excess cost) to 1.8 for the large hotel with the 75 percentile values among the feasible equipment combinations being even larger (about 2.0). The large school has the highest variability between the three RTP scenarios, with the poorest near-optimal solutions having the largest CPR values. However, the best near-optimal solutions have CPR values close to unity. This suggests that schools are prime candidates where the incorporation of a BCHP supervisory tool will have the most benefit. For the large hotel, the best near-optimal solutions have CPR values of 1.2-1.4 suggesting that an operating strategy involving equipment combination changes partway into the planning horizon may be advantageous. This does not seem necessary for the two other RTP buildings (school and hospital).

For TOU price signals, the analysis has been done for two cases: for the peak setting day of the month (during which day the demand charges apply) and for the non-peak setting day, when the demand charges do not apply and only the energy use rate applies. It is clearly noted that the CPR values are much larger for the peak setting days: the 75th percentile among the feasible candidates is about 1.7 -2.0. However, the best near-optimal values are close to 1.0 in all cases for both scenarios. This suggests that the need for proper control is very crucial for peak-setting days, and that not selecting the best nonoptimal solution can have large cost penalties. The analysis demonstrated that care should be taken towards supervisory control of BCHP systems for small buildings as well. Though the number of possible equipment combinations is small, it was found that certain combinations are clearly better than others with the best near-optimal ones being very close to the ideal one. It was also found (at least for the days selected) that large CPR values can result, especially so for peak-setting days. Though the need for incorporating a supervisory tool may not be as acute as for large buildings, the need still exits to develop some simple optimization tool or even a look-up table for such smaller buildings.

This research revealed that there are no clear or simple rules for near-optimal scheduling of BCHP systems that apply to different building types, seasons and price signals. A cookbook approach is likely to lead to large cost penalties, and this highlights the need to have a software tool for optimal scheduling and control of BCHP plants. However, some general trends were identified, which are summarized in tabular form for each of the seven scenarios and the various seasons. It must be cautioned that the above findings are very specific to the buildings, price signals and selected days in this research and should not be viewed as appropriate to all BCHP plants.

Units: I-P