本研究阐述了商业/机构建筑热电联产（CHP）系统设备合理调度控制的重要性。与工业热电联产相比，这些涉及多台原动机、冷却器和锅炉的建筑热电联产（BCHP）厂需要更仔细、更复杂的设备调度和控制方法，因为热负荷和电负荷的变化较大，并且存在设备调度问题。设备调度涉及确定要操作的众多设备组合中的哪一个，即与启动或停止原动机、锅炉和冷却器有关。第二级和较低级别的监控类型称为连续控制，它涉及在特定的设备计划组合下确定控制参数（如原动机、锅炉和冷却器的负载分数）的最佳值。 迄今为止，HVAC&R文献中的大部分工作都与多台电动和混合式制冷机和冷却装置有关。此外，这些研究侧重于较低层次的连续控制问题，因为这些研究涉及的是较简单的系统，其中可能的设备组合数量相对较少。需要区分两个术语:最优和接近最优，不同的专业人员使用的术语不同。区别这些问题的一种方式是，根据描述各种设备性能的建模方程、构建和求解优化函数的方法，将后者视为前者的简化，以及该问题是被视为静态问题还是动态问题（即，以每小时为基础或在一天中几个小时或一个整月的规划周期内处理该问题）。另一个观点是考虑- optimal是简化和启发式策略的同义词，这些策略接近最佳策略，但在实际应用中更容易实现。从设备调度的角度来看，最优控制不同于连续设备控制。在本研究中，近似最优调度控制的定义有所不同。从实际操作的角度来看，BCHP运营商不愿意在规划期内开启和关闭设备，他们更愿意选择一组特定的BCHP设备，在规划期开始时启动，并保持这组设备的运行直到结束，但是，以最佳方式每小时控制单个已运行设备的能力。在本研究中，这种操作策略被称为接近最优的策略，而在这种策略中，设备调度可以在每小时时间步长开始时以准静态方式进行更改，并以最优方式进行控制。 因此，有尽可能多的接近最优的解决方案，因为在选定的一天内有可行的组合（即那些允许在规划期的每个小时内满足建筑负荷的解决方案）。这种近乎最优的操作和控制将导致更高的运营成本。一个称为CPR（成本惩罚比率）的量被定义为接近最优解与最优解的比率，它是该量随建筑类型、位置、年份和价格信号的变化和大小，这是本研究的主要重点。第二个目标是为此类BCHP电厂的成本效益运行确定初步启发式指导原则。该研究项目分为两个阶段。第一个涉及使用合理设计和尺寸的BCHP设备生成特定特征建筑类型的必要数据。这需要详细说明研究范围，包括选择具有代表性的建筑类型和地理气候； 仔细设计并确定BCHP系统和设备的尺寸；并使用详细的仿真程序生成小时负荷。选择了七栋建筑:三栋采用实时电价（RTP）的大型建筑（医院、学校和酒店），四栋采用分时电价（TOU）的建筑（两大两小）。随后，为每栋建筑确定了一年中执行优化研究的特定天数。第二阶段包括进行参数模拟，研究选定的七种建筑场景中CPR值的大小和可变性，并提取结果。通常，此类建筑有1-2台原动机、两台锅炉、两台蒸汽压缩制冷机和一台吸收式制冷机。并非所有组合都是可行的解决方案。七种情景和选定天数的可行组合数量在10个之间- 对于大型建筑，30英镑是一个很大的选择。分析表明，可行解决方案之间的CPR值存在较大差异。此外，CPR的中值会随着场景的变化而变化，并且每天都在变化，通常都很大。对于三个RTP案例，发现大型酒店的中位CPR值从1.10（即10%超额成本）到1.8，可行设备组合中的75%值甚至更大（约2.0）。大型学校在三种RTP方案之间具有最高的可变性，最差的接近最优的解决方案具有最大的CPR值。然而，最佳近似最优解的CPR值接近于一。这表明，学校是合并BCHP监管工具将带来最大益处的首选学校。对于大型酒店而言，最佳的近似最优解决方案的CPR值为1.2-1.4，这表明在规划期内，涉及设备组合变化的运营策略可能是有利的。 对于其他两座RTP建筑（学校和医院）来说，这似乎没有必要。对于分时电价信号，已经对两种情况进行了分析:当月的高峰设置日（适用需求电价的那一天）和非高峰设置日，当需求电价不适用且仅适用能源使用率时。显然，峰值设定日的CPR值要大得多:可行候选方案中的第75个百分位约为1.7-2.0。然而，在所有情况下，两种方案的最佳近似最佳值都接近1.0。这表明，对峰值设定日进行适当控制的需求非常关键，如果不选择最佳的非最优解决方案，可能会导致较大的成本损失。分析表明，还应注意小型建筑BCHP系统的监控。虽然可能的设备组合数量很少，但发现某些组合明显优于其他最佳组合- 最佳方案与理想方案非常接近。还发现（至少对于选定的天数），可能会导致较大的CPR值，尤其是在峰值设定日。尽管整合监管工具的需求可能不像大型建筑那么迫切，但仍需要开发一些简单的优化工具，甚至是此类小型建筑的查找表。这项研究表明，对于适用于不同建筑类型、季节和价格信号的BCHP系统的近似最优调度，没有明确或简单的规则。食谱法可能会导致巨大的成本损失，这突出表明需要有一个软件工具来优化BCHP工厂的调度和控制。然而，确定了一些总体趋势，并以表格形式总结了七种情景和不同季节的总体趋势。必须注意的是，上述调查结果仅针对本研究中的建筑、价格信号和选定日期，不应视为适用于所有BCHP工厂。 单位:I-P

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