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Article Excerpt
INTRODUCTION
Combined heat and power (CHP) components and systems are described in several books and technical papers (Petchers 2003; ASHRAE 2004). Systems meant for commercial/institutional buildings (building combined heat and power [BCHP]) involve multiple prime movers, chillers, and boilers and require more careful and sophisticated equipment scheduling and control methods 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., it is concerned with starting or stopping prime movers, boilers, and chillers. The second and lower-level type of control is called supervisory control, which involves determining the optimal values of the control parameters (such as loading of prime movers, boilers, and chillers) under a specific combination of equipment schedule.
Research into methods to optimize energy consumption or cost of operation of building systems is not new (Braun et al. 1989a, 1989b; Braun 2006; Cumali 1988; Henze 2003; House and Smith 1995; Jiang and Reddy 2007; Sun and Reddy 2005; Wang 1998; Wang and Ma 2008). However, many of the efforts address optimization of one or two specific building systems (e.g., thermal storage or start/stop of chillers and/or boiler systems). Furthermore, most previous cost optimization efforts were based on flat electric rate schedules (non-time variant) with a demand charge. A primary intent for optimization is to avoid demand charges. Further, there are only a few papers that deal with optimization of CHP plants, and these are more academic than practical. Most of the work to date in the heating, ventilating, air-conditioning, and refrigerating (HVAC&R) literature on plant operational optimization is concerned with multiple electric and hybrid chillers and cooling plants. These focus on the lower level objective since the studies were concerned with simpler systems where the number of possible equipment combinations is relatively few.
Currently, little optimization of the interactions among systems is done in buildings. Attempts in practice to optimize operations usually involve applying rules of thumb regarding when to turn on boilers or chillers, how to reset set points, and other heuristic actions. There is little or no analytic basis for control of scheduling and interactions in real time. Shedding of loads in response to day-ahead or hour-ahead notifications of need from utilities works well in practice, but as electric rate structures become increasingly time variant, real-time control of scheduling and system interactions become essential for cost-effective operation. Heuristic control normally used by plant operators often results in off-optimal operation due to the numerous control options available to them, as well as to dynamic, time-varying rate structures and relative changes in gas and electricity prices. Though reliable estimates are lacking in the technical literature, the consensus is that 5%-15% of cost savings can be realized if these multiple-equipment BCHP plants were operated more rationally and optimally.
There are a few computer software programs that have been developed by federal agencies (e.g., Fischer and Glazer 2002), consultants, and equipment companies for designing BCHP plants. Many use simple models of equipment and simplistic operating scenarios. Most of these programs are design tools that are add-ons to existing programs such as DOE-2 (LBL 1989) or adopt bin-type analysis methods to determine type and size of BCHP systems to be used during the design and selection process. Several papers and books describe heuristic practices for operating cooling plants (Kelly and Chan 1999; Braun et al. 1989a, 1989b; ASHRAE 2007), hybrid cooling plants (Koeppel et al. 1995; Siemens 2004; Braun 2006) and even cogeneration plants (Honeywell 2006; Petchers 2003). However, there has been no systematic guidance on how to operate BCHP plants, and a proper understanding of the cost penalties associated with operating them in a nonoptimal manner is lacking.
OBJECTIVES AND SCOPE
The objective of this paper is to report on research results involving proper scheduling of equipment in BCHP systems for commercial/institutional buildings. Most of the work to date in the HVAC&R literature (specifically chiller plants) focuses on the lower level objective, since the studies are concerned with simpler systems where the number of possible equipment combinations is relatively few. Also, 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). A second viewpoint is to consider near-optimal as synonymous with simplified and heuristic strategies that are close to the optimum bit much simpler to implement in actual practice.
Here, we have defined near-optimal scheduling control 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 start up at the beginning of the planning horizon and keep this set operational till the end with the ability to control the individual already operating equipment at smaller time steps (e.g., each hour) in an optimal manner. While optimal control is where both the equipment scheduling and control can be done optimally each hour, near-optimal is defined in this research as an operational strategy where one cannot change the equipment scheduling during the planning horizon, but whichever equipment is operating can be controlled so as to result in minimum operating cost. Thus, there will be as many near-optimal solutions as there are feasible combinations during the selected day. A quantity called cost penalty ratio (CPR) was defined as the ratio of the near-optimal to the optimal solutions, and the magnitude and variation of this quantity with building type, location, and price signal are studied.
The research project (Maor and Reddy 2008) on which this paper is based involved two phases. The first involved the generation of necessary data for certain characteristic building types with rationally designed and sized BCHP equipment (Maor and Reddy 2009). This entailed specifying a methodology to select representative building types and geographic climates to perform careful design and sizing of the BCHP systems and equipment and to generate hourly building loads using a detailed simulation program. A matrix of representative building types at different geographic locations was defined, after which representative BCHP equipment and electric utility dynamic rate schedules were selected to study the effect of near-optimal control under several days of the year representative of seasonal variations. The second phase, whose results are reported here, involved performing the parametric simulations and studying the magnitude and variability of the CPR values across the various building scenarios selected and then distilling the results.
LITERATURE REVIEW
Successful operation of BCHP systems requires controls that can integrate information on the building load, the HVAC system, and the electric generator to identify optimal set points for the generator and HVAC systems in the buildings. The optimization problem has several notable characteristics of a large set of system equations: problem variables that are a mixture of integer and continuous variables, nonlinear inequality and equality constraints, and objective functions that can be discontinuous (e.g., Edgar et al. 2001). It seems that neither traditional gradient-based methods nor direct search methods are effective for the optimization problem. Several papers point out the appealing features of mixed integer programming (MIP) (Hui and Natori 1996; Sakawa et al. 2002; Yokoyama et al. 2002; Dotzauer 1997) and some heuristic methods like genetic algorithms (GA) and simulated annealing (SA) (Maia et al. 1995; Sakamoto et al. 1999; Curti et al. 2000).
For simplified systems, some simple optimization algorithms, such as linear programming, can be used to solve the optimization problems. Baughman et al. (1989) developed a computer program to minimize the present worth of the electric and thermal energy costs as a function of the type and amount of cogeneration and thermal storage. The plant model, as well as a base model without thermal storage or cogeneration, was proposed, and a linear constrained optimization problem was formulated. The proposed model proved to be valuable in accurately determining the energy savings that various combinations of cogeneration and thermal storage equipment configurations might offer. Ehmke (1990) developed a methodology to extend an existing linear programming model for the optimization of the cogeneration plants. The new approach introduced capital cost and maintenance cost. The aim was to optimize the size of the cogeneration equipment, depending on the characteristics of plant load and tariff conditions.
Increasing sophistication of the optimization algorithm allows more complicated models and conditions to be treated. Spakovsky and Frangopoulos (1994) proposed methodologies that combined not only energy use and financial resources expended but also environmental considerations in the construction of mathematical models for the analysis, improvement, and optimization of energy systems. This methodology was applied to a gas turbine cycle with cogeneration to demonstrate the applicability of this methodology, and results were then analyzed and compared with the results of the thermoeconomic optimization of the same cycle obtained in earlier works.
Maia et al. (1995) used a combinatorial optimization technique (SA) to derive the flow sheets for systems that satisfy fixed demands of steam, electricity, and mechanical power. SA has been shown to be a powerful technique in the synthesis of a utility system. Also, the authors mentioned that further improvements in the model could consider uncertainty in the input data, since SA does not offer a simple way to perform sensitivity analysis.
Sakamoto et al. (1999) described an optimization method for electric-type district heating and cooling plants based on the GA. First, in order to examine the characteristics of the GA method by simulation, a simulated plant was assumed to supply chilled and hot water simultaneously. Second, a pilot plant was actually constructed at a sewage treatment plant in order to better determine the benefits of using low-temperature energy. In the pilot plant, the GA method was applied to optimize the operation schedule for online processing. The study showed that the optimality of the plant schedule obtained by the GA was almost equal to that obtained by mathematical programming.
Curti (2000) presented an environomic model (i.e., a model that also includes environmental considerations) for a district heating network based on centralized and decentralized heat pumps by applying the general environomic methodology. A complete set of results for the optimal synthesis, design, and operation of the network is given and discussed. The resulting solution space was highly nonlinear and noncontiguous and was effectively determined using GA. Results were shown for various district heating user distributions, as well as fuel and electricity prices. When properly optimized, solutions with heat pumps were economically very close to traditional district heating solutions, particularly when the main pollution costs are internalized. For comparison purposes, the same approach and models can be used to identify the life cycle exergetic optimum. This approach provides for a fast, comprehensive, and optimal reassessment of design options when economic conditions or the emphasis on pollution vary.
Hui et al. (1996) studied the application of mixed integer programming (MIP) techniques for the optimization of site utility systems. The intent was to determine the best investment scheme to the utility system that maximizes the merit of exporting electricity. In other words, the objective was to identify the best combination of equipment to be added to the existing system that would maximize and stabilize the electricity export throughout the year. Decisions on process modifications, such as changing the steam pressure on some local waste heat boilers, were also taken into account....
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