1、Erik Bonnett was an Analyst at Rocky Mountain Institute, Boulder, Colo. and is a student at the University of Oregon, Eugene. Michael Bendewald is an Analyst at Rocky Mountain Institute, Boulder, Colo Victor Olgyay is a Principal at Rocky Mountain Institute, Boulder, Colo Finding the “Switching Poin
2、t:“ Cost Optimization for New NZE Commercial Buildings Erik Bonnett Michael Bendewald Victor Olgyay, AIA Student Member ASHRAE Affiliate Member ASHRAE Member ASHRAE ABSTRACT Achieving a Net Zero Energy (NZE) building requires incorporating costly onsite renewable energy generation, which can be redu
3、ced with energy efficiency measures (EEMs). NRELs Building Energy Optimization software (BEopt) automates building simulation runs to determine the cost-optimal combination of energy generation and energy efficiency for residential construction. However, several factors prevent BEopt from being an e
4、ffective tool for commercial building design. This paper proposes an adaptation of the BEopt method, referred to here as the adapted Commercial Building Energy Optimization process (CBEO), to accommodate the greater complexities and demands of commercial building design. The CBEO process begins by s
5、etting an economic performance benchmark, against which EEMs are analyzed, the cost of renewable energy generation onsite. Comparison to a fixed economic benchmark avoids the need to analyze all measures simultaneously, allowing, for instance, massing to be optimized earlier than mechanical equipmen
6、t. Later in design, EEMs are bundled to capture synergies that reduce capital cost and increase energy efficiency. This paper presents the application of the CBEO process to the Archbold Biological Station Lodge and Learning Center. In this project, significant cost savings were achieved. For instan
7、ce, highly effective daylighting eliminated the need for dimmable electric lighting and daylight sensors in most spaces because design light levels were achieved with daylight alone. As shown in the case study, the adapted process has some limitations in comparison to the BEopt software including de
8、creased automation and loss of the ability to accurately identify optimal packages of EEMs at lower efficiency targets (such as 20 percent below an energy code). INTRODUCTION Achieving a net zero energy1(NZE) building requires two basic strategies: energy efficiency measures (EEMs)and onsite renewab
9、le energy generation. Both strategies offset site energy onsite renewable generation produces watts and efficiency produces “negawatts” (Lovins, 1990) and, presumably, there is some financially optimum combination to reach net zero. However, comparing the economic performance of EEMs with that of re
10、newable energy generation to find this optimum combination may often be bypassed because of the complexity and speed of commercial construction projects. Cost-optimization of energy efficiency and onsite generation can save significant building capital and operation 1When using “net zero energy” or
11、“NZE” in this paper, we refer to the “net zero site energy” building definition from the ASHRAE Vision 2020 Ad Hoc Committee. LV-11-C038314 ASHRAE Transactions2011. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). Published in ASHRAE Transactions, Vol
12、ume 117, Part 1. For personal use only. Additional reproduction, distribution, or transmission in either print or digital form is not permitted without ASHRAES prior written permission.expenses. This is especially true for building projects with aggressive performance targets, such at net zero energ
13、y, which may require a large amount of costly onsite renewable generation. This paper describes a method of cost-optimization for high-performance and net zero energy projects that accommodates the speed and complexity of commercial construction. This Commercial Building Energy Optimization process
14、(CBEO) is an adaptation of NRELs residential Building Energy Optimization (BEopt) method and software. THE BEOPT METHODS VALUE AND LIMITATIONS The BEopt software enables users to quickly analyze and compare efficiency measures with each other and with onsite renewable energy generation. The automate
15、d process of BEopt would be ideally suited for the speed of commercial construction, however several factors limit BEopt in this application. Below, we provide a brief overview of the BEopt method, which serves as the model for CBEO, and discuss the relevant limitations. Overview of the BEopt Method
16、 NRELs Building Energy Optimization software automates hundreds to thousands of building-simulation iterations to determine the combination of EEMs and renewable energy options that achieves a range of energy use targets for residential buildings at least cost. BEopts algorithm selects discrete EEMs
17、 for inclusion using a sequential analytical process to identify the optimum combination of energy efficiency and renewable energy measures. BEopt uses the sum of the mortgage payment and energy bill of a home as the metric for lifecycle cost-effectiveness. Beginning with a code compliant home, BEop
18、t analyses dozens of EEMs, selecting the EEM with the greatest cost-effectiveness. Then assuming that EEM is implemented, the software re-analyzes the remaining EEMs, selecting the most cost-effective. This process continues, with some algorithms to account for relationships between measures, until
19、the cost of remaining EEMs is greater than the cost of photovoltaic energy generation. At that switching point, PV is applied to reach NZE. This process is illustrated in Figure 1. Figure 1 The BEopt conceptual plot of the path to NZE. Cost is graphed as a combination of mortgage and utilities on th
20、e y-axis. Point 1 is the base case, point 2 is the annual cost optimal case, point 3 is the switching point from EEMs to renewable energy, and point 4 is the NZE case. (Christensen et al 2006) The output is a set of EEMs, each of which contributes to reaching net zero site energy, graphed by order o
21、f cost-effectiveness. Users can select the most cost-effective solution for their building at any performance level, such as NZE, most cost-effective, or most energy efficient at the same lifecycle cost as a “conventional” home. 2011 ASHRAE 315Limitations of the BEopt Software and Method applied to
22、Commercial Construction The primary software limitation of applying BEopt to commercial construction is the geometrical interface, which does not currently accommodate the complexity of commercial building forms. A more fundamental limitation is that the BEopt method considers all modeled design dec
23、isions simultaneously. This is appropriate for residential construction, where building assemblies and mechanical systems are often known from the outset of design. However commercial building design occurs over a significant period of time, divided into distinct phases during which certain design d
24、ecisions are made. For instance, building massing is typically determined before mechanical equipment. The segmented nature of commercial building design makes a direct application of BEopts simultaneous method problematic. In addition, several capacity issues would result from direct application of
25、 the BEopt method to commercial building design. To accurately program the energy model with the diversity and complexity of commercial EEMs, building simulation runs would need to be conducted manually or semi-manually. This would require extensive consultant time and fees. Second, these EEMs would
26、 also need to be cost-estimated manually due to their complexity. Cost databases such as RS Means do not contain specific enough information for this purpose, probably due the complexity and quantity of commercial EEMs; for instance the cost of many various high performance glazing combinations is n
27、ot available. Very few cost-estimators would be willing to price the multitude of building products necessary to establish a full range of energy performance data for each building component. Though these limits prevent direct adoption of BEopt for commercial building projects, many of its features
28、can be adopted as part of a CBEO process. The CBEO process can create solutions of similar quality while meeting the challenges of commercial building design. THE ADAPTED COMMERCIAL BUILDING ENERGY OPTIMIZATION PROCESS The basis of the adapted CBEO process is the “switching point,” the location alon
29、g a building energy optimization curve when additional energy efficiency becomes less cost effective than renewable energy generation. Graphically this is the tangent point of the downward sloping energy efficiency curve and the straight line of renewable energy generation. Figure 2 The “switching”
30、point. Investment in energy efficiency eventually becomes less cost effective than on-site renewable energy generation, despite tunneling through the cost barrier (see www.10xE.org: related articles). The point at which additional EEMs are more costly than onsite renewable energy is the “switching p
31、oint.” Note: this figure depicts value, thus the poles of the y-axis are reversed compared to Figure 1, which depicts cost. Marginal Value (Cost) (for each addtl.unit of energy savings)PV(+)(-)0Net Present Value(relative to no-efficiency base case)EfficiencyEfficiency + PVTunneling Through the Cost
32、Barrier: Investment in passive design beyond what is conventionally considered cost effective can lead to system downsizing expanding (not deminishing returns. (+)(-)0% Energy Savings20% 40% 60% 80% 100%Switching Point: Switch from adding more efficiency to adding PV when the marginal cost of anothe
33、r unit of savings is lower for PV than efficiencyEfficiencyEnergy Savings (kWh)316 ASHRAE TransactionsBecause the cost of renewable energy generation can be determined for a commercial building project with little project-specific information, it is possible to establish the cost-effectiveness of on
34、site renewable energy early on (i.e., net present value per unit energy saved the slope of the red line on Figure 2). The estimated cost of onsite renewable energy provides the economic performance benchmark that is used to help guide selection of energy efficiency measures. The other critical facto
35、r when considering efficiency measures is identifying opportunities to downsize HVAC equipment (i.e., tunneling through the cost barrier). Ideally, as illustrated by Figure 2, the designers would tunnel through the cost barrier first then use the economic performance benchmark to find the switching
36、point. Several steps are necessary for integrating cost optimization with the commercial building process, as follows: CBEO Process Description 1. The first step of the CBEO process is to set an economic performance benchmark for the optimization process, typically the return on investment (ROI) or
37、net present value per kilowatt-hour (NPV/kWh) of the anticipated onsite renewable energy technology. It may seem unusual to set the economic benchmark for EEM selection using the cost of renewable energy rather than the clients hurdle rate. However, assuming the client wants to build a net zero ener
38、gy building, some quantity of renewable energy will be required. The quantity of renewable energy that will achieve NZE at least cost will be determined by first aggressively reducing the buildings energy use intensity, until the cost of additional EEMs becomes higher than the cost of renewable ener
39、gy. At that “switching point,” it makes sense to begin investing in onsite renewable energy. The same process can be used for near net zero energy buildings, or any high-performance building for which the design team is considering the application of onsite renewable energy generation. 2. Second, EE
40、Ms are analyzed during the conceptual and schematic design phases to estimate energy performance values in comparison to the economic performance benchmark. During these phases of design, the largest decisions are made in terms of impact on energy efficiency, while the least information is available
41、. This requires back-of-the-envelope calculations, approximated computer simulation, and rough cost estimates to conduct cost-optimization. Fortunately, many of the early decisions with the greatest impact also have low costs, such as building massing and orientation. At this stage, careful attentio
42、n is required to preference decisions that will increase the cost-effectiveness of later decisions. For instance, cardinal orientation will facilitate smaller and more cost-effective daylight control devices later in design. Because it is difficult to optimize dependent EEMs, especially early in des
43、ign with rough data, it is important that professionals using this method estimate the impact of dependencies early in design to inform early decisions, while confirming estimates later with more robust data. This process of considering and accounting for dependencies between EEMs will cumulatively
44、“tunnel through the cost barrier” to achieve greater energy efficiency. Analysis of EEMs in isolation will yield a “diminishing returns” curve, while optimizing dependencies between EEMs will yield a cumulative solution at lower cost and higher efficiency than any point on the “diminishing returns”
45、curve. Comparison to an aggressive economic benchmark enables tunneling through the cost barrier while avoiding the need to analyze all measures simultaneously. For instance, massing can be optimized earlier than mechanical equipment. To accomplish this, EEM performance values are preliminarily set
46、“in the ballpark” to allow the design process to move forward, while allowing the flexibility to refine values later in design. Accounting for dependencies is critical to avoiding a false “switching point,” at for instance 20 or 30 percent below typical US energy codes, and instead achieving deeper
47、and more cost effective energy savings through efficiency. 3. Third, EEMs are bundled during design development to fully capture synergies that reduce capital cost and increase energy efficiency. Rather than analyzing all potential performance values for each EEM in combination as with the BEopt met
48、hod, sets of measures are bundled and analyzed at several performance levels. Creating the bundles requires significant knowledge of building energy use patterns and dependencies between measures. Professional judgment should supplant a time-intensive analysis of all possible options. 2011 ASHRAE 31
49、7Bundles should be structured to “tunnel through the cost barrier,” specifically to decrease the size and cost of HVAC equipment and electric lighting, thus making aggressive passive design more cost effective. Some potential bundles could include: lighting, windows and shading, and cooling equipment; envelope and heating equipment; and low flow water fixtures and water heating equipment. If bundles are not properly structured, it may appear that the “switching point” occurs relatively early along the energy efficiency curve, such as twenty or thirty percent belo
