- Email: [email protected]
Minimizing Energy Consumption and Carbon Emissions of Aging Buildings
Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 118 (2015) 886 – 893
International Conference on Sustainable Design, Engineering and Construction
Minimizing energy consumption and carbon emissions of aging buildings Moatassem Abdallaha*, Khaled El-Rayesb, Caroline Clevenger a a
University of Colordo Denver, 1200 Larimer Street, North classroom, Devver Colorado, 80217, USA b University of Illinois at Urbana Champaign, 205 N. Mathews Ave., Urbana, Illinois, 61801, USA
Abstract The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model. ©2015 2015The M Authors. oatassem Abdallah, KhaledLtd. El-Rayes, Caroline Clevenger. Published by Elsevierlicense Ltd. © Published by Elsevier This is an open access article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and and Construction Construction 2015 2015. Keywords: Energy Consumption, Sustainability Measures, Aging Buildings, Carbon emissions
* Corresponding author. Tel.: +1-303-556-5287; fax: +1-303-556-2368. E-mail address: [email protected]
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015
doi:10.1016/j.proeng.2015.08.527
Moatassem Abdallah et al. / Procedia Engineering 118 (2015) 886 – 893
887
1. Introduction Buildings in the United States are responsible for significant percentage of energy use (41%) and carbon emissions (39%) while aging buildings represent 70% of existing buildings [1– 3]. The energy consumption and carbon emissions of aging building can be significantly reduced by implementing sustainability measures such as energy efficient lighting, efficient HVA C systems, and renewable energy systems . Decision makers in the private and public sectors are frequently challenged to identify the optimal selection of building upgrade measures that can minimize their building energy consumption wh ile co mply ing with available budgets. To support decision makers and building owners in this challenging task, there is a pressing need to develop an optimization model that is capable of min imizing building energy demand and carbon emissions according to available budgets and building operational performance. Several studies have been conducted to evaluate the performance of imp lementing various sustainability measures in existing buildings. These studies focused on evaluating the imp lementation of energy -efficient HVAC systems in buildings [4–7], energy-efficient lighting systems in buildings and streets [8–10], installation of occupancy sensors to control lighting systems in commercial buildings [11], installat ion of renewable energy systems to generate electricity at bu ild ing sites and offset energy consumption such as wind power technology and photovoltaic systems [12–15], and imp lementation of solar water heating systems to reduce energy consumption of water heaters [16,17]. Other studies focused on developing optimization models and decision support systems to min imize building negative environmental impacts during operation [18], minimize operational cost of existing buildings [19], evaluate existing building conditions and identify optimal decisions pertaining to building renovations [20– 22], and select optimal structural and architecture design of new buildings [23,24]. Despite the significant contribution of the aforementioned research studies, there is limited or no studies that focused on developing a novel optimization model that is capable of identifying the optimal selection of build ing upgrade measures to simultaneously minimize energy consumption and carbon emissions of aging buildings while co mply ing with a specified upgrade budget and preferred operational performance. Furthermore, there is limited research that considers all sustainability measures of building fixtures and equipment, and the use of renewable energy systems simu ltaneously to min imize carbon emissions of existing buildings. 2. Objective The objective of this research paper is to develop an optimizat ion model to minimize energy consumption and carbon emission of aging buildings. The optimizat ion model is designed to provide the optimal selection of building upgrade measures to minimize simu ltaneously energy consumption and carbon footprint of aging building s while comply ing with a specified upgrade budget and building operational performance. Th is optimization model will support building owners and operators in ongoing efforts to identify the optimal allocation of their budgets to minimize energy consumption and carbon footprint of their buildings. This optimization model is developed by identifying decision variables, objective function and constraints to identify the optimal replacement of building upgrade fixtures and equip ment that minimizes energy consumption and carbon emissions. The energy consumption of buildings is calculated in the model using eQuest energy simulat ion software package. The optimizat ion model is then implemented using Genetic Algorithms (GA) to execute the optimization computations. The model uses databases of build products to facilitate the model input data and the results output. The following sections describe the development of the optimizat ion model and analy ze an application example of an ag ing build ing to illustrate the capabilities of the model and demonstrate its use. 3. Research development The optimizat ion model is designed to identify the optimal replacements of building fixtures and equipment, and the installation of onsite renewable energy systems to minimize energy consumption and carbon emissions of aging buildings. This optimization model is developed in two main development steps. The first step is to identify the model decisions variables, object ive function, and constraints while the second step is to imp lement the model using
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an optimization technique to identify the optimal replacements of building fixtures and equipment and selection of renewable energy systems. The decision variables of this optimizat ion model are designed represent all build ing fixtures and equipment, including HVAC systems, interior and exterior lighting fixtures and bulbs, water heaters, refrigerators and vending mach ines, and hand dryers. Furthermo re, the decision variables of this optimization model are designed to represent the installation of solar photovoltaic systems including solar panels, inverters, and percentage of renewable energy at the building site. All of these decision variables are designed to represent a large search space of feasible sustainability measures that have impact in the energy consumption and carbon emissions. The objective function of this optimizat ion model is designed to min imize building carbon emissions by summing up the carbon emissions of building fixtures and equipment except for the saved carbon emissions due to renewable electricity that is generated at the building site. To calculate the carbon emissions of aging buildings, energy simu lation software packages such as eQuest are used to calculate energy consumption [25]. The carbon emissions of aging buildings are calcu lated based on the total energy consumption and location of the building, which accounts for the utility p lants that are generating electricity to the build ings and their associated weighted emissions and transmission factors. The Environ mental Protection Ag ency provide emission factors and average percentages of transmission loss in the major electricity grids in the Un ited States. These emission factors can be used to estimate emissions of energy use in buildings [26]. Installing more energy efficient fixtures or installing renewable energy systems will reduce energy demand of buildings and subsequently the carbon emissions. The reduction in carbon emissions can be estimated based on the non-base emission factors, wh ich assume that the reduction in build ings’ energy demand will result in reducing the generation of energy from inefficient plants [26]. To ensure the practicality of the optimizat ion model, a number of constraints are integrated in the model to comply with (1) available upgrade budgets, (2) existing buildin g operational performance, (3) design constraints of photovoltaic systems, and (4) feasibility of model decision variables. The availab le upgrade budget constraint is designed to ensure that the upgrade cost of replacing building fixtures and equipment in addition to the installation of renewable energy systems will not exceed the specified availab le budget by the building owner or operator. The building performance constraints are designed to ensure that the operational performance of the building will not change after the replacement of build ing fixtures and equip ment including lighting systems output, heating and cooling capacities, and water heating capacity. The constraints of photovoltaic systems are design ed to satisfy the requirements of the photovoltaic system design. The constraints of decision variables are designed to identify the type of decision variab les and their bounds. Integer decision variab les are used in the model to represent products of building fixtures and equipment fro m databases of building products. Furthermore, integer decision variables are used to represent the components of photovoltaic systems and the percentage of generated renewable energy. In terms of the model imp lementation, the developed optimizat ion model is integrate d with several databases of building products, which include data of sustainability building fixtures and equip ment and co mponents of renewable energy systems. These databases are designed to include energy and cost data, general products data, and physical characteristics of HVAC equip ment and ground source heat pumps, interior and exterior lighting fixtures and bulbs, water heaters, hand dryers, refrigerators, vending machines, solar panels, and inverters. The model is designed to allo w the decision maker to select the existing fixtures and equip ment fro m the model databases. The model is also designed to provide detailed recommendations when replacing the existing building fixtures and equipment with sustainability measures from the model database by prov ided details for the reco mmended upgrade measures, which include brand name, model nu mber, upgrade cost, expected savings and reduction in carbon emission, and supplier informat ion. The developed optimization model and its databases are flexibility to inte grate new and updated sustainability measures based on their availability in the market. The developed optimization model is imp lemented using Genetic Algorithms (GAs) due to its capability of modeling step changes and nonlinearity in the model objective functions and constraints and efficiently modeling the optimizat ion problem with the least number of decision variab les. Furthermore, GAs have the capability of identifying near optimal solution for this type
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of problems in reasonable computational time [27– 30]. The co mputations of the optimization model starts by searching the model databases to identify feasible replacements for HVA C systems and water heaters. The model then creates eQuest input files based on the identified feasible replacements of HVAC systems and water heaters and next sends them to eQuest to calculate energy consumption of each feasible alternative. The calculated energy consumption of each feasible alternative is stored in the model databases where they can be used during the optimization computations. The GA co mputations starts by generating an initial solutions that represent the random replacements of build ing fixtures and equipment, selection of the co mponents of renewable energy systems, the percentage of renewable energy that can generated at the building site. The fitness of these solutions are evaluated based on their carbon emissions. Solutions that satisfy all the constraints and provide low carbon emissions are classified as solutions with high fitness value. Other solutions are classified as infeasible solutions or solutions with low fitness values. Solutions with high fitness values are ranked based on their carbon emissions and the GA operators of selection, crossover, and mutation are applied to generate a new set of solutions. This process is repeated iteratively until no further imp rovements can be achieved within a predefined number of iterations. The intimal population, mutation and crossover rates are set based on the GA string and possib le alternatives of the model decision variables [31,32]. 4. Application example An application example of an aging build ing in Illinois is analyzed to illustrate the model capabilities and demonstrate its use. This building has an area of 3,500 square feet and was built in 1980. The major contributors of the building energy consumption include space heating and cooling, interior and exterior lighting, water heating, hand dryers, vending machines. The input data of the optimization model include characteristics of building fixtures and equipment, build ing geometry and occupants use; electricity and gas consumption, and utility billing rates for a previous year. The optimization model is designed to allow the decision maker to select building fixtures and equipment fro m the model databases, as shown in Table 1. The building geo metry and occupants use are used to create an energy simulat ion model to estimate the energy consumption of the ag ing build ing after rep lacing its fixtures and equipment. The energy simu lation model was calibrated using the reported energy b ills to ensure the practicality and accuracy of the optimization model results. The annual energy consumption of the building is reported in the energy bills at 212,381KW H and 3,139 Therms. The existing conditions of the building leads to annual carbon emissions of 159,188 metric tons of CO2 emissions based on the building energy use. T able 1. Sample Input Data for Building Fixtures and Equipment of Public Building Building fixture or e quipment Fluorescent light fixture – 4’’ with 4 T8 Lamps of 34 Watts Square fluorescent fixture with 2 T 12 U-shaped lamps of 34 Watts Fluorescent light fixture – 5’’ with 2 T10 Lamps of 60 Watts Fluorescent light fixture – 4’’with 2 T12 Lamps of 34 Watts Snacks vending machine with average usage of 138 Watts HVAC system with a cooling capacity of 3.5 tons and heating capacity of 100Kbtu Water heater with a capacity of 60 gallons Hand dryer - 2300 Watts
Number of fixtures or e quipment 4 6 4 1 2
working hours pe r day (hrs) 24 24 0.33 2 24
1
24
1 4
24 per use
The developed optimization model is used to analyze and optimize the selection of bu ild ing upgrade measures of the aforementioned aging build ing to min imize its energy consumption and carbon emissions within specified upgrade budgets. For each of the specified upgrade budget, the model was able to identify the optimal selection of building fixtures and equip ment in addition to the components of renewable energy systems to minimize the building carbon emissions. The developed optimization model searched a large nu mber of possible solutions to min imize the carbon emissions of the building with a specified budget of $10K, as shown in Figure 1. It should be noted that the feasible solutions that the optimizat ion model searched in Figure 1 are separated in two groups due to the impact of replacing the HVA C system which results in high reduction in energy consumption and high init ial cost as compared
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to other building replacements such as lighting fixtures and bulbs, hand dryers, vending mach ines, and water heater. The model is designed to provide the results in a graphical and tabular form which include reco mmendations for replacing build ing fixtures and equipment; upgrade costs, carbon emission before and after the implementing the recommended upgrade measures, expected annual savings, and payback periods (if any). Table 2 shows an examp le of the model results for the building example with an upgrade budget of $10K. The results of the model identify the optimal selection of building upgrades based on an identified upgrade budget which helps decision makers and building owners in their ongoing task of maximizing the sustainability of their building wh ile co mp lying with their available budgets.
Figure 1. Feasible solutions searched during the optimization computations with upgrade budget of $30,000 T able 2. Optimization Results for an Upgrade Budget of $10,000 Building Sustainability Me asure
Upgrade Cost
Efficient Interior Lighting Exterior Lighting HVAC System Water Heater Hand Dryers Vending Machines Other Devices and Loads Photovoltaic System T otal
$1,389 $0 $5,821 $0 $1,734 $0 $699 $0 $9,985
Carbon Emissions (me tric ton CO2 emissions) Be fore Afte r 22,806 9,722 16,930 16,930 61,146 1576 6 6 6,024 903 17,569 17,569 34,707 34,112 0 0 159,188 80,818
Savings in ope rational costs $1,969 $0 $4,341 $0 $603 $0 $70 $0 $6,983
Payback pe riod (ye ars) 1.1 0 1.3 0 2.6 0 N.A. 0 1.5
5. Summary and conclusions This paper presented the development of an optimization model that is capable of identifying the optimal replacement of building fixtures and equip ment and the installation o f renewab le energy systems at the building site to simu ltaneously reduce energy consumption and carbon emissions. The optimizat ion model is designed to analyze
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the replacement of interio r and exterior lighting systems, HVA C systems, water heaters, hand dry ers, and the installation of renewable energy systems. The model is also designed with a set of constraints to perform its analysis within a specified upgrade budget while maintaining the existing operational performance of buildings. An application exa mple of an aging build ing is used to illustrate the model capabilit ies and demonstrate its use. The developed model was able to identify the optimal selection of building upgrade measures to minimize carbon emissions and energy consumption with various upg rade budgets. The optimization model is designed to generate the output results in a tabular and graphical form which provide detailed reco mmendations of the selected upgrade measures, includ ing brand name and model nu mber of the building fixtures and equipment, and renewable energy systems; savings in energy consumption and carbon emissions, savings in operational costs, required upgrade cost; and payback period (if any). The developed model provides new and unique capabilities that allow decision makers, building owners, and operators to identify, fro m a range of feasible alternative, the optimal selection of building upgrade measures that helps them in their ongoing task of maximizing the sustainability of their bu ild ings. Future expansion of the model is needed to further study the impact of feasible upgrade measures of the building envelope such as type of insulation, windows, and doors to consider their effects on reducing energy consumption and carbon emissions for buildings that have their energy cons umption dominated by HVAC systems. References [1]
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ScienceDirect Procedia Engineering 118 (2015) 886 – 893
International Conference on Sustainable Design, Engineering and Construction
Minimizing energy consumption and carbon emissions of aging buildings Moatassem Abdallaha*, Khaled El-Rayesb, Caroline Clevenger a a
University of Colordo Denver, 1200 Larimer Street, North classroom, Devver Colorado, 80217, USA b University of Illinois at Urbana Champaign, 205 N. Mathews Ave., Urbana, Illinois, 61801, USA
Abstract The building sector in the United States is responsible for 41% of energy consumption and 39% of carbon footprint while the majority of energy consumption and carbon footprint are caused by aging buildings which represent 70% of existing buildings in the United States. The energy consumption of aging buildings can be significantly reduced by identifying and implementing green building upgrade measures based on available budgets. Aging buildings are often in urgent need for upgrading to improve their operational, economic, and environmental performance. This paper presents the development of an optimization model that is capable of identifying the optimal selection of building upgrade measures to minimize energy consumption of aging buildings while complying with limited upgrade budgets and building operational performance. This optimization model is designed to estimate building energy consumption using energy simulation software packages such as eQuest and it is integrated with databases of building products. This optimization model performs analysis of replacing existing building fixtures and equipment during the optimization computations to identify the optimal replacement of building products that minimizes building energy consumption and carbon emissions. The model is designed to provide detailed results for building owners and operators, which include specifications for the recommended upgrade measures and their location in the building; upgrade cost; expected energy, operational, and life-cycle cost savings; and expected payback period. This paper illustrates the new and unique capabilities of the developed optimization model. ©2015 2015The M Authors. oatassem Abdallah, KhaledLtd. El-Rayes, Caroline Clevenger. Published by Elsevierlicense Ltd. © Published by Elsevier This is an open access article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and and Construction Construction 2015 2015. Keywords: Energy Consumption, Sustainability Measures, Aging Buildings, Carbon emissions
* Corresponding author. Tel.: +1-303-556-5287; fax: +1-303-556-2368. E-mail address: [email protected]
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015
doi:10.1016/j.proeng.2015.08.527
Moatassem Abdallah et al. / Procedia Engineering 118 (2015) 886 – 893
887
1. Introduction Buildings in the United States are responsible for significant percentage of energy use (41%) and carbon emissions (39%) while aging buildings represent 70% of existing buildings [1– 3]. The energy consumption and carbon emissions of aging building can be significantly reduced by implementing sustainability measures such as energy efficient lighting, efficient HVA C systems, and renewable energy systems . Decision makers in the private and public sectors are frequently challenged to identify the optimal selection of building upgrade measures that can minimize their building energy consumption wh ile co mply ing with available budgets. To support decision makers and building owners in this challenging task, there is a pressing need to develop an optimization model that is capable of min imizing building energy demand and carbon emissions according to available budgets and building operational performance. Several studies have been conducted to evaluate the performance of imp lementing various sustainability measures in existing buildings. These studies focused on evaluating the imp lementation of energy -efficient HVAC systems in buildings [4–7], energy-efficient lighting systems in buildings and streets [8–10], installation of occupancy sensors to control lighting systems in commercial buildings [11], installat ion of renewable energy systems to generate electricity at bu ild ing sites and offset energy consumption such as wind power technology and photovoltaic systems [12–15], and imp lementation of solar water heating systems to reduce energy consumption of water heaters [16,17]. Other studies focused on developing optimization models and decision support systems to min imize building negative environmental impacts during operation [18], minimize operational cost of existing buildings [19], evaluate existing building conditions and identify optimal decisions pertaining to building renovations [20– 22], and select optimal structural and architecture design of new buildings [23,24]. Despite the significant contribution of the aforementioned research studies, there is limited or no studies that focused on developing a novel optimization model that is capable of identifying the optimal selection of build ing upgrade measures to simultaneously minimize energy consumption and carbon emissions of aging buildings while co mply ing with a specified upgrade budget and preferred operational performance. Furthermore, there is limited research that considers all sustainability measures of building fixtures and equipment, and the use of renewable energy systems simu ltaneously to min imize carbon emissions of existing buildings. 2. Objective The objective of this research paper is to develop an optimizat ion model to minimize energy consumption and carbon emission of aging buildings. The optimizat ion model is designed to provide the optimal selection of building upgrade measures to minimize simu ltaneously energy consumption and carbon footprint of aging building s while comply ing with a specified upgrade budget and building operational performance. Th is optimization model will support building owners and operators in ongoing efforts to identify the optimal allocation of their budgets to minimize energy consumption and carbon footprint of their buildings. This optimization model is developed by identifying decision variables, objective function and constraints to identify the optimal replacement of building upgrade fixtures and equip ment that minimizes energy consumption and carbon emissions. The energy consumption of buildings is calculated in the model using eQuest energy simulat ion software package. The optimizat ion model is then implemented using Genetic Algorithms (GA) to execute the optimization computations. The model uses databases of build products to facilitate the model input data and the results output. The following sections describe the development of the optimizat ion model and analy ze an application example of an ag ing build ing to illustrate the capabilities of the model and demonstrate its use. 3. Research development The optimizat ion model is designed to identify the optimal replacements of building fixtures and equipment, and the installation of onsite renewable energy systems to minimize energy consumption and carbon emissions of aging buildings. This optimization model is developed in two main development steps. The first step is to identify the model decisions variables, object ive function, and constraints while the second step is to imp lement the model using
888
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an optimization technique to identify the optimal replacements of building fixtures and equipment and selection of renewable energy systems. The decision variables of this optimizat ion model are designed represent all build ing fixtures and equipment, including HVAC systems, interior and exterior lighting fixtures and bulbs, water heaters, refrigerators and vending mach ines, and hand dryers. Furthermo re, the decision variables of this optimization model are designed to represent the installation of solar photovoltaic systems including solar panels, inverters, and percentage of renewable energy at the building site. All of these decision variables are designed to represent a large search space of feasible sustainability measures that have impact in the energy consumption and carbon emissions. The objective function of this optimizat ion model is designed to min imize building carbon emissions by summing up the carbon emissions of building fixtures and equipment except for the saved carbon emissions due to renewable electricity that is generated at the building site. To calculate the carbon emissions of aging buildings, energy simu lation software packages such as eQuest are used to calculate energy consumption [25]. The carbon emissions of aging buildings are calcu lated based on the total energy consumption and location of the building, which accounts for the utility p lants that are generating electricity to the build ings and their associated weighted emissions and transmission factors. The Environ mental Protection Ag ency provide emission factors and average percentages of transmission loss in the major electricity grids in the Un ited States. These emission factors can be used to estimate emissions of energy use in buildings [26]. Installing more energy efficient fixtures or installing renewable energy systems will reduce energy demand of buildings and subsequently the carbon emissions. The reduction in carbon emissions can be estimated based on the non-base emission factors, wh ich assume that the reduction in build ings’ energy demand will result in reducing the generation of energy from inefficient plants [26]. To ensure the practicality of the optimizat ion model, a number of constraints are integrated in the model to comply with (1) available upgrade budgets, (2) existing buildin g operational performance, (3) design constraints of photovoltaic systems, and (4) feasibility of model decision variables. The availab le upgrade budget constraint is designed to ensure that the upgrade cost of replacing building fixtures and equipment in addition to the installation of renewable energy systems will not exceed the specified availab le budget by the building owner or operator. The building performance constraints are designed to ensure that the operational performance of the building will not change after the replacement of build ing fixtures and equip ment including lighting systems output, heating and cooling capacities, and water heating capacity. The constraints of photovoltaic systems are design ed to satisfy the requirements of the photovoltaic system design. The constraints of decision variables are designed to identify the type of decision variab les and their bounds. Integer decision variab les are used in the model to represent products of building fixtures and equipment fro m databases of building products. Furthermore, integer decision variables are used to represent the components of photovoltaic systems and the percentage of generated renewable energy. In terms of the model imp lementation, the developed optimizat ion model is integrate d with several databases of building products, which include data of sustainability building fixtures and equip ment and co mponents of renewable energy systems. These databases are designed to include energy and cost data, general products data, and physical characteristics of HVAC equip ment and ground source heat pumps, interior and exterior lighting fixtures and bulbs, water heaters, hand dryers, refrigerators, vending machines, solar panels, and inverters. The model is designed to allo w the decision maker to select the existing fixtures and equip ment fro m the model databases. The model is also designed to provide detailed recommendations when replacing the existing building fixtures and equipment with sustainability measures from the model database by prov ided details for the reco mmended upgrade measures, which include brand name, model nu mber, upgrade cost, expected savings and reduction in carbon emission, and supplier informat ion. The developed optimization model and its databases are flexibility to inte grate new and updated sustainability measures based on their availability in the market. The developed optimization model is imp lemented using Genetic Algorithms (GAs) due to its capability of modeling step changes and nonlinearity in the model objective functions and constraints and efficiently modeling the optimizat ion problem with the least number of decision variab les. Furthermore, GAs have the capability of identifying near optimal solution for this type
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of problems in reasonable computational time [27– 30]. The co mputations of the optimization model starts by searching the model databases to identify feasible replacements for HVA C systems and water heaters. The model then creates eQuest input files based on the identified feasible replacements of HVAC systems and water heaters and next sends them to eQuest to calculate energy consumption of each feasible alternative. The calculated energy consumption of each feasible alternative is stored in the model databases where they can be used during the optimization computations. The GA co mputations starts by generating an initial solutions that represent the random replacements of build ing fixtures and equipment, selection of the co mponents of renewable energy systems, the percentage of renewable energy that can generated at the building site. The fitness of these solutions are evaluated based on their carbon emissions. Solutions that satisfy all the constraints and provide low carbon emissions are classified as solutions with high fitness value. Other solutions are classified as infeasible solutions or solutions with low fitness values. Solutions with high fitness values are ranked based on their carbon emissions and the GA operators of selection, crossover, and mutation are applied to generate a new set of solutions. This process is repeated iteratively until no further imp rovements can be achieved within a predefined number of iterations. The intimal population, mutation and crossover rates are set based on the GA string and possib le alternatives of the model decision variables [31,32]. 4. Application example An application example of an aging build ing in Illinois is analyzed to illustrate the model capabilities and demonstrate its use. This building has an area of 3,500 square feet and was built in 1980. The major contributors of the building energy consumption include space heating and cooling, interior and exterior lighting, water heating, hand dryers, vending machines. The input data of the optimization model include characteristics of building fixtures and equipment, build ing geometry and occupants use; electricity and gas consumption, and utility billing rates for a previous year. The optimization model is designed to allow the decision maker to select building fixtures and equipment fro m the model databases, as shown in Table 1. The building geo metry and occupants use are used to create an energy simulat ion model to estimate the energy consumption of the ag ing build ing after rep lacing its fixtures and equipment. The energy simu lation model was calibrated using the reported energy b ills to ensure the practicality and accuracy of the optimization model results. The annual energy consumption of the building is reported in the energy bills at 212,381KW H and 3,139 Therms. The existing conditions of the building leads to annual carbon emissions of 159,188 metric tons of CO2 emissions based on the building energy use. T able 1. Sample Input Data for Building Fixtures and Equipment of Public Building Building fixture or e quipment Fluorescent light fixture – 4’’ with 4 T8 Lamps of 34 Watts Square fluorescent fixture with 2 T 12 U-shaped lamps of 34 Watts Fluorescent light fixture – 5’’ with 2 T10 Lamps of 60 Watts Fluorescent light fixture – 4’’with 2 T12 Lamps of 34 Watts Snacks vending machine with average usage of 138 Watts HVAC system with a cooling capacity of 3.5 tons and heating capacity of 100Kbtu Water heater with a capacity of 60 gallons Hand dryer - 2300 Watts
Number of fixtures or e quipment 4 6 4 1 2
working hours pe r day (hrs) 24 24 0.33 2 24
1
24
1 4
24 per use
The developed optimization model is used to analyze and optimize the selection of bu ild ing upgrade measures of the aforementioned aging build ing to min imize its energy consumption and carbon emissions within specified upgrade budgets. For each of the specified upgrade budget, the model was able to identify the optimal selection of building fixtures and equip ment in addition to the components of renewable energy systems to minimize the building carbon emissions. The developed optimization model searched a large nu mber of possible solutions to min imize the carbon emissions of the building with a specified budget of $10K, as shown in Figure 1. It should be noted that the feasible solutions that the optimizat ion model searched in Figure 1 are separated in two groups due to the impact of replacing the HVA C system which results in high reduction in energy consumption and high init ial cost as compared
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to other building replacements such as lighting fixtures and bulbs, hand dryers, vending mach ines, and water heater. The model is designed to provide the results in a graphical and tabular form which include reco mmendations for replacing build ing fixtures and equipment; upgrade costs, carbon emission before and after the implementing the recommended upgrade measures, expected annual savings, and payback periods (if any). Table 2 shows an examp le of the model results for the building example with an upgrade budget of $10K. The results of the model identify the optimal selection of building upgrades based on an identified upgrade budget which helps decision makers and building owners in their ongoing task of maximizing the sustainability of their building wh ile co mp lying with their available budgets.
Figure 1. Feasible solutions searched during the optimization computations with upgrade budget of $30,000 T able 2. Optimization Results for an Upgrade Budget of $10,000 Building Sustainability Me asure
Upgrade Cost
Efficient Interior Lighting Exterior Lighting HVAC System Water Heater Hand Dryers Vending Machines Other Devices and Loads Photovoltaic System T otal
$1,389 $0 $5,821 $0 $1,734 $0 $699 $0 $9,985
Carbon Emissions (me tric ton CO2 emissions) Be fore Afte r 22,806 9,722 16,930 16,930 61,146 1576 6 6 6,024 903 17,569 17,569 34,707 34,112 0 0 159,188 80,818
Savings in ope rational costs $1,969 $0 $4,341 $0 $603 $0 $70 $0 $6,983
Payback pe riod (ye ars) 1.1 0 1.3 0 2.6 0 N.A. 0 1.5
5. Summary and conclusions This paper presented the development of an optimization model that is capable of identifying the optimal replacement of building fixtures and equip ment and the installation o f renewab le energy systems at the building site to simu ltaneously reduce energy consumption and carbon emissions. The optimizat ion model is designed to analyze
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the replacement of interio r and exterior lighting systems, HVA C systems, water heaters, hand dry ers, and the installation of renewable energy systems. The model is also designed with a set of constraints to perform its analysis within a specified upgrade budget while maintaining the existing operational performance of buildings. An application exa mple of an aging build ing is used to illustrate the model capabilit ies and demonstrate its use. The developed model was able to identify the optimal selection of building upgrade measures to minimize carbon emissions and energy consumption with various upg rade budgets. The optimization model is designed to generate the output results in a tabular and graphical form which provide detailed reco mmendations of the selected upgrade measures, includ ing brand name and model nu mber of the building fixtures and equipment, and renewable energy systems; savings in energy consumption and carbon emissions, savings in operational costs, required upgrade cost; and payback period (if any). The developed model provides new and unique capabilities that allow decision makers, building owners, and operators to identify, fro m a range of feasible alternative, the optimal selection of building upgrade measures that helps them in their ongoing task of maximizing the sustainability of their bu ild ings. Future expansion of the model is needed to further study the impact of feasible upgrade measures of the building envelope such as type of insulation, windows, and doors to consider their effects on reducing energy consumption and carbon emissions for buildings that have their energy cons umption dominated by HVAC systems. References [1]
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