Best - Fuel Cycle CCHP-Powered Community

Proceedings of the International Symposium on Sustainable Systems and Technologies, v2 (2014)

Optimizing the Total Fuel Cycle Efficiency of an Idealized CCHP-Powered Community in Oakland, CA

Robert Best Stanford University, [email protected] Flager Stanford University, [email protected] Nowacki Stanford University, [email protected] Fischer Stanford University, [email protected] Lepech Stanford University, [email protected]

Abstract. Energy production by combined cooling, heating, and power (CCHP) can achieve combustion efficiencies of 80-90% by simultaneously generating electricity and useful heating and cooling. In urban applications, systems typically only achieve efficiencies of 45-60%. One challenge facing design of these systems is that the relative demand for cooling, heating, and electricity for the district-scale networks they serve varies significantly over time while the production ratio between heat and electrical power is relatively constant. Hence an opportunity exists to improve the Total Fuel Cycle Efficiency by accounting for downstream losses and this temporal mismatch and better aligning supply and demand. No models exist that capture these impacts by simultaneously considering the supply and demand of a district CCHP network.

This paper presents an integrated urban energy model that evaluates different urban planning schemes (building use types and densities) to predict and improve the Total Fuel Cycle Efficiency of a microgrid and district heating and cooling network. Sequential quadratic programming is used to optimize the mix of buildings by modeling the energy demand and supply of all buildings within an idealized model of Oakland, CA. Over 16,000 solutions were tested; compared to a baseline case representing Oakland's current mix of building types, the optimal design resulted in a 12% improvement in annual average efficiency. However, maximum annual average efficiency was still only 57%, far lower than the theoretical upper limit of a CCHP plant. Inefficiencies are observed primarily in higher commercial cooling loads during the summer that do not match temporally with higher electrical loads. Simultaneous electrical peaking from residential and commercial buildings without corresponding heating and cooling loads also was found to contribute to inefficiency.

Proceedings of the International Symposium on Sustainable Systems and Technologies (ISSN 2329-9169) is published annually by the Sustainable Conoscente Network. Melissa Bilec and Jun-Ki Choi, co-editors. [email protected].

Copyright © 2014 by Robert Best, Forest Flager, Caroline Nowacki, Martin Fischer, and Michael Lepech Licensed under CC-BY 3.0.Cite as:Optimizing the Urban Plan of a CCHP-Powered Community for Total Fuel Cycle Efficiency Proc. ISSST, Best, R., et al. Doi information v2 (2014)

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Optimizing the Urban Plan of a CCHP-Powered Community for Total Fuel Cycle Efficiency

Introduction. Cities take up less than 2% of Earth's land area and consume more than 75% of its resources (Ramsar COP11 Scientific and Technical Review Panel, 2012). Furthermore, the systems that provide energy to buildings and other end-uses suffer from extremely high loss rates; in the United States, 58% of primary energy is lost to inefficiency prior to being used by the customer (The National Academy of Sciences, 2013). One increasingly common solution to reduce losses is Combined Cooling, Heating, and Power (CCHP) which converts a single primary energy source into three energy services. CCHP plants were first commercially operated in the late 1800s as primary sources of electricity and heat for urban centers (Thornton, 2005). Their recent resurgence can be attributed to their higher thermodynamic efficiencies. Whereas traditional electricity generation has efficiencies of 30-60%, CCHP can achieve efficiencies of 80-90% in certain cases (Horlock, 1987). CCHP units are often sized to maximize profit based on interconnection agreements with utilities rather than provide the maximum environmental benefit (Kolanowski, 2000). When operated as a primary energy source, the constant generation of CCHP systems is mismatched from the electricity, heating, and cooling consumption of the buildings to which it provides energy. This results in the Total Fuel Cycle Efficiency being significantly lower than efficiencies reported by manufacturers.

Few attempts to minimize the resulting loss of fuel cycle efficiency exist. A published survey shows a rise in publications on urban energy planning (Keirstead, et al., 2012), but most of these have tended toward multi-criteria decision analysis supported by analytical models, but without computational rankings or optimization (Pohekar & Ramachandran, 2004) (De Sousa, et al., 2012). Robinson, et al., modeled energy flows in communities using annual energy information but no optimization of the community was performed (2009). SynCity, developed at Imperial College, evaluates urban energy systems based on a common ontology incorporating agent-based simulation of individuals and vehicles, and energy modeling of large blocks of building uses (Keirstead, et al., 2009) (Van Dam & Keirstead, 2010). SynCity does not include hourly assessment of energy performance and does not provide enough detail to vary building form and mix of uses.

None of these urban-scale simulations provides the ability to quantify efficiency loss when CCHP systems are used as a primary energy source for a new community, especially where the community is a microgrid disconnected from a larger source of energy. Furthermore, no means of optimizing demand to match the output of a CCHP plant is present. This paper presents a study based on an urban energy model capable of quantifying the Total Fuel Cycle Efficiency of CCHP plants, the energy distribution network, and the energy use of a community.

Goals and Case Study Definition. The goal of this study was to assess the Total Fuel Cycle Efficiency of scaled models of Oakland (referred to herein as “City”) and its Central Business District (referred to herein as “CBD”) if they were powered by a CCHP plant. The City was used since it is a representation of traditional, historic growth guided by zoning and planning regulations using a typical process. The CBD represents a specially designated zone designed to create higher social and economic impacts per unit area; often CBDs are expected to be environmentally more efficient as well, making it an interesting scenario for comparison. These scenarios were compared to an optimized model that varied heights and mix of uses of common building types in Oakland simultaneously with the operation and size of the CCHP plant to determine improvement in Total Fuel Cycle Efficiency through optimization of demand. Though such a model would be more appropriately applied to a true greenfield project (i.e., new urban development in China or India), Oakland was chosen as a case study due to the availability of data for energy consumption of U.S. buildings and availability of planning and zoning information for the City of Oakland.


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The reported use of each zoned parcel in Oakland was taken from the 2014 Assessor’s database. For the CBD, additional information was available on building height and floor area. The average building height and use per zoning type were used to correlate 173 parcel use types in the Assessor's file to 26 building types used for simulation. Appendix 1 details the resulting 26 building types and their simulated floor areas and heights.

Model Framework. As noted above, previous studies have failed to capture tradeoffs between urban form, mix of uses, and Total Fuel Cycle Efficiency. To characterize and maximize efficiency in this manner required a novel modeling framework. This framework involved bottom-up energy modeling of all 26 building types distilled from the Assessor’s File, deterministic assessment of energy loss, and deterministic modeling of energy supply. Three software packages were used to create an integrated energy model and optimization platform, the conceptual architecture of which is given in Figure 1.

Integrated Energy Model. Energy demand for all 26 buildings was simulated in EnergyPlus, a free multiphysics-based building energy simulation tool developed by the U.S. Department of Energy (DOE) (Crawley, et al., 2001). Prototype building simulation files for EnergyPlus published by DOE based on the Commercial Building Energy Consumption Survey and the Residential Energy Consumption Survey were matched to the 26 building types in Oakland (Deru, et al., 2011). Where exact prototypes were unavailable, characteristic components of existing prototypes were recombined to match the desired use and height. Additional industrial energy consumption information was also gathered from life-cycle assessment studies of relevant industries (UNIDO, 1998; Rue, 2007; Therkelsen, 2014; Metso Global, 2014).

Figure 1: Model Architecture. Architecture of the model to assess Total Fuel Cycle Efficiency of community energy supply and demand. The model uses an optimization routine linked with an integrated energy model containing supply, demand, and loss components, each built from component models of buildings and power equipment.

Simulations were run on all 26 buildings for 8,760 hours using Typical Meteorological Year weather files for Oakland Airport. Hourly profiles are important for modeling CCHP given the wide swings in efficiency based on changes in heating and electrical loads (Hawkes & Leach, 2005). These EnergyPlus simulations are preprocessed prior to optimization, creating a look-up table by building. For any particular community plan, the energy performance was determined by aggregating the profiles of all buildings in the community in Microsoft Excel.

Losses were considered deterministically based on a literature regarding energy loss in district heating and cooling networks and electrical distribution (Boehm, 2001). Since losses depend on the distance energy resources travel, they are deterministically quantified based on the total number of buildings in each simulation iteration. An average distance between buildings was



calculated to be 35 m given the lot sizes of buildings in Oakland. A loss factor of 3 W/m of pipe was used for heating distribution, and a loss factor of 17 W/m of pipe was used for cooling distribution; electrical losses were assumed to be minimal over short distances. Losses are added to the energy profiles to generate hourly requirements for the CCHP plant.

The plant was assumed to utilize between one and five reciprocating gas engines, an auxiliary boiler, absorption chillers, and centrifugal chillers. The gas engines produce both heat and power, while the boiler provides supplemental heat as needed and the chillers provide cooling energy. This set up was based on a study of optimal arrangements and control algorithms for CCHP plants by Kavvadias, et al. (2010). The control strategy for the plant was based on the Electric Load Equivalent method from Kavvadias, et al., (2010). Parameters for the simulated equipment were based on highly efficient models currently on the market, and are shown in Appendix 2.

Total Fuel Cycle Efficiency (TFCE, denoted ƞ) is calculated according to the second law of thermodynamics as a ratio between the energy consumed for heating, cooling, and electricity, and the energy present in the natural gas consumed to power the CCHP plant. This is shown in Equation 1.

η=∑b∑tvb (Qb ,t+Cb ,t+Eb ,t )

∑tLHV t

(1)

In Equation 1, b indexes all building types in the demand model, t indexes time and is bounded from 1 to 8,760. Q, C, and E are the heating, cooling, and electrical loads (J/m2) in each hour for each building, respectively. vb denotes the floor area of each building type. LHV is the lower heating value (J) of natural gas consumed in each hour.

Optimization. Optimization was performed in ModelCenter, a commercial software package from Phoenix Integration that allows integration of simulation programs via plug-ins and software wrappers written in the Python programming language (PHX, 2008). Decision variables are chosen in ModelCenter and used to control linked simulation files (e.g., Microsoft Excel) for calculation of the objective function. This process is repeated for each candidate solution until an optimum is found. For this case study, a single objective of maximizing TFCE was used (see Equation 1). The SEQOPT sequential quadratic programming algorithm was used to find a solution. This algorithm statistically samples the potential solution space using an orthogonal array and then uses quadratic programming on simplified surrogate models and pattern search to iteratively improve the local optima (Booker, et al., 1999). The algorithm ceases when incremental improvement in the objective function is lower than 10-5.

The decision variables, vb, were 26 continuous parameters describing the gross floor area of each building type. A dependent integer variable ranged between 1 and 5 as a multiplier for the number of gas engines in the CCHP plant, dictating its size. A constraint was placed on the total electrical demand, Emax, such that it could not exceed 47.5 MWh, the capacity of the 5 gas engines. Constraints were also placed on the maximum percentage of each of building type, Vb, so that no single building could exceed 25% of the total built-up area. These ensure a more realistic scenario by varying heights and uses. Non-negativity constraints on each building were also included. The constraints are shown in Equations 2 and 3.

Emax≥∑bvbEb ,t ∀ t (2)


Best, et al.

V b≥vb≥0 ∀ b(3)Results, Analysis, and Validation. The optimization program was run on a computer with two Quad Core Xeon E5440 processors (2.83 GHz) and 16 GB of RAM. The maximum efficiency optimization ran to completion in 3,951 iterations in 8.5 hours; 7 similar trials were run to generate additional solutions and find the minimum efficiency. The mix of uses in the CBD and City were specified in the Integrated Energy Model and the TFCE values recorded without optimization. A second optimization was performed to minimize TFCE to characterize the feasible solution space. Table 2 shows the TFCE and hourly standard deviation of all four scenarios. The average fraction of electrical and heat demand from the gas engines is also shown. In the CBD, City, and Maximum Efficiency scenarios, five combustion engines were chosen by the simulation for an electrical capacity of 47.5 MW; the Minimum Efficiency scenario used two combustion engines, for a capacity of 19 MW. An ideal scenario where all energy from the CCHP plant is consumed is also shown for comparison.

Table 2: Characteristics of the City, CBD, Maximum Efficiency, and Minimum Efficiency scenarios.Run Total Fuel Cycle

EfficiencyHourly Standard

Deviation in TFCEAverage Electrical

FractionAverage Heat

FractionIdeal Scenario 87.5% 0.00% 0.55 0.45Maximum Efficiency 55.67% 6.95% 0.65 0.35City Baseline 45.33% 9.91% 0.79 0.21CBD Baseline 44.92% 9.46% 0.76 0.24Minimum Efficiency 35.77% 7.93% 0.67 0.33

Figure 2 shows the mix of building uses for each scenario. Uses are characterized into 8 primary use types by aggregating similar uses of different forms present in the 26 prototype buildings.

Maxim

um E

fficien

cy

Minimum

Effic

iency

City S

cena

rio

CBD Sce

nario

0%20%40%60%80%

100%

Mix of Building Uses for Simulated Scenarios

ResidentialOfficeMedicalLodgingIndustrialEducation#REF!

Figure 2: Results of Optimization. Chart showing the mix of uses for the Maximum Efficiency, Minimum Efficiency, City, and CBD Scenarios. The Maximum Efficiency scenario shows significantly higher proportions of medical, lodging, and retail buildings than the City and CBD. The Minimum Efficiency has a large amount of residential space.

The results of the simulation and optimization indicate the potential to increase TFCE 10% and 12% from the City and CBD scenarios, respectively. Examination of the building types in the Maximum Efficiency scenario reveals that the efficiency increase stems from an unviable, large percentage of medical, retail, and hospitality buildings. The hourly profiles of these types contain



temporally similar space conditioning and electrical load profiles, creating a more constant utilization of electricity and heat from the gas engine throughout the year. The baseline studies, by comparison, have a larger share of office and residential buildings, which contribute to a coincident afternoon electrical peak without corresponding heating or cooling. This is also reflected in the average heat and electricity fractions shown in Table 2; more equal heat and electricity fractions exist in the Maximum Efficiency scenario. These fractions are closer to the ideal as well, indicating more complete use of the heat and electricity generated from natural gas combustion. Furthermore, the reduced standard deviation in hourly TFCE values indicates reduced numbers of extreme lows and highs in efficiency, and greater clustering of hourly TFCE values around the higher mean. This is partially a result of the more constant loads generated by the mix of uses chosen in the optimal scenario; such clustering can also be in part an artifact of the optimization.

It is worth noting that the maximum efficiency scenario is still far short of meeting the ideal utilization of heat, cooling, and electricity, indicating that for this scenario, it is impossible to balance perfectly the output characteristics of the CCHP plant by only varying the mix of building uses. A gap still exists in many hours between the thermal and electrical demand and supply; additional schedulable electrical and thermal uses not tied to the mix of buildings or load-shifting technologies may help balance the supply and demand of electricity, heating, and cooling. However a study of these potential solutions is beyond the scope of this work.

To validate the results, a Monte Carlo analysis (MCA) was performed incorporating three main sources of variability. The first is the accuracy of the prototype building hourly energy results as representations of the actual buildings. Fumo, et al., among others, have characterized the standard deviation of the mean of this uncertainty as 10% annually (2010). The distribution is assumed to be normal by the Central Limit Theorem given that the error results from hundreds of choices, each with unique distributions. The second is the hourly forecast error inherent in using energy modeling as a predictive tool for planning electricity, heating, and cooling production. Sevlian and Rajagopal have shown that for clusters of 200 buildings or greater, the standard deviation of the mean is stable at 10%, and has a normal distribution (2013). Sevlian and Rajagopal show this for electricity; it is assumed to be true for heating and cooling as well. The final error is in efficiency of the CCHP engine at a given operating point. This was deduced from documentation from General Electric to be normal with a standard deviation of 0.75 at any point along the efficiency curve (Payrhuber, K. and Trapp, C., 2011).

MCA was performed on all four scenarios. 30 trials were simulated for each drawing from all three error distributions. The resulting distributions are shown in Figure 3. The distributions show no overlap between the maximum and minimum efficiency scenarios and either baseline. Furthermore, a t-test shows that the minimum and maximum efficiency cases are different from each other and from both baselines with greater than 99% confidence. The two baselines cannot be shown to be unique from one another.


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0.3 0.35 0.4 0.45 0.5 0.55 0.60

50100150200250300350400

Probability Density Functions for Efficiencies of All Four Scenarios

Max Efficiency Min Efficiency City CBDFigure 3: Probability Density Functions from MCA of results. The distributions above were generated from a Monte Carlo Analysis of the Total Fuel Cycle Efficiency of all four scenarios. No perceptible overlap is evident, indicating that the maximum efficiency scenario is distinct from both baselines and the minimum efficiency case.

Conclusions and Future Work. A retrospective case study of repowering a scaled model of Oakland, CA, with a 47.5 MW CCHP plant demonstrated that optimization of the mix of building uses can improve Total Fuel Cycle Efficiency up to 12%. This increase results from a higher percentage of uses with similar electricity and space conditioning hourly load profiles. This study assumed that the hourly heating, cooling, and electrical demand of the community could be estimated by aggregating the 16 EnergyPlus Reference Prototypes and 10 additional models. Additional research is required to fully validate the accuracy of this approach for Oakland, CA, and for this type of modeling. Additional research should also explore the implications of using more prototype buildings that provide a more granular set of uses and densities. Specific technology was selected for the CCHP plant as well; exploration of the solution space with different equipment and control schemes should be explored. In addition, this study used a simplified method to calculate heating, cooling, and electrical losses based solely on the distance of the building from the CCHP plant. Further work is planned to model the layout and corresponding losses of the distribution network in more detail.

Future research will focus on extending simulation to other sources of energy and other uses to allow simultaneous optimization of supply and demand that may better balance the thermal and electrical demand and supply. It is also recognized that the mix of uses in the maximum efficiency scenario presented here is not socially viable; we plan to consider additional social or economic objectives and constraints in the future to better reflect the variety of considerations urban planners face when designing new communities.

Acknowledgements. The authors would like to thank the Center for Integrated Facility Engineering at Stanford University, Walt Disney Imagineering and Dr. Ben Schwegler, and the National Science Foundation Graduate Research Fellowship Program for supporting this work.



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Supplementary Information

Optimizing the Total Fuel Cycle Efficiency of an Idealized CCHP-Powered Community in Oakland, CA

Robert Best Stanford University, [email protected] Flager Stanford University, [email protected] Nowacki Stanford University, [email protected] Fischer Stanford University, [email protected] Lepech Stanford University, [email protected]

Appendix 1: Summary of building types simulated for case study including heights in stories, gross floor area, and annual heating, cooling, and electrical loads in MJ/m2.

Building Type Stories

Gross Floor Area (m2)

Heating (MJ/m2)

Cooling (MJ/m2)

Electricity (MJ/m2)

Single Family Residential 1 223 112.7 1.018 214.9Residential Townhouses 2 392 64.21 71.27 470.5

Mid-Rise Residential Apartments 4 3,135 17.12 119.9 321.7High-Rise Residential Condominiums 12 9,405 4.864 165.9 285.0

Small Hotel 4 4,013 70.11 51.02 530.7Large Hotel 6 11,345 79.87 387.7 1,089Small Office 1 511 12.80 74.98 435.5

Medium Office 3 4,982 50.16 143.5 401.9Large Office 12 46,320 31.28 119.9 398.7Warehouse 1 4,835 57.10 1.820 173.1

Refrigerated Warehouse 1 4,835 0.00 580,700 580,700Bakery 1 4,835 19.67 7.123 647.1

Foundry 1 4,835 1.970 612.0 695.3Steel Recycler 1 4,835 25.83 5.393 2,972Glass Factory 1 4,835 41.32 3.642 301.6

Hospital 5 22,422 480.1 796.2 877.4Outpatient Care 3 3,804 477.8 1,019 905.5Primary School 1 6,871 34.88 111.1 431.5

Secondary School 2 19,592 19.63 68.66 386.8Mixed Use: Condos, First Floor Retail 12 9,405 4.35 174.3 287.1Mixed Use: Offices, First Floor Retail 12 46,320 157.1 593.8 1,961

Strip Mall 1 2,090 133.6 34.15 446.5Stand-Alone Retail 1 2,294 118.4 31.20 409.2

Quick Service Restaurant 1 232 273.0 81.74 3,201Full Service Restaurant 1 511 459.0 115.3 2,552

Supermarket 1 4,181 343.0 9.941 1,409


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Appendix 2: Equipment types and operating parameters simulated for the CCHP plant in this study.Equipment Type Chosen Model Operating Characteristics

Prime Mover (Heat and Power Generation Technology)

GE Jenbacher 920 Gas-Fired Combustion Engine

9.5 MW0.85 Heat to Power Ratio7006 Btu/kWh Heat Rate

Max Electrical Efficiency: 0.475

Centrifugal Chiller McQuay MPV Centrifugal Compressor Water Chillers COP: 3.5

Absorption Chiller York YHN Double-Effect Absorption Chillers COP: 1.5

Boiler Standard Industrial Boiler Efficiency: 80%


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