Summary for CIFE Seed Proposals for Academic Year 2015-16
Proposal number:
Proposal title:
Decision Support for Community and Infrastructure Planning
through Coupled Analysis and Optimization
Principal investigator(s)
and department(s):
Michael Lepech
Research staff:
Forest Flager, Rob Best, Additional Graduate Researcher
Total funds requested:
$ 64,776
Project URL for
continuation proposals
To be determined
Project objectives
addressed by proposal
Operable, Sustainable
Expected time horizon
> 10 years
Type of innovation
Breakthrough
Abstract
(up to 150 words)
The problem: Community planning and infrastructure design
operate sequentially with little cross-discipline integration. This
results in underestimation of distributed infrastructure operating
cost and overestimation of carbon reductions by up to 40%. There
is no framework for balancing diverse stakeholder objectives or
exploring coupled infrastructure solutions.
Civil and Environmental Engineering
The proposed solution: Coupled analytical models encompassing
resource generation, transportation, and consumption, paired with
multidisciplinary design optimization can balance objectives and
constraints of planning stakeholders. Decision support tools
created from optimization outputs would exponentially increase
the solutions examined and the ability of a design team to meet all
goals.
The proposed research approach: A coupled model will be
constructed for wastewater treatment and energy supply based on
work done at CIFE and ReNUWIt at Stanford. An optimization
study will be undertaken for a Bay Area development, and the
optimized result compared to the currently proposed design.
Uncertainty will be ascertained through Monte Carlo analysis.
Decision Support for Community and Infrastructure Planning through Coupled Analysis
and Optimization
Engineering or Business Problem
While architects, engineers and contractors have embraced Integrated Project Delivery to achieve
more cost-effective and sustainable buildings, urban and infrastructure planning continue to lack
integrated design methods, leading to large inefficiencies and compromises that increase life cycle
cost and environmental impact. While practitioners leverage technology such as energy efficiency,
distributed generation, microgrids, district heating and cooling, rain- and graywater capture, and
small-scale wastewater treatment solutions to reduce carbon emissions, water consumption, and
cost, these efforts are uncoordinated and result in suboptimal solutions that fail to meet design goals.
For instance, operational data from Combined Cooling, Heating, and Power (CCHP) installations
at Walt Disney theme parks show that efficiencies are approximately 20-40% lower and costs 2040% higher than anticipated during planning because of poor coordination between energy demand
and supply planningi. Disney’s case is not unique; interviews with other practitioners revealed that
the common practice of sizing all infrastructure on peak loads for campus or district infrastructure
causes similar changes in efficiency and cost.ii,iii One engineer commented that lack of early
coordination between urban planners and infrastructure planners could cause a doubling of life
cycle infrastructure cost from missed design opportunities.iv Research has confirmed that this
practice can cause up to a 40% overestimation of carbon emissions reductions in some cases.v
Anecdotal evidence and observations from three planning and development projects along with
published research suggest that the barriers to coordinated urban and infrastructure planning are:
(1) a sequential design process where not all objectives are considered early by key decision makers;
(2) lack of analytical frameworks for assessing performance of combined infrastructure systemsvi;
and (3) long design cycle times that do not allow for exploration of novel approaches to integrated
design. Figure 1 demonstrates the sequential nature of this process and the roles and barriers
experienced throughout.
Figure 1: Sequential design and lack of integration of urban and infrastructure planning leads to
a suboptimal solution with higher costs and lower efficiencies for the owner/end-user.
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Decision Support for Community Planning
2
Currently, when urban and infrastructure planning do not align, or the entire infrastructure system
(generation through end-use) is not accurately modeled, the end-user bears the higher price of
energy, water, etc. No methods exist to provide decision support to planners to test multiple cases
with all objectives and systems considered. Similarly, some practitioners and researchers argue
that novel technologies or connections between infrastructure systems can take advantage of the
gap between anticipated and actual performance.vii,viii However, no analytical tools exist for
coupled infrastructure systems, or for decision support for integrated planning
Theoretical and Practical Points of Departure
Recent practices in energy and infrastructure planning have begun to embrace the concept that
additional savings and reductions in resource use can be generated through integrated planning of
energy supply and demand infrastructure.vi However, the majority of these do not include
analytical modeling and rapid generation of multiple solutions, leaving a void for computational
tools that can balance multiple resources while exploring vast design spacesix. Furthermore, these
attempts do not include additional infrastructure systems (e.g., water), despite consideration in
literature that intelligent scheduling of water and wastewater treatment and inclusive modeling of
coupled energy and water consumption can lead to more efficient infrastructure planningx. This is
not for lack of understanding of other infrastructure systems and their interaction with energy;
anaerobic digesters and nitrogen-recovery treatment systems such as CANDO, developed at the
ReNUWIt Center at Stanford, have demonstrated the potential to produce excess energy and
benefit from heat inputs in the treatment process.xi
Additional evidence of improved efficiencies from combined planning of resource supply and
demand comes from eco-industrial parks. These are cases of smart planning based on industrial
ecology such that energy and material flows cascade from one industrial process to another,
resulting in higher resource use and recovery and lower waste ratesxii. One example, Kalundborg
Eco-Industrial Park in Denmark, saves 240,000 tons of carbon dioxide emissions and 3 million
cubic meters of water annuallyxiii. Surveys of additional eco-industrial parks, including planned
projects in the US and Denmark, indicate similar environmental and economic savingsxiv. The
experience of these parks shows both the benefits of combining infrastructure system and
simultaneously planning flows of resource demand and supply. However eco-industrial parks are
almost universally focused on cascading industrial resource flows. No work has been done
applying the same concepts of coupling and cascading resources in civic infrastructure.
Based on these theoretical underpinnings, research was undertaken at CIFE to translate
simultaneous supply and demand planning practices and industrial ecology themes to urban
systems. Oakland, California, was used as the basis for a “what-if” analysis assessing the potential
improvement in resource utilization if the current mix of building types (e.g., residential,
commercial, etc.) were optimized to run on a CCHP plant. Using building simulations from
EnergyPlus, the ratio of existing uses was simulated on an hourly basis to determine electricity,
heating, and cooling loads. These loads were used as inputs to a CCHP simulator, and a baseline
resource efficiency of 45% for the entire city was found. An optimization routine was then used to
determine the upper limit with flexibility in the ratios of each building type. An improvement of
11% to a total of 56% efficiency was foundxv. This shows the potential improvement from
computational optimization and integrated infrastructure planning. However, as noted previously,
this efficiency is still drastically lower than the theoretical maximum efficiency of over 80%. Even
in scenarios where energy supply technology was also varied, efficiency was not found to be higher
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Decision Support for Community Planning
3
than approximately 60% for constrained developments; integration of additional systems may help
compensate for this gap.
When findings such as these are used properly as a decision support tool, they have shown to be
effective both in building design and urban planning. Basbagill, Flager, and Lepech presented a
method for visualizing life cycle financial and environmental impact of building demand choices
using probability density functions that updated as decisions were madexvi Tsoutsos, et al., used a
Multi-Criteria Decision Analysis tool for planning on the isle of Crete and found that with 20
stakeholders, a compromise solution that improved overall utility relative to individual optimal
outcomes was reached.xvii Abraham, et al., found that data visualization decision support tools
helped practitioners improve the quality of building design solutions 3-5%.xviii
Research Methods and Work Plan
To address this challenge, the proposed research seeks to create an analytical model of coupled
building resource consumption, energy supply infrastructure, and water treatment infrastructure.
Multidisciplinary design optimization (MDO) will then be applied to test thousands of scenarios
for financial and environmental performance, with the goal of substantially reducing operational
cost and carbon emissions and reducing the cycle time for solution generation from weeks to
seconds. The results will be tested for statistical difference using a Monte Carlo analysis and used
to generate decision support tools for planners. A case study on a 25 acre development in the San
Francisco Bay Area will be performed to benchmark the change in solution quality, time, and total
number of solutions explored. This work involves three major stages.
Stage 1: Generate Coupled Wastewater/Energy Analytical Model and Formalize Design Problem
The proposed research would build on two models developed at Stanford to create a coupled local
wastewater and energy model. The first is the CANDO model of wastewater treatment via an
anaerobic digester developed as part of the ReNUWIt NSF Research Center. The second is a model
for balancing building energy demands with distributed CCHP and solar resources. Both models
will be extended to integrate flows of energy and water between the two systems. New flows of
waste heat to the wastewater treatment plant will be modeled to improve treatment efficiency and
lower cost. Similarly, flows of water to CCHP installations for heating, cooling, and cooling towers
will be identified and modeled. This analytical model is shown in Figure 2.
To provide decision support, a multi-objective optimization will be formulated as follows:
OBJECTIVES


Environmental Impact: Minimize the quantity of greenhouse gas emissions (CO2e) released
to meet the utility demand service life of the infrastructure (50 years)
Financial Cost: Minimize the total life-cycle cost of meeting the utility demand over the
service life of the infrastructure (50 years)
CONSTRAINTS

Lepech
Gross Floor Area, Floor Area Ratio, Percent of Each Building Type: Maxima and Minima
will be used to set upper and lower bounds on all constraints
Decision Support for Community Planning
4
VARIABLES

Building Types, Type of Energy Supply Infrastructure, Size of Energy Supply
Infrastructure, Size of Wastewater Treatment Infrastructure, Hourly Water Treatment,
Hourly Heat Diverted to Water Treatment
Figure 2: Description of the major process components and dependencies (coupling). Feedback
to allow for integrated decision making and design exploration is also shown.
The result of the optimization is expected to be a suite of equally “good” solutions when
benchmarked against both objective functions. Visualization and decision support tools and
interfaces will be created to communicate the chosen decision variables, massing, and operation to
the project owners.
Stage 2: Demonstrate Value of Decision Support Using Case Study
The proposed analytical model and optimization method will be applied to a proposed development
in the San Francisco Bay Area. The optimization method will be compared to the currently proposed
design generated using conventional methods, which is attempting to target relatively aggressive
sustainability and economic goals. It is hypothesized that optimization will allow exploration of a
significantly greater number of objectives in a shorter amount of time, and that this approach will
allow discovery of more solutions that meet or exceed the project goals. The value of the analytical
and optimization methods will be tested by evaluating:



CYCLE DURATION: The time to generate and evaluate a given design alternative.
CYCLE TOTAL: The total number of alternatives evaluated.
SOLUTION QUALITY: The degree to which the design objectives are satisfied.
Stage 3: Validation and Uncertainty Analysis
Given the novelty of the coupled wastewater/energy community planning and infrastructure model,
it is necessary to validate and assess the uncertainty of findings as much as possible. Where
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Decision Support for Community Planning
5
possible, data sets will be sought from partners and in literature to validate each piece of the
analytical model. Where data is unavailable, comparison will be made to industry-standard models
of power and water infrastructure using reference cases.
To assess uncertainty, a Monte Carlo analysis will be performed on the results of the case study.
Uncertainty in each step of the coupled model will be quantified through a literature review and
direct testing where possible and used to generate probabilistic uncertainty curves. Statistical tests
will be used to evaluate if the base case and optimal case are significantly different.
Expected Results: Findings, Contributions, and Impact on Practice
One anticipated result of this work are a novel coupled analytical model for wastewater and energy
infrastructure that uses a modular framework to allow testing of a variety of energy and wastewater
treatment technologies. A second anticipated outcome is an optimization method for balancing
building mix, energy infrastructure, wastewater infrastructure, and the operation of a coupled
system. The research team is not aware of a framework that exists currently for balancing
wastewater and energy infrastructure in a community; current models do not even allow balancing
of energy supply and demand rapidly using multidisciplinary design optimization. As such, the
model, optimization framework, and results of the case study will be submitted to the Journal of
Industrial Ecology for publication.
If this work is translated into industry, it is anticipated that adoption of the decision support tools
based on the ability to rapidly evaluate multiple infrastructure solutions and building use mixes will
greatly enhance planning of new communities and large developments. Understanding the
relationship of resource supply and demand has the potential to greatly reduce total energy and
water consumption on every site implementing the project, potentially reducing the footprint of
new construction by 20-30% at zero or reduced cost. These benefits will be spread among the
project owners and developers and the end-users who will benefit from lower operational costs.
Industry Involvement
CIFE member Walt Disney Imagineering (Ben Schwegler) has been consulted on the development
of this project. Disney has supported past Multidisciplinary Design Optimization (MDO) studies
through CIFE and continues to be deeply interested in the application of MDO to new problems.
The case study discussed previously has been identified through a relationship with a large energy
company with an office in San Francisco.
Opportunities for CIFE member involvement will exist during the course of this work. First,
identification of a CIFE member project for a second case study in an area distinct from the Bay
Area will allow more general testing of the analytical model, optimization framework, and resulting
decision support tools. Second, data sets on the operation of wastewater plants and buildings will
be required for validation. These could be gathered from past projects of CIFE members. Third,
cost information on new construction and operations and maintenance of infrastructure will be
required and may be obtained from CIFE members.
Research Milestones and Risks
The following milestones will be used to measure progress:
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Decision Support for Community Planning
6
Milestone
Completion Date
1. CREATE COUPLED ANALYTICAL MODEL
September, 2015
Description: Integrate existing energy and wastewater models in same framework with active,
accurate communication between each.
Risks: No similar analytical model has been found in literature. While the performance of each
type of infrastructure is well-studied, effects of coupling the two are unknown. Validating the
coupled model will be challenging as a result, but several approaches to validation have been
discussed previously in this proposal.
2. COLLECT CASE STUDY DATA
October, 2015
Description: Collect all data on financial and environmental parameters related to the problem,
as well as decision variables.
Risks: Companies that hold datasets are often unwilling to share what they know. The partner
with which we have worked thus far has been very open to sharing information, but may not have
all of the required information for the model. Finding sources to supplement the owner’s data
may be challenging. It is hoped that CIFE member companies can help alleviate this by sharing
their data.
3. COMPLETE FIRST CASE STUDY
January, 2016
Description: Simulate base case and optimized developments in coupled analytical model and
compare results.
Risks: Technological challenges in wrapping the coupled model in an optimization framework
may exist. Long simulation times may also require the use of additional computing resources or
process parallelization. These options will be considered once initial model testing has been
performed.
4. COMPLETION OF MONTE CARLO ANALYSIS
February, 2016
Description: Quantify uncertainty and apply Monte Carlo to both the base case and the optimal
solution.
Risks: Few studies have been performed on the uncertainty of building energy models,
technological models of CCHP and wastewater operational models, and other components of the
coupled model described herein. It is possible that sources of uncertainty will be missed or
incorrectly quantified. Sensitivity testing will help identify the limits of the solutions and provide
opportunities for future improvement in uncertainty assessments.
Next Steps
This work is currently supported in part by a grant from the Precourt Institute for Energy. Beyond
the duration of that grant and the CIFE SEED Grant, additional research will be proposed to the
National Science Foundation (NSF). An extension of this work will seek to explore how coupled
infrastructure systems increase urban resilience in collaboration with the John A. Blume Earthquake
Engineering Center. NSF has supported similar work through its Critical Resilient Interdependent
Infrastructure Systems and Processes (CRISP) solicitation and Civil Infrastructure Systems grants.
Both of these funding opportunities seek research necessary for designing and operating efficient,
resilient, sustainable civil infrastructure systems. The CIFE SEED grant would allow for
development of a first example of coupled infrastructure modeling and decision support necessary
for applying to larger opportunities.
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References
i
Schwegler, B. (May 2011). Personal Communication, R. Best Interviewer.
Tahir, A. (August 2014). Personal Communication, R. Best Interviewer.
iii
Coster, J. (August 2014). Personal Communication, R. Best Interviewer.
iv
Adams, K. and Naqvi, A. (June 2013). Personal Communication, R. Best and F. Flager
Interviewers.
v
Hawkes, A. and Leach, M. (2005). Impacts of Temporal Precision in Optimisation Modelling of
Micro-Combined Heat and Power. Energy, 30(10), pgs. 1759-1779.
vi
Keirstead, J., Jennings, M., & Sivakumar, A. (2012). A Review of Urban Energy System Models:
Approaches, Challenges, and Opportunities. Renewable and Sustainable Energy Reviews, 16,
3847-3866.
vii
Camci, F., Ulanicki, B., Boxall, J., Chitchyan, R., Varga, L., & Karaca, F. (2012). Rethinking
Future of Utilities: Supplying All Services Through One Sustainable Utility Infrastructure.
Environmental Science and Technology, 46, 5271-5272.
viii
Jaccard, M. (2005). Sustainable Fossil Fuels: The Unusual Suspect in the Quest for Clean and
Enduring Energy. Cambridge, England: Cambridge University Press.
ix
Pohekar, S., & Ramachandran, M. (2004). Application of Multi-Criteria Decision Making to
Sustainable Energy Planning--A Review. Renewable and Sustainable Energy Reviews, 8(4), 365381.
x
Camci, F., Ulanicki, B., Boxall, J., Chitchyan, R., Varga, L., & Karaca, F. (2012). Rethinking
Future of Utilities: Supplying All Services Through One Sustainable Utility Infrastructure.
Environmental Science and Technology, 46, 5271-5272.
xi
Scherson, Y. and Criddle, C. (2014). Recovery of Freshwater from Wastewater: Upgrading
Process Configurations To Maximize Energy Recovery and Minimize Residuals. Environmental
Science and Technology, 48(15) 8420-8432.
xii
Cote, R. P., & Cohen-Rosenthal, E. (1998). Designing Eco-Industrial Parks: A Synthesis of
Some Experiences. Journal of Cleaner Production, 6, 181-188.
xiii
Kalundborg. (2014). Kalundborg Symbiosis. Retrieved from Kalundborg Symbiosis:
http://www.symbiosis.dk/en
xiv
Heeres, R., Vermeulen, W., & de Walle, F. (2004). Eco-industrial park initiatives in the USA
and the Netherlands: first lessons. Journal of Cleaner Production, 12, 985-995.
xv
Best, R., Flager, F., Nowacki, C., Fischer, M., and Lepech, M. (2014) Optimizing the Total Fuel
Cycle Efficiency of an Idealized CCHP-Powered Community in Oakland, CA. Proceedings of the
ISSST v2.
xvi
Basbagill, J., Flager, F., &Lepech, M. (2014). A multi-objective feedback approach for
evaluating sequential conceptual building design decisions. Automation in Construction, 45, 136150.
xvii
Tsoutsos, T., Drandaki, M., Frantzeskaki, N., Iosifidis, E., & Kiosses, I. (2009). Sustainable
Energy Planning By Using Multi-Criteria Analysis Application in the Island of Crete. Energy
Policy, 37, 1587-1600.
xviii
Abraham, K., Flager, F., Macedo, J., Gerber, D., & Lepech, M. (2014). Multi-Attribute
Decision-Making and Data Visualization for Multi-Disciplinary Group Building Project
Decisions. Engineering Project Organization Conference.
ii
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Decision Support for Community Planning
8
Sponsor:
Submission Type:
Budget Preparation Date:
Budget Start Date:
Project Name:
Department:
Principal Investigator:
Administrator:
CIFE
New
4/15/2015
10/1/2015
Decision Support for Community Planning
Civil Engineering
Michael Lepech
Blanca Rebuelta
From
To
Personnel Salaries
Graduate Students
Research Assistant ‐ 2 quarters
Total Graduate Student Salaries
Period 1
10/1/2015
9/30/2016
All Periods
10/1/2015
9/30/2016
Academic
50.0% 19,156
19,156
19,156
19,156
Calendar
20.0% 19,825
19,825
19,825
19,825
38,981
38,981
996
6,433
7,429
996
6,433
7,429
46,410
46,410
50.0% 13,366
13,366
13,366
13,366
Foreign Travel
5,000
5,000
Total Other Direct Costs
18,366
18,366
64,776
64,776
Research Staff
Forest Flager
Total Research Staff Salaries
Total Salaries
Benefits
Graduate
Research Staff
Total Benefits
Total Salaries and Benefits
Other Direct Costs
Tuition
Research Assistant
Total Tuition
Total Amount Requested
Rates Used in Budget Calculations
Benefit Rates
Graduate: FY 1 05.20%;
Research: FY 1 32.45%;
Academic
FY 2 05.20%; FY 3+ 05.20%;
FY 2 32.45%; FY 3+ 32.45%;