Annual Acquisition Research SymposiumNaval Postgraduate School Naval Postgraduate School Acquisition Research Progra

McDonald, M.L.

Morse, M.M.

Naval Postgraduate School Acquisition Research Program

Nussbaum, D.A.

Sanchez, S.M.

Upton, S.C.

Calhoun, Institutional Archive of the Naval Postgraduate School

Calhoun: The NPS Institutional ArchiveReports and Technical Reports All Technical Reports Collection2014-05-01A Robust Design Approach to CostEstimation: Solar Energy for MarineCorps Expeditionary OperationsSanchez, S.M.http://hdl.handle.net/10945/45105A ROBUST DESIGN APPROACH TO COST ESTIMATION: SOLAR ENERGY FOR MARINE CORPS EXPEDITIONARY OPERATIONS S.M. Sanchez, M.M. Morse, S.C. Upton, M.L. McDonald, D.A. NussbaumDepartment of Defense Distribution Statement:  Approved for public release; distribution is unlimited.Bottom Line Up Front- Assessing life cycle cost and risk are important – and tricky – problems!- Motivation: USMC Expeditionary Energy- E2O initiatives- HOMER model- Sources of variability- Designed experiments can help- Find out more…2Background- Cost estimates underpin many important decisions in the Marine Corps, DoD, and beyond.- Computational models may provide useful insights—but they are typically too complex to study with brute-force methods - “Robust design” incorporates many sources of uncertainty that can influence life cycle costs, in terms of expected cost and the risk of exceeding or falling under a threshold.- NPS’s SEED Center specializes in new methods for designing and conducting computational experiments—leading to revolutionary changes in the way we can leverage computational models3Expeditionary Energy2011 USMC E2O Strategy • Goal: 50% of bases “net-zero” by 2020• First focus: forward operating bases– 32% of fuel consumed by MEB (2009, Afghanistan) used for electric power generation(Schwartz et al., 2012)– Ground Renewable Energy System (GREENS) one successful renewable energy asset4MPEM (MAGTF Power and Energy Model)- Mission-level model used to assess potential impact of energy investments on fuel consumption. - Inputs include unit type and size (e.g. MEU, MEB, etc.), length of the operation and OPTEMPO phases, equipment type and efficiencies, and environmental conditions (solar, wind, temperature). 5Cost/Usage model in use at USMCOutputs depend on the inputs, could be converted to costs for direct comparison with other alternatives and acquisition costsOutputs include: daily requirement for liquid fuel and electricity (kW) to sustain the operation  secondary measures (days of supply, number/weight of batteries required, …)6Cost/Usage model under considerationHOMER (Hybrid Optimization Model for Electric Renewables)- Assists in identifying the optimal composition of a power system for decreasing life cycle fuel consumption when given a specified load profile and location- Power system assets  considered include generators, battery banks, solar arrays and wind turbines-Micropower System Modeling With HOMER“Optimal” results depend on specific fixed cost inputs, such as diesel costs, capital costs, and non-fuel O&M costs Model inputs: operational, environmental, and cost7Spatial variability8Annual solar irradiance in the United States (from USEIA, 2013).Temporal variability9Monthly direct normal irradiance (DNI) interannual coefficient of variation (COV) in the United States (Gueymard & Wilcox, 2011)Temporal variability: Salt Lake City10Histogram of total solar irradiation over days 75-134 for Salt Lake City, by year, 1961-2010Temporal variability: Salt Lake City11Scatterplot of total solar irradiation over days 75-134 for Salt Lake City, by year, 1961-2010Cost projections:  oil and solar12Oil cost projections (from USEIA, 2014) and PV array cost projections (adapted from USDOE, 2014)PV Cost of EnergyCost of Energy (cents/kWh)1008060402001980                    2000                    2020(a)(b)Reveal risk of exceeding a target budgetExploring robustness of cost estimatesReplace fixed cost estimates with distributionsNote that variability is much largerExploring robustness of cost estimatesExamine impact of correlated submodel costs on overall costExploring robustness of cost estimatesBehind the scenes:  Design of Experiments• For simple models with few input factors, we can use Monte Carlo simulation• For models with many factors that have interactions, or nonlinear effects, this doesn’t work• Fortunately, not all factors / sources of variation are equally important.  Structured exploration helps identify driving factors, knees in the curve, “robust” alternatives, etc.• Large-scale models will require large-scale experiments.16Behind the scenes:  Design of Experiments• Consider a model with 100 factors• Study each factor at only 2 levels• This would require 2100 experiments, approximately 1,000,000,000,000,000,000,000,000,000,000…not good enough to be of practical use!17Behind the scenes:  Design of Experiments18• Designed experiments (developed by NPS’s SEED Center) allow 100’s of factors to be explored in days or weeks• Analysis makes use of a variety of statistical data mining techniques• A revolution in capabilities for gaining insights from computational modelsFuture• Effects of (correlated) uncertainties in submodel costs• What if high fuel prices tend to increase O&M transportation/spare part costs, but also tend to hasten economies of scale for new energy technologies?• Incorporate with operational simulations• How robust are particular energy strategies over a set of likely MAGTF mission types and AORs?19Find out more• Details and references for this study  Acquisition Research Symposium Proceedings• Much broader study of energy modeling in HOMER, use of renewable energy for USMC expeditionary opsMorse, M. (2014).  An analysis of the HOMER energy micropoweroptimization model's robustness for Marine Corps expeditionary operations (Master’s thesis, Naval Postgraduate School).  In process.• More on large-scale design of experimentsSanchez, S. M., T. W. Lucas, P. J. Sanchez, C. J. Nannini, and H. Wan (2012).  “Designs for large-scale simulation experiments, with application to defense and homeland security.”  Chapter 12 in Design of Experiments, V. 3 (ed. K. Hinkelmann).  http://harvest.nps.edu (SEED Center website)20Define ObjectivesQuestions?21updated 05-12-2014http://harvest.nps.edu

A Robust Design Approach to Cost Estimation: Solar Energy for Marine Corps Expeditionary Operations

http://calhoun.nps.edu/bitstream/handle/10945/45105/NPS-AM-14-C11P22Z01-142.pdf?sequence=3&isAllowed=y

A Robust Design Approach to Cost Estimation: Solar Energy for Marine Corps Expeditionary Operations

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Calhoun, Institutional Archive of the Naval Postgraduate School