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dc.contributor.advisorRajeev Ram and Munther Dahleh.en_US
dc.contributor.authorMehra, Varun, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2017-05-11T19:57:54Z
dc.date.available2017-05-11T19:57:54Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/108959
dc.descriptionThesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, 2017.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 199-209).en_US
dc.description.abstractSolar-based community micro-grids and individual home systems have been recognized as key enablers of electricity provision to the over one billion people living without energy access to-date. Despite significant cost reductions in solar panels, these options can still be cost-prohibitive mainly due over-sizing of generation assets corresponding with a lack of ability to actively manage electricity demand. The main contribution shared is the methodology and optimization approach of least-cost combinations of generation asset sizes, in solar panels and batteries, subject to meeting reliability constraints; these results are based on a techno-economic modeling approach constructed for assessing decentralized micro-grids with demand-side management capabilities. The software model constructed is implemented to represent the technical characteristics of a low-voltage, direct current network architecture and computational capabilities of a power management device. The main use-case of the model presented is based on serving representative, aggregated, household-level load profiles combined with simulated power output from solar photovoltaic modules and the kinetic operating constraints of lead-acid batteries at hourly timesteps over year-long simulations. The state-space for solutions is based on available solar module and battery capacities from distributors in Jharkhand, India. Additional work presented also extends to real-time operation of such isolated micro-grids with requisite local computation. First, for load disaggregation and forecasting purposes, clustering algorithms and statistical learning techniques are applied on quantitative results from inferred load profiles based on data logged from off-grid solar home systems. Second, results from an optimization approach to accurately parametrize a lead-acid battery model for potential usage in real-time field implementation are also shared. Economic results, sensitivity analyses around key technical and financial input assumptions, and comparisons in cost reductions due to the optimization of solar and battery assets for decentralized micro-grids with demand-side management capabilities are subsequently presented. The work concludes with insights and policy implications on establishing differentiated willingness-to-pay, tiers of service, and dynamic price-setting in advanced micro-grids.en_US
dc.description.statementofresponsibilityby Varun Mehra.en_US
dc.format.extent209 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectTechnology and Policy Program.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleOptimal sizing of solar and battery assets in decentralized micro-grids with demand-side managementen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Technology and Policyen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.contributor.departmentTechnology and Policy Program
dc.identifier.oclc986485495en_US


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