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dc.contributor.authorHaider, Rabab.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2021-10-06T19:56:59Z
dc.date.available2021-10-06T19:56:59Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/132741
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, February, 2020en_US
dc.descriptionCataloged from the PDF version of thesis. "Some pages in the original document contain text that runs off the edge of the page. See Appendix C - MATLAB Code p.213 - 301"--Disclaimer page.en_US
dc.descriptionIncludes bibliographical references (pages 303-310).en_US
dc.description.abstractModern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these DERs can only be achieved at the nexus of new technological approaches and policies, primarily distributed computation and transactive energy. Transactive energy is a control mechanism which uses economic incentives, such as time-of-use or real-time electricity prices, to influence the behaviour of independent agents (i.e. DERs in the grid) as needed by the grid operator. This thesis tackles the problem of DER coordination by considering the role of distributed optimization algorithms in solving the optimal power flow (OPF) problem, when a large number of small scale DERs are present. The OPF problem minimizes costs to operate the grid, while subject to network constraints. The distributed implementation makes large-scale problems computationally tractable, while also maintaining privacy of local information. First, we utilize a new convex formulation of the power grid based on current injection (CI) and McCormick Envelopes, to model distribution grids of meshed topology and unbalanced structure. The OPF problem is then solved for such grid structures, using the distributed proximal atomic coordination (PAC) algorithm, which has several advantages over other distributed algorithms. These advantages include reduced network communication requirements, reduced local computational effort, and improved privacy. The DER coordination problem is then extended to consider storage devices. This requires a multi-period OPF formulation, which is also solved using the PAC algorithm. Results from active configurations of the IEEE 123-bus network corroborate the need for a multi-period optimization approach under high penetration of renewable resources. Finally, we propose a retail market mechanism which can be viewed as a transactive energy scheme within the distribution grid. Through the retail market, DERs are optimally scheduled in an energy market, and leveraged in alert system cases in an ancillary services market. The transactions of the energy market are carried out at each primacy feeder through bilateral agreements between the Distribution System Operator (DSO) and agents representing DERs at feeder buses, leveraging the PAC algorithm. These interactions determine the optimal real-time resource schedules and distributed Locational Marginal Price. The PAC algorithm and proposed market are extensively validated on a real distribution grid in Tokyo, a balanced IEEE 123-bus distribution grid, and a three feeder model using the IEEE 13-bus. The energy market is shown to result in an overall increase in revenue for the DSO.en_US
dc.description.statementofresponsibilityby Rabab Haider.en_US
dc.format.extent310 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleOptimal coordination of distributed energy resources in smart grids enabled by distributed optimization and transactive energyen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.identifier.oclc1263579884en_US
dc.description.collectionS.M. Massachusetts Institute of Technology, Department of Mechanical Engineeringen_US
dspace.imported2021-10-06T19:56:59Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentMechEen_US


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