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dc.contributor.advisorRajamani, Haile S.
dc.contributor.advisorAbd-Alhameed, Raed A.
dc.contributor.authorOkpako, Oghenovo
dc.date.accessioned2021-12-07T15:20:44Z
dc.date.available2021-12-07T15:20:44Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10454/18669
dc.description.abstractOne feature that is hoped for in the smart grid is the participation of energy prosumers in a power market through demand response program. In this work, we consider a third-party virtual power plant (VPP) that has “real-time” control over a number of prosumers’ storage units within an envisaged free market. Typically, a VPP with domestic energy storage will involve a bidirectional flow of energy, where energy can either flow from the grid to the prosumers’ battery or from the prosumers’ battery to the grid. Such a system requires prices to be set correctly in order to meet the market objectives of all the VPP stakeholders (VPP Aggregator, prosumers, and grid). Previous work has shown how VPPs could operate, and the benefits of using energy storage, coupled with pricing, in terms of reducing energy cost for stakeholders and providing the grid with its required load shape. The published work either assumes prices or costs or then optimises for least cost within the grid parameters i.e. losses, voltage limits, etc. However, the setting of prices in such a way that energy can be traded among VPP stakeholders that satisfies all stakeholders’ objectives has not been fully explored in the literature, particularly with real-time VPP aggregators. In this thesis, we present novel strategies for evaluating and setting the prices of a community VPP with domestic storage based on the bidirectional flow of energy through the VPP aggregator between the grid and the prosumers that mutually meet all VPP stakeholders’ objectives. This showed that depending on pricing and the VPP objectives, demand-side management could be attractive. However, the effect on the grid in terms of the load was not what was desired. A new performance index called the “Cumulative Performance Index” CPI is proposed to measure the VPP’s performance. Using the CPI, it was possible to compare and contrast between the VPP technical performance and its business case for stakeholders. Optimizing with respect to the grid’s requirement for DSM from the VPP, it was possible to achieve a CPI of 100%. This work was implemented using a novel approach on a genetic algorithm platform.en_US
dc.description.sponsorshipNiger Delta Development Commission of Nigeriaen_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectBattery storageen_US
dc.subjectVirtual power planten_US
dc.subjectProsumersen_US
dc.subjectSmart griden_US
dc.subjectState of chargeen_US
dc.subjectPricingen_US
dc.subjectStakeholdersen_US
dc.subjectGenetic algorithmen_US
dc.subjectDemand side managementen_US
dc.subjectCumulative performance indexen_US
dc.titleEnergy storage in the future smart grid. An investigation of pricing strategies and dynamic load levelling for efficient integration of domestic energy storage within a virtual power plant and its evaluation using a genetic algorithm optimization platformen_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentSchool of Engineering and Informaticsen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2019
refterms.dateFOA2021-12-07T15:20:44Z


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