Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445662
Title: A probabilistic method of modelling energy storage in electricity systems with intermittent renewable energy
Author: Barton, John P.
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2007
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Abstract:
A novel probabilistic method has been developed for modelling the operation of energy storage in electricity systems with significant amounts of wind and solar powered generation. This method is based on a spectral analysis of the variations of wind speed and solar irradiance together with profiles of electrical demand. The method has been embodied in two Matlab computer programs: Wind power only: This program models wind power on any time scale from seconds to years, with limited modelling of demand profiles. This program is only capable of modelling stand-alone systems, or systems in which the electrical demand is replaced by a weak grid connection with limited export capacity. 24-hours: This program models wind power, solar PV power and electrical demand, including seasonal and diurnal effects of each. However, this program only models store cycle times (variations within a time scale) of 24 hours. This program is capable of modelling local electrical demand at the same time as a grid connection with import or export capacity and a backup generator. Each of these programs has been validated by comparing its results with those from a time step program, making four Matlab programs in total. All four programs calculate the power flows to and from the store, satisfied demand, unsatisfied demand and curtailed power. The programs also predict the fractions of time that the store spends full, empty, filling or emptying. The results obtained are promising. Probabilistic program results agree well with time step results over a wide range of input data and time scales. The probabilistic method needs further refinement, but can be used to perform initial modelling and feasibility studies for renewable energy systems. The probabilistic method has the advantage that the required input data is less, and the computer run time is reduced, compared to the time step method.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.445662  DOI: Not available
Keywords: Energy storage ; Modelling ; Wind power generation ; Photovoltaics ; Stand alone power systems ; Interconnected power systems ; Voltage control ; Embedded generation
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