Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770398
Title: Hybridisation of high penetration photovoltaic, anaerobic digestion biogas power plant and electrical energy storage
Author: Lai, Chun Sing
ISNI:       0000 0004 7652 4038
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Abstract:
This thesis presents the development of methodologies for the hybridisation of an energy system with a solar Photovoltaic (PV), an Anaerobic Digestion biogas power plant (AD) and an electrical energy storage (EES). This includes the system sizing and operating regime. The key aspect is to evaluate the Levelized Cost of Electricity (LCOE) for the system and the generation assets. The challenge arises in analysing the economic projections on PV and EES hybrid systems. EES does not produce electricity as it is not a conventional generation source. Commonly, the cost of a generating asset or the power system is evaluated using LCOE. Levelized Cost of Delivery (LCOD) has been proposed to calculate the LCOE for the EES. A deterministic approach for sizing an off-grid PV and EES with AD biogas power plant to meet a proportional scaled-down demand of the national load has been implemented. The aim is to achieve a minimal LCOE for the system while minimising the energy imbalance between generation and demand due to both AD and PV generator constraints. To reduce the amount of irradiance data and to provide a standardised methodology for PV system design and analysis, feature extraction technique has been used to discover Clearness Index (CI) patterns and to construct centroids for the daily CI profiles. Fuzzy C-Means with Dynamic Time Warping has been used for daily CI profiles' classification. An operating regime has been proposed for the hybrid system, and the EES degradation cost has been considered via a capacity fade model. The work presented in this thesis has been supported with case studies from real-life solar irradiance and national load data. The proposed methodologies have contributed to a more efficient and cost-effective method for PV-AD-EES hybrid system sizing and operation.
Supervisor: McCulloch, Malcolm D. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.770398  DOI: Not available
Keywords: Data analytics ; Photovoltaic power systems ; Energy economics
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