Use this URL to cite or link to this record in EThOS:
Title: Quantifying the system balancing cost when wind energy is incorporated into electricity generation system
Author: Issaeva, Natalia
ISNI:       0000 0004 2729 1207
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2009
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Incorporation of wind energy into the electricity generation system requires a detailed analysis of wind speed in order to minimize system balancing cost and avoid a significant mismatch between supply and demand. Power generation and consumption in the electricity networks have to be balanced every minute, therefore it is necessary to study wind speed on a one-minute time scale. In this thesis, we examine the statistical characteristics of one-minute average values of wind speed. One-minute wind speed is available from a single site in Great Britain while there are records of ten-minute wind speed available. We apply a modified Gibbs sampling algorithm to generate one-minute wind speed required for optimization modelling from the available ten-minute wind speed. System balancing costs are estimated through optimization modelling of the short-term electricity generation with wind energy contributing to the total supply. Two main drivers of additional system cost caused by wind energy are variability and unpredictability of one-minute wind speed. Further, a linear mathematical optimization model for a problem of short-term electricity generation is presented to calculate an additional balancing cost that appears as a result of wind energy variability. It is then shown that this additional balancing cost can be estimated using the statistical characteristics of wind energy present in the system. The unpredictable characteristic of wind speed is analysed with the techniques of stochastic programming. Uncertainty of the expected wind speed is represented through scenario trees and stochastic linear optimization models are used to calculate the extra cost due to uncertainty. Alternative optimization models are compared by calculating the additional balancing cost and the extent of imbalance between power generation and consumption in the system.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: wind energy ; electricity networks ; modelling