Use this URL to cite or link to this record in EThOS:
Title: Least-cost expansion planning in the electricity supply industry
Author: Holmes, Jane Hope
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1995
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Formerly, to meet increases in electricity demand or to replace obsolete stations, the decision to build a new power station of a particular fuel type was not based wholly on economic grounds. Often political pressure (e.g. using coal to keep mines open), national strategy (e.g. building of nuclear reactors) or government policy (e.g. introduction of hydro generation in Scotland to counter population drift) dominated the expansion planning process. With the advent of the availability of inexpensive computing power, planning based solely on economics, with calculations that are mathematically complex, repetitive and time consuming, can be applied more readily to such decision making. In addition, a wider range of factors can be taken into account. Sensitivity analysis and comparative assessments can be made easily, allowing the Planning Engineer to consider more options and to arrive at decisions with more confidence. Occasionally, however, the preferred options indicated by such planning (termed least-cost expansion planning) may be overruled through influence of externalities such as those mentioned above, e.g. political pressure. Thus, although least-cost expansion planning software will never replace totally the human involvement in the process, such software has the considerable advantage that it can be used to rank a range of options in order of economic cost. The Planner can then quantify in economic terms the effects of overriding indicated minimum-cost options by making decisions on the basis of some other grounds, e.g. governmental policy. This thesis examines the factors and techniques which are used in least-cost expansion planning. Their integration into a decision support system is described and suitable software is developed. Using realistic data a typical run of this software demonstrates ranking of minimum-cost candidates that successfully meet expected future electricity demands and planning criteria set before a run is executed. The merits of using the software in a practical application are then discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available