Abstract:
The development of power generation technologies and the deregulation of the
power market has led to an increasing interest in distributed power generation, mainly
the simultaneous exploitation of electricity and heat from the same energy source,
known as combined heat and power (CHP) systems.
As a consequence of the high competitiveness of power markets and increasing
environmental concerns, distributed power generators have to make reasonable choices
at multiple levels of complexity. A key issue to successfully approaching these
problems is the development of decision making support tools that rely on service life
prediction, intelligent economic dispatch optimisation techniques and condition
monitoring.
This research introduces the concept and development of a decision making
support tool for a mini-pool nerve centre based on distributed gas turbine generation
units operating in a competitive market. The nerve centre framework leads naturally to a
multi-criteria optimisation problem which is solved in this research with a hybrid
genetic algorithm adapted priority list and creep life assessment. The proposed hybrid
approach can result in a significant saving to generators as it efficiently optimises mini-
pool profits and service hours between failures in an acceptable computation time and
accurately. Life cycle assessment combined with generation schedule optimisation can
enhance the maintenance strategy activities and the competitiveness of gas turbine
distributed generation plants, particularly for generators trading energy in a highly
competitive market.
Gas turbine combined heat and power distributed generators are unlikely to
succeed in competing individually with centralised generation technologies within the
present market framework, but they can be more competitive in an information
technology based mini-pool. Additionally, results show that the development of a low
carbon emission power industry can result in an outstanding opportunity for combined
heat and power mainly in power markets currently highly dependent on coal and oil
powered stations.