Managing sustainable demand-side infrastructure for power system ancillary services

Date

2011-12-22

Authors

Parkinson, Simon Christopher

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Abstract

Widespread access to renewable electricity is seen as a viable method to mitigate carbon emissions, although problematic are the issues associated with the integration of the generation systems within current power system configurations. Wind power plants are the primary large-scale renewable generation technology applied globally, but display considerable short-term supply variability that is difficult to predict. Power systems are currently not designed to operate under these conditions, and results in the need to increase operating reserve in order to guarantee stability. Often, operating conventional generation as reserve is both technically and economically inefficient, which can overshadow positive benefits associated with renewable energy exploitation. The purpose of this thesis is to introduce and assess an alternative method of enhancing power system operations through the control of electric loads. In particular, this thesis focuses on managing highly-distributed sustainable demand-side infrastructure, in the form of heat pumps, electric vehicles, and electrolyzers, as dispatchable short-term energy balancing resources. The main contribution of the thesis is an optimal control strategy capable of simultaneously balancing grid- and demand-side objectives. The viability of the load control strategy is assessed through model-based simulations that explicitly track end-use functionality of responsive devices within a power systems analysis typically implemented to observe the effects of integrated wind energy systems. Results indicate that there is great potential for the proposed method to displace the need for increased reserve capacity in systems considering a high penetration of wind energy, thereby allowing conventional generation to operate more efficiently and avoid the need for possible capacity expansions.

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Keywords

Resource management, Renewable Energy, Power system operation and control, Computational modelling

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