Application of Bayesian network (BN) and influence diagram (ID) to multi-criteria decision making (MCDM).
Development of a novel methodology for improvement of power generation efficiency in renewable energy applications.
Theoretical Integration of the influencing parameters and costs associated with power generation in decision making process.
Development of a utility function for representation of wave energy converter (WEC) implementation.
Research and development of alternative energy resources such as wave energy has always attracted significant attention due to their abundant and sustainable nature. The uncertainties associated with the marine environment and the significant costs required for implementation of Wave Energy Converters (WECs) require a sound decision making methodology. This paper presents a novel risk-based methodology for selecting sites for WEC installation to minimize the overall economic risk. It provides WEC developers, investors, governments and policy makers a methodology for evaluating influencing parameters for potential site locations whilst also optimizing wave energy extraction. A Bayesian network is developed to model the probabilistic influencing parameters and then it is extended to an influence diagram for estimating the expected utility of installing the WEC equipment in a selected location. To demonstrate the application of the developed methodology, three sites in the south coast of Tasmania are considered. Based on actual sea state data, the optimum location for installing WEC equipment is determined as location 2 and the economic risk associated with energy extraction is minimized by suggesting a specific wave height (HS = 5 m) as a design criteria.
- Decision making;
- Renewable energy;
- Wave energy converter;
- Bayesian network;
- Influence diagram;
- Expected utility
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