The significant cost required for implementation of WEC sites and the uncertainty associated with their performance, due to the randomness of the marine environment, can bring critical challenges to the industry. This paper presents a probabilistic methodology for predicting the long-term power generation of WECs. The developed method can be used by the operators and designers to optimize the performance of WECs by improving the design or in selecting optimum site locations. A Markov Chain model is constructed to estimate the stationary distribution of output power based on the results of hydrodynamic analyses on a point absorber WEC. To illustrate the application of the method, the performance of a point absorber is assessed in three locations in the south of Tasmania by considering their actual long-term sea state data. It is observed that location 3 provides the highest potential for energy extraction with a mean value for absorbed power of approximately 0.54MW, while the value for locations 1 and 2 is 0.33MW and 0.43MW respectively. The model estimated that location 3 has the capacity to satisfy industry requirement with probability 0.72, assuming that the production goal is to generate at least 0.5MW power.

Wave energy converters (WEC) are reaching a pinnacle in their prototype phase. World leaders in the energy sector are looking for renewable energy sources to replace the expiry of fossil-fuel energy capacity. For WECs to become a viable solution to the fossil-fuel challenges, there is a need to have a deeper understanding of the associated costs and the operational impacts of this technology. This study investigates the relationship between these two characteristics and finds an improved implementation strategy by developing a dynamic risk-based methodology. The methodology developed from this study will aid WEC technology to move towards a commercialised state by implementing an array or farm of WEC devices. Bayesian network (BN) is adopted to analyse the probability of a collision accident within the farm as well as the likelihood of meeting the desired level of power production. The BN is later extended to an influence diagram (ID) for selecting the optimum configuration of the WEC farm. The ID assists in decision-making based on the investigated probabilities, required capital investment, and economic impact of the accident scenario. To demonstrate the application of the developed method, a case study is adopted including three decision alternatives, each representing a farm with different layouts of point absorber WECs. The performance of the facility is assessed under real-life offshore environmental conditions. The developed methodology assists in finding the WEC layout which minimises the economic risk of an array implementation and also increases the reliability of these structures.