Identification of cost-effective multihazard capacity of nuclear power plant using multi-objective genetic algorithm

Abstract

After the Tohoku earthquake-tsunami (Japan, 2011), the regulatory efforts to mitigate the external hazards increase both safety requirements and the total capital cost of nuclear power plant (NPP). In these circumstances, identify not only the robust but also the cost-effective capacity of NPP becomes one of the most important tasks for the nuclear power industry. A few studies performed to relocate the seismic capacity of NPP, yet the effect of multihazard had not been accounted for NPP capacity optimization. The major challenges in extending this problem to the multihazard dimension are (1) high computational cost for both multihazard risk quantification and system-level optimization and (2) lack of capital cost database of NPP. To resolve these issues, this paper presents an effective method that identifies the optimal multihazard capacity of NPP using a multi-objective genetic algorithm, two-stage direct quantification of fault tree using Monte Carlo simulation (two-stage DQFM) method, and indirect capital cost measure. The proposed multihazard capacity optimization framework for NPP is demonstrated and tested by the NPP example.