Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions

R. Rocchetta (1) , Yanfu Li (2) , Enrico Zio (3)

1 UNIBO - Alma Mater Studiorum Università di Bologna = University of Bologna
2 Center for Genomics and Systems Biology
3 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

Security and reliability are major concerns for future power systems with distributed generation. A comprehensive evaluation of the risk associated with these systems must consider contingencies under normal environmental conditions and also extreme ones. Environmental conditions can strongly influence the operation and performance of distributed generation systems, not only due to the growing shares of renewable-energy generators installed but also for the environment-related contingencies that can damage or deeply degrade the components of the power grid. In this context, the main novelty of this paper is the development of probabilistic risk assessment and risk-cost optimization framework for distributed power generation systems, that take the effects of extreme weather conditions into account. A Monte Carlo non-sequential algorithm is used for generating both normal and severe weather. The probabilistic risk assessment is embedded within a risk-based, bi-objective optimization to find the optimal size of generators distributed on the power grid that minimize both risks and cost associated with severe weather. An application is shown on a case study adapted from the IEEE 13 nodes test system. By comparing the results considering normal environmental conditions and the results considering the effects of extreme weather, the relevance of the latter clearly emerges.

Mots clés: Monte Carlo simulation; weather modelling; extreme weather conditions; Distributed generation; AC power flow; probabilistic risk assessment

Domaines: Energie électrique

R. Rocchetta, Yanfu Li, Enrico Zio. Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions. Reliability Engineering and System Safety, 2015, 136, pp.47-61. ⟨10.1016/j.ress.2014.11.013⟩. ⟨hal-01109499⟩