8th International Conference on Advanced Power System Automation and Protection (APAP2019)

1064 / 2019-05-20 02:47:15

A Data-driven Approach to Select Representative Scenarios for Risk-Oriented Assessment of Long-Distance Renewable Consumption

clustering algorithm,renewable energy,Risk assessment,Scenario analysis

Large-scale integration of renewable sources has become a global trend of energy development, which brings great challenges to the safe operation of power systems. This paper presents a data-driven scenario selection approach to support risk-oriented assessment of long-distance renewable consumption based on massive power system operation scenarios. As serious risks of renewable consumption mainly occur in the so-called extreme operation scenarios, this paper develops a customized data mining technique to recognize and extract the areas where extreme operation scenarios are located. Then, a specialized strategy is designed to enhance the selection of extreme operation scenarios in each scenario cluster. Furthermore, a heuristic optimization model is established to determine a set of representative scenarios with high precision. The effectiveness of the proposed scenario selection methodology is verified through comparison with traditional methods. Numerical results also validate the obtained representative scenarios for use in risk-oriented assessment of renewable consumption.