Data-driven Probabilistic Static Security Assessment for Power System Operation Using High-order Moments
Guanzhong Wang, Zhiyi Li, Feng Zhang, Ping Ju, Hao Wu, Changsen Feng
Abstract
In this letter, a new formulation of Lebesgue integration is used to evaluate the probabilistic static security of power system operation with uncertain renewable energy generation. The risk of power flow solutions violating any pre-defined operation security limits is obtained by integrating a semi-algebraic set composed of polynomials. With the high-order moments of historical data of renewable energy generation, the integration is reformulated as a generalized moment problem which is then relaxed to a semi-definite program (SDP). Finally, the effectiveness of the proposed method is verified by numerical examples.
Topics & Concepts
Probabilistic logicMoment (physics)Renewable energyMathematical optimizationElectric power systemComputer scienceSet (abstract data type)Order (exchange)Power (physics)Applied mathematicsMathematicsEngineeringElectrical engineeringQuantum mechanicsClassical mechanicsFinanceProgramming languageEconomicsArtificial intelligencePhysicsOptimal Power Flow DistributionPower System Reliability and MaintenancePower System Optimization and Stability