IET Renewable Power Generation · 2014 · 84 citations · 35 references
AgricultureComputational EngineeringEnergy SystemsRenewable Energy SystemsUncertainty QuantificationDistributed Energy ResourcesEnergy System ModelingHybrid Renewable Energy SystemsPower SystemsRenewable EnergyWind Energy SystemsDistributed Energy Resources IntegrationDistributed Energy SystemsHybrid Power SystemsPower System ModelingProbabilistic AnalysisSolar-wind Hybrid SystemsSystem Performance ModelingUncertain Systems Modeling
As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies such as wind and solar, is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system context, consequently, necessitates probabilistic analysis of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognised as an appropriate probabilistic method. This study proposes a new methodology for probabilistic power flow studies for such a problem by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables, but the proposed method has been equipped with this ability. To justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6‐bus and the IEEE118‐bus test systems are examined using the proposed method, then the obtained results are compared against the Monte Carlo simulation results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.
35
Unscented Filtering and Nonlinear Estimation
Simon Julier, Jeffrey Uhlmann · Proceedings of the IEEE · 2004 · 6.5K citations
Computing, Data Science, Forecasting +14
B. Borkowska · IEEE Transactions on Power Apparatus and Systems · 1974 · 791 citations
Engineering, Electrical Engineering, Stochastic Modeling +11