A Probabilistic Optimal Power Flow Calculation Method with Latin Hypercube Sampling

Abstract:

Among the solving methods of probabilistic optimal power flow (P-OPF), Monte Carlo Simulation (MCS) combined with random sampling (RS) is widely used due to its high accuracy. In order to further improve that, this paper proposes a way of using Monte Carlo Simulation with Latin hypercube sampling (LHS) to calculate the consumption of generating cost under many random variables. Numerical results of IEEE 14-bus and IEEE 118-bus systems show that the Latin hypercube sampling method provides more accurate performance in dealing with POPF under the condition of a smaller sample size, comparing with random sampling method. Thus the Latin hypercube sampling method can replace the MCS with random sampling as the benchmark method of other algorithms.

• Periodical: Advanced Materials Research (Volume 918)
• Pages: 183-190
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.918.183
• Online since: April 2014
• Authors: Jin Quan Zhao, Chen Lu Zhang*, Wei Hua Luo, Jun Zhao
• Keywords: Latin Hypercube Sampling, Monte Carlo Simulation, Probabilistic Optimal Power Flow, Random Sampling

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