Random Fuzzy Power Flow Analysis for Power System considering the Uncertainties of Renewable Energy and Load Demands

Abstract

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE.

Probability confidence correlation analysis for many‐objective optimal operation considering a set of conflict interests

“…Literature [10] proposes a stochastic two-objective optimization method to solve the optimal power flow problem with uncertain wind power, taking fuel cost and wind power permeability as the optimal goals, and obtains more comprehensive results than the traditional deterministic single-objective optimization. Literature [11,12] establish the multi-objective reactive power optimization problems of high-proportion renewable energy power system, which takes network loss, voltage deviation and quiescent voltage stability margin as three independent objectives to evaluate and improve the reactive power regulation performance of new energy units, and the effects of network loss and voltage deviation on system stability can be analyzed from the solution results. However, with the optimization objectives increasing, the computational complexity and solution time of many-objective optimization problems (MaOPs) will increase dramatically.…”