Evolutionary Optimization Approach for Performing Interval Power Flow Considering Uncertainties in Electric Power Systems

The paper deals with problem of power flow calculation if there are uncertainties present in power distribution network variables or parameters. Load, network parameters and supply uncertainties can be represented by ranges of values. From mathematical point of view these ranges can be defined as intervals, fuzzy numbers or a probability density function. Since the input data are interval instead crisp numbers, the question is what bounds of the power flow output variables are. Literature suggests various methods such as probabilistic power flow, bound power flow and interval and fuzzy arithmetic for calculating power flow considering uncertainties. The proposed approach is based on optimization problem formulation. The presence of uncertainties is defined in optimization problem form with minimization and maximization of power flow output quantities as objective functions of the optimization. The uncertainties in input quantity are taken into account through optimization problem inequality constraints. The evolutionary algorithm is used to solve the optimization problem. The standard power flow calculation for crisp numbers is used instead power flow based on interval or fuzzy arithmetic. The proposed procedure is realized through co-simulation between PSAT and MATLAB computer programs. The proposed approach is tested on IEEE 14 node test distribution feeder. The simulation results indicate that proposed procedure has capability to solve considered problem.