The optimal planning and dynamic operation of distributed generation method based on modified multiobjective optimization in power distribution system

This paper proposes an optimal method to plan and dynamically operate the distributed generation (DG) based on the modified nondominated sorting genetic algorithm II (NSGA-II). First, the uncertainty of load and DG (photovoltaic panels) output are considered. Daily summer load data and ideal photovoltaic (PV) panels output data are taken from a local electrical company in Albuquerque. To model the uncertainty of load, the daily load is randomized to each bus for every time interval (15 minutes), but with their sum equal to the daily load. The uncertainty of PV output is modeled by adding the cloudy index and frequency factor to the ideal PV output. Second, the placement of a DG is defined by voltage sensitivity analysis. The bus with high voltage sensitivity is considered to have priority for the installation of DG. To find the optimal daily operation of DG, a multiobjective problem is formulated that focuses on the minimization of a circuit's voltage deviations, active and reactive power losses. To solve the problem, the traditional NSGA II is modified by incorporating a fuzzy logic decision model. The fuzzy logic model selects an optimally compromised solution from the Pareto front by analyzing its weights of voltage deviations, active and reactive power losses. The selected solution includes the optimal outputs for every generator, synchronous compensator, capacitor bank, and PV panels for each time interval. Furthermore, to operate DG optimally and dynamically, the method's computation speed is crucial. To increase the modified NSGA II computation speed, the population initialization space is reduced and the population is selected and saved for the next generation based on load analysis and experiments. The method is tested on the IEEE 14 bus and a local residential circuit. The results on reducing the power losses, voltage deviations, and increasing the algorithm speed demonstrate the effectiveness of this method.