Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

Abstract

For the sustainability of power supply and operation systems, planners aim to deliver power at an optimum value to consumers, while maintaining stability in the system. The load-shedding approach has proven to be an effective means of achieving the desired stability. This paper presents a nodal analysis to establish critical bus identification in the power grid. A power simulation for load shedding was created using the power system analysis toolbox (PSAT) for identifying and isolating weak buses on the power system. A computational algorithm was developed using differential evolution (DE) for minimizing service interruptions and blackouts, and was tested against the conventional genetic algorithm (GA). The proposed algorithm was implemented on an IEEE 30-bus test system. The simulation results were analyzed before and after the application of DE. It was observed that after the application of DE, load shedding gives an efficient result of 10.6%, 8.7%, and 13.4% improvement at buses 26, 29, and 30, respectively, after being tested using a genetic algorithm (GA), with a result of 10.2%, 7.6% and 13.1% on the same respective buses. This work will further expand the reliability and availability of power systems toward a sustainable, steady power supply that is void of nodal or bus cutoffs.

A Blocking Method for Overload-Dominant Cascading Failures in Power Grid Based on Source and Load Collaborative Regulation

“…Solving the Cascading Failure Model. According to the solving process of the CFB control model in Section 4, taking the initial fault line [39,46] as an example, the NSGA-II algorithm and DE (differential evolution) algorithm [26] were used to calculate all optimal solutions that meet the model. The Pareto frontier point sets under different algo-rithms are shown in Figure 6, and the comparison results under different algorithms are shown in Table 3.…”

Pelican Optimization Algorithm for Optimal Demand Response in Islanded Active Distribution Network Considering Controllable Loads

“…In [24], non-critical and critical buses are identified for UFLS by evaluating power imbalance using polynomial regression and the optimal load shed problem is solved using MILP. In [25], nodal analysis and participation factors are employed for identifying weak buses, and later, differential evaluation (DE) is used for optimal load shed for improving voltage profile. In [26], to balance the hourly load demand with the available DG generation under islanding conditions, a grasshopper optimization algorithm (GOA) is proposed for voltage stability guarantee with a distributed optimal amount of load shed is analysed.…”