Multi-objective reactive power optimization strategy of power system considering large-scale renewable integration

Abstract

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

“…As optimization problems continue to evolve, new techniques encompassing artificial intelligence, as well as the metaheuristic search-based optimization approaches were designed to tackle the D-OPF problem. Recent efforts focused on search-based optimization approaches, which include the genetic algorithm (GA) optimization method [10], particle swarm optimizer (PSO) method [11,12], differential evolution optimization method [13,14], enhanced genetic algorithms optimization method [15], gravitational searching algorithm (GSA) method [16,17], multi-phase searching optimization algorithm [18,19], improving colliding bodies method [20], improved PSO method [21], biogeography-based optimizing approach [22], fuzzy-based hybrid PSO method [23], blackhole optimization approach [24], imperialist competitive optimization algorithm [25], harmony search optimization algorithm [26], PSO hybrid with GSA method [27], grey wolf optimization technique [28], and bee colony optimization approach [29]. Additionally, many multi-objective functions have been introduced for the D-OPF in [30,31].…”