Congestion Management of Power System With Integration of Renewable Resources Considering Demand Response Based on Improved Ecosystem-Based Optimization Method

Abstract

Recently, transmission congestion remains a critical challenge in power systems, especially in deregulated markets. While Generation Rescheduling (GR) is the conventional approach for Congestion Management (CM), integrating Demand Response (DR) and Distributed Generation (DG) has also proven to offer system operational benefits. However, coordinating these three elements (GR, DR, and DG) within an AC model imposes a computational burden, making the problem highly challenging for standard optimization techniques. To address this problem, this paper proposes an Improved Artificial Ecosystem-Based Optimization (IAEO) algorithm. The proposed IAEO incorporates stochastic search and random crossover mechanisms to significantly enhance the exploration and exploitation capabilities of the original AEO, preventing premature convergence in non-convex search spaces. The proposed framework is validated on the IEEE 30-bus and IEEE 118-bus systems and benchmarked against standard and recent algorithms (AEO, EEFO, SPO, PSO, and DE). Simulation results indicate two major findings. First, incorporating DR reduces CM costs by 2.8% and 32.3%, while the fully coordinated GR, DR, and DG strategy achieves remarkable cost reductions of 53.8% and 35.9% for the respectively considered systems compared to the conventional GR approach. Second, the optimality of the proposed method is improved up to 9.85% and 21.2% in the IEEE 30-bus and IEEE 118-bus systems, respectively. Furthermore, statistical evaluations using the Wilcoxon signed-rank test confirm that the performance improvements achieved by the IAEO are statistically significant compared to others.