Work

Collaborative optimization of distribution and microgrid based on combined forecasting and improved analytical target cascading method

Year: 2026

Type: article

Abstract: The stochasticity and volatility of renewable energy generation pose significant challenges to the secure and economic operation of power grids. To address the issues of insufficient prediction accuracy and the collaborative optimization of distribution networks and microgrids (MGs), this study conducts a comprehensive investigation. First, a hybrid prediction model based on Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis, and Bidirectional Long Short-Term Memory is constructed. EEMD is utilized to decompose renewable power sequences, which are subsequently filtered according to their contribution values to enhance prediction accuracy at the source. Second, operational models for the distribution network and microgrids are established. In the subsequent solution of the coordinated dispatch model, an improved Analytical Target Cascading method incorporating a balance coefficient is designed to eliminate the influence of improper initial penalty multiplier selection on model convergence. Simulation results based on an IEEE 33-node system integrated with microgrids demonstrate that the proposed method effectively reduces network losses and voltage deviation rates while further enhancing the overall economic efficiency of the system.

Source: AIP Advances

DOI: 10.1063/5.0315319

Authors: Wei Bao, Hairui Xu, Qilong Chen, Shang Gao, Yanan Yang, Fang Zhao

Institution: State Grid Henan Electric Power Company (China)

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $1,350