Self-adaptive fully connected weight network for intraday dispatch optimization in integrated park-level energy systems
ZHANG Tao
DONG Nan-jiang
WANG Rui
Abstract：The intraday dispatch optimization model is a nonlinear multi-objective problem involving semi-continuous variables,while also imposing high requirements on computational efficiency.This poses a challenge to the convergence speed of existing multi-objective intelligent optimization algorithms.To address this,this paper constructs an intraday dispatch model with two time granularities(15-minute and 5-minute intervals)and a corresponding optimization frame-work.Given the high timeliness requirement of intraday dispatch optimization,a self-adaptive update strategy for the fully connected weight network model is designed based on the two-fully-connected-weight-network evolutionary algorithm(TFCWNEA),denoted as S-TFCWNEA.By adaptively adjusting the standard deviation of model parameters during the optimization process,the strategy balances global and local search capabilities,accelerating population convergence.The two-time-granularity dispatch optimization achieves a stepwise refinement of the day-ahead scheduling plan.Simulation results demonstrate that the proposed self-adaptive update strategy for the fully connected weight network significantly improves the algorithm's convergence speed,enabling rapid response to fluctuations in renewable generation and load demand.The method efficiently adjusts the dispatch plan within a limited time frame.
Keywords：fully connected weight networkmulti-objective optimizationenergy system dispatch optimisationpa-rameter self-adaptation
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：11( 2231-2241 )