百兆瓦级源网荷储一体化园区多时间尺度调度策略OA

With the global transition toward clean and low-carbon energy systems,the randomness and volatility associated with high-penetration renewable energy integration pose significant challenges to power system balance.Traditional"source-follows-load"dispatching models struggle to accommodate large-scale renewable energy consumption.This study focuses on a 100 MW-level source-grid-load-storage integrated industrial park in Xinjiang and proposes a three-stage multi-timescale dispatching strategy,including day-ahead optimization,intraday rolling correction,and real-time rapid-response scheduling.To efficiently solve(PSO)the resulting high-dimensional dispatch model,an elite-preferred particle swarm optimization algorithm is developed.The algorithm generates initial solutions through logical judgment and introduces an elite particle screening mechanism,which significantly reduces the search dimension and effectively avoids local optima.Practical implementation demonstrates that monthly wind and solar curtailment was reduced to 4.9%,achieving renewable energy absorption of 62200.13 MWh per month.A self-developed smart control platform addresses the challenges of renewable energy integration and power balance in 100 MW-level industrial parks.This research fills the technical gap in large-scale integrated energy system regulation,provides a replicable solution for low-carbon transformation,and supports the construction of new power systems.