Research on wind-thermal-energy storage optimization configuration method for large consumer loads

Configuring wind power for newly increased large consumer load has become an important planning mode for new energy generation. The planning mode needs to achieve local power without influencing the peak regulation burden of utility grids while maximizing the installed wind power capacity. To address this issue, regional wind power characteristics are firstly modelled using a scenario reduction method utilizing synchronous back-propagation algorithm (SBR) and K-means clustering to obtain yearly wind power scenarios. To realize local autonomous peak regulation, a bundled wind-thermal-storage optimization configuration method was proposed to achieve generation planning. Case studies show that with the decrease of wind curtailment rates, installed wind capacity increases and more wind power generation can be consumed locally. If the monthly bundled capacity is variable, the average bundled thermal power capacity will decrease, which is beneficial to increase the installed wind power capacity. Under the wind-thermal-load-storage bundling mode, the configured wind power capacity increases compared to the wind-thermal-load bundling mode. The local utilization rate of the bundled thermal power unit also increases when configuring energy storage.