Optimized Scheduling Based On Hydrogen Energy Utilization In Green Industrial Parks

In comprehensive energy systems incorporating various forms of energy, coordinating and optimizing the operation of each system component to maximize economic efficiency while meeting energy demands is crucial. This paper investigates the operational optimization and scheduling problem of a green industrial park's integrated energy system based on hydrogen utilization. Building upon modeling of key equipment, it addresses the stochastic nature of wind and solar outputs and the fluctuations in energy loads, constructing a multi-timescale optimization scheduling model encompassing both day-ahead and intra-day periods. Two scenarios are primarily studied: firstly, to mitigate the impact of uncertainty in wind and solar outputs and load, the PSO-BP neural network method is employed for forecasting; secondly, with economic optimization as the objective, penalty terms for curtailed wind and solar power and carbon emissions costs are introduced, and the park's coordinated optimization scheduling is conducted based on this.