Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.

翻译：能源枢纽通过多个转换和存储组件结合不同能源输入，实现能源的转换与分配。能源枢纽的优化运行可充分利用其灵活性，提高能源效率并降低运营成本。然而，需求的不确定性给能源枢纽优化带来挑战。本文提出一种随机模型预测控制器，利用机会约束处理不确定的电力和热力需求，从而最小化能源成本。基于历史数据，采用高斯过程构建需求预测模型，以生成未来电力和热力需求的预测。随机优化问题通过场景方法求解，从推导的预测模型中采样多步需求轨迹。所提出的预测器与随机控制器的性能在模拟能源枢纽模型及真实建筑需求数据上得到验证。