Finck Christian, Li Rongling, Zeiler Wim
Department of the Built Environment, Eindhoven University of Technology, de Rondom 70, 5612 AP, the Netherlands.
Department of Civil Engineering, Technical University of Denmark, Brovej, Building 118, 2800 Kgs. Lyngby, Denmark.
MethodsX. 2020 Mar 19;7:100866. doi: 10.1016/j.mex.2020.100866. eCollection 2020.
Controllers employing optimal control strategies will path the way to enable flexible operations in future power grids. As buildings will increasingly act as prosumers in future power grids, optimal control of buildings' energy consumption will play a major role in providing flexible operations. Optimal controllers such as model predictive controller are able to manage buildings' operations and to optimise their energy consumption. For online optimisation, model predictive controller requires a model of the energy system. The more accurate the system model represents the system dynamics, the more accurate the model predictive controller predicts the future states of the energy system while optimising its energy consumption. In this article, we present a system model that can be used in online MPC, including dynamic programming as optimisation strategy. The system model is validated using a building and heating system, including heat pump and thermal energy storage. The following bullet points summarise the main requirements for the configuration of the system model:• 1 s;••
采用最优控制策略的控制器将为未来电网实现灵活运行铺平道路。随着建筑物在未来电网中越来越多地扮演产消者的角色，建筑物能耗的最优控制将在提供灵活运行方面发挥重要作用。诸如模型预测控制器之类的最优控制器能够管理建筑物的运行并优化其能耗。对于在线优化，模型预测控制器需要一个能源系统模型。系统模型对系统动态的表示越准确，模型预测控制器在优化其能耗时对能源系统未来状态的预测就越准确。在本文中，我们提出了一种可用于在线模型预测控制（MPC）的系统模型，包括将动态规划作为优化策略。该系统模型使用一个建筑物和供暖系统进行了验证，该系统包括热泵和热能存储。以下要点总结了系统模型配置的主要要求：• 1秒；••
