论文标题
通过广泛的析取编程对离散的能源系统的预测性控制
Model Predictive Control of Discrete-Continuous Energy Systems via Generalized Disjunctive Programming
论文作者
论文摘要
广义分离编程（GDP）提供了一个替代框架，可以模拟离散变量和连续变量的优化问题。 GDP背后的关键思想涉及使用逻辑析出来表示连续空间中的离散决策，以及在离散空间中表示代数约束的逻辑命题。与传统的混合构成编程（MIP）相比，GDP中固有的逻辑结构产生了更严格的放松，这些放松被全球分支和绑定算法所利用，以提高解决方案质量。在本文中，我们提出了一个通用的GDP模型，用于最佳控制既具有离散和连续动力学的混合系统。具体而言，我们使用GDP来制定具有隐式开关逻辑的分段式系统系统的模型预测控制（MPC）模型。例如，将基于GDP的MPC方法用作监督控制，以提高具有二进制开/关基于基于继电器的恒温器的住宅建筑物的能源效率。与现有的基于MIP的控制方法相比，模拟研究用于证明所提出的方法的有效性以及解决方案质量的提高。
Generalized Disjunctive Programming (GDP) provides an alternative framework to model optimization problems with both discrete and continuous variables. The key idea behind GDP involves the use of logical disjunctions to represent discrete decisions in the continuous space, and logical propositions to denote algebraic constraints in the discrete space. Compared to traditional mixed-integer programming (MIP), the inherent logic structure in GDP yields tighter relaxations that are exploited by global branch and bound algorithms to improve solution quality. In this paper, we present a general GDP model for optimal control of hybrid systems that exhibit both discrete and continuous dynamics. Specifically, we use GDP to formulate a model predictive control (MPC) model for piecewise-affine systems with implicit switching logic. As an example, the GDP-based MPC approach is used as a supervisory control to improve energy efficiency in residential buildings with binary on/off, relay-based thermostats. A simulation study is used to demonstrate the validity of the proposed approach, and the improved solution quality compared to existing MIP-based control approaches.
