A data-driven robust optimization approach to scenario-based stochastic model predictive control

Chen, Shang; You, Fengqi

doi:10.1016/j.jprocont.2018.12.013

Cited by 106 publications

(27 citation statements)

References 54 publications

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“…Building energy control has always been an important issue, applied in preset temperature tracking, thermostatic indoor greenhouse, and heat regulation of air gardens, etc. [52][53][54] Different from the widely implemented rule-based control law, which barely considers the system dynamics and makes decisions simply by virtue of measuring current states, intelligent and energy-saving control approaches are becoming more popular nowadays thanks to the emerging data-driven techniques. 55 Arguments could be found in supporting publications that taking the data information into consideration indeed improves the energy efficiency.…”

Section: Model Setup and Data Acquisitionmentioning

confidence: 99%

Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

Lee

You

2020

AIChE Journal

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This article proposes a novel distributionally robust optimization (DRO)‐based soft‐constrained model predictive control (MPC) framework to explicitly hedge against unknown external input terms in a linear state‐space system. Without a priori knowledge of the exact uncertainty distribution, this framework works with a lifted ambiguity set constructed using machine learning to incorporate the first‐order moment information. By adopting a linear performance measure and considering input and state constraints robustly with respect to a lifted support set, the DRO‐based MPC is reformulated as a robust optimization problem. The constraints are softened to ensure recursive feasibility. Theoretical results on optimality, feasibility, and stability are further discussed. Performance and computational efficiency of the proposed method are illustrated through motion control and building energy control systems, showing 18.3% less cost and 78.8% less constraint violations, respectively, while requiring one third of the CPU time compared to multi‐stage scenario based stochastic MPC.

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Section: Model Setup and Data Acquisitionmentioning

confidence: 99%

Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

Lee

You

2020

AIChE Journal

Self Cite

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“…Clustering and supervised machine-learning approaches were used in [GGJ20]. In [SHY17,SY19] the authors derive a kernel-based support vector clustering model to construct polyhedral uncertainty sets.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust Optimization using Unsupervised Deep Learning

Goerigk¹,

Kurtz²

2020

Preprint

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Robust optimization has been established as a leading methodology to approach decision problems under uncertainty. To derive a robust optimization model, a central ingredient is to identify a suitable model for uncertainty, which is called the uncertainty set, containing all scenarios against which we wish to protect. An ongoing challenge in the recent literature is to derive uncertainty sets from given historical data.In this paper we use an unsupervised deep learning method to construct non-convex uncertainty sets from data, which have a more complex structure than the typically considered sets. We show that the trained neural networks can be integrated into a robust optimization model by formulating the adversarial problem as a convex quadratic mixed-integer program. This allows us to derive robust solutions through an iterative scenario generation process. In extensive computational experiments, we compare this approach to a current state-ofthe-art approach, which derives uncertainty sets by kernel-based support vector clustering. We find that uncertainty sets derived by the unsupervised deep learning method can give a better description of data, leading to robust solutions that considerably outperform the comparison method both with respect to objective value and feasibility.

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“…In fact, the microgrid operation problem is often formulated as a model predictive control (MPC) problem, because MPC is widely accepted in varieties of industrial scenarios, and its effective ability to deal with optimization problems subject to large numbers of constraints (Shang and You, 2019). In fact, several methods can be adopted to solve the MPC problem.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Dispatching Performance Improvement of Multiple Multi-Energy Supply Microgrids Using Neural Network Based Approximate Dynamic Programming

Roche

2021

Front.Electron.

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In the multi-energy supply microgrid, different types of energy can be scheduled from a “global” view, which can improve the energy utilization efficiency. In addition, hydrogen storage system performs as the long-term storage is considered, which can promote more renewable energy installed in the local consumer side. However, when there are large numbers of grid-connected multi-energy microgrids, the scheduling of these multiple microgrids in real-time is a problem. Because different types of devices, three types of energy, and three types of utility grid networks are considered, which make the dispatching problem difficult. In this paper, a two-stage coordinated algorithm is adopted to operate the microgrids: day-ahead scheduling and real-time dispatching. In order to reduce the time taken to solve the scheduling problem, and improve the scheduling performance, approximate dynamic programming (ADP) is used in real-time operation. Different types of value function approximations (VFA), i.e., linear function, nonlinear function, and neural network are compared to study about the influence of the VFA on the decision results. Offline and online processes are developed to study the impact of the historical data on the regression of VFA. The results show that the neural network based ADP one-step decision algorithm has almost the same performance as the Global optimization algorithm, and the highest performance among all others Local optimization algorithms. The total operation cost relative error is less than 3%, while the running time is only 31% of the Global algorithm. In the neural network based ADP, the key technology is continuously updating the training dataset online, and adopting an appropriate neural network structure, which can at last improve the scheduling performance.

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A data-driven robust optimization approach to scenario-based stochastic model predictive control

Cited by 106 publications

References 54 publications

Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

Data-Driven Robust Optimization using Unsupervised Deep Learning

Real-Time Dispatching Performance Improvement of Multiple Multi-Energy Supply Microgrids Using Neural Network Based Approximate Dynamic Programming

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