Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules

Jongh, Steven de; Steinle, Sina; Hlawatsch, Anna; Mueller, Felicitas; Suriyah, Michael

doi:10.1109/upec50034.2021.9548179

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The aforementioned grid-focused studies all apply their method to simplified use cases and learn the output of static optimal power flow problems. De Jongh et al [26] are the first to use ANNs to learn a dynamic MPC-based problem of a smart distribution grid. The distribution grid covers 15 nodes and 13 households and considers electric vehicles and heat pumps.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Based Approximation of the Hierarchical Model Predictive Control of Multi-Use PV-Battery Systems in Non-Residential Buildings

Maier,

Quast,

Hering

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Model predictive control (MPC) has proven to be a promising method to exploit energy saving potentials in building energy systems. However, they are not widespread in practice due to high hard-and software requirements, high computational effort, and missing trust and know-how among practitioners. Approximate MPC can address these challenges by replacing the hard-and software-intensive optimization program by black box models. Machine learning models such as Artificial Neural Networks or tree-based algorithms have been widely investigated by the scientific community. However, a comparison of Artificial Neural Networks with advanced tree-based models like Random Forest and Gradient Boosting is still missing. In addition, the relation between the models' training and the resulting control performance has not yet been assessed. We close these gaps by investigating the optimal control based on an MPC of a PV-battery system in a non-residential building. The MPC optimizes the battery's power based on a preceding peak load optimization. The MPC is imitated by three machine learning models, namely, an Artificial Neural Network, a Random Forest, and Gradient Boosting, whose performance is subsequently evaluated open-and closed loop. While Gradient Boosting results in the highest open-loop performance with an R 2 of 0.83, it deviates more significantly from the optimal control trajectory than, e.g., the Artificial Neural Network. Nonetheless, Gradient Boosting even outperforms the teacher MPC when considering the system's annuity. This is explained by its ability to push beyond the peak load constraints which are set within the optimization. A rule-based backup controller is, therefore, included for all approximator-based controllers. Based on this, the approximators result in a peak load reduction between 5 % and 7 % compared to the benchmark and a change in annuity between −1 % and 4 % compared to the MPC. To summarize, all approximators can retain most of the MPC's advantages but do not surpass its overall performance.

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Section: Introductionmentioning

confidence: 99%

Machine-Learning Based Approximation of the Hierarchical Model Predictive Control of Multi-Use PV-Battery Systems in Non-Residential Buildings

Maier,

Quast,

Hering

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Advanced Control Strategies for Resilient Voltage and Frequency Regulation in Smart Grids

Rahul Kumar Jha,

Birendra Kumar Shah,

Patel

2024

JEEA

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This study discusses advanced control strategies for voltage and frequency regulation in smart grids, particularly in the integration of renewable energy sources and electrification. These strategies, including Model Predictive Control (MPC), adaptive control, optimal control, robust control, and distributed control, aim to optimize control actions while adhering to system constraints. Case studies show their effectiveness in high photovoltaic penetration, wind power integration, and microgrid operation. However, challenges persist, such as managing uncertainties and coordinating multiple controllers in decentralized power systems. The study acknowledges ongoing research and development in this field, emphasizing the potential for enhancing voltage and frequency regulation in smart grids.

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Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules

Cited by 2 publications

References 8 publications

Machine-Learning Based Approximation of the Hierarchical Model Predictive Control of Multi-Use PV-Battery Systems in Non-Residential Buildings

Machine-Learning Based Approximation of the Hierarchical Model Predictive Control of Multi-Use PV-Battery Systems in Non-Residential Buildings

Advanced Control Strategies for Resilient Voltage and Frequency Regulation in Smart Grids

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