Artificial-intelligence-enabled dynamic demand response system for maximizing the use of renewable electricity in production processes

The International Journal of Advanced Manufacturing Technology, vol. 138, pp. 247–271

Abstract

Abstract The transition towards renewable electricity provides opportunities for manufacturing companies to save electricity costs through participating in demand response programs. End-to-end implementation of demand response systems focusing on manufacturing power consumers is still challenging due to multiple stakeholders and subsystems that generate a heterogeneous and large amount of data. This work develops an approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs. It also proposes a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules. This paper reports on developing and evaluating multiple machine learning models for power generation forecasting and load prediction, and also mixed-integer linear programming as well as reinforcement learning for production optimization considering dynamic electricity pricing represented as Green Electricity Index (GEI). The experiments show that the hybrid auto-regressive long-short-term-memory model performs best for solar and convolutional neural networks for wind power generation forecasting. Random forest, k -nearest neighbors, ridge, and gradient-boosting regression models perform best in load prediction in the considered use cases. Furthermore, this research found that the reinforcement-learning-based approach can provide generic and scalable solutions for complex and dynamic production environments. Additionally, this paper presents the validation of the developed system in the German industrial environment, involving a utility company and two small to medium-sized manufacturing companies. It shows that the developed system benefits the manufacturing company that implements fine-grained process scheduling most due to its flexible rescheduling capacities.

Authors 12

1. Hendro Wicaksono corresponding

   Constructor University

   Affiliation as printed

   School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany

   School of Business, Social and Decision Sciences, Constructor University, Campus Ring 1, 28759, Bremen, Germany

2. Martin Trat corresponding

   FZI Research Center for Information Technology

   Affiliation as printed

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Karlsruhe, Germany

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Baden-Württemberg, Germany

3. Atit Bashyal

   Constructor University

   Affiliation as printed

   School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany

   School of Business, Social and Decision Sciences, Constructor University, Campus Ring 1, 28759, Bremen, Germany

4. Tina Boroukhian

   Constructor University

   Affiliation as printed

   School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany

   School of Business, Social and Decision Sciences, Constructor University, Campus Ring 1, 28759, Bremen, Germany

5. Mine Felder

   FZI Research Center for Information Technology

   Affiliation as printed

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Karlsruhe, Germany

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Baden-Württemberg, Germany

6. Mischa Ahrens

   FZI Research Center for Information Technology

   Affiliation as printed

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Karlsruhe, Germany

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Baden-Württemberg, Germany

7. Janek Bender

   FZI Research Center for Information Technology

   Affiliation as printed

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Karlsruhe, Germany

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Baden-Württemberg, Germany

8. Sebastian Groß

   FZI Research Center for Information Technology

   Affiliation as printed

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Karlsruhe, Germany

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Baden-Württemberg, Germany

9. Daniel Steiner

   FZI Research Center for Information Technology

   Affiliation as printed

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Karlsruhe, Germany

   Intelligent Systems and Production Engineering, FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131, Karlsruhe, Baden-Württemberg, Germany

10. Christoph July Aachen devolo AG

    Devolo (Germany)

    Affiliation as printed

    devolo AG, Aachen, Germany

    devolo AG, Charlottenburger Allee 67, 52068, Aachen, North Rhine-Westphalia, Germany

11. Christoph Dorus
    Affiliation as printed

    StromDAO GmbH, Mauer, Germany

    StromDAO GmbH, Gerhard Weiser Ring 29, 69256, Mauer, Baden-Württemberg, Germany

12. Thorsten Zoerner
    Affiliation as printed

    StromDAO GmbH, Mauer, Germany

    StromDAO GmbH, Gerhard Weiser Ring 29, 69256, Mauer, Baden-Württemberg, Germany