IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v385y2025ics0306261925002260.html
   My bibliography  Save this article

A modular Python framework for rapid development of advanced control algorithms for energy systems

Author

Listed:
  • Eser, Steffen
  • Storek, Thomas
  • Wüllhorst, Fabian
  • Dähling, Stefan
  • Gall, Jan
  • Stoffel, Phillip
  • Müller, Dirk

Abstract

Due to the advance in energy engineering and necessary adaptations due to climate change, building energy systems are becoming increasingly complex, necessitating the development of advanced control strategies. However, there is often a gap between control algorithms developed in research and their practical adoption. To bridge this gap, we present AgentLib – a modular Python framework to aid the development, testing and deployment of advanced control systems for energy applications. AgentLib allows researchers and engineers to gradually scale up controller complexity, supporting the full development lifecycle from simulation and testing to distributed real-time implementation. The framework and its plugins provide a set of extensible modules for common agent functions like optimization, simulation and communication. Control engineers can leverage familiar tools for mathematical optimization and machine learning in Python. AgentLib is agnostic to specific communication protocols, allowing flexible interfacing with diverse energy systems and external services. To demonstrate the framework’s capabilities, we present a case study on developing a distributed model predictive controller from concept to real-world experiment. We showcase how AgentLib enables a true parallel implementation of cooperative agents and supports gradual transition from development to deployment. By analyzing the system’s performance, we highlight the real-world impacts of communication overhead on distributed control. The framework’s capability to bridge the gap between theoretical research and practical applications marks a significant step forward in deploying sophisticated control strategies within the building energy management sector, and possibly other domains.

Suggested Citation

  • Eser, Steffen & Storek, Thomas & Wüllhorst, Fabian & Dähling, Stefan & Gall, Jan & Stoffel, Phillip & Müller, Dirk, 2025. "A modular Python framework for rapid development of advanced control algorithms for energy systems," Applied Energy, Elsevier, vol. 385(C).
    • Handle: RePEc:eee:appene:v:385:y:2025:i:c:s0306261925002260
    DOI: 10.1016/j.apenergy.2025.125496
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925002260
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125496?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
    2. Kim, Donghun & Wang, Zhe & Brugger, James & Blum, David & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization," Applied Energy, Elsevier, vol. 321(C).
    3. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
    4. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    5. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    6. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    7. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    8. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    9. Kalliopi Kravari & Nick Bassiliades, 2015. "A Survey of Agent Platforms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-11.
    10. Li, Zhikang & Ma, Chengbin, 2022. "A temporal–spatial charging coordination scheme incorporating probability of EV charging availability," Applied Energy, Elsevier, vol. 325(C).
    11. Xu, Zhiwei & Hu, Zechun & Song, Yonghua & Zhao, Wei & Zhang, Yongwang, 2014. "Coordination of PEVs charging across multiple aggregators," Applied Energy, Elsevier, vol. 136(C), pages 582-589.
    12. Hany Elgamal, Ahmed & Kocher-Oberlehner, Gudrun & Robu, Valentin & Andoni, Merlinda, 2019. "Optimization of a multiple-scale renewable energy-based virtual power plant in the UK," Applied Energy, Elsevier, vol. 256(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Xiong, Chang & Su, Yixin & Wang, Hao & Dong, Zhengcheng & Tian, Meng & Shi, Binghua, 2024. "Consensus-based decentralized scheduling method for collaborative operation in seaport virtual power plant," Applied Energy, Elsevier, vol. 373(C).
    3. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    4. Li, Jiaxin & Xu, Zhanbo & Zhou, Yuzhou & Li, Yuting & Wu, Jiang & Guan, Xiaohong, 2024. "Optimal scheduling method and fast-solving algorithm for large-scale virtual power plants communication networks," Applied Energy, Elsevier, vol. 371(C).
    5. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    6. El Shamy, Ahmed R. & Al-Sumaiti, Ameena S., 2025. "Optimal cost predictive BMS considering greywater recycling, responsive HVAC, and energy storage," Applied Energy, Elsevier, vol. 377(PC).
    7. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    8. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    9. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    10. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).
    12. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    13. Dong, Lianxin & Wu, Qing & Hong, Juhua & Wang, Zhihua & Fan, Shuai & He, Guangyu, 2023. "An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings," Applied Energy, Elsevier, vol. 330(PA).
    14. Li, Wenzhuo & Tang, Rui & Wang, Shengwei & Zheng, Zhuang, 2023. "An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings," Applied Energy, Elsevier, vol. 349(C).
    15. Deng, Zhipeng & Wang, Xuezheng & Jiang, Zixin & Zhou, Nianxin & Ge, Haiwang & Dong, Bing, 2023. "Evaluation of deploying data-driven predictive controls in buildings on a large scale for greenhouse gas emission reduction," Energy, Elsevier, vol. 270(C).
    16. Wang, Peng & Sun, Junqing & Yoon, Sungmin & Zhao, Liang & Liang, Ruobing, 2024. "A global optimization method for data center air conditioning water systems based on predictive optimization control," Energy, Elsevier, vol. 295(C).
    17. Guanjing Lin & Armando Casillas & Maggie Sheng & Jessica Granderson, 2023. "Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building," Energies, MDPI, vol. 16(20), pages 1-21, October.
    18. Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(C).
    19. Li, Wenzhuo & Wang, Shengwei, 2022. "A fully distributed optimal control approach for multi-zone dedicated outdoor air systems to be implemented in IoT-enabled building automation networks," Applied Energy, Elsevier, vol. 308(C).
    20. Sha, Xinyi & Ma, Zhenjun & Sethuvenkatraman, Subbu & Li, Wanqing, 2025. "Online learning-enhanced data-driven model predictive control for optimizing HVAC energy consumption, indoor air quality and thermal comfort," Applied Energy, Elsevier, vol. 383(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:385:y:2025:i:c:s0306261925002260. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.