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Real time optimal control of district cooling system with thermal energy storage using neural networks

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  • Cox, Sam J.
  • Kim, Dongsu
  • Cho, Heejin
  • Mago, Pedro

Abstract

Thermal energy storage can be utilized as an effective component in energy systems to maximize cost savings when time-of-use (TOU) pricing or real-time pricing (RTP) is in place. This study proposes a novel approach that can effectively predict performance and determine control strategy of thermal energy storage (i.e., ice storage) in a district cooling system. The proposed approach utilizes Neural Network (NN) based model predictive control (MPC) strategy coupled with a genetic algorithm (GA) optimizer and examines the effectiveness of using a NN model for a district cooling system with ice storage. The NN offers a relatively fast performance estimation of a district cooling system with given external inputs. To simulate the proposed MPC controller, a physics-based model of the district cooling system is first developed and validated to act as a virtual plant for the controller to communicate system states in real times. Next, the NN modeling the plant is developed and trained during a cooling period so that the control strategy is tested under the RTP and TOU pricing. This model is optimized using the GA due to the on/off controls for the district cooling network. Finally, a thermal load prediction algorithm is integrated to test under perfect weather inputs and weather forecasts by considering 1-hour discretization in the MPC scheme. Results indicate that for the month of August, the optimal control scheme can effectively adapt to varying loads and varying prices to effectively reduce operating costs of the district cooling network by approximately 16% and 13% under the TOU pricing and the RTP, respectively.

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  • Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:466-480
    DOI: 10.1016/j.apenergy.2019.01.093
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    19. Marius Reich & Jonas Gottschald & Philipp Riegebauer & Mario Adam, 2020. "Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory," Energies, MDPI, vol. 13(24), pages 1-25, December.
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    22. Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2021. "Design and dispatch optimization of packaged ice storage systems within a connected community," Applied Energy, Elsevier, vol. 298(C).
    23. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).

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