Open AccessExergy-based model predictive control for design and control of a seasonal thermal energy storage system
Author(s) -
Matthieu Jonin,
Mohammad Khosravi,
Annika Eichler,
Willy Villasmil,
Philipp Schuetz,
Colin N. Jones,
Roy S. Smith
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1343/1/012066
Subject(s) - model predictive control , linearization , sequential quadratic programming , thermal energy storage , computer science , mathematical optimization , quadratic programming , bilinear interpolation , optimal control , optimization problem , energy storage , control (management) , control engineering , control theory (sociology) , engineering , mathematics , quantum mechanics , nonlinear system , artificial intelligence , computer vision , ecology , physics , biology , power (physics)
In this paper, we investigate the problem of controlling a seasonal thermal energy storage (STES). The STES considered here is a large scale tank of heated water installed in a building and connected to a solar panel. The stored energy in the STES can be used for providing the building with the space heating (SP) and the domestic hot water (DHW). In order to utilize the STES efficiently, we design a suitable model predictive control (MPC) scheme. In this regard, we develop an appropriate model for the system with an emphasis on the computational tractability of problem. Toward this end, we introduce a bilinear model with analytical linearization. Subsequently, we solve the optimization problem using a sequential quadratic programming (SQP) framework in a reasonable computational time. For controlling the system, in addition to solving the corresponding optimization problem, the main challenge is incorporating seasonal features in the MPC. This issue is resolved by augmenting the cost function with an additional term which is defined based on the exergy of system. Moreover, we address the challenging question of deriving minimal achievable size of the STES tank while satisfying user demand of DHW and SP. Finally, the efficiency of the proposed method is verified numerically.