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Title Model Predictive Control Based Optimal Operation Of Smart City
ID_Doc 37429
Authors Ishibashi T.; Furukakoi M.; Uehara A.; Masrur H.; Rashwan A.; Krishna N.; Mandal P.; Takahashi H.; Senjyu T.
Year 2024
Published Sustainable Cities and Society, 114
DOI http://dx.doi.org/10.1016/j.scs.2024.105759
Abstract Rapid urbanization has resulted in a significant proportion of the world's population residing in urban areas. Cities are undergoing a transition towards the designation of smart cities, which aims to facilitate the efficient management of electrical and heating energy systems derived from renewable energy sources (RES). This paper presents a smart city model that incorporates RES, battery, grid and a combined cooling heat and power (CCHP) system. The smart city model deals with cooling, heating, and electric energy and applies a simple model predictive control (MPC) based approach for optimal operation. MPC method can be operated for future fluctuations by optimizing at the control horizon (NC) while including the prediction horizon (NP) in the optimization interval. The NP and NC parameters have a significant impact on the results, and their selection is an important consideration. This study uses MATLAB simulation to validate the effectiveness of MPC-based operations with a simplified forecasting model in a smart city utilizing RES and CCHP systems. The proposed method is easy to implement and shows sufficient performance while avoiding model complexity. In addition, the impact of NC and NP parameters on performance is investigated. The results show that the proposed method performs better when NP is about 12 h. © 2024 Elsevier Ltd
Author Keywords Combined cooling heat and power; Mixed integer linear programming; Model predictive control; Optimization problem; Renewable energy; Smart city


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