Model predictive control based optimal operation of smart city

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 (N_C) while including the prediction horizon (N_P) in the optimization interval. The N_P and N_C 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 N_C and N_P parameters on performance is investigated. The results show that the proposed method performs better when N_P is about 12 h.