Version 1
: Received: 10 July 2020 / Approved: 11 July 2020 / Online: 11 July 2020 (09:00:22 CEST)
How to cite:
Wu, K.; Wu, J.; Feng, L.; Yang, B.; Liang, R.; Yang, S.; Zhao, R.; Wang, H. Study on Optimal Control Strategy for Cooling, Heating and Power (CCHP) System. Preprints2020, 2020070233. https://doi.org/10.20944/preprints202007.0233.v1
Wu, K.; Wu, J.; Feng, L.; Yang, B.; Liang, R.; Yang, S.; Zhao, R.; Wang, H. Study on Optimal Control Strategy for Cooling, Heating and Power (CCHP) System. Preprints 2020, 2020070233. https://doi.org/10.20944/preprints202007.0233.v1
Wu, K.; Wu, J.; Feng, L.; Yang, B.; Liang, R.; Yang, S.; Zhao, R.; Wang, H. Study on Optimal Control Strategy for Cooling, Heating and Power (CCHP) System. Preprints2020, 2020070233. https://doi.org/10.20944/preprints202007.0233.v1
APA Style
Wu, K., Wu, J., Feng, L., Yang, B., Liang, R., Yang, S., Zhao, R., & Wang, H. (2020). Study on Optimal Control Strategy for Cooling, Heating and Power (CCHP) System. Preprints. https://doi.org/10.20944/preprints202007.0233.v1
Chicago/Turabian Style
Wu, K., Ren Zhao and Hao Wang. 2020 "Study on Optimal Control Strategy for Cooling, Heating and Power (CCHP) System" Preprints. https://doi.org/10.20944/preprints202007.0233.v1
Abstract
An optimal scheduling strategy for cooling, heating and power (CCHP) joint-power-supply system is proposed to improve energy utilization and minimize costs in this paper. Firstly, the mathematical model of CCHP system is established. Particle swarm optimization (PSO) is used to optimize the regularization coefficient C and the kernel parameter λ which can affect the prediction accuracy of KELM(PSO-KELM). Then PV generation and load prediction model are established by PSO-KELM. In order to jump out of local optimal solution, Cauchy variation is introduced in SFLA local update, and adaptive mutation operation is carried out on SFLA individuals. The predictions of PV generation and load power by PSO-KELM are imported into the objective function, and the microgrid dispatching model is solved by the improved SFLA algorithm. Compared with the traditional GA-KELM and KELM, PSO-KELM has faster convergence and prediction accuracy. Compared with the power supply division, the operation cost of the power grid is reduced by the proposed optimization dispatching strategy of CCHP micro-grid.
Keywords
cooling; heating and power (CCHP) microgrid; kernel learning machine (KELM); particle swarm optimization (PSO); shuffled frog leaping algorithm (SFLA)
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
