Abstract
This study proposes a novel operational planning method for polymer electrolyte fuel cell cogeneration systems (PEFC-CGSs). PEFC-CGSs provide hot water by utilizing waste heat produced in the electricity generation process, and hot water is stored in an attached tank. Generating and storing hot water based on an optimal operational plan according to household demand leads to further energy saving; therefore, operational planning methods based on household demand prediction have received significant attention. However, the improvement in the demand prediction accuracy does not necessarily lead to efficient PEFC-CGS operation in terms of operational costs; in other words, the accuracy in the demand prediction does not directly indicate the resulting operational efficiency. In this study, the authors propose a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates. In the proposed scheme, the error between expected and actual operational costs explicitly represents the relevance of the operational plan, so that the optimal operational plan can be selected directly from the perspective of the resulting operational efficiency. The practicality of the proposed approach is evaluated with the existing demand prediction-based approach via numerical simulations using real-world measurements of multiple customers in Japan. The proposed method reveals 30% reduction of the excessive operational costs by avoiding the inefficient operation of the auxiliary gas-heater in the experiments and will further enhance the value of introducing highly efficient residential fuel cell system that contributes to a low-carbon society.
Highlights
Recent efforts in the development of demand-side energy management are vital to reducing non-essential energy consumption and CO2 emissions
The contributions of this paper are briefly described as follows: 1) it is revealed that the accuracy of the demand prediction which is emphasized in the existing operation approaches of PEFC-CGSs based on the demand forecast does not directly represent the efficiency of the operation, 2) a novel PEFC-CGS operation scheme aimed at directly minimizing the expected operational cost is provided from the viewpoint of surrogate modeling, 3) the effect of demand pattern and seasonality on the proposed method is clarified via numerical experiments targeting multiple households collected at various points in Japan, and 4) the validity of the proposed method is revealed by the comparison with the conventional operation approach based on the demand prediction scheme
CONCLUDING REMARKS In this study, we examined the PEFC-CGS operational planning methods and proposed a novel approach based on a surrogate model for deriving an appropriate plan that minimizes the expected operational costs among the operational plan candidates
Summary
Recent efforts in the development of demand-side energy management are vital to reducing non-essential energy consumption and CO2 emissions. Fujimoto et al have proposed a K-nearest neighbor-based multiple-scenario forecast scheme for operational planning of household energy system with the PEFC-CGS [24] These approaches based on machine learning techniques improve prediction accuracy in terms of reducing the average error between predictions and the actual measurements at each time slice in the demand sequence. The contributions of this paper are briefly described as follows: 1) it is revealed that the accuracy of the demand prediction which is emphasized in the existing operation approaches of PEFC-CGSs based on the demand forecast does not directly represent the efficiency of the operation, 2) a novel PEFC-CGS operation scheme aimed at directly minimizing the expected operational cost is provided from the viewpoint of surrogate modeling, 3) the effect of demand pattern and seasonality on the proposed method is clarified via numerical experiments targeting multiple households collected at various points in Japan, and 4) the validity of the proposed method is revealed by the comparison with the conventional operation approach based on the demand prediction scheme. The detailed formulation of the entire system is presented in the Appendix
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
