Optimal Probabilistic Scheduling of a Proposed EH Configuration Based on Metaheuristic Automatic Data Clustering

Abstract

A combination of various energy conversion units offers a multi-carrier energy system, namely Energy Hub (EH). Optimal scheduling of EHs in the interconnected systems besides providing flexible management in input units stands as a challenging task. The uncertainties of input variables increase the complexity of this task. In this paper, an efficient EH structure is proposed and the conventional model is extended to tackle this issue in a short time operation interval. Moreover, a Metaheuristic Automatic Data Clustering (MADC) scheme used to solve the demand side uncertainty. This strategy omits the use of any additional variable needed for conventional techniques. The final solution is used to solve non differential, and high-dimension EH Economic Dispatch (EHED) problems. For best achievements an enhanced revised configuration by using a variable cost strategy for gas and electricity market simultaneously is proposed too. By comparing the effects of the EH feeding point on the objective function, a management side medium to reducing the computation burden proposed in the proceeding. The simulation results of the proposed model show an increase in EH benefit by reducing the cost. Considering mentioned issues as well as decreasing data dimensions due to reduced computing and probabilistic MADC scheduling of demands which could define the uncertainty. Besides, bypassing the need for a separate load management system, the results of this study are encouraging and warrant further analysis and research. The study is carried out in GAMS© and is connected by MATLAB© to implement the MADC.