Optimal planning and operation of energy hub by considering demand response algorithms and uncertainties based on problem-solving approach in discrete and continuous space

In this research, the two-level structure of optimal planning and operation of the energy hub (EH) based on demand uncertainty and renewable energy resources (RES) is presented. The optimal planning based on stochastic-probability models is presented at the primary and optimal operation based on stochastic-probability models is also presented at the secondary level. The proposed method is planned based on the problem-solving approach in continuous and discrete space. The optimal planning objectives include determining the optimal capacity of EH equipment and minimizing investment costs. Optimal operation objectives are also planned and formulated based on minimizing EH operation cost, reducing greenhouse gas emissions, increasing RES utili-zation based on stochastic-probability modelling, and examining the demand response/integrated demand response (DR/IDR) effect. The DR programs implementation causes a reduction of 14.3% of the EH total cost, and the IDR programs implementation also causes a reduction of 16.56% of the EH total cost. The use of the proposed optimal two-level model, in addition to efficiency in different operation scenarios, has also reduced the calcu-lation time. The optimization problem is solved based on Mixed Integer Linear Programming (MILP) and Binary Real-Coded Hybrid Genetic Algorithm (HRBC-GA).

Automated summary

This research presents a two-level structure for the optimal planning and operation of an energy hub (EH) based on demand uncertainty and renewable energy resources (RES). The primary level focuses on optimal planning using stochastic-probability models, while the secondary level focuses on optimal operation. The proposed method uses a problem-solving approach in continuous and discrete space, with objectives such as determining optimal capacity and minimizing costs.