Optimal Scheduling of a Hydrogen-Based Energy Hub Considering a Stochastic Multi-Attribute Decision-Making Approach

Nowadays, the integration of multi-energy carriers is one of the most critical matters in smart energy systems with the aim of meeting sustainable energy development indicators. Hydrogen is referred to as one of the main energy carriers in the future energy industry, but its integration into the energy system faces different open challenges which have not yet been comprehensively studied. In this paper, a novel day-ahead scheduling is presented to reach the optimal operation of a hydrogen-based energy hub, based on a stochastic multi-attribute decision-making approach. In this way, the energy hub model is first developed by providing a detailed model of Power-to-Hydrogen (P2H) facilities. Then, a new multi-objective problem is given by considering the prosumer’s role in the proposed energy hub model as well as the integrated demand response program (IDRP). The proposed model introduces a comprehensive approach from the analysis of the historical data to the final decision-making with the aim of minimizing the system operation cost and carbon emission. Moreover, to deal with system uncertainty, the scenario-based method is applied to model the renewable energy resources fluctuation. The proposed problem is defined as mixed-integer non-linear programming (MINLP), and to solve this problem, a simple augmented e-constrained (SAUGMECON) method is employed. Finally, the simulation of the proposed model is performed on a case study and the obtained results show the effectiveness and benefits of the proposed scheme

Optimal Robust Control Scheme for SSSC System Based on H_∞to Improve Power System Small Signal Stability

Static Series Synchronous Compensator (SSSC) is one of the controllable reactive power series compensators, which is applied to modify the power flow in the transmission line and enhance the electrical grid's small-signal stability. In this paper, a novel damping controller for the electrical network equipped with SSSC is presented considering H∞ control theory and Teaching Learning Based Optimization (TLBO) algorithm based on a fixed structure control scheme to reduce the controller order and minimize the system error. The suggested mechanism guarantees both meet the robustness criteria and move the undamped and lightly damped nodes toward the specified stability region. Therefore, a fixed structure controller is employed and designed by proposing a novel optimization problem. Moreover, SMIB connected to the SSSC system is applied to analyze the electrical network's small-signal stability. Finally, the proposed controller performance is compared to other conventional robust control methods and the simulation results show the controller achieves both excellent damping and acceptable robust performance compared to other methods.</p