A series multi-step approach for operation Co-optimization of integrated power and natural gas systems

Power to gas units and gas turbines have provided considerable opportunities for bidirectional interdependency between electric power and natural gas infrastructures. This paper proposes a series of multi-step strategy with surrogate Lagrange relaxation for operation co-optimization of an integrated power and natural gas system. At first, the value of coordination capacity is considered as a contract to avoid dysfunction in each system. Then, the uncertainties and risks analysis associated with wind speed, solar radiation, and load fluctuation are implemented by generating stochastic scenarios. Finally, before employing surrogate Lagrange relaxation, the non-linear and non-convex gas flow constraint is linearized by two-dimension piecewise linearization. In the proposed procedure, constraints for energy storages and renewable energy sources are included. Two case studies are employed to verify the effectiveness of the proposed method. The surrogate Lagrange relaxation approach with coordination branch &amp; cut method enhances the accuracy of convergence and can effectively reduce the decision-making time.</p

A Mixed Epistemic-Aleatory Stochastic Framework for the Optimal Operation of Hybrid Fuel Stations

The fast development of technologies in the smart grids provides new opportunities such as co-optimization of multi-energy systems. One of the new concepts that can utilize multiple energy sources is a hybrid fuel station (HFS). For instance, an HFS can benefit from energy hubs, renewable energies, and natural gas sources to supply electric vehicles along with natural gas vehicles. However, the optimal operation of an HFS deals with uncertainties from different sources that do not have similar natures. Some may lack in term of historical data, and some may have very random and unpredictable behavior. In this study, we present a stochastic mathematical framework to address both types of these uncertainties according to the innate nature of each uncertain variable, namely: epistemic uncertainty variables (EUVs) and aleatory uncertainty variables (AUVs). Also, the imprecise probability approach is introduced for EUVs utilizing the copula theory in the process, and a scenario-based approach combining Monte Carlo simulation with Latin Hypercube sampling is applied for AUVs. The proposed framework is employed to address the daily operation of a novel HFS, leading to a two-stage mixed-integer linear programming problem. The proposed approach and its applicability are verified using various numerical simulations

Performance analysis of organic solar cells: Opto-electrical modeling and simulation

In this paper, the effect of mobility and active layer thickness on the properties of bulk heterojunction solar cells have been investigated using the drift-diffusion model. It was observed that open circuit voltage and power conversion efficiency are affected by two loss mechanisms. The recombination of charge carriers in wrong contacts destroys photogenerated electron-hole pairs, and the bulk recombination increases the act of coupling between electron-hole pairs with different binding energies and consequently lowers the open circuit voltage. The first loss mechanism rises as the mobility of each of the two carriers increases. The bulk recombination not only depends on the slow carrier mobility but also affected by the mobility balance of two carriers. Thus, it generates two optimal points for power conversion efficiency at none unity electron to hole mobility ratio. In the active layer, light does not get absorbed uniformly, and the profile of the photogenerated excitons depends on the device thickness. Therefore, similar changes in the electron and hole mobility do not bring about the same changes in power conversion efficiency. Also, this study indicates that with the simultaneous increase of mobility and thickness, considerable enhancement in the efficiency of the bulk heterojunction solar cells would be achieved