High voltage gain DC-DC converters are widely used in various applications like low voltage sustainable sources. In traditional boost converters, the voltage gain is limited by high voltage stress, high current ripple, and low efficiency due to employing a high duty cycle ratio. In this paper, we propose converters that are a combination of four sub-structures, namely active switched inductor, passive switched inductor, switched capacitor cell, as well as an auxiliary switch with non-isolated configuration. The proposed structures have high voltage gain compared to the conventional either switched inductor-based or switched capacitor-based ones in addition to a low duty cycle ratio. By adding an auxiliary switch, the efficiency is improved, particularly for high voltage gains. The principle of operation and steady-state analysis are discussed in detail. Also, simulation results from PSCAD/EMTDC software are validated by a prototype built for experimental examination. The results are demonstrated that the voltage gain and efficiency could be improved by utilizing the auxiliary switch.

In recent years, there has been a rapid change in the portfolio of road vehicles. This transformation needs to be supported by reshaping fuel stations. The existing structures can be modified to supply multiple vehicle types while utilizing renewable energies. In this study, we introduce a hybrid fuel station (HFS) to supply both electric and natural gas vehicles. We incorporate an energy hub and thoroughly explain the structure and components in detail. The configuration of the proposed HFS includes renewable energy sources (RESs), a power to gas unit (P2G), a natural gas distributed generator (NGDG), an energy storage device, a compressor, and a gas storage device. The optimal operation of the HFS will be under uncertainty due to multiple parameters that display unpredictable behaviors. Thus, a stochastic scheduling scheme considering features of the proposed HFS is presented. The resulted optimization is modeled as a mixed-integer linear programming. The validity and capabilities of structure and scheduling schemes are evaluated with several numerical simulations. In particular, the impact of RESs, P2G, and NGDG on the operating cost of the HFS is analyzed. The results demonstrate that the economic benefits of the proposed HFS are significantly higher than a conventional one.

In recent years, due to the several advantages of permanent magnet synchronous generator (PMSG), the number of wind farms utilizing this technology has been significantly grown. The determination of the failure mechanism in these devices is the major challenge which has been addressed in many studies. Particularly, response to grid code compliance by wind power in the voltage drop situation needs to be comprehensively analyzed. In this paper, a small signal model of a PMSG-based wind turbine for low voltage ride-through (LVRT) and suitable for stability and artificial intelligent studies is presented. Accordingly, the generator side converter controls the dc-link voltage, and the maximum power point tracking is performed by the grid side converter. Given the proposed model, the speed of the simulation for stability analysis studies can be significantly increased by intelligent methods. Furthermore, the simplified approach can be achieved for calculating the optimal coefficients of the proportionality-integral controller by intelligent methods in a short time. By simulating the proposed small-signal model and comparing it with the block-based simulation in MATLAB/SIMULINK software, the appropriate accuracy and efficiency of the proposed model are confirmed.

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.

This article presents a novel methodology for analyzing the resilience of an active distribution system (ADS) integrated with an urban gas network (UGN). It demonstrates that the secure adoption of gas turbines with optimal capacity and allocation can enhance the resilience of the ADS during high-impact, low-probability (HILP) events. A two-level tri-layer resilience problem is formulated to minimize load shedding as the resilience index during post-event outages. The challenge of unpredictability is addressed by an adaptive distributionally robust optimization strategy based on multi-cut Benders decomposition. The uncertainties of HILP events are modeled by different moment-based probability distributions. In this regard, considering the nature of each uncertain variable, a different probabilistic method is utilized. For instance, to account for the influence of power generated from renewable energy sources on the decision-making process, a diurnal version of the long-term short-term memory network is developed to forecast day-ahead weather. In comparison with standard LSTM, the proposed approach reduces the mean absolute error and root mean squared error by approximately 47% and 71% for wind speed, as well as 76% and 77% for solar irradiance network. Finally, the optimal operating framework for improving power grid resilience is validated using the IEEE 33-bus ADS and 7-node UGN.