ABSTRACT A new optimized design of a hybrid AlexNet/Extreme Learning Machine (ELM) network to provide an optimal identification tool for the Proton-exchange membrane fuel cells (PEMFCs) is presented in this study. The major concept is to reduce the error amount between the empirical output voltage and the evaluated output voltage of the PEM fuel cell stack model using the proposed hybrid AlexNet/ELM. For enhancing the model formation of the AlexNet/ELM, a modified version of the African Vulture Optimization (MAVO) Algorithm, which is a new metaheuristic, is suggested. To analyze the efficiency of the suggested method, it is applied to a practical PEMFC benchmark case study for identification purposes. Then, the method is confirmed by comparison of the experimental data and standard AlexNet/ELM. The achievements indicated the better confirmation of the suggested AlexNet/ELM network with the experimental data. The results show that the highest relative error for training and test is 0.03% and 0.05342%, respectively, which shows a promising result for the study.

Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African Vulture Optimization Algorithm

The present study introduces an economical–functional design for a polymer electrolyte membrane fuel cell system. To do so, after introducing the optimization problem and solving the problem based on the presented equations in the fuel cell, a cost model is presented. The final design is employed for minimizing the construction cost of a 50 kW fuel cell stack, along with the costs of accessories regarding the current density, stoichiometric coefficient of the hydrogen and air, and pressure of the system as well as the temperature of the system as optimization parameters. The functional–economic model is developed for the studied system in which all components of the system are modeled economically as well as electrochemically–mechanically. The objective function is solved by a newly improved metaheuristic technique, called converged collective animal behavior (CCAB) optimizer. The final results of the method are compared with the standard CAB optimizer and genetic algorithm as a popular technique. The results show that the best optimal cost with 0.1061 $/kWh is achieved by the CCAB. Finally, a sensitivity analysis is provided for analyzing the consistency of the method.

Optimization of PEMFC Model Parameters Using Meta-Heuristics

ABSTRACT This study proposes a new optimal hybrid renewable energy system (HRES) arrangement, including a photovoltaic system, wind turbine, and fuel cell, for electrifying a remote area in Turkey. The study is based on considering system cost and reliability. To deliver an optimal configuration, system sizing has been designed based on an Amended version of the DragonFly optimizer. The achievements of the method have been then compared with some other published methods, including Particle Swarm Optimizer (PSO)-based algorithm and Firefly (FA)-based method. Simulation results show that the proposed method with 1,888,827.5 USD provides the minimum Net Present Cost value among the others. The main idea is to assess the objective function by lessening the Net Present Cost (NPC) by confirming based on the loss of power supply probability (LPSP). Final simulations indicated that the proposed approach provides lower NPC and LCOE toward the others.

Optimum structure of a combined wind/photovoltaic/fuel cell-based on amended Dragon Fly optimization algorithm: a case study

ABSTRACT The present study proposes an optimal design of combined cooling, heating, and power systems (CCHP) for a watersport complex. The main purpose is to reduce the energy losses in economic, energy, and environmental terms of view. The method has been established by optimizing the nominal capacity of the CCHP components for the watersport complex. The design parameters for proper configuration include the number of gas engines and their nominal capacity, boiler heating capacity, partial load, and the cooling capacity of the electric and absorption chillers, which should be selected optimally by the relation annual benefit based on two different scenarios of considering and not considering the capability of selling the extra electrical power to the network. To provide optimal results for the system, an improved metaheuristic technique called the developed African Vulture Optimization (dAVO) algorithm has been utilized. The results of the actual annual benefit for the suggested method are compared with different state-of-the-art methods to show the method’s superiority. The results show that without considering the CCHP system, the total cost of electricity purchasing equals 420959 $/year, while the cost function value is achieved positive, which shows the positive effect of the CCHP system in reducing the system costs.

Optimal modeling of combined cooling, heating, and power systems using developed African Vulture Optimization: a case study in watersport complex

The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers. 

A comprehensive review on electric vehicles smart charging: Solutions, strategies, technologies, and challenges

Planning of an islanded hybrid system (IHS) with different sources and storages to supply clean, flexible, and highly reliable energy at consumption sites is of high importance. To this end, this paper presents the design of an IHS with a wind turbine, photovoltaic, diesel generator, and stationary (battery) and mobile (electrical vehicles) energy storage systems (ESS). The proposed method includes a multi-objective optimization to minimize the total cost of construction, maintenance, and operation of sources and ESSs within the IHS and the emission level of the system using two separate objective functions. The problem is subject to operational and planning constraints of sources and ESSs and power. Employing the Pareto optimization technique based on the e-constraint method forms a single-objective optimization problem for the proposed design. The problem involves uncertainties of load, renewable energy, and energy demand of mobile ESSs and has a nonlinear form. Adaptive robust optimization based on a hybrid meta-heuristic algorithm that utilizes a combination of the sine-cosine algorithm (SCA) and crow search algorithm (CSA) is proposed to achieve an optimal robust structure for the suggested scheme. In this scheme, operation model of the mobile storage systems in the IHS considering the uncertainties prediction errors and its model using HMA-based ARO besides adopting the HMA to achieve a unique optimal solution are among the novelties of this research. Eventually, considering the climate data and energy consumption of a region in Rafsanjan, Iran, capabilities of the method in extracting a robust IHS for sources and ESSs are validated depending on optimal economic and environmental conditions. The HMA succeeds to reach an optimal solution with an SD of 0.92% in the final response and this underlines its capability in achieving approximate conditions of unique responsiveness. The proposed scheme with proper planning and operation of sources and storages in the form of a HIS finds optimal values for economic and environmental conditions so that the difference between pollution and cost values from its minimum values at the compromise point is roughly 22%. For 17% uncertainty parameters prediction errors, the scheme obtains a robust structure for the IHS.

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

A zonal optimization solution to reliability security constraint unit commitment with wind uncertainty

DC smart grids have become very popular over the last decade. Furthermore, protection of DC grids has always been a challenge that must be addressed. DC fault has a different behavior from AC fault in which zero crossing current helps current interruption in circuit breakers. Moreover, DC fault current increases to much higher levels and in a faster pace as there is a small amount of reactance in DC systems comparing to AC systems. Hybrid Circuit Breakers (HCBs) are attractive for slow dynamic traditional load based microgrids due to their excellent conduction loss. However, the effect of their added reactance to the grid, which is a necessity to limit the fault current, severely decreases the grid response time to the pulse loads disqualifies them for most of the high demand modern micro grids. The all new Twin Contact HCB (TCHCB) offers a significantly smaller Current Limiting Reactance (CLR) making it a great alternative to Solid State CBs (SSCBs) with considerably high conduction loss and prerequisite of a cooling system.

Novel Hybrid Circuit Breaker Topology Using a Twin Contact Mechanical Switch

Due to dc microgrid nature, dc fault current has no zero-crossing current and could increase up to a thousand amps. Because of that, a dc circuit breaker (DCCB) with the ultra-fast response and high efficiency is required. Regarding this issue, this paper presents a novel thyristor-based DCCB. Then the optimal values of the proposed DCCB components are obtained by cost-power loss multi-objective optimization method. Finally, to keep the maximum temperature of the thyristor below the maximum allowed value, an optimum forced-air microchannel that has high reliability, low cost, and high efficiency is proposed for the proposed thyristor-based DCCB.

Optimal Design of a Novel High-Power Thyristor-based DC Circuit Breaker

Conventional distribution systems typically utilize TN earthing systems with overcurrent time-trip protection. However, in microgrids overcurrent protection alone is inadequate for fault discrimination. A combination of differential, directional overcurrent and overcurrent time-trip protection is required. This paper represents a practical microgrid model and investigates fault behaviour of the microgrid during one phase to chassis ground fault (L-G), called insulation fault, which is the most common type of fault in distribution systems. The results will be presented for three different earthing systems, TN, TT and IT, in both grid connected and islanded mode. The restrictions of protection scheme for each earthing system is presented and eventually, proper protection scheme will be proposed. All models of the generators and controllers and eventually the grid, are built using PSCAD V6.4.2.

Farzad Banihashemi

Papers

Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems

A zonal optimization solution to reliability security constraint unit commitment with wind uncertainty

Novel Hybrid Circuit Breaker Topology Using a Twin Contact Mechanical Switch

Optimal Design of a Novel High-Power Thyristor-based DC Circuit Breaker

Earthing Arrangements Impacts on Protection Schemes for a Commercial Microgrid