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The waste heat management process from various thermal sources is associated with promising results in the energy conversion field. Thermoelectric generators integrated with phase change materials containing metal foams are useful means for recovering waste heat and converting it into electrical energy. Installing these hybrid cases on common heat sources and providing a practical strategy for waste heat management addresses a significant research gap in this field. The aim of this study is to manage waste heat from a three-phase asynchronous electric motor of a milling machine for the first time using a novel prototype thermoelectric system. Four thermoelectric modules (connected electrically in series) with zero-cooling energy phase change material and copper foam heat sink were embedded on the motor body. Hence, continuous and fluctuating loads were imposed on the motor, starting from ambient temperature. This caused the motor body to heat up and transfer the wasted heat to the module and the environment. The results of this study show that the proposed system was successfully started via the waste heat of the motor body. Thermal energy was absorbed from the hot side of the module, and the hybrid heat sink controlled the temperature of the cold side. Moreover, an appropriate temperature difference was provided across the module, and the system produced electricity for a long time during milling operation. The maximum output voltage in constant and periodic loads, with on/off durations of 180 s and 360 s, was recorded as 960 mV, 1280 mV, and 1440 mV, respectively. The mentioned hybrid thermoelectric system provides a unique solution for waste heat management in industrial applications, improving economic savings.

This study introduces a versatile variable-output isolated AC-DC converter design distinguished by its incorporation of soft-switching and a novel control algorithm. The converter configuration comprises two full-bridge diode rectifiers, positioned at both input and output sides, complemented by a full-bridge isolated resonant DC–DC converter. By providing soft-switching across all semiconductor components, the proposed design could achieve a significant power density increase while still preserving the power conversion efficiency in an acceptable range. The novel control algorithm concurrently governs power factor and output voltage, employing the duty cycle as the primary control variable and the phase shift as an auxiliary parameter. Using this synergistic method fulfills more efficiency, increases power density, and improves power factor performance. The effectiveness and reliability of the converter were carefully confirmed by experiments done on a prototype, which provided the capacity to provide over 400 watts in its maximum state of operation. Notably, the converter's adaptability makes it an ideal candidate for a wide variety of applications, with a specific emphasis on integrating renewable energy sources into power systems.

This paper presents a flexibility-oriented generation expansion planning (GEP) model with the goal of increasing renewable energy penetration. The model incorporates GEP and unit commitment while considering improved reserve requirements. Utilizing GEP and unit commitment results in considering short-term operational constraints. Furthermore, given the increase in renewable portfolio standards (RPS) and the need for accurate reserve requirements determination, it is essential to consider improved reserve requirements in the proposed model. Moreover, this model utilizes a metric that measures power system flexibility deficiency and leads to flexible power system expansion by integrating it into the objective function. The flexibility-oriented GEP model is developed using mixed-integer linear programming and applied to a scaled-sized case study representing the national power system of Iran with a twenty-year planning horizon. The results show that neglecting improved reserve requirements leads to plans short on flexibility, and considering these requirements substantially declines the upward flexibility violation costs by 68% under the base scenario and 87% under increasing non-hydro RPS scenario. Furthermore, using the flexibility metric coupled with improved reserve requirements changes the optimal path for power system expansion and reduces the risk of flexibility deficiency. Also, increasing RPS by 35% would strongly depend on developing fast-response dispatchable generation units in the years before renewable energy resources expansion; however it doesn’t impose a significant extra cost of planning and the total cost will increase just by 9.43%.

The development of proper storage medium for renewable sources with high intermittency (such as solar or wind) is an essential steps towards the growth of green energy development and enabling them to compete with fossil fuel resources in the current market. While the emerging of new generation of storage mediums, such as lithium based batteries is revolutionizing the world of renewable energy storage systems, many counties are still far behind in the growing market of storage technologies due to budget-related issues and hindering policies. In other world for many countries investment on new technologies which are still under the experimental stage of marketing, is too risky. It is more efficient for such countries to focus on well-developed and reliable storing technologies. This is the main reason why fossil fuel systems still thriving significantly in this field. Under these circumstances relying on “water-based” storage systems to compete with fossil fuels dominancy is an efficient solution due to various advantages of water-based systems including high specific heat, non-toxicity, lower costs, chemical stability, availability and high capacity rate during charge and discharge. While liquid water storage are highly suitable for operating temperature of 20–80 °C, using the steam accumulation form of such medium is easily suitable for high temperature applications such as power generation or other industrial applications. Aside from thermal applications of water-based storages, such systems can also take advantage of its mechanical energy in the form of pumped storage systems which are vastly use for bulk energy storage applications and can be used both as integrated with power grid or standalone and remote communities. The main goal of this study is to comprehensively explore the exciting water-based storage systems (including ice and steam) in terms of technical advances, economic growth and environmental challenges which have been significantly overlooked in the previous similar studies.

In the present study, an optimal Proton-Exchange Membrane Fuel Cell-based includes Cooling, Heating, and Power system has been proposed to cover the required energy of the households for cooling, heating and electricity. The system is optimized by a modified version of the Mayfly Optimization (MMFO) algorithm to recover and develop the efficiency of the CCHP system. The innovation is the application of the proposed MMFO algorithm to provide the optimal configuration of the CCHP system in terms of environmentally and economically. the CCHP system is then simulated and its results are compared with NSGA-II from the literature, and also the original MFO method to indicate the method efficiency. The results indicate that the proposed optimal CCHP system offers good results and prominence toward the compared algorithms in both terms of environmental and efficiency.

In this paper, a new effective technique is presented for model identification of the Proton-exchange membrane (PEM) fuel cells by a new hybrid model of Rotor Hopfield Neural Network (RHNN). The concept is to lessen the Error between the empirical output voltage and the modeled produced voltage of the PEM fuel cells based on the proposed hybrid RHNN. To improve the structure of the RHNN model, a new metaheuristic, called collective guidance factor-based Pathfinder algorithm (GFPF) has been proposed. To validate the performance of the proposed model, it is performed to a case study and its results are compared with the original RHNN as two Blackbox models. Simulation results indicate 0.5665% and 0.4141% maximum relative error for testing of the original RHNN-based model and the GFPF-RHNN-based model, respectively which is a good accuracy for PEM fuel cells modeling.

The optimum size of the prime mover (PM) for combined cooling, heating and power (CCHP) systems is determined taking account of economics, environmental impacts, energy, and exergy. The optimum size of the PM greatly increases the justification of establishing a CCHP system. Optimum sizing of the PM is conducted here considering exergy efficiency, exergy destruction rate, total energy efficiency, overall fuel energy consumption, carbon release, carbon release decrease, equivalent uniform annual benefit, and payback duration. To determine the optimum size, an objective function is presented in which some of the aforementioned criteria are minimized and some others are maximized. To solve the problem arisen from this objective function, the improved pathfinder optimization algorithm is proposed. To prove its superior performance, it is compared to six other popular optimization algorithms used in CCHP optimization using six benchmark functions. After obtaining the optimum size of the PM, sensitivity analyses are conducted on all of the eight assessment sub-criteria versus the PM capacity, and the results are given in figures. In addition, their values are given with the optimum size of the PM. The results indicate that the studied CCHP with the optimum PM size yields exergy efficiency of 55.53%, exergy destruction rate of 48.34%, total energy efficiency of 68.79%, and overall fuel energy consumption of 209 kWh. Also, this system will produce 36.82kg of carbon per hour which is 23.46% lower than the initial separated generation system. Moreover, with the equivalent uniform annual benefit of 26376$, it will pay back in 3.21 years.

The present study presents a new method for optimal identification of the output voltage of a Proton Exchange Membrane Fuel Cell based on an optimized Deep Belief Network. According to the designed method, the Deep Belief Network structure is optimized based on a modified version of the Water Strider Algorithm. The main idea is to minimize the error value between the observed and the obtained output voltages of the Proton Exchange Membrane Fuel Cell. Simulations specified that the suggested Deep Belief Network-Modified Water Strider Algorithm method provides accurate tracking for the voltage signal prediction of the Proton Exchange Membrane Fuel Cell. Final results are also compared with Deep Belief Network-Water Strider Algorithm, and DBN/TLBO-DE from the literature to show the proposed method dominance.

In hybrid electric vehicles, combining fuel cells with battery brings advantages such as high-power density, fast startup, and improved dynamic set response. In such a combination, the performance quality of the driving forces depends on how the power sources are controlled and distributed. In this paper, by providing a smart strategy, the fuel cell stack and the battery sizing have been proposed to obtain a desired low cost and reliable battery/fuel cell motor and to resolve the barrier of cost as a significant problem of hybrid motors. To achieve optimal configuration, a developed metaheuristic called Converged Barnacles Mating Optimizer (CBMO) algorithm has been proposed. The results show that by reducing the battery size, the FC stack lifetime has been reduced, such that the stack needs to provide peak power demand by operating under high power for a long time. The FC stack size reduction also increases the rate of fuel consumption because of the prompting to supply higher power.

In this paper, a multi-objective optimization method has been established on a hybrid PV, wind, fuel cell, and battery system. The optimization is based on three models including energy supply reliability, electricity efficiency, and capital cost of the hybrid system. A new model of the Elephant Herding Optimization (BEHO) Algorithm is utilized to solve the multi-objective optimization problem and is validated based on different algorithms and benchmark functions. The main purpose is to determine the Pareto surface including a set of possible design solutions to help the decision-makers obtaining the global optimum solution. The final results indicated that the proposed method is an applicable approach for designing of the proposed hybrid system.

Today, the security of energy has become one of the primary concerns of all the countries around the world due to the limited energy sources, increasing population, energy prices fluctuations and limitations in energy supply. Japan is considered as one of the largest energy consumers and energy importers throughout the world which almost 96% of its primary energy supply in national level relies on the imports from other countries. After experiencing several harsh energy supply conditions over the last 40 years, Japan realized the sensitivity of its energy supply and decided to fundamentally restructure its energy supply and rely more on energy mix diversification, renewable energies, energy efficiency improvement, and carbon emissions reduction. In this article the energy security improvement and the government actions to boost diversification of energy by focusing more on renewable energies and reducing energy intensity, increasing energy efficiency and decreasing energy import dependency in Japan is studied. In addition, various renewable energy technologies as a substitute source of energy and promising ways of domestic energy supply as well as the factors to improve the security of energy supply are studied thoroughly. Moreover, it was realized that the most primary energy sources in Japan are biofuel and waste energy followed by hydropower and geothermal energy respectively.

This paper presents an exergy assessment for a proposed power generation system that is used the organic Rankine cycle to recycle the waste heat from a high-temperature proton exchange membrane fuel cell (HT-PEMFC). To do so, mathematical model for the studied PEMFC along with the water management system have been introduced. Parametric analysis has been directed to study the impact of different economic and thermodynamic parameters, like the fuel cell irreversibility, exergy efficiency, and its work. For optimal designing the PEMFC, its parameters have been optimized by considering three objective functions, i.e. irreversibility, exergy efficiency, and its work. The optimization process has been performed based on a new model of fibrous root optimization algorithm improved. Simulation results of the presented algorithm have been compared with empirical results, genetic algorithm, and the basic of fibrous root optimization algorithm. The optimized values of irreversibility, exergy efficiency, work for the proposed algorithm are achieved 0.012, -0.439, and -0.4993, respectively which has the best values compared with the other analyzed algorithms.

This paper presents a new optimal method for model estimation of the unknown parameters of circuit-based proton exchange membrane fuel cells (PEMFCs). The main idea is to minimize the sum of squared error (SSE) value between the actual data and the estimated results. The optimization process here is based on an Improved Fluid Search Optimization Algorithm (IFSO). For verification of the suggested method, it is applied to three practical case studies including Horizon H-12 stacks, NedStack PS6, and Ballard Mark V 5 kW under different operating conditions with temperature variations between 30 oC and 55oC and pressure variations between 1.0/1.0 Bar and 3.0/3.0 Bar. The results of these case studies are also compared with CGOA, MRFO, and basic FSO algorithm to show the proposed method’s effectiveness. The results show that the minimum value of SSE among different algorithms is 0.7845, 2.15, and 0.084, respectively that are reached by the suggested IFSO algorithm.