• Energy Research
• 2021-2025close
• CN close
• Energy Conversion and Management close

Abstract This study proposes a novel interconnector, termed beam and slot interconnector (BSI), for the anode-supported planar solid oxide fuel cell (SOFC). A detailed comparative investigation is conducted on various transport characteristics and electrical performance of the SOFC stacks with conventional straight channel interconnectors (SCIs) or with novel interconnectors. Results show that the peak power density of a SOFC stack with BSIs is 24.8% higher than it with SCIs at 700 °C and a fuel–air flow rate of 16–40 Nml/(min·cm2). Moreover, BSI can reduce the fuel–air feeding and enhance the fuel–air utilization while maintaining high output power density. Compared with SCI, BSI promotes the gas disturbance, significantly increases the gas velocity and vorticity, and leads the gas to flow in the direction perpendicular to the channel. BSI eliminates the limitation of SCIs on gas diffusion in the electrode and transfers sufficient reactant gas into the electrode function layers for electrochemical reactions. BSI shortens the charge transfer path in SOFC and almost avoids the adverse effects of the electrode-interconnector contact resistance. Compared with the conventional SOFC stack, the novel SOFC stack with BSIs significantly reduces various overpotentials. At 700 °C and 0.5 A/cm2, the activation, concentration, and contact overpotentials are reduced by 8.5%, 47.4%, and 96.4% respectively, and the total overpotential finally drops by 20.0%. Overall, the electrical performance of the SOFC stack with novel interconnectors significantly exceeds the one with conventional interconnectors under the same operating conditions.

Abstract This paper presents an ultra-low frequency vibration energy harvester using a zigzag piezoelectric spring oscillator, which consists of two piezoelectric zigzag springs and a rolling metal ball. The metal ball rolls and drives the piezoelectric springs to deform to harvest energy when a slight vibration occurs in the external environment. The natural frequency of zigzag spring oscillator piezoelectric energy harvester (ZSO-PEH) is related to the length of the spring and the weight of the ball, correlation analysis is carried out by theoretical derivation and ANSYS simulation. It is found experimentally that the proposed device offers efficient energy output in ultra-low frequency excitation. A maximum output power of 5.68 mW is achieved under the best matching resistance of 5.1 k Ω at the excitation frequency of 3 Hz. The performance of energy harvester can be optimized by adjusting the length of the spring and the mass of the ball. The results show that the proposed piezoelectric energy harvester has the potential to power low-power electronic devices and wireless sensor nodes.

Thermally integrated pumped-thermal electricity storage (TI-PTES) offers the opportunity to store electricity as thermal exergy at a large scale, and existing studies are primarily focused on TI-PTES systems based on high-temperature thermal energy storage. This paper presents a thermo-economic analysis of a “cold TI-PTES” system which converts electricity into cold energy using a vapor compression refrigeration (VCR) unit and stores it at sub-ambient temperatures during the charging process, and generates electricity by using an organic Rankine cycle (ORC) working between the sub-ambient temperature and an external low-grade heat source during the discharging process. The effects of key parameters, i.e., mass flowrate and temperature of the storage medium, ORC evaporation temperature, component efficiencies, and pinch-point temperature differences, on the system performance are evaluated based on a whole-system thermo-economic model. The results reveal that the roundtrip efficiency and levelized cost of storage (LCOS) of the system increases while the electrical energy storage capacity decreases as the temperatures of the two cold storage tanks approach each other. When the temperature of the cold storage tank 1 rises from 1 °C to 8 °C while the cold storage tank 2 remains as 13 °C, there is an increase of 25% and 20% in the roundtrip efficiency and LCOS respectively while the energy storage capacity decreases by 69%. A roundtrip efficiency of 0.74 and LCOS of 0.32 $/kWh are achieved with a heat source temperature of 85 °C, using a mass flowrate and temperature of the cold storage medium of 50 kg/s and 1 °C. Furthermore, any change in cold storage medium mass flowrate changes both electrical energy storage capacity and power output by the same proportions. With a continuous high-flowrate external heat source, the LCOS can be as low as 0.17 $/kWh. By providing sufficient heat from an external heat source, the proposed system possesses a high potential for medium-to-large scale energy storage with a unique hybrid ...

Abstract Parameter estimation of photovoltaic cells is essential to establish reliable photovoltaic models, upon which studies on photovoltaic systems can be more effectively undertaken, such as performance evaluation, maximum output power harvest, optimal design, and so on. However, inherent high nonlinearity characteristics and insufficient current–voltage data provided by manufacturers make such problem extremely thorny for conventional optimization techniques. In particular, inadequate measured data might save computational resources, while numerous data is also lost which might significantly decrease simulation accuracy. To solve this problem, this paper aims to employ powerful data-processing tools, for instance, neural networks to enrich datasets of photovoltaic cells based on measured current–voltage data. Hence, a novel improved equilibrium optimizer is proposed in this paper to solve the parameters identification problems of three different photovoltaic cell models, namely, single diode model, double diode model, and three diode model. Compared with original equilibrium optimizer, improved equilibrium optimizer employs a back propagation neural network to predict more output data of photovoltaic cell, thus it can implement a more efficient optimization with a more reasonable fitness function. Besides, different equilibrium candidates of improved equilibrium optimizer are allocated by different selection probabilities according to their fitness values instead of a random selection by equilibrium optimizer, which can achieve a deeper exploitation. Comprehensive case studies and analysis indicate that improved equilibrium optimizer can achieve more desirable optimization performance, for example, it can achieve the minimum root mean square error under all three different diode models compare to equilibrium optimizer and several other advanced algorithms. In general, the proposed improved equilibrium optimizer can obtain a highly competitive performance compared with other state-of-the-state algorithms, which can efficiently improve both optimization precision and reliability for estimating photovoltaic cell parameters.