• Energy Research
• 2016-2025close
• CN close
• GB close
• AU close
• Energy Conversion and Management close

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.

Abstract Global hydropower growth continues to accelerate with 25% of total capacity installed in just the last 10 years. This accelerating expansion and the important storage facility hydropower means it is increasingly important to understand the reasons for operational failures. This is a challenge because the major reason for failures involves the complex interaction of hydraulic, mechanical and electric subsystems. Historically, reliability modelling has been split in two directions, focusing on different sub-systems, and has not yet been unified. Here these approaches are unified with a novel expression of unbalanced forces. This model with operational data are validated and the important modes of oscillation in the shaft are identified. Finally, the mechanism of the first-order oscillation mode exciting a second-order mode is presented. This integrated and accurate mathematical model is a major advance in the diagnosis and prediction of failures in hydropower operation.

Abstract Techniques for evaluating water management are critical to diagnose the performance of polymer electrolyte membrane fuel cells (PEMFCs). Acoustic emission as a function of polarisation (AEfP) has been recently introduced as a non-invasive, non-destructive method to analyse the water generation and removal inside a PEMFC during polarisation. AEfP was shown to provide unique insight into water management within a conventional PEMFC and correlating it to cell performance. Here, AEfP is used to characterise the performance of fractal PEMFCs by evaluating the hydration conditions inside them. This is achieved by probing the water dynamics inside two different fractal flow-field based PEMFCs, namely 1-way and 2-way fractal PEMFCs, and measuring the corresponding acoustic activity generated from them. AEfP is performed on the fractal PEMFCs under relatively humid (70% RH) and fully humidified (100% RH) reactant relative humidity (RH) conditions. Flooding in the 2-way fractal PEMFC, as opposed to the 1-way fractal PEMFC, is demonstrated under different operating conditions by the relatively higher acoustic activity it generates. Corroborating evidence of flooding in the 2-way fractal flow-field under different conditions is provided by its polarisation curves, impedance tests and galvanostatic (current hold) measurements.

Abstract With the application of advanced information technology for the integration of electricity and natural gas systems, formulating an excellent energy conversion and management strategy has become an effective method to achieve established goals. Differing from previous works, this paper proposes a peak load shifting model to smooth the net load curve of an integrated electricity and natural gas system by coordinating the operations of the power-to-gas unit and generators. Moreover, the study aims to achieve multi-objective optimization while considering the economy of the system. A dynamic energy conversion and management strategy is proposed, which coordinates both the economic cost target and the peak load shifting target by adjusting an economic coefficient. To illustrate the complex energy conversion process, deep reinforcement learning is used to formulate the dynamic energy conversion and management problem as a discrete Markov decision process, and a deep deterministic policy gradient is adopted to solve the decision-making problem. By using the deep reinforcement learning method, the system operator can adaptively determine the conversion ratio of wind power, power-to-gas and gas turbine operations, and generator output through an online process, where the flexibility of wind power generation, wholesale gas price, and the uncertainties of energy demand are considered. Simulation results show that the proposed algorithm can increase the profit of the system operator, reduce wind power curtailment, and smooth the net load curves effectively in real time.

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.