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Register a new userMaximum Production Point Tracking of a High-Temperature Power-to-Gas System: A Dynamic-Model-Based Study
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Power-to-gas (P2G) can be employed to balance renewable generation because of its feasibility to operate at fluctuating loading power. The fluctuating operation of low-temperature P2G loads can be achieved by controlling the electrolysis current alone. However, this method does not apply to high-temperature P2G (HT-P2G) technology with auxiliary parameters such as temperature and feed rates: Such parameters need simultaneous coordination with current due to their great impact on conversion efficiency. To improve the system performance of HT-P2G while tracking the dynamic power input, this paper proposes a maximum production point tracking (MPPT) strategy and coordinates the current, temperature and feed rates together. In addition, a comprehensive dynamic model of an HT-P2G plant is established to test the performance of the proposed MPPT strategy, which is absent in previous studies that focused on steady states. The case study suggests that the MPPT operation responds to the external load command rapidly even though the internal transition and stabilization cost a few minutes. Moreover, the conversion efficiency and available loading capacity are both improved, which is definitely beneficial in the long run.
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High-temperature electrolysis (HTE) is a promising technology for achieving high-efficiency power-to-gas, which mitigates the renewable curtailment by transforming wind or solar energy into fuels. Different from low-temperature electrolysis, a considerable amount of the input energy is consumed by auxiliaries in an HTE system for maintaining the temperature, so the studies on systematic description and parameter optimization of HTE are essentially required. A few published studies investigated HTEs systematic optimization based on simulation, yet there is not a commonly used analytical optimization model which is more suitable for integration with power grid. To fill in this blank, a concise analytical operation model is proposed in this paper to coordinate the necessary power consumptions of auxiliaries under various loading conditions of an HTE system. First, this paper develops a comprehensive energy flow model for an HTE system based on the fundamentals extracted from the existing work, providing a quantitative description of the impacts of condition parameters, including the temperature and the feedstock flow rates. A concise operation model is then analytically proposed to search for the optimal operating states that maximize the hydrogen yield while meeting the desired system loading power by coordinating the temperature, the feedstock flows and the electrolysis current. The evaluation of system performance and the consideration of constraints caused by energy balances and necessary stack requirements are both included. In addition, analytical optimality conditions are obtained to locate the optimal states without performing nonlinear programming by further investigating the optimization method. A numerical case of an HTE system is employed to validate the proposed operation model, which proves to not only improve the conversion efficiency but also enlarge the system load range.
We present a nonlinear equivalent resistance tracking method to optimize the power output for solar arrays. Tracking an equivalent resistance results in nonlinear voltage step sizes in the gradient descent search loop. We introduce a new model for the combined solar module along with a DC-DC converter which results in a highly nonlinear dynamical system due to the inherent non-linearity of the PV cell topology and the switched DC-DC converter system. To guarantee stability over a range of possible operating regimes, we utilize a feedback linearization control approach to exponentially converge to the setpoint. Simulations are presented to illustrate the performance and robustness of the proposed technique.
The analysis of the effect of noisy perturbations on real heat engines, working on any steady-state regime has been a topic of interest within the context of Finite-Time Thermodynamics (FTT). The study of their local stability has been proposed through the so-called performance regimes: maximum power output, maximum ecological function, among others. Recently, the global stability analysis of an endoreversible heat engine was also studied taking into account the same performance regimes. We present a study of local and global stability analysis of power plant models (the Curzon-Ahlborn model) operating on a generalized efficient power regime called maximum k-efficient power. We apply the Lyapunov stability theory to construct the Lyapunov functions to prove the asymptotically stable behavior of the steady-state of intermediate temperatures in the Curzon-Ahlborn model. We consider the effect of a linear heat transfer law on the phase portrait description of real power plants, as well as the role of the $k$ parameter in the evolution of perturbations to heat flow. In general, restructured operation conditions show better stability in external perturbations.
Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to accurately calculate the ion stopping power in distinct target materials. Deep learning algorithm is a newly emerged method to solve multi-factors physical problems and can mine the deeply implicit relations among parameters, which make it a powerful tool in energy loss prediction. In this work, we developed an energy loss prediction model based on deep learning. When experimental data are available, our model can give predictions with an average absolute difference close to 5.7%, which is in the same level compared with other widely used programs e.g. SRIM. In the regime without experimental data, our model still can maintain a high performance, and has higher reliability compared with the existing models. The ion range of Au ions in SiC can be calculated with a relative error of 0.6~25% for ions in the energy range of 700~10000 keV, which is much better than the results calculated by SRIM. Moreover, our model support the reciprocity conjecture of ion stopping power in solid proposed by P. Sigmund, which has been known for a long time but can hardly been proved by any of the existing stopping power models. This high-accuracy energy loss prediction model is very important for the research of ion-solid interaction mechanism and enormous relevant applications of energetic ions, such as in semiconductor fabrications, nuclear energy systems and the space facilities.
Advancements in electrochemical double-layer capacitor (EDLC) technology require the concomitant use of novel efficient electrode materials and viable electrode manufacturing methods. Cost-effectiveness, scalability and sustainability are key-drivers for fulfilling product development chain accepted by worldwide legislations. Herein, we report a scalable and sprayable green electrode material-based ink based on activated carbon and single-/few-layer graphene (SLG/FLG) flakes. We show that, contrary to commercial reduced graphene oxide, defect-free and flat SLG/FLG flakes reduce the friction of ions over the electrode films, while spray coating deposition of our ink maximises the electrolyte accessibility to the electrode surface area. Sprayed SLG/FLG flakes-based EDLCs display superior rate capability performance (e.g., specific energies of 31.5, 23.7 and 12.5 Wh kg-1 at specific powers of 150, 7500 and 30000 W kg-1, respectively) compared to both SLG/FLG flakes-free devices and commercial-like EDLCs produced by slurry-coating method. The use of SLG/FLG flakes enables our sprayed EDLCs to operate in a wide range of temperature (-40/+100{deg}C) compatible with ionic liquid/organic solvent-based electrolytes, overcoming the specific power limits of AC-based EDLCs. A prototype EDLCs stack consisting of multiple large-area EDLCs, each one displaying a capacitance of 25 F, demonstrates the industrial potential of our technology.
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