Numerical Study on Hydrodynamic Characteristics and Electrochemical Performance of Alkaline Water Electrolyzer by Micro-Nano Surface Electrode.

This study constructed a two-dimensional alkaline water electrolyzer model based on the two-phase flow Euler-Euler model. In the model, the micro-nano surface electrodes with different structure types and graphic parameters (distance, height, and width) were used and compared with the vertical flat electrode to evaluate their influence on electrolysis performance. The simulation results show that the performance of the micro-nano surface electrode is much better than that of the vertical flat electrode. The total length of micro-nano structural units relates to the contact area between the electrode and the electrolyte and affects the cell voltage, overpotential, and void fraction. When rectangular structural units with a distance, height, and width of 0.5 µm, 0.5 µm, and 1 µm are used, the total length of the corresponding micro-nano surface electrode is three times that of the vertical flat electrode, and the cathode overpotential decreases by 65.31% and the void fraction increases by 54.53% when it replaces the vertical flat electrode.

Thermodynamic Analysis of a Hybrid Trigenerative Compressed Air Energy Storage System with Solar Thermal Energy.

The comprehensive utilization technology of combined cooling, heating and power (CCHP) systems is the leading edge of renewable and sustainable energy research. In this paper, we propose a novel CCHP system based on a hybrid trigenerative compressed air energy storage system (HT-CAES), which can meet various forms of energy demand. A comprehensive thermodynamic model of the HT-CAES has been carried out, and a thermodynamic performance analysis with energy and exergy methods has been done. Furthermore, a sensitivity analysis and assessment capacity for CHP is investigated by the critical parameters effected on the performance of the HT-CAES. The results indicate that round-trip efficiency, electricity storage efficiency, and exergy efficiency can reach 73%, 53.6%, and 50.6%, respectively. Therefore, the system proposed in this paper has high efficiency and flexibility to jointly supply multiple energy to meet demands, so it has broad prospects in regions with abundant solar energy resource.

Technological Research of a Clean Energy Router Based on Advanced Adiabatic Compressed Air Energy Storage System.

As a fundamental infrastructure of energy supply for future society, energy Internet (EI) can achieve clean energy generation, conversion, storage and consumption in a more economic and safer way. This paper demonstrates the technology principle of advanced adiabatic compressed air energy storage system (AA-CAES), as well as analysis of the technical characteristics of AA-CAES. Furthermore, we propose an overall architectural scheme of a clean energy router (CER) based on AA-CAES. The storage and mutual conversion mechanism of wind and solar power, heating, and other clean energy were designed to provide a key technological solution for the coordination and comprehensive utilization of various clean energies for the EI. Therefore, the design of the CER scheme and its efficiency were analyzed based on a thermodynamic simulation model of AA-CAES. Meanwhile, we explored the energy conversion mechanism of the CER and improved its overall efficiency. The CER based on AA-CAES proposed in this paper can provide a reference for efficient comprehensive energy utilization (CEU) (93.6%) in regions with abundant wind and solar energy sources.

Thermodynamic Analysis of a Hybrid Power System Combining Kalina Cycle with Liquid Air Energy Storage.

Liquid air energy storage (LAES) is a promising energy storage technology in consuming renewable energy and electricity grid management. In the baseline LAES (B-LAES), the compression heat is only utilized in heating the inlet air of turbines, and a large amount of compression heat is surplus, leading to a low round-trip efficiency (RTE). In this paper, an integrated energy system based on LAES and the Kalina cycle (KC), called KC-LAES, is proposed and analyzed. In the proposed system, the surplus compression heat is utilized to drive a KC system to generate additional electricity in the discharging process. An energetic model is developed to evaluate the performance of the KC and the KC-LAES. In the analysis of the KC subsystem, the calculation results show that the evaporating temperature has less influence on the performance of the KC-LAES system than the B-LAES system, and the optimal working fluid concentration and operating pressure are 85% and 12 MPa, respectively. For the KC-LAES, the calculation results indicate that the introduction of the KC notably improves the compression heat utilization ratio of the LAES, thereby improving the RTE. With a liquefaction pressure value of eight MPa and an expansion pressure value of four MPa, the RTE of the KC-LAES is 57.18%, while that of the B-LAES is 52.16%.

Policy Approximation in Policy Iteration Approximate Dynamic Programming for Discrete-Time Nonlinear Systems.

Policy iteration approximate dynamic programming (DP) is an important algorithm for solving optimal decision and control problems. In this paper, we focus on the problem associated with policy approximation in policy iteration approximate DP for discrete-time nonlinear systems using infinite-horizon undiscounted value functions. Taking policy approximation error into account, we demonstrate asymptotic stability of the control policy under our problem setting, show boundedness of the value function during each policy iteration step, and introduce a new sufficient condition for the value function to converge to a bounded neighborhood of the optimal value function. Aiming for practical implementation of an approximate policy, we consider using Volterra series, which has been extensively covered in controls literature for its good theoretical properties and for its success in practical applications. We illustrate the effectiveness of the main ideas developed in this paper using several examples including a practical problem of excitation control of a hydrogenerator.

Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration.

The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation.

A boundedness result for the direct heuristic dynamic programming.

Approximate/adaptive dynamic programming (ADP) has been studied extensively in recent years for its potential scalability to solve large state and control space problems, including those involving continuous states and continuous controls. The applicability of ADP algorithms, especially the adaptive critic designs has been demonstrated in several case studies. Direct heuristic dynamic programming (direct HDP) is one of the ADP algorithms inspired by the adaptive critic designs. It has been shown applicable to industrial scale, realistic and complex control problems. In this paper, we provide a uniformly ultimately boundedness (UUB) result for the direct HDP learning controller under mild and intuitive conditions. By using a Lyapunov approach we show that the estimation errors of the learning parameters or the weights in the action and critic networks remain UUB. This result provides a useful controller convergence guarantee for the first time for the direct HDP design.