Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning

Microgrid with hydrogen storage is an effective way to integrate renewable energy and reduce carbon emissions. This paper proposes an optimal operation method for a microgrid with hydrogen storage. The electrolyzer efficiency characteristic model is established based on the linear interpolation method. The optimal operation model of microgrid is incorporated with the electrolyzer efficiency characteristic model. The sequential decision-making problem of the optimal operation of microgrid is solved by a deep deterministic policy gradient algorithm. Simulation results show that the proposed method can reduce about 5% of the operation cost of the microgrid compared with traditional algorithms and has a certain generalization capability.

Nonlinear Modeling of Dynamic Characteristics of Pump-Turbine

Hydropower is a kind of clean energy, which can effectively reduce the consumption of fossil energy and is one of the main fields of new energy development. Pumped storage power station not only provides a solution for storing electric energy and generating excess, but also is a clean, efficient, economical and safe power system regulation method with high quality. Accurate modeling of a pump-turbine, as the core equipment of a pumped storage unit, is the key to safe and stable operation of the pumped storage unit. At present, a method of simplifying the external characteristics of a pump-turbine into a first-order linear model is widely used, which cannot effectively and accurately reveal the nonlinear dynamic characteristics of the unit in transition process. In order to meet the demand of high-precision simulation of the unit, a new method of identifying Taylor series expansion considering nonlinearity based on the torque characteristic formula and the flow characteristic formula is proposed, which is applied to the pump-turbine external characteristic model, and retains the second derivative term, making the model a second-order nonlinear model, and thus, the pump-turbine model becomes a nonlinear model. The nonlinear model established is used to simulate the load increase and load rejection of the unit, and the results are compared with those for the linear model. The comparison shows that the nonlinear model established for the pump-turbine can reveal the dynamic response of the unit more effectively and accurately than the linear model, and provide a further guarantee for the safe and stable operation of pumped-storage units, which is of great significance to hydropower energy development.

Studies on Frequency Response Characteristics of High-Speed Railway Train End Relationship Test System with Flexible Bases

A train end relationship test system was installed with a Stewart parallel robot on the reaction bases perpendicular to the ground, to test the fatigue and durability of train end relationship components, such as the transfixion way of a high-speed railway train. The flexibility of the reaction bases affected the test accuracy of the train end relationship components within a test frequency band range. In this paper, a coupling characteristic model was established between the flexible bases and the parallel robot. Then, the analytical relationship was analyzed between the natural frequency of the bases and the natural frequency of the parallel robot. Moreover, a design criterion was proposed for the natural frequency of the reaction bases. It was considered that when the natural frequency of the bases was not less than five times the natural frequency of the parallel robot, the influence of the flexibility of the bases on the test accuracy can be ignored. The validity of the design criterion was verified by the simulation results.

Adaptive control and disturbance compensation for gear transmission servo systems with large-range inertia variation

In gear transmission servo systems, backlash effect and inertia variation often generate low precision, oscillations or even affect the stability. Focusing on this issue, an adaptive terminal integral sliding mode control (ATISMC) strategy and a discrete linear extended state observer (DLESO) are proposed in this article. First, different from using traditional dynamic model, a characteristic model is established with online parameter identification to describe inertia variation. Then, a DLESO is proposed to observe and compensate the modeling error caused by parameter identification and backlash effect. An ATISMC is newly designed based on the characteristic model to suppress the uncertainties and stabilize the whole closed-loop system. Both the uniform ultimate boundedness of observation error and the practical finite-time stability of the system is proved. Finally, simulation and experimental results demonstrate that the proposed strategy can adapt to large-range inertia variation and suppress the backlash effect.