Yi Zhang

Bio: Yi Zhang is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Electric power system & Base load power plant. The author has an hindex of 2, co-authored 2 publications receiving 132 citations. Previous affiliations of Yi Zhang include North China University of Science and Technology.

Coordinated Distributed MPC for Load Frequency Control of Power System With Wind Farms

Abstract: Load frequency control (LFC) is crucial for the operation and design of modern electric power systems. This becomes quite challenging, as more wind power is included into the power system. This paper proposes a coordinated distributed model predictive control (DMPC) for the LFC of a power system that includes inherently variable wind-power generations. This DMPC communicates power system measurement and prediction data, and considers the information of other controllers for their local objective to realize effective coordination. The controllers solve the optimization problem while considering given constraints, e.g., generation rate constraints, wind speed, pitch angle, and load input constraints for each area. Since the wind-power output depends largely on the wind speed, different optimization modes for the DMPC were used. Both simulation and experimental tests of a four-area interconnected power system LFC, which consists of thermal plants, hydro units, and a wind farm, demonstrate the improved efficiency of the coordinated DMPC.

A Robust Load Frequency Control Scheme for Power Systems Based on Second-Order Sliding Mode and Extended Disturbance Observer

Abstract: This paper proposes a new robust load frequency control (LFC) scheme for multiarea power systems based on the second-order sliding mode control and an extended disturbance observer. First, a reduced-order model of the power system LFC is derived. In this model, the load variations and net exchange tie-line power deviations are combined as a lumped disturbance which can be estimated by the extended disturbance observer. Second, a novel sliding surface is designed with the new transformed state variables obtained from the estimated disturbance. The system dynamics can be indicated by sliding surface design using the eigenvalue assignment or the optimal sliding manifold technique. The sliding variable is driven to the sliding surface with a second-order sliding mode algorithm named supertwisting algorithm. The stability of the proposed LFC scheme and the extended disturbance observer is proved using Lyapunov method. The merits of the scheme include faster response speed, stronger robustness against disturbances arising from power system parameter errors, and unmodeled dynamics, and the full consideration of tie-line power flow scheduling variations. Finally, numerical simulations verify the effectiveness of the LFC scheme and reveal its advantages over the state of the arts.

Robust Load Frequency Control for Power System Considering Transmission Delay and Sampling Period

Abstract: Uncertain transmission delays, sampling periods, parameters uncertainties regarding the power system, load fluctuations, and the intermittent generation of renewable energy sources (RESs) will significantly influence a power system's frequency. This article designs a robust delay-dependent PI-based load frequency control (LFC) scheme for a power system based on sampled-data control. First, a sampled-data-based delay-dependent LFC model of power system is constructed. Then, by applying the Lyapunov theory, and the linear matrix inequality technique, a novel stability criterion is developed for the LFC of the power system by considering the sampling period, and transmission delay of the communication network, which ensures that the proposed scheme operates in large sampling periods, and under transmission delays. Next, an exponential decay rate (EDR) is introduced to guide the design of a robust PI-based LFC scheme. The LFC scheme with robustness is designed by setting a small EDR. The values of EDR are adjusted by the given robust performance evaluation conditions of parameter uncertainties, and $H_\infty$ performance. Finally, case studies are carried out based on a one-area power system, and a three-area power system with RESs. Simulation results show that the proposed LFC scheme performs strong robustness against parameter uncertainties regarding the power system, and communication network, load fluctuations, and the intermittent generation of RESs.

Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System

Abstract: A microgrid is a distributed networked generation system, which can effectively integrate various sources of distributed generation, especially renewable energy sources into the information network. The standalone wind/solar/battery power system is a typical standalone microgrid, in which the wind and solar power generations are the intermittent systems with complex dynamics and multiconstraints. Coordinated optimization between the wind power and solar power generations can effectively meet the load demand, reduce wear and tear of generating units, prolong the lifetime and, thus, guarantee the safety of the power grid. Regarding the large-scale, geographically dispersed standalone wind/solar/battery power generation system, this paper constituted a hierarchical distributed model predictive control (HDMPC). In this HDMPC, the upper layer utilizes an iterative distributed control strategy to realize the coordination of the power dispatch. It thus reaches the economic object, e.g., reducing the torsional shaft torque transmitted to gearbox in wind turbine system. The lower layer utilizes the supervisory predictive control to realize both the economic and tracking property. Under this hierarchical structure, the back-calculation from the lower control layer to the upper layer is utilized to keep the consistency of constraints. Through coordinated optimization among the subsystems, the proposed HDMPC realizes the plug and play of distributed energy. The simulation and the experiment validate the advantages of the proposed method in that it can realize the reliability, high efficiency, flexibility, and interactivity for the microgrid control.

Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System

Abstract: Fast and stochastic power fluctuations caused by renewable energy sources and flexible loads have significantly deteriorated the frequency performance of modern power systems. Power system frequency control aims to achieve real-time power balance between generations and loads. In practice, it is much more difficult to exactly acquire the values of unbalance power in both transmission and distribution systems, especially when there is a high penetration level of renewable energies. This paper explores a deep learning approach to identify active power fluctuations in real-time, which is based on a long short-term memory recurrent neural network. The developed method provides a more accurate and faster estimation of the value of power fluctuations from the real-time measured frequency signal. The identified power fluctuations can serve as control reference so that the system frequency can be better maintained by automatic generation control, as well as emerging frequency control elements, such as energy storage system. A detailed model of Singapore power system integrated with distributed energy storage systems is used to verify the proposed method and to compare with various classical methods. The simulation results clearly demonstrate the necessity for power fluctuation identification, and the advantages of the proposed method.