The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs) and prediction models using wavelet neural network (WNN). Lower upper bound estimation (LUBE) of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC) algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system.

To realize the national strategies for carbon emission reduction, electric power industries should undergo reforms to cope with the multiple challenges of decarbonization, marketization, and energy transition. How to design a dispatch strategy that considers both low‐carbon demand and economic cost has become a major concern in integrated energy systems. To realize multi‐energy complementation and low‐carbonization, a scheduling framework of combined heat and power‐virtual power plants (CHP‐VPPs) considering carbon capture and power‐to‐gas is proposed. The stochastic dynamic optimization problem is modelled as a Markov decision process. An improved deep reinforcement learning‐based algorithm named multi‐level backtracking prioritized experience replay‐twin delayed deep deterministic policy gradient (MBEPR‐TD3) is employed to solve the low‐carbon economic dispatch problem. The results show that: (1) The profits have been increased by 85.8% and carbon emissions have been reduced by 30.3% because of the addition of power‐to‐gas in CHP‐VPP; (2) the revenues have been improved by 22.24% and carbon emissions have been decreased by 37.04% owing to the introduction of carbon capture and trading; (3) compared with results using DQN, DDPG, and TD3, the optimal dispatch strategy obtained by MBEPR‐TD3 has higher reward returns and more stable convergence.

Vibrations occur in the wind turbine drivetrain due to the drivetrain’s elasticity and gear clearance. Typically, the PID control method is used to suppress elastic vibration, while the method with the disturbance suppression function is used to suppress nonlinear gear clearance vibration. The purpose of this paper is to propose an equivalent PID control method based on the internal model control (IMC) for suppressing vibration caused by the wind turbine drivetrain’s elasticity and gear clearance. The equivalent PID controller can suppresses elastic vibration; the IMC controller can suppresses gear clearance vibration. First, the vibration principle of the two-mass wind turbine drivetrain with clearance is discussed. After analyzing the nonlinear characteristics of the gear clearance, the nonlinear clearance system is decomposed into a linear unit and a nonlinear bounded disturbance unit. To suppress nonlinear bounded disturbances, a disturbance suppression method based on IMC is proposed; simultaneously, an equivalent PID controller based on IMC is designed to resolve the vibration issue caused by the wind turbine drivetrain’s elasticity. The simulation experimental results show that the clearance vibration is suppressed by the original IMC method. The PID controller obtained by the IMC equivalent transformation can suppress the elastic vibration.

A robust sliding mode observer (SMO) is proposed to achieve multiple fault reconstruction for a wind energy conversion system (WECS) with simultaneous actuator and sensor faults. Firstly, the state equation of the WECS is established. The orthogonal transformation matrix and a post filter are introduced, and a new augmented system is constructed; then, the sensor fault is converted into an actuator fault to diagnose. The fault information is collected by the equivalent output control, and the simultaneous reconstruction algorithm of the sensor fault and the actuator fault is given. Through compensation control, the reliable control input of the WECS is guaranteed, and the function of active fault tolerant control for multiple faults is achieved. Simulation experiments show that the proposed method can accurately reconstruct the actuator and sensor faults, and maximum wind energy capture can be achieved by active fault-tolerant control.

An augmented sliding mode observer is proposed to solve the actuator fault of an uncertain wind energy conversion system (WECS), which can estimate the system state and reconstruct the actuator faults. Firstly, the mathematical model of the WECS is established, and the non-linear term in the state equation is separated as the uncertain part of the system. Then, the states of the system are augmented, and the actuator fault is considered as part of the augmented state. The augmented sliding mode observer is designed to estimate the system state and actuator fault. A robust fault-tolerant controller is designed to ensure the reliable input of the WECS, maintain the stability of the fault system and maximize the acquisition of wind energy. The numerical simulation results verify the effectiveness of the control strategy.