Search

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Large‐scale grid‐connected electric vehicles (EVs) and wind power generation have brought new challenges to economical optimal scheduling of power system because of their uncertainties. It is essential to establish accurate scheduling models and arrive at the economical optimal scheduling strategy. This paper develops a probability model of wind power output, which adopts the normal distribution to depict the randomness of wind speed, considering turbulence. The probability model of vehicle‐to‐grid (V2G) is also established, and the corresponding probability distribution is used to describe the initial charge/discharge time, daily mileage, and charge state of the EV. A collaborative scheduling model is built for the grid and consumers, taking account of the fuel cost of thermal power unit, the uncertainty cost of wind power output, and the cost of V2G service. The scheduling model is solved by an improved particle swarm optimization algorithm, which applies the inertia weight obeying the standard normal distribution and the nonlinear anticosine learning factors to avoid premature convergence and the loss of population space diversity. Finally, the validity and practicability of the proposed method are verified by the simulation of a ten‐machine power system integrated with wind power and EVs. Simulation results show that V2G can optimize the output of thermal power units and eliminate the surplus output of wind power. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]