Optimized Active Power Dispatching Strategy Considering Fatigue Load of Wind Turbines During De-Loading Operation

Abstract

With the increase of wind power penetration rate in the power grid, power dispatching including wind curtailment often happens to guarantee the safety of the power system. Considering the fatigue load of wind turbines during de-loading operation, how to optimally dispatch them along with the required power instructions becomes a hot issue. Under this background, this paper mainly studies the active power dispatching based on fatigue load optimization. In order to reduce the calculation complexity of the fatigue load, this paper proposes a simplified active power dispatching model for wind turbines via a look-up table. Subsequently, the ultra-short-term wind speed prediction is performed using the least squares support vector machine for the dynamic's prediction of wind turbines, which will be applied in fatigue load pre-calculation. Moreover, in order to fast solve the optimal dispatching problem, the accelerated particle swarm optimization algorithm is adopted. Under different de-loading scenes, the simulation experiments show that the fatigue load of the wind turbines has certain regularity while different de-loading strategies yield different effects on the fatigue load. Finally, based on the evaluation of damage equivalent load (DEL), the proposed optimal dispatching strategy in this paper is proved to be effective to meet the external dispatching from the power grid while reducing the total fatigue load level of wind farm via optimizing the power instruction to each wind turbine. INDEX TERMS Active power dispatching, de-loading operation, fatigue load, wind farm, wind speed prediction.

Dispatching of a Wind Farm Incorporated With Dual-Battery Energy Storage System Using Model Predictive Control

“…However, wind power exhibits intermittent characteristics due to natural factors such as climate, which reduces the dispatchability of active power in wind farms. Given the increasing penetration of wind power in the power system, the reduction of dispatchable power would in turn increase operational risks and affect the use of wind energy efficiency [3]- [5].…”

Hierarchical two‐stage robust optimisation dispatch based on co‐evolutionary theory for multiple CCHP microgrids

“…A number of methods have been proposed for predicting the output power of wind farms, such as simulation methods based on scenario prediction [6] and the functions sampling method [7], which are employed to form a mathematical model of WP output as a time series. However, the uncertainty associated with WP power output is practically impossible to address in power control and dispatch operations simply through prediction owing to the inevitability of prediction errors.…”

Stochastic Multi-Objective Optimized Dispatch of Combined Cooling, Heating, and Power Microgrids Based on Hybrid Evolutionary Optimization Algorithm

“…Then, the comprehensive influence of the probability distribution of these random variables on the objective function value and the risk value (or opportunity cost) is described according to the occurrence probability and simulation result of each scenario. An example of this method is the development of a probability model between the fatigue load of wind turbines and WP output [10]. Since each wind farm has different factors, such as topography, latitude, fan size, and the wind turbine material, there is an over-reliance on experience using simulation methods.…”

“…The randomness of WP output has also been characterized in a model combining a stacked auto encoder (SAE) and a back-propagation (BP) algorithm [13]. Moreover, intelligent algorithms and fitting models have also been applied to WP prediction [10], [14]. However, these typical uses of single-layer prediction models increases the computational burden of these models, and often fails to obtain sufficiently accurate confidence probability intervals.…”