Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction

Abstract

This paper looks at the ability to cope with the uncertainty of wind power and reduce the impact of wind power forecast error (WPFE) on the operation and dispatch of power system. Therefore, several factors which are related to WPFE will be studied. By statistical analysis of the historical data, an indicator of real-time error based on these factors is obtained to estimate WPFE. Based on the real-time estimation of WPFE, a multi-time scale rolling dispatch model for wind/storage power system is established. In the real-time error compensation section of this model, the previous dispatch plan of thermal power unit is revised according to the estimation of WPFE. As the regulating capacity of thermal power unit within a short time period is limited, the estimation of WPFE is further compensated by using battery energy storage system. This can not only decrease the risk caused by the wind power uncertainty and lessen wind spillage, but also reduce the total cost. Thereby providing a new method to describe and model wind power uncertainty, and providing economic, safe and energy-saving dispatch plan for power system. The analysis in case study verifies the effectiveness of the proposed model.

Strength Pareto Evolutionary Algorithm for the Dynamic Economic Emission Dispatch Problem incorporating Wind Farms and Energy Storage Systems

“…The resolutions of such problems have been based on quadratic programming, Genetic Algorithm (GA) [3], Particle Swarm Optimization (PSO) [4], simulated annealing [5], harmony search [6], firefly algorithm [7], chemical reaction optimization [8], etc. The uncertainty of the wind power output is handled with different manners, such as scenario method [9], forecast error method [10], stochastic programming [11], probability theory-based model [12], fuzzy logic [13], and chance constraint model [14]. For example, a chance constraint-based formulation for the dispatch problem was described in [15].…”

Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees

“…According to (18) and (19), the calculation of weights depends on the error distributions, and the error is calculated after the model is trained; however, the model is trained after the weights are set.…”

“…Probabilistic wind power forecasting is proposed to model the uncertainty of wind power by estimating the probability distribution of wind power generation. Based on the probability distribution, many decision-making applications, including unit commitment [3][4][5][6][7][8], wind power trading [9][10][11][12], reserve procurement [13,14], demand response [15,16], probabilistic power flow [17,18], and economic dispatch [19][20][21], can be developed.…”

Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

“…In Reference [9], a dispatch model including CHP, conventional thermal power units, and renewable energy source was proposed, and the heating process by a three-stage heat transfer model of the extraction steam was described. In Reference [10], a multi-timescale rolling dispatch model based on real-time error compensation was proposed. The power of thermal power unit was revised in real-time to compensate for the error of wind power forecast.…”