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.
Highlights
In recent years, with the increasing prominence of energy consumption and environmental pollution, renewable energy generation, represented by wind power generation, has been paid more and more attention [1,2]
To estimate the wind power forecast error (WPFE), an error indicator λe is defined in Equation (7): From the previous discussion, it can be concluded that λ1, λ2, λ3, λ4 have relationships to WPFE
The charge/discharge power and the states of charge (SOC) of the battery energy storage system (BESS) in each sample time are shown in Figure curtailment decreases from 324.0 MW to 32.4 MW, with a reduction rate of 90.0%
Summary
With the increasing prominence of energy consumption and environmental pollution, renewable energy generation, represented by wind power generation, has been paid more and more attention [1,2]. In Reference [11], the network loss and the spinning reserve requirements for wind power uncertainty are considered, a multi-objective dispatch model considering both emission and cost is established. This type of model needs to set a large capacity of spinning reserve to ensure the safety of the system operation, which is too conservative and not economical. In Reference [19], the authors regard the wind power output as a fuzzy variable and establish a multi-time scale dispatch model considering chance constraints of spinning reserve. The dispatch results and the comparison analysis with alternative models from case study represent the comprehensive performance of proposed method in flexibility, improving wind power accommodation, reducing load shedding, saving computational time and decrease the total cost
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