Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series c...Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.展开更多
The grid-connected converter(GCC) is widely used as the interface between various distributed generations and the utility grid. To achieve precise power control for GCC, this paper presents a model predictive direct p...The grid-connected converter(GCC) is widely used as the interface between various distributed generations and the utility grid. To achieve precise power control for GCC, this paper presents a model predictive direct power control(MPDPC)with consideration of the unbalanced filter inductance and grid conditions. First, the characteristics of GCC with unbalanced filter inductance are analyzed and a modified voltage control function is derived. On this basis, to compensate for the power oscillation caused by unbalanced filter inductance, a novel power compensation method is proposed for MPDPC to eliminate the DC-side current ripple while maintaining sinusoidal grid current. Besides, to improve the control robustness against mismatched filter inductance, a filter inductance identification scheme is proposed. Through this scheme, the estimated value of filter inductance is updated in each control period and applied in the proposed MPDPC. Finally, simulation results in PSCAD/EMTDC confirm the validity of the proposed MPDPC and the filter inductance identification scheme.展开更多
基金This work was supported by the National Key Research and Development Program of China(No.2017YFB0902600).
文摘Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.
基金supported by the Science and Technology Projects of State Grid Corporation of China “Key Technologies and Demonstration Application of Distributed Power Clusters Regulation”(No. 52153220000U)。
文摘The grid-connected converter(GCC) is widely used as the interface between various distributed generations and the utility grid. To achieve precise power control for GCC, this paper presents a model predictive direct power control(MPDPC)with consideration of the unbalanced filter inductance and grid conditions. First, the characteristics of GCC with unbalanced filter inductance are analyzed and a modified voltage control function is derived. On this basis, to compensate for the power oscillation caused by unbalanced filter inductance, a novel power compensation method is proposed for MPDPC to eliminate the DC-side current ripple while maintaining sinusoidal grid current. Besides, to improve the control robustness against mismatched filter inductance, a filter inductance identification scheme is proposed. Through this scheme, the estimated value of filter inductance is updated in each control period and applied in the proposed MPDPC. Finally, simulation results in PSCAD/EMTDC confirm the validity of the proposed MPDPC and the filter inductance identification scheme.
