Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach. (15th November 2018)

• Record Type: Journal Article
• Authors: Fang, Xin; Hodge, Bri-Mathias; Du, Ershun; Zhang, Ning; Li, Fangxing
• Is Part Of: Applied energy. Volume 230(2018)
• Page Start: 531
• Page End: 539
• Publication Date: 2018-11-15
• Subjects: Electricity market -- Economic dispatch -- Spatial-temporal correlation -- Adjustable and distributional robustness -- Chance constrained optimal power flow; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.08.123
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Spatial and temporal correlation of wind power and uncertainty considered. Sparse correlation matrix efficiently models the spatial-temporal correlation. Distributionally robust chance constrained OPF model considers economics and security. Considering spatial-temporal correlation leads to lower system operating cost. Comparison with scenario-based stochastic model shows efficiency of proposed model. Abstract: The significantly increasing deployment of wind power necessitates that system operation considers the spatial-temporal correlation of power forecast from different wind power plants. How to model this spatial-temporal correlation in the actual system dispatch is challenging. In this paper, a sparse correlation matrix is utilized to efficiently model the spatial-temporal correlation of wind power forecast in the generation dispatch model. A novel, adjustable, and distributionally-robust chance-constrained multi-interval optimal power flow (ADRCC-MIOPF) model is proposed to obtain reliable economic dispatch (ED) solutions. The spatial-temporal correlation of wind power plants power forecasts is represented by the sparse correlation covariance matrix obtained from historical, time series wind power forecast error data. The chance constraints in the ADRCC-MIOPF model are transformed into a set of second-order-cone (SOC) constraints in which an adjustable coefficient in the chance constraints controls the robustness of the ADRCC-MIOPF model to the wind power forecast errors distribution. Case studies performed on the PJM 5-bus system and IEEE 118-bus system verify the proposed method to improve the system security and reduce the cost especially under the high wind penetration levels. All the cases can be solved within several minutes for both the small and large cases which validates the efficiency of the proposed sparse matrix model. In addition, considering the spatial-temporal correlation of wind power forecast and the distributional robustness of wind power forecast error leads to a more reliable economic dispatch with lower system violations.