Efficient Solution of Adjustable Affine Chance-Constrained Real-Time Dispatch

Abstract

Chance-constrained real-time dispatch (CCRTD), which coordinates hour-ahead schedules and automatic generation control (AGC) to jointly optimize the generation and affine policies of units, is crucial for handling uncertainties. However, the chance constraints in CCTRD are essentially intractable due to the affine coupling of random and decision variables. To address this issue, we recursively decompose the quantiles with non-Gaussian uncertainties into difference of convex (DC) functions, which are implicitly piecewise but second-order continuous to ensure computational reliability. Then, CCRTD is reformulated as a DC programming and is efficiently solved utilizing the penalty convex-concave procedure (CCP). The effectiveness of the approach is verified by numerical tests.