Surrogate‐assisted optimal re‐dispatch control for risk‐aware regulation of dynamic total transfer capability

Abstract

To enable reliable power delivery through transmission tie-lines, total transfer capability (TTC) must be calculated and regulated to accommodate the transferred amount. However, the traditional optimal power flow (OPF)-based total transfer capability calculation is computationally expensive for efficient total transfer capability control due to the inclusion of a large set of differential-algebraic equations (DAEs) to verify transient stability constraints. In order to enable practicable total transfer capability regulation, a novel risk-aware deep learning-assisted paradigm is proposed here. First, a deep belief network (DBN) is employed to establish the total transfer capability predictor and surrogate the computation-intensive differential-algebraic equations in original optimal power flow formulas, simplifying the high-dimensional and intractable constraints deep belief networks without loss of nonlinearity. Particularly, in order to be aware of control risk from the predictive error of the deep belief networks, prediction intervals (PIs) are produced improved by using ensemble learning and used to disclose the probability of insufficient actions, further guaranteeing the sufficient and cost-effective control by compromising the tradeoff between cost and risk. Symbiotic organisms search (SOS) is then applied to solve the proposed risk-aware deep belief network-assisted total transfer capability control problem globally. The numerical studies testify that the proposed method enables economical, reliable, and full nonlinearity-retained dynamic total transfer capability regulation control within a risk-free surrogate-assisted and tractable physical model-driven hybrid framework.