When a single-line grounding (SLG) fault occurs in a small current grounding system (SCGS), the weak fault characteristics pose a challenge to the fault line selection (FFS). To improve the reliability of FFS, a new FFS method based on harmonic active injection of converter interfaced distributed generator (CIDG) is proposed in the paper. First, by analyzing the distribution law and attenuation characteristics of the harmonic current actively injected by CIDG in the system, the active injection characteristic harmonic current signal that can meet the needs of FFS is selected, and then, a characteristic harmonic current signal injection method based on CIDG active control is designed. On this basis, an FFS method based on the amplitude-phase relationship of harmonic current is proposed, enabling the method to simultaneously meet the requirements of fault line selection and phase selection requirements. PSCAD/EMTDC simulation analysis verifies the effectiveness and superiority of the proposed method compared to existing methods.

Increasing distributed generators (DGs) and flexible loads (FLs) enable distribution systems to provide both active and reactive power reserves (P-Q reserves) in supporting the frequency and voltage regulations of transmission systems. However, such requirements at the interface between the transmission system operator (TSO) and distribution system operator (DSO) affect the distribution system operation security, considering the uncertainties of DGs and FLs. To exploit the reserve potential of distribution systems, this paper investigates the voltage-dependent P-Q reserve capacity (V-PQRC) of such types of distribution systems. V-PQRC reflects the feasible space of P-Q reserves that the DSO can provide to the TSO taking the voltage deviation limit at TSO-DSO interface into consideration, while ensuring the distribution system operation security under uncertainties of DGs and FLs. An evaluation method for V-PQRC at the TSO-DSO interface is proposed. To improve the robust performance of the evaluation method, the DG uncertainty is captured by a generalized ambiguity set and the FL uncertainty is addressed by designing a constrained sliding mode controller (CSMC). Three objectives are considered in the evaluation, i.e., P reserve capacity, Q reserve capacity, and the voltage deviation limit at the TSO-DSO interface. Then, a multi-objective optimization model integrating the generalized robust chance-constrained optimization and CSMC (GRCC-CSMC) is established for V-PQRC evaluation to obtain the Pareto optimal reserve schemes. Finally, a non-approximated selecting (NAS) method is proposed to build up a simplified V-PQRC linear model, which can be convenient to apply in the transmission-distribution system coordination. Simulation results reveal that the V-PQRC evaluation method can achieve a good performance of accuracy and robustness against uncertainties.

The air conditioning cluster (ACC) is a potential candidate to provide frequency regulation reserves. However, the effective assessment of the ACC willing reserve capacity is often an obstacle for existing demand response (DR) programs, influenced by incentive prices, temperatures, etc. In this paper, the complex relationship between the ACC willing reserve capacity and its key influence factors is defined as a demand response characteristic (DRC). To learn about DRC along with real-time frequency regulation, an online deep learning-based DRC (ODL-DRC) modeling methodology is designed to continuously retrain the deep neural network-based model. The ODL-DRC model trained by incoming new data does not require massive historical training data, which makes it more time-efficient. Then, the coordinate operation between ODL-DRC modeling and optimal frequency regulation (OFR) is presented. A robust decentralized sliding mode controller (DSMC) is designed to manage the ACC response power in primary frequency regulation against any ACC response uncertainty. An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration. Thereby, the ODL-DRC model can be applied to minimize the total operational cost while maintaining frequency stability, without waiting for a well-trained model. The simulation cases validate the superiority of the OFR based on characterizing the ACC by online learning, which can capture the real DRC and simultaneously optimize the regulation performance with strong robustness against any ACC response uncertainty and learning error.