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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 development of the Energy Internet has improved the efficiency of energy utilization and promoted sustainable development of power and energy systems. The multi-energy system modeling considering the dynamic process of transmission line is one of the key research points of Energy Internet operation control. Through the energy circuit theory, the lumped parameter model of natural gas pipelines is built and the dynamic characteristic parameters under the control instruction are extracted. Combined with dynamic characteristic parameters, the long short-term memory (LSTM) neural network is designed to fit the natural gas pipeline dynamic process into discrete linear time-varying (LTV) equations. Combined with the equations, an energy hub method is used to build a control model of industrial parks with multi-energy distribution system. Using the rolling optimal control strategy given in this paper, the model is solved by the Matlab-Yalmip solver and rolling control instructions of each energy conversion unit are obtained. Finally, the case study demonstrates that the LSTM neural network-based modeling method presented in this paper can accurately fit the dynamic process of a natural gas pipeline system. The rolling control model of the multi-energy system can improve the efficiency of energy utilization, exhibit the transmission line status constraints during the optimization control process and improve reliability of the multi-energy system operation.

The early detection of cascading failure plays an important role in the safe and stable operation of the power system with high penetration of renewable energy. This paper proposes a fault propagation dynamic model based on the epidemic model, and further puts forward a method to detect the development of cascading failures. Through the simulation of the IEEE 39-bus and 118-bus systems, this model is proven to be valid and capable of providing practical technical support for the prevention of cascading failures in power systems with high penetration of renewable energy. This paper also provides an analysis method for the choice of different protection and control measures at each stage of cascading failure, which has critical significance and follow-up value.