An increased use of the high-voltage direct current (HVDC) technologies can have important effects on frequency performance and voltage stability of the receiving-end grid during normal operation as well as during blocking failure. The main reasons are the inherent characteristics of the HVDC such as its much larger capacity than thermal plants and lack of voltage supporting ability to the alternating current (AC) grid. These has led to new challenges for AC/direct current (DC) power grid operators in terms of ensuring power system security. To address these challenges, a unit commitment (UC) of the receiving-end in the AC/DC hybrid grid is presented in this paper. In the proposed model, primary frequency modulation constraints are added to provide sufficient capacity for HVDC blocking. Besides, grid security constraint after secondary frequency regulation is also considered because HVDC blocking failure would cause large range power transfer and transmission lines overload. Meanwhile, voltage stability constraints are employed to guarantee enough voltage supporting capacity from thermal plants at the HVDC feed-in area. Based on the characteristics of the model, Benders decomposition and mixed integer programming algorithm are used to get the optimal transmission power of the HVDC and schedule of thermal units. The study is done by considering the IEEE-39 and Jiangsu power grid in eastern China, containing two HVDC transmission projections respectively. The results are also validated by simulation of different HVDC blocking failure scenarios.

As one of the most effective approaches in dealing with the energy crisis, combined electricity and natural gas systems have become more and more popular worldwide. To take full advantages of such hybrid energy networks, a proper operation and control method is required. In this paper, a novel approach coordinating combined heating and power generation is proposed. First, state excursion rate, a criterion describing the deviation of system operation, is defined for system state evaluation. Then, thermal energy storage is allocated to provide more and better operation modes for combined generation. By investigating the state excursion rate of hybrid energy systems, the optimal operation mode is chosen through an analytical strategy. Case studies on hybrid energy networks show that all state variables, including voltages in electric systems and pressures in gas networks, are adjusted to follow proper operation constraints by the implementations of the proposed strategy. In addition to providing sufficient auxiliary services for hybrid systems, it is also possible to maintain the economic and energy-efficient benefits of energy supply. This study provides an effective method to utilize the regulation capability of combined heating and power generations, which is a technical basis of energy internet.

When conducting research on the static voltage stability of AC/DC systems with voltage source converter-high voltage direct current (VSC-HVDC) transmission lines, the focus is often given to reactive power control, neglecting the potential from active power support. Based on the minimum modulus eigenvalue, this paper proposes to coordinately control active and reactive power of VSC-HVDC to improve the static voltage stability of AC/DC systems. Firstly, the converter loss is quantified and taken into account to solve the power flow of the AC/DC system. Secondly, the minimum modulus eigenvalue of the system is calculated based on the Jacobian matrix in the power flow solution process to characterize the static voltage stability of the system. Then, taking the minimum modulus eigenvalue of the AC/DC system as the optimization objective, with power flow, node voltage, and converter power as constraints, and with the active and reactive power injections of HVDC as optimization variables, an optimization model is built to determine the optimal adjustment of active and reactive power of VSC-HVDC. Finally, the particle swarm optimization algorithm is utilized to solve the optimization model. Simulations in MATLAB show that compared with only active power control and only reactive power control, the proposed control method can significantly improve the static voltage stability of the system while ensuring its safe operation.

This study proposed an algorithm for calculating the maximum transmission capacity considering unified power flow controller (UPFC). At the beginning, the equivalent virtual generator, located on ends of UPFC's branch, is obtained based on the source model of UPFC. Then, for the purpose of obtaining the range of transmission power, the maximum (minimum) active/reactive power is treated as an optimising object. Meanwhile, the series voltage and shunt current of the UPFC, as well as other generators’ reactive power, are selected as control variables. As a result, the optimisation model could be formed in UPFC, SSSC and STATCOM modes, respectively, in which buses voltage and transmission limits are also supplied. Based on the characteristics of the above model, the interior point method is used to achieve the optimal solution. In the end, Nanjing western power grid and Suzhou southern power grid are tested to prove the effective and correctness of the introduced algorithm.

The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training task of security correction into two stages: in the first stage, the stacked auto-encoder (SAE) classification model is established, and the node correction state (0/1) output based on the fault information; in the second stage, the DNN learning model is established, and the correction amount of each action node is obtained based on the action nodes output in the previous stage. In this paper, the UPFC demonstration project of Nanjing West Ring Network is taken as a case study to validate the proposed method. The results show that the proposed method can fully meet the real-time security correction time requirements of power grids, and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.