This paper proposes an approximate dynamic programming (ADP) based approach for the economic dispatch (ED) of microgrid with distributed generations (DGs). The time-variant renewable generation, electricity price and the power demand are considered as stochastic variables in this work. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo (MC) method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.

This paper proposes an approximate dynamic programming (ADP) based algorithm for the real-time operation of the microgrid under uncertainties. First, the optimal operation of the microgrid is formulated as a stochastic mixedinteger nonlinear programming (MINLP) problem, combining the AC power flow and the detailed operational character of the battery. For this NP-hard problem, the proposed ADP based energy management algorithm (ADP-EMA) decomposes the original multi-time periods MINLP problem into singletime period nonlinear programming (NLP) problems. Thus the sequential decisions can be made by solving Bellman's equation. Historical data is utilized offline to improve the optimality of the real-time decision, and the dependency on the forecast information is reduced. Comparative numerical simulations with several existing methods demonstrate the effectiveness and efficiency of the proposed algorithm.

Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from the power system operators. This paper proposes a novel data-adaptive robust optimization method for the economic dispatch of active distribution network with renewables. The scenario-generation method and the two-stage robust optimization are combined in the proposed method. To reduce the conservativeness, a few extreme scenarios selected from the historical data are used to replace the conventional uncertainty set. The proposed extreme-scenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all the possible scenarios if they hold in the selected extreme scenarios, which guarantees the robustness of the decision. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm with the selected uncertainty set is less conservative but equally as robust as the existing two-stage robust optimization approaches. This leads to the improved economy of the decision with uncompromised security.

This paper investigates the in-depth mechanism of commutation failure for a line-commuted converter based high voltage direct current (LCC-HVDC) system. The commutation failure prevention control (CFPREV) and the initial fault voltage angle (IFVA) are considered from the view of the voltage-time area (VTA) in the analysis. It is revealed that the IFVA is among the dominant factors for commutation failures when the voltage drop of the inverter bus is relatively small, and CFPREV further intensifies the impact of the IFVA on commutation failures, while the fluctuation of the direct current plays a dominant role in commutation failures under a greater voltage reduction at the inverter bus. A quantitative division of the severity of AC faults is proposed to determine dominant factors for commutation failures. The relationship between the chance of commutation failures to occur and the IFVA is built, and the method used for computing probability of commutation failures is proposed. Simulations based on a typical monopole LCC-HVDC system using PSCAD/EMTDC software are conducted to verify the correctness of the theoretic analysis and the effectiveness of the proposed computing methods.

The growing penetration of hydrogen vehicles and modern energy conversion technologies strengthen the coupling of transportation and energy networks. This article proposes a hydrogen supply infrastructure planning model for the integration of hydrogen vehicles and renewable energy. To flexibly meet the energy demand of hydrogen vehicles, the proposed model makes investment decisions for various facilities, including hydrogen pipelines, hydrogen refueling stations, power to gas devices, and renewable energy generators. Besides, with the pipeline transportation method applied, the hydrogen network is constructed and coordinated with the electricity and transportation networks. The multinetwork synergistic effect is thus fully utilized, bringing higher operational flexibility and investment economy. Furthermore, a two-stage stochastic planning model is provided to accommodate the variability of renewable energy and traffic loads. To reduce the computational complexity of the proposed stochastic planning model, both linearization techniques and Benders decomposition algorithm are applied. Simulation results of the 8-node system and the 24-node system demonstrate the effectiveness of the proposed model and algorithm. Compared to the uncoordinated model, the proposed model saves by 11% of the total cost for the 24-node test system. Also, the computational performance of the Benders decomposition algorithm surpasses that of the basic algorithm by more than 24.6% in the two test systems.

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) connected with offshore wind farms could suffer serious challenge: surplus power of MMC-HVDC caused by AC fault in onshore power grid. This paper proposes an active energy control (AEC) scheme for converter stations utilizing MMC sub-module capacitance. It can be used to actively absorb the surplus power of the MMC-HVDC in the stage of grid-side AC fault and enhance the AC fault ride-through (ACFRT) ability. First, the energy decoupling principle of MMC is analyzed, and a steady-state control scheme is proposed for MMC-HVDC with energy decoupling capability. Based on this strategy, an AEC scheme between onshore and offshore converter stations is proposed for ACFRT. It is divided into the following four stages: passive energy recovery, active energy recovery, active energy maintenance, and active energy release. Then, a set of coordinated control schemes for AEC and active power reduction of offshore wind farms are designed. This is used as an example to illustrate that the proposed AEC can effectively enhance the fault ride-through ability of the existing strategies, such as active power reduction of offshore wind farms. Finally, the correctness and effectiveness of the proposed control schemes are verified via the PSCAD/EMTDC platform.