The modern power system is becoming more complicated due to the ever increasing penetration of power electronics, which is referred to as a power electronics-dominated system (PEDS). In this case, the analysis, control, and operation of the entire power system should tone with the energy-paradigm transition pace, where the challenges should be properly tackled. After the brief introduction of grid-connected converters, this paper explores stability challenges of PEDSs, especially on the low inertia issue and the multi-timescale characteristics, as well as the dynamics when connected to weak grids. The low inertia issue is considered as one of great challenges that power electronics introduce to the conventional power grids. On this basis, the stability issues of PEDSs are discussed. The exploration reveals that the multi-timescale coupling among various control loops and the mutual effects of multiple converters demand much more attention than ever before. Also, the coordinated control of converters for the global stability of PEDSs is summarized, part of which offers the possibility to solve the inertia problem. This paper serves as inspiration on potential solutions to these issues. In order to provide a more intuitive impression of the inertia problem in PEDSs, a case study is exemplified to further highlight the analysis and discussion.

This paper proposes a distributionally robust scheduling model for the integrated gas-electricity system (IGES) with electricity and gas load uncertainties, and further studies the impact of integrated gas-electricity demand response (DR) on energy market clearing as well as locational marginal electricity and gas prices (LMEPs and LMGPs). The proposed model maximizes the base-case system social welfare (i.e., revenue from price-sensitive DR loads minus energy production cost) minus the worst-case expected load shedding cost. Price-based gas-electricity DRs are formulated via price-sensitive demand bidding curves while considering DR participation levels and energy curtailment limits. By linearizing nonlinear Weymouth gas flow equations via Taylor series expansion and further approximating recourse decisions as affine functions of uncertainty parameters, the formulation is cast into a mixed-integer linear programming problem to enhance computational tractability. Case studies illustrate effectiveness of the proposed model for ensuring system security against uncertainties, avoiding potential transmission congestions, and increasing financial stability of DR providers.

The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics. This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub, with collocated gas-fired units, power-to-gas (PtG) facilities, and natural gas storages, is considered to store or convert one type of energy (i.e., electricity or natural gas) into the other form, which could analogously function as large-scale electrical energy storages. The column-and-constraint generation (C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.

Commutation failure, primarily caused by voltage drops and distortion, may lead to high‐voltage direct current (HVDC) power interruption and AC system oscillation. To date, there have been few studies on the mechanism and prevention of commutation failure based on voltage distortion caused by harmonics. This study analyses the influence of harmonic voltage on commutation failure based on voltage time area and proposes a method for quantitative analysis of harmonic effects. The analysis shows that limiting the DC current is an effective method to mitigate commutation failure caused by harmonics. A voltage distortion‐dependent commutation failure prevention strategy is proposed, and the controller is applied on the inverter side. The selection of parameters is based on the influence of various harmonic orders. The validity and effectiveness of the proposed methods are verified by simulations, which show that voltage distortion‐dependent commutation failure prevention strategy can improve the DC system recovery characteristics and suppress subsequent commutation failure caused by harmonics.

SCR-based Solid-state DC circuit breaker (SCR-DCCB) topologies have been widely studied for replacing traditional expensive IGBT-based DCCB. Yet, the existing SCR-DCCB cannot be actively turned-off since SCR is a half-controlled device. In this letter, an active Z-source DCCB (AZ-DCCB) is proposed by the combination of IGBT and SCR. The proposed AZ-DCCB has advantages of simple and compact topology, economical design, low conduction losses, active and bidirectional current breaking capacity. The operation process of the proposed AZ-DCCB is discussed in detail, and the designing principle of circuit parameters is also presented. Experimental results verified the effectiveness of the proposed AZ-DCCB.

Wind power forecasting plays a vital role in renewable energy production. Accurately forecasting wind energy is a significant challenge due to the uncertain and complex behavior of wind signals. For this purpose, accurate prediction methods are required. This paper presents a new hybrid approach of principal component analysis (PCA) and deep learning to uncover the hidden patterns from wind data and to forecast accurate wind power. PCA is applied to wind data to extract the hidden features from wind data and to identify meaningful information. It is also used to remove high correlation among the values. Further, an optimized deep learning algorithm with a TensorFlow framework is used to accurately forecast wind power from significant features. Finally, the deep learning algorithm is fine-tuned with learning error rate, optimizer function, dropout layer, activation and loss function. The algorithm uses a neural network and intelligent algorithm to predict the wind signals. The proposed idea is applied to three different datasets (hourly, monthly, yearly) gathered from the National Renewable Energy Laboratory (NREL) transforming energy database. The forecasting results show that the proposed research can accurately predict wind power using a span ranging from hours to years. A comparison is made with popular state of the art algorithms and it is demonstrated that the proposed research yields better predictions results.