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Single hydrogen production methods cannot meet the stable hydrogen demand for many engineering applications under carbon emission requirements. This paper proposes a hydrogen energy hub (HEH) for stable and green hydrogen production from variable renewable energy inputs. The HEH integrates water electrolysis, biomass gasification, and natural gas reforming, which are coupled and coordinated with complementary utilization of both materials and energy. The uncertainties associated with biomass moisture content (MC) and photovoltaic (PV) generation for hydrogen production are considered. The impact of MC on the production rate of biomass is quantified and embedded in a thermodynamic equilibrium model, and inaccurate PV forecasts are also taken into account. A systematic model of HEH is developed to improve the economic benefit of system operation while providing probabilistic guarantees on the robustness of stable hydrogen production. A double-loop robust algorithm is adapted to efficiently solve the robust model, with the outer loop developing a day-ahead scheduling scheme and the inner loop figuring out the worst-case scenario. Finally, the effectiveness of the proposed model is demonstrated through case studies in terms of cost reduction, decision robustness, and computational efficiency. A 9.9% improvement in energy efficiency can be achieved through complementary utilization. The robust optimization is able to achieve an average total cost reduction of 37.52% over the deterministic optimization in 1000 random scenarios. • A hydrogen energy hub integrating electrolysis, gasification, and reforming is proposed. • A 9.9% energy efficiency increase by using material and energy complementarity. • A two-stage scheduling model and robust optimization approach are proposed. • The uncertainties in PV generation and biomass MC for hydrogen production are considered. [ABSTRACT FROM AUTHOR]

With the rapid development of voltage source converter based high-voltage direct current, power flow analysis becomes increasingly important for AC/DC hybrid systems. This study extends the holomorphic embedding (HE) method to the VSC-based AC/DC power flow problem. With an appropriate embedding technique, a general embedded model is established for AC/DC power flow with VSCs, which can not only preserve the deterministic property of HE that enables it to derive the desired solution in an explicit manner but also flexibly accommodate various bus types, VSC configurations, control strategies and operating conditions. Moreover, the sparsity of the recursive matrix in power series calculations and the introduction of the iterative calculation of the inverse matrix (ICIM) to rational approximants contribute to the high computational efficiency of the model. The proposed method is shown to be feasible and efficient by illustrative cases and is further extended to multi-terminal (VSC-MTDC) configurations to verify its flexibility. [ABSTRACT FROM AUTHOR]

With the rapid development of voltage source converter based high‐voltage direct current, power flow analysis becomes increasingly important for AC/DC hybrid systems. This study extends the holomorphic embedding (HE) method to the VSC‐based AC/DC power flow problem. With an appropriate embedding technique, a general embedded model is established for AC/DC power flow with VSCs, which can not only preserve the deterministic property of HE that enables it to derive the desired solution in an explicit manner but also flexibly accommodate various bus types, VSC configurations, control strategies and operating conditions. Moreover, the sparsity of the recursive matrix in power series calculations and the introduction of the iterative calculation of the inverse matrix (ICIM) to rational approximants contribute to the high computational efficiency of the model. The proposed method is shown to be feasible and efficient by illustrative cases and is further extended to multi‐terminal (VSC‐MTDC) configurations to verify its flexibility. [ABSTRACT FROM AUTHOR]

Peak regulation ancillary service market (PRSM) is an emerging and effective mechanism for renewable energy accommodation in China, especially under Carbon Neutral. To comprehensively analyze its long-term impact on the power system, a chronological operation simulation (COS) model is proposed. Besides, the computational-burden balancing technique based on the time-domain decomposition is developed to improve the computational efficiency of COS. The real-world northeast China power system with PRSM has been analyzed. Case studies validate the simulation with published statistical data and demonstrate the accuracy. Besides, 52.16% computation time is reduced compared to the widely used rolling horizon method. The necessity of PRSM for renewable energy accommodation is verified through chronological simulation. The relationship between peak regulation costs and renewable energy penetration is also given, which demonstrates its applicability. • Model for peak regulation ancillary service market. • Improve efficiency by computation-burden balance and time-domain decomposition. • Validate the accuracy and efficiency in the northeast China power system. • Applicable to the market rule study for policymakers. [ABSTRACT FROM AUTHOR]

This paper proposes a resilience enhancement strategy for power transmission system against ice storms by the optimal coordination of power system schedule with the pre-positioning and routing of mobile dc de-icing devices (MDIDs). A two-stage robust optimization model is established to accommodate the variable ice thickness on transmission lines. The first stage coordinates the pre-positioned MDIDs and unit commitment in day-ahead. These decisions, which are based on a robust approach, can accommodate the variable ice thickness in which the coordinated real-time schedule would always be feasible with respect to day-ahead decisions. At the second stage, the real-time operation, which integrates the power system dispatch, de-icing schedule, and MDID routing, is scheduled according to the real-time ice thickness. Auxiliary variables are adopted to convert the proposed nonconvex nonlinear model to a mixed-integer second-order cone programming (MISOCP) problem. The nested column-and-constraint generation algorithm is utilized to solve the two-stage robust MISOCP problem. Several computational enhancement strategies including Lagrangian relaxation are proposed to improve the performance of the proposed resilience enhancement strategy. Numerical results for an integrated 6-bus 6-node electricity-road network and a real-world example employed in China show the effectiveness of the proposed model and solution technique for enhancing the transmission grid resilience. [ABSTRACT FROM AUTHOR]

Highlights • A security-constrained co-planning of power lines and energy storage is proposed. • The corrective control with multi-energy storages and generator units is studied. • The large-scale formulation is solved using Benders decomposition algorithm. • Case studies on the real-world power system in Gansu, China are conducted. Abstract This paper presents a security-constrained co-planning of transmission line expansion and energy storage with high penetration of wind power. The energy storage can not only improve the accommodation of renewable generation but also help to mitigate the emergency overflow under the post-contingency state. Also, the adjustment capabilities of the wind farms are considered, and the risk of excessive wind curtailment under the post-contingency state is assessed quantitatively. The coordinated multistage corrective control strategy for the battery storage, the pumped storage, thermal generators, and wind farms is studied. The mixed-integer linear programming problem based on the proposed corrective control strategy has been formulated. The Benders decomposition algorithm is introduced to divide the co-planning problem into one master problem and two sub-problems. In the master problem, the system is operating under normal conditions, so the security constraints are not considered. It minimizes the sum of the investment cost of newly built facilities and the operational cost. In the sub-problems, the corrective control during different post-contingency stages is formulated, minimizing the risk costs under fault conditions. The two-step technique is developed to generate the Benders cuts from the mixed-integer linear programming sub-problems, and return to the master problem. The real-world power system in Gansu, China is studied to validate the applicability and scalability of the proposed model and solution technique. The results denote that the co-planning model with security constraints is profitable in the real power system, with savings of 25% for the newly built transmission lines and a 5.5% reduction in total cost. [ABSTRACT FROM AUTHOR]

Highlights • Real-time subsidy based scheduling for integrated power and gas system proposed. • Gaming between the power and gas system formulated as bi-level optimization. • Relationship between the dual variables and the regulation coefficients revealed. • Simulation with real-world data demonstrates the effectiveness of the proposed. Abstract In this work, a real-time subsidy based robust scheduling method for the integrated gas and power system is proposed. Bi-directional energy conversion, including the power-to-gas and gas-fired generation, is operated via dynamic variant price signals. The gaming between the electrical power system and the natural gas system is formulated in the bi-level optimization. The upper-level problem minimizes the operational cost, in which the real-time subsidy for power-to-gas and gas-fired units is obtained. The lower-level problem maximizes the profit for the power-to-gas and gas-fired units, in which the transient gas flow is introduced. In addition, in order to counter the uncertainties brought on by wind power, a real-time subsidy update strategy based on robust optimization is proposed to stimulate the regulation capabilities of the power-to-gas and gas-fired units. This bi-level optimization is reformulated as a mixed-integer quadratic programming problem using the Karush-Kuhn-Tucker optimization conditions. Simulation results show that the real-time subsidy scheduling can make power-to-gas and gas-fired units follow the system operator's preferences such as wind power accommodation, mitigation of unsupplied load, and relieving network congestion. [ABSTRACT FROM AUTHOR]

Highlights • A systematic approach for the joint dispatch of energy and reserve is proposed. • A new demand response model is proposed to balance the flexibility and reliability. • Dynamic scenario generation and evaluation methods are proposed for wind profiles. • The inactive constraint reduction method is used to reduce the calculation burden. Abstract The intermittent nature of wind power increases the need for flexibility of the power system. This paper proposes the systematic approach for the joint dispatch of energy and reserve incorporating demand response, including the formulation of the two-stage optimization, dynamic scenario generation, and inactive constraint identification. The incentive-based demand response model is adopted to improve flexibility by its cooperation with conventional units. The dynamic scenario generation method is developed to provide reasonable input for the two-stage optimization, considering the temporal correlations of the wind power. Three indicators are proposed to evaluate the quality of scenarios. To speed up the solution, the inactive constraint reduction has been applied to reduce the computational burden raised by the number of the scenarios and the system scale. Finally, the modified IEEE 118-bus test system with fifty incentive-based demand response aggregators is utilized to evaluate the effectiveness of the proposed method to improve operational economics and to promote wind power utilization. Simulation results show that 89.53% of the transmission line constraints can be removed, leading to a maximal reduction of 69.81% of the computational time. Compared to the conventional sampling method, the dynamic scenario set performs better in terms of three proposed indicators, and can reduce the total cost by 1.99%. Neglecting the constraint of response times, the economic efficiency would be overestimated by 0.98%. [ABSTRACT FROM AUTHOR]

Probabilistic load flow (LF) calculation is the first step to evaluate the potential impact of the integrated wind power on the power system. Although research works show that the wind speed can be modeled by a Weibull probability density function (pdf), due to the nonlinear relationship between the wind speed and the wind power as well as many other influencing factors, it is hard to fit wind power to any common pdfs. At the same time, the relationship between the input and output variables of LF calculation is nonlinear. In view of the two characteristics, the point estimate method and Gram–Charlier expansion method are combined. Based only on the sample data of the wind power, the expectation, variance, and cumulative distribution of the output random variables can be estimated with the method by 2n + 1 times of LF calculation, where n is the number of input stochastic variables, eliminating the need for the distribution of the input variables. The simulation results on the New England Test System and New York Power Pool (NETS-NYPP) system and the actual Northeast China Grid show that the proposed method provides higher precision with less computation burden. The method can also be applied to other problems with uncertainty factors whose distribution is unknown in the power system. [ABSTRACT FROM AUTHOR]

Optimal pump scheduling ensures the safe and economic operation of the oil pipelines, which is also of vital significance for energy conservation as well as carbon emission reduction. This article proposes a framework adapting the data-driven pressure loss estimation with the long short-term memory neural network (LSTM-NN) and pump characteristics fitting with quadratic polynomials to model-based optimal pump scheduling for the multiproduct refined oil transmission system. The data-driven methods are data-adaptive with periodical field data in a rolling-training manner, and thus can reflect actual working conditions in the transmission pipelines. The presented LSTM-NN for pressure loss estimation integrating the characteristics of multiproduct refined oil pipeline transmission has good performance with an acceptable mean squared error of 0.016 MPa2 in real applications. The optimization model makes the time precision down to the minute level, which is more convenient for the system dispatchers to operate the pumps following the optimal schedule. Practical implementation and field test are carried out in a real-world pipeline system, which verifies that the proposed framework is more practical and better-performed than the manual schedule. [ABSTRACT FROM AUTHOR]

With the increasing amounts of terminal equipment with higher requirements of communication quality in the emerging fifth generation mobile communication network (5G), the energy consumption of 5G base stations (BSs) is increasing significantly, which not only raises the operating expenses of telecom operators but also imposes a burden on the environment. To solve this problem, a two-step energy management method that coordinates 5G macro BSs for 5G networks with user clustering is proposed. The coordination among the communication equipment and the standard equipment in 5G macro BSs is developed to reduce both the energy consumption and the electricity costs. A novel user clustering method is proposed together with Benders decomposition to accelerate the solving process. Simulation results show that the proposed method is computationally efficient and can ensure near-optimal performance, effectively reducing the energy consumption and electricity costs compared with the conventional dispatching scheme. [ABSTRACT FROM AUTHOR]

Power flow equations (PFEs) are the fundamental of power flow (PF) calculations and optimal PF (OPF) for power system analysis, but existing PFEs suffer from the trade-off among various requirements in practice. This study presents a novel pyramidal approximation (PA) for PF and OPF. A multi-sided pyramid is used to approximate the feasible region of PFEs. The PA-based PFEs not only guarantee the linearity, tightness, and no dependence on the initial guess but also have high solution accuracy. The rotation-and-fold strategy is developed to balance the computational efficiency and approximation accuracy, so that the problem for a relatively large power system can be solved in a reasonable time. Case studies in different test systems validate the tightness and accuracy of the proposed PA method. The balance of accuracy and efficiency is also discussed, and the PA has a good performance in the small or medium power systems. [ABSTRACT FROM AUTHOR]

Power flow equations (PFEs) are the fundamental of power flow (PF) calculations and optimal PF (OPF) for power system analysis, but existing PFEs suffer from the trade‐off among various requirements in practice. This study presents a novel pyramidal approximation (PA) for PF and OPF. A multi‐sided pyramid is used to approximate the feasible region of PFEs. The PA‐based PFEs not only guarantee the linearity, tightness, and no dependence on the initial guess but also have high solution accuracy. The rotation‐and‐fold strategy is developed to balance the computational efficiency and approximation accuracy, so that the problem for a relatively large power system can be solved in a reasonable time. Case studies in different test systems validate the tightness and accuracy of the proposed PA method. The balance of accuracy and efficiency is also discussed, and the PA has a good performance in the small or medium power systems. [ABSTRACT FROM AUTHOR]

The construction of the uncertainty set is a key aspect of robust unit commitment (RUC) for the power systems with wind power integrated. The existing uncertainty sets based on the convex hull of historical data are limited to the specific shape of the historical data set or with high computational burden. In this letter, we propose the partition-combine method to build the minimal uncertainty set with the irregularly-distributed historical data. The partition of the box set is used to reduce the size of the uncertainty so that the set can properly adapt the data provided and is no longer limited to a specific shape. The combination of non-empty subsets is used to formulate the model of uncertainty set instead of the extreme points. The scale of uncertainty variables is reduced with quick identification of inner subsets which contain no extreme points. The simulation results validated the enhanced performances of the proposed method from both the conservativeness and computational burden perspectives. [ABSTRACT FROM AUTHOR]

• Proposing a new robust co-planning of AC/DC hybrid transmission network and energy storage. • Embedding BFM within the proposed co-planning model. • Using data-adaptive robust optimization to cope with uncertainties. • Validating the effectiveness of the proposed method in practical power system. This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver's 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model. [ABSTRACT FROM AUTHOR]

This paper presents an open‐access Matlab/Simulink‐based power system simulation toolbox (MatPSST) for research and education. In MatPSST, dynamic modeling is implemented by Simulink. Only the initialization process is coded in Matlab. This structure makes full use of Simulink, enabling users to fully use the rich solvers and built‐in toolboxes in Matlab/Simulink, such as small‐signal analysis, without additional programming. The interaction with real‐time platforms like RTLAB and dSPACE in MatPSST is convenient through the Simulink interface. In addition, MatPSST provides a uniform interface and flexible framework for user‐defined functions. Users do not need to consider the interaction between models and dynamic solvers when implementing state‐of‐the‐art user‐defined models. To avoid the heavy work (replicate units and change parameters) in modeling detailed wind farm or photovoltaic station with hundreds of renewable generation units, the idea of the vector‐variable‐based model is proposed with MatPSST. Case studies introduce some selected application examples of MatPSST in research and education and demonstrate its accuracy by the comparisons with Simscape and PSAT. The cases reveal the advantages of MatPSST in promoting power system research and education. [ABSTRACT FROM AUTHOR]

This letter proposes a parametric cost function approximation (CFA)-based on-line operation strategy for microgrids, considering the uncertainty from distributed renewable energy. It aims to obtain on-line scheduling solutions with the minimal expectation of the operational cost. For the non-convex optimization problem in this letter, the stochastic gradient descent-based parameter optimization method is proposed to find the optimal parameter used in the parametric CFA policy. The proposed algorithm is demonstrated to be highly effective by using numerical experiments, indicating the high potential for practical applications. [ABSTRACT FROM AUTHOR]

• A unified multiphysics model for AE, PEM and SOEC is derived to bridge the gap among different PtH researches. • Various definitions on cell efficiency are collected and evaluated to unify the definitions of efficiency. • Cell performances are compared and explained from multiphysics process. • Main drawbacks of the cell performances are reviewed to explain the parametric gaps. This paper provides a comparative study of alkaline water electrolysis (AE), proton exchange membrane water electrolysis (PEM) and solid oxide electrolysis cells (SOEC) through multiphysics modeling. A unified multiphysics model for the three types of electrolyzers, considering current density distribution, single-phase and diphasic flow, mass transfer and temperature dependence in the electrolyzers, is derived to make a thorough interpretation of their differences in terms of I-V characteristics, cell efficiency and part-load performances. Besides, various definitions of electrolysis efficiency from different researches on PtH are compared and evaluated from thermodynamics to unify the definitions of efficiency. Main drawbacks of the cell performances are then reviewed respectively to explain the parametric gap among three types of water electrolysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hybrid hydrogen-electric vehicles (H2EVs) are developing rapidly in recent years. Evidently, the planning of H2EVs energy-supplying facilities (HVESFs) is important. It involves the coupled power flow of power network (PN), mass flow of hydrogen network (HN), and traffic flow of traffic network (TN), simultaneously. These coupled flows would indeed challenge economic and secure performances of HVESFs planning, especially when uncertain renewable energy is introduced. Therefore, a multinetwork framework considering coupled flow constraints is proposed for the planning issue of HVESFs, involving all the PN, HN, and TN. Therein, we first propose a modified maximum covering location method, in order to satisfy the energy demand of H2EVs. On this basis, a many-objective optimization-based bi-level HVESFs planning model is developed. Then, we use a hybrid differential evolution-interior point algorithm to solve this model, and obtain the planning solutions. Simulation results verify the feasibility and effectiveness of the proposed planning model, which has better performances compared to the existing approach. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is effective in identifying the dominant instability mode (DIM) of power systems. However, regular supervised learning for training DL models requires a large number of training samples with accurate labels provided by power system experts, which is prohibitively costly in practice. To address this issue, this paper proposes a weakly supervised learning framework to train DL models for DIM identification based on cheap but potentially inaccurate (noisy) labels from non-expert engineers. The framework comprises two stages to mitigate the detrimental effects of label noise. At Stage I, an auxiliary model is proposed to intelligently detect detrimental noisy samples while preserving truly-labeled informative hard samples based on the entire training loss dynamics of base DL models. Then Stage II incorporates virtual adversarial training to utilize all samples, including noisy ones with labels removed, to train a smooth DIM identification model in a semi-supervised learning way. It can help further mitigate the effects of undetected label noise. Case studies are conducted on CEPRI 36-bus system and Northeast China Power System (2131 buses). The results verify that the proposed framework can tolerate high-intensity feature-(in)dependent label noise and build reliable DL models for DIM identification with significantly less reliance on experts. • Robust deep learning for power system dominant instability mode identification. • Two-stage weakly supervised learning to mitigate label noise from non-experts. • Noisy samples are detected while hard ones are preserved. • Virtual adversarial training is proposed to use noisy samples as unlabeled ones. • Intelligent dominant instability mode identification becomes more affordable. [Display omitted] [ABSTRACT FROM AUTHOR]

Electrochemical water splitting plays a vital role for production of hydrogen (H 2). Enhancing the rate of hydrogen separation from the electrode is crucial for improving the performance of water electrolyzers. The water electrolyzers often encounter issues such as air bubble adhesion to the electrode plate, leading to increased electrical resistance, reduced current density, and thus lower hydrogen generation rates. In addition, the compact configuration of electrolyzer and non-transparent electrode plates make it impracticable to observe and analyze the gas-liquid two-phase flow between the anode and cathode plates. Hence, a methodology is in high demand to experimentally investigate the two-phase flow within the electrolyzer, which could be further used to guide the design and optimization of the electrolyzer. In this work, we propose to utilize a microfluidic system as a phantom of electrolyzer. The transparent material of the microfluidic chip allows optical inspection and measurement of the two-phase flow. Specifically, we propose to optimize the two-phase flow by manipulating the micro-structure of the flow, which has been theoretically and experimentally demonstrated to effectively remove the gas bubbles attached to the wall and consequently enhance the hydrogen production rate. The dimensions of the proposed micro-structures can be potentially extended and applied to industrial electrolyzers according to the principle of similarity. • A new methodology to investigate the two-phase flow within an electrolyzer using a microfluidic chip as a phantom is proposed. • A theory is proposed that the manipulation of the surface tension can effectively reduce the resistance of bubble detachment. • Two micro-flow configurations are demonstrated to have lower resistance of bubble detachment against the drag force. • The proposed micro-flow structures have the potential to be extended for industrial electrolyzers based on principle of similarity. [ABSTRACT FROM AUTHOR]

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. The influence of the dynamic of CFPREV output on our research is studied. 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. [ABSTRACT FROM AUTHOR]

Highlights: The 2D CoOOH sheet-encapsulated Ni2P into tubular arrays electrocatalytic system with expediting mass transport, structural stability, and tuned electron was conceptually proposed. The designed electrocatalysts realize expectant 1000 mA cm−2-level-current-density hydrogen evolution in neutral water for over 100 h.Water electrolysis at high current density (1000 mA cm−2 level) with excellent durability especially in neutral electrolyte is the pivotal issue for green hydrogen from experiment to industrialization. In addition to the high intrinsic activity determined by the electronic structure, electrocatalysts are also required to be capable of fast mass transfer (electrolyte recharge and bubble overflow) and high mechanical stability. Herein, the 2D CoOOH sheet-encapsulated Ni2P into tubular arrays electrocatalytic system was proposed and realized 1000 mA cm−2-level-current-density hydrogen evolution over 100 h in neutral water. In designed catalysts, 2D stack structure as an adaptive material can buffer the shock of electrolyte convection, hydrogen bubble rupture, and evolution through the release of stress, which insure the long cycle stability. Meanwhile, the rich porosity between stacked units contributed the good infiltration of electrolyte and slippage of hydrogen bubbles, guaranteeing electrolyte fast recharge and bubble evolution at the high-current catalysis. Beyond that, the electron structure modulation induced by interfacial charge transfer is also beneficial to enhance the intrinsic activity. Profoundly, the multiscale coordinated regulation will provide a guide to design high-efficiency industrial electrocatalysts. [ABSTRACT FROM AUTHOR]

• Proposed an improved two-stage optimization method for network and load recovery. • Decouples integer and continuous decision variables and solves them separately. • Proposed method significantly can improve the computational efficiency. • Verified the advantage in computational efficiency and plan optimality. • Demonstrated potential application through simulation in a practical system. Network and load recovery (NLR) during power system restoration is a multi-step mixed-integer nonlinear programming (MINP) problem. The NLR is difficult to be solved as it is NP-hard. Thus, NLR is commonly solved step by step, which is short-sighted and will result in longer restoration time. To obtain a far-sighted NLR plan, this paper proposes an improved two-stage optimization method for NLR. The first stage adopts a mixed-integer linear programming (MILP) model to obtain optimal solutions of integer variables in NLR, namely the load pick-up schedules and transmission line charging schedules. Then in the second stage, a continuous non-linear optimization method based on AC power flow with frequency constraints and load model is established to minimize the restoration duration of the plan generated in the first stage step by step. Case studies are undertaken on a 10-machine 39-bus system and Southeast Hubei Provincial power system of China. Simulation results indicate that the restoration plan obtained from the improved two-stage optimization method is highly effective, while the computational efficient meets the intensive need for restoration scheduling after blackouts. [ABSTRACT FROM AUTHOR]

• Dynamic event-triggered robust secondary frequency controller is proposed. • It provides a good balance between dynamic performance and communication burden. • The controller design does not depend on the full-order state matrices of the plant. • Communication intervals are enlarged by the dynamic event-triggered mechanism. • Simulation on detailed models verifies the effectiveness of Dynamic event-triggered robust secondary frequency controller. The permeability of renewable energy sources rapidly increases in the microgrid for economic and environmental concerns. However, the uncertainty of renewable energy may lead to frequency fluctuation or even instability in the microgrid. This paper proposes a dynamic event-triggered robust secondary frequency control scheme for the islanded microgrid to handle the uncertainty of the renewable energy source. In addition to the enhancement of frequency stability, the communication burden is also considered in this work. The proposed dynamic event-triggered communication scheme provides a good balance between dynamic performance and communication burden. Based on the discontinuous Lyapunov function, the stability analysis helps the optimal gain selecting. Given the designed controller, the derived theorems in this paper can help to examine whether the communication system fulfills the requirements from the controllers. To validate the proposed method, case studies are carried out based on two typical microgrids. The simulation results are compared with conventional frequency controllers. It shows that the proposed controller outperforms other types of controllers in term of the frequency response performance and communication burden. [ABSTRACT FROM AUTHOR]

The porous electrode of vanadium redox flow batteries (VRBs) is subject to deformation due to mechanical stress during stack assembling. The forces compress the electrode fiber into the flow channel and thus alter the electrode porosity ratio. Due to the complex mechanisms, the effects of resulting electrode morphological changes on VRB performance were usually ignored in existing studies. This paper proposes a three-dimensional VRB model considering the uneven electrode deformation to investigate the cell performance under different electrode compression ratios with three flow-field designs. Compression ratio (CR) and the intrusive part of the electrode are obtained under various mechanical stress by adjusting gasket thickness in the experiment. The proposed electrochemical model is established based on the comprehensive description of conservation laws and analyzed using the COMSOL platform. Three indices, namely the concentration overpotential, pressure drop, and distribution uniformity, are selected for the analysis under the three flow field designs and different CRs. The numerical study reveal that the pressure drop and the concentration overpotential are sensitive to the CR but less affected by the concentration uniformity. The minimum overpotential can be reached when the CR is around 40%–50%, depending on flow field designs, while a higher CR can cause a drastically increased pressure drop. It is also found that the interdigitated flow field with a CR of 45% is considered optimal. The insights from the proposed method demonstrate the significance of considering the effects of electrode deformation in the stack design under various flow fields. • A 3D electrochemical model considering unevenly electrodes deformation is proposed. • A non-uniform partitioning method for unevenly deformed electrodes is proposed. • The compression ratios under various mechanical stress are measured. • The interdigitated flow channels under CR of 45% demonstrate maximum performance. [ABSTRACT FROM AUTHOR]

• The multiphysics model predicts the I-V curve more accurately than traditional models, especially at high current density. • The consideration of the interaction among multiphysics processes is necessary to improve the model's accuracy. • The compactly-assembled structure of the cell is considered to reveal the influence of the shunting current effect. Electrolysis occupies a dominant position in the long-term application of hydrogen energy, as it can use the power surplus directly from renewable energies to produce hydrogen. Alkaline water electrolysis (AWE) is a mature and reliable technology standing out from other types of electrolysis because of its simplicity and low cost. Several multiphysics processes inside the AWE cell, such as the electrochemical, thermal, and fluidic processes. Developing the multiphysics model to quantify the relationship between these physics fields is essential for cell design. This paper establishes a three-dimensional numerical model to consider the quantitative relationship between the electrochemical process and fluidic process inside the cell of industrial AWE. The model considers the structural design of industrial AWE equipment, revealing that the shunting current effect introduced by the structure design cannot be ignored in the model. The simulation results present that the multiphysics model considering the bubble effect can estimate the current–voltage (I-V) characteristic curve more accurately with a relative error smaller than 5%, especially at a current density higher than 2500 A/m2. The model established is supposed to advance the development of water electrolysis models and guide the electrolyzer design of industrial AWE cell. [ABSTRACT FROM AUTHOR]

• The multi-area integrated energy system is constructed for enhanced flexibility. • Robust optimization is applied to accommodate uncertainties of wind power. • A tri-level algorithm is designed to solve robust multi-area problem distributedly. • Three simulation systems are conducted to validate the model and its algorithm. Large-scale integrated energy systems often encompass several subsystems divided by geographic areas. Coordinated operation of multi-area integrated energy systems can enhance operational flexibility, which is crucial for systems with high wind power penetration. Traditional centralized methods have some inherent defects, such as privacy protection, communication burden, and computational capability, etc. To address such challenges, this paper proposes a decentralized computation method for the robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power. The interconnected multiple areas can cooperate for improved system flexibility and wind power integration capability. Using the decentralized method, the area operator optimizes its operation decisions independently without transferring its dispatch rights. The shared information is limited to exchanged energy flows. The proprietary information is thus preserved while the communication burden is significantly reduced. To solve the robust operation problem distributedly, the decentralized method is enhanced to cope with both deterministic and robust problems. A tri-level algorithm composed of the iterative alternating direction multiplier method and the column constraints generation method is utilized to realize the decentralized optimization. Numerical results for three test systems show the effectiveness, computational quality, and scalability of the proposed method. Compared to the isolated method, the proposed method fully utilizes the coordination effect of multi-area RD-IES and has obvious economic advantages, especially in scenarios with high wind power penetration levels. The proposed method brings a 12.4% cost reduction in the two-area system and a 22.4% cost reduction in the eight-area system. [ABSTRACT FROM AUTHOR]

• CNN is designed for both transient stability and instability mode prediction. • Stochastic gradient descent with warm restarts training algorithm is employed. • Case studies are conducted on both benchmark and practical larger power system. • Superior accuracy and robustness to input signal noise and loss. Online transient stability assessment (TSA) is vital for power system control as it provides the basis for operators to decide emergency control actions. But none of previous TSA research has taken into consideration the difference between two instability modes (aperiodic instability and oscillatory instability), which may threaten secure operation of power system. To address this problem, a TSA and instability mode prediction method based on convolutional neural network is proposed. The method takes the bus voltage phasor sampled by phasor measurement units (PMUs) during a short observation window after disturbance as input, and outputs the prediction result promptly: stable, aperiodic unstable or oscillatory unstable. The end-to-end model automatically extracts needed features from the raw measurement data, thus freeing itself from reliance on expertise. At the offline training stage, stochastic gradient descent with warm restart (SGDR) optimization algorithm is employed so that the model tends to converge to 'flat' and 'wide' minima with better generalization ability. Case studies conducted on New England 39-bus system and Western Electricity Coordinating Council (WECC) 179-bus system demonstrate superior accuracy, adaptability and scalability of the proposed method compared with conventional machine learning methods. Furthermore, the proposed model is empirically proven to be robust to PMU noise and loss. [ABSTRACT FROM AUTHOR]

The distributed maximum power point tracking (DMPPT) technologies, based on a DC optimizer (DCO) for every single photovoltaic (PV) panel, are increasingly proposed to mitigate the waste of solar energy due to the mismatch problems of PV arrays. However, the stability problem of DMPPT based distributed PV grid-connected systems that involve a large amount of DCOs remains to be further studied. Therefore, modeling, deservedly, is the basis for stability analysis. Usually the model of the PV power plant consists of hundreds or even thousands of DCOs, which results in a heavy computation burden during the simulation. To solve the modeling problem, this paper proposes a matrix variables based modeling method for the distributed PV grid-connected system. The core idea of the modeling method is to convert the complex model that contains plenty of PV-DCO generation units to an average model consisting of only two typical submodules, by constructing the block matrix formed variables. In this way, the model has the advantages of good scalability and high simulation efficiency, and can be realized by the vector simulation feature of Matlab/Simulink. Besides, it is easy to obtain the linearization results directly by using the Linearization Toolbox in Simulink, which avoids the complexity of writing programs for linearization calculation when studying the stability of large-scale systems. Based on the average model and corresponding linearization results, the key impacts on the small signal stability of the system are determined via the eigenvalue analysis method and root locus method. It is shown that the total active power output by PV array, the controller parameters of the grid-connected inverter, and the strength of the AC system are critical factors affecting the small signal stability of distributed PV grid-connected system. Different simulation results verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights: The 2D CoOOH sheet-encapsulated Ni2P into tubular arrays electrocatalytic system with expediting mass transport, structural stability, and tuned electron was conceptually proposed. The designed electrocatalysts realize expectant 1000 mA cm−2-level-current-density hydrogen evolution in neutral water for over 100 h. Water electrolysis at high current density (1000 mA cm−2 level) with excellent durability especially in neutral electrolyte is the pivotal issue for green hydrogen from experiment to industrialization. In addition to the high intrinsic activity determined by the electronic structure, electrocatalysts are also required to be capable of fast mass transfer (electrolyte recharge and bubble overflow) and high mechanical stability. Herein, the 2D CoOOH sheet-encapsulated Ni2P into tubular arrays electrocatalytic system was proposed and realized 1000 mA cm−2-level-current-density hydrogen evolution over 100 h in neutral water. In designed catalysts, 2D stack structure as an adaptive material can buffer the shock of electrolyte convection, hydrogen bubble rupture, and evolution through the release of stress, which insure the long cycle stability. Meanwhile, the rich porosity between stacked units contributed the good infiltration of electrolyte and slippage of hydrogen bubbles, guaranteeing electrolyte fast recharge and bubble evolution at the high-current catalysis. Beyond that, the electron structure modulation induced by interfacial charge transfer is also beneficial to enhance the intrinsic activity. Profoundly, the multiscale coordinated regulation will provide a guide to design high-efficiency industrial electrocatalysts. [ABSTRACT FROM AUTHOR]