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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]

• A hydrogen production method integrating electrolysis, gasification, and reforming is proposed. • Integrated system exploits energy and material complementarity of the three methods. • Integrated system provides green and stable hydrogen production. • Integrated system exhibits energy efficiency of 64.19 % and carbon emissions of 2.42 kgCO 2 /kgH 2. Engineering industries such as petroleum and petrochemicals have strict requirements for the stability of hydrogen supply to ensure the safety and reliability of system operations. However, relying on a single hydrogen production technology is insufficient to meet the requirements of both stable and green hydrogen. This study proposes an integrated hydrogen production system combining water electrolysis, biomass gasification, and natural gas reforming to achieve stable and green hydrogen. To capitalise on the advantages of the individual technologies, the integration among the different hydrogen production methods considers not only energy but also materials. As a by-product of water electrolysis, oxygen can create a pure oxygen atmosphere for biomass gasification and natural gas reforming, thereby considerably improving the energy conversion rate. The heated gases from the biomass gasification transport thermal energy to the natural gas reforming. Stable and maximised hydrogen production can be achieved through optimal coordination of the three hydrogen production technologies and storage devices. Optimisation models are built for each component, and case studies show that the proposed integrated system exhibits energy efficiency of 64.19 %, which is 9.87 % higher than the 54.32 % efficiency of single-energy hydrogen production, and achieves an 67 % carbon emission reduction compared with the existing industrial technology. The integrated system proposed in this study provides a solution for stable hydrogen production under the broad requirement of carbon neutrality, and it will provide valuable guidance for future integrated hydrogen production system development and offer information that is of interest to operators and investors. [ABSTRACT FROM AUTHOR]

The synergy between electric power systems and natural gas systems brings significant energy storage potentials yet challenges the modeling and solution techniques of partial-differential-equation (PDE) constrained optimization. This paper proposes a function-space optimization to coordinate multi-energy storage across the integrated electricity and natural gas system. Compared to general algebraic-space optimization, the proposed function-space optimization (FSO) uses continuous spatial–temporal functions as variables, for which time and space can be explicitly expressed and the PDE constraints of gas dynamics can be directly embedded into optimization. Considering the bidirectional energy conversion enabled by gas-fired units and power-to-gas, linepack gas storage and battery energy storage are coordinated through FSO to improve the operational economy. For tractable calculation, the Bernstein polynomial spline-based solution space transformation is adopted to transform FSO into mixed-integer linear programming. Case studies on a single-pipe example and two practical test systems are conducted and the simulation results validate the effectiveness of the proposed FSO method. • Establish the function-space optimization to coordinate multi-energy storages. • Explicitly express space/time in optimization with partial-differential-equation. • Coordinate battery energy storage and linepack gas storage for complementation. • Shift function-space optimization into algebraic space for tractable calculation. [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]

Developing an effective and reliable power flow (PF) tool is of great significance for the steady-state analysis of autonomous AC/DC hybrid microgrids (MGs). In this paper, a generalized PF model is constructed based on holomorphic embedding (HE). With a well-designed embedding technique, the nonlinear PF problem is modeled into an embedded system, which comprehensively accounts for the distinct features of islanded hybrid MGs (such as droop-regulated distributed generations (DGs) and AC/DC interlinking converters (ICs), the variation of system frequency and its effect on admittance matrix and loads in AC part). The embedded system has a generalized structure with a constant sparse matrix, which not only enables it to be solved recursively and efficiently, but also facilitates wide applicability and convenient operation. Moreover, the proposed HE-based model features a deterministic germ that relates to a physical state, which allows it to inherit the merit of the canonical embedding in converging to upper-branch solution reliably and unambiguously, without dependency on initial estimates of state variables. The feasibility and applicability of the proposed model are validated on 3 test systems of different sizes. Furthermore, the computational efficiency and convergence performance are also evaluated. • Holomorphic embedding power flow modeling of islanded AC/DC hybrid microgrids. • Compatibility with droop regulation and distinct features of hybrid MGs. • Deriving reliable power flow solutions without dependency on initial guesses. • Generalized framework for wide applicability and efficient operation. • Experimental validation has been done on hybrid MGs of different sizes. [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]

• Building the stochastic multi-time period optimization model for IHPS. • Proposing a new stochastic optimization for IHPS namely MADP. • Taking advantage of multi-energy integration in real-time schedule. • Coping with uncertainties with empirical knowledge embedded. • Validating the effectiveness of the MADP in real-time optimization. This paper proposes a multi-dimensional approximate dynamic programming (ADP) algorithm for the real-time schedule of integrated heat and power system (IHPS) with battery and heat storage tank (HST). The multi-time period optimization problem is reformulated under the Markov Decision Process. The high dimensional state variables are aggregated into the state of charge (SOC) of battery and overall available heat (OAH) of HST to reduce the computation of value function approximation (VFA) while ensuring approximate accuracy. Under sufficient training by uncertainty scenarios of wind power, electricity price, electrical and heat load, the approximate value function (AVF) can derive empirical knowledge and help IHPS make decisions to cope with uncertainties. The proposed ADP algorithm can efficiently take advantage of multi-energy integration and provide a near-optimal operation strategy to ensure the economy of IHPS by recursively solving the Bellman's equation. Simulation results compared with existing methods validate the superiority of the proposed algorithm. [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]

• Building the stochastic multi-time-period optimization model for IGPS. • Proposing a new stochastic optimization approach for IGPS namely HADP. • Advantages of ADP and MPC combined to improve the optimality of the decision. • By using the historical data, the forecast error distribution no longer needed. • Validating the effectiveness of the HADP in real-time optimization. This paper proposes a hybrid approximate dynamic programming (HADP) approach for the optimal operation of integrated gas and power systems (IGPS) under the stochastic environment. The proposed HADP, combining the advantages of the model predictive control (MPC) and approximate dynamic programming (ADP), decomposes the multi-time-period optimization into multiple sequential subproblems by solving Bellman's equation forward through time. Historical data is utilized to build the approximate value functions so that the influence of current decisions on the future is estimated. And hence, the HADP algorithm can obtain a near-optimal solution through the whole time horizon of interest. Meanwhile, the MPC policy is embedded in the HADP to replace the long-term forecast with short-term or even real-time prediction. This further improves the optimality of the decisions made by the proposed HADP. The simulation results on the IGPS demonstrate the proposed HADP outperforms alternative solutions. [ABSTRACT FROM AUTHOR]

• A stack-level multiphysics model with bubble and shunt current effects is proposed. • The proposed ER curve is applied to quantify various factors on stack performance. • The reduction of single cell area will be a trend in size design for on-grid stacks. • The practical scale-up AWE stack coupled with RES is investigated. Large-capacity hydrogen production systems are crucial for promoting green hydrogen development. However, the scaling-up of electrolysis stacks through larger electrode areas or additional electrodes may increase the bubble coverage effect and shunt current effect, leading to efficiency degradation. Therefore, a quantitative model that considers interrelated factors is needed to optimize the size design during the scaling-up process. This paper presents a stack-level multiphysics model that describes the electrochemical and two-phase flow processes within the stack. The model is validated experimentally, with a relative error of the current–voltage (IV) curve within 4%. Based on the model, the energy efficiency-reaction current density (ER) curves of different stacks are analyzed and used to provide performance optimization strategies for stacks coupled with renewable energy sources (RES). The findings demonstrate that in on-grid mode, the small cell design leads to a hydrogen production rate increase of over 6%, driving a trend towards miniaturization of cell areas. In addition, during off-grid operation, the hydrogen production variations among different designs can exceed 4% due to performance differences under heavy and light loads. Therefore, optimizing electrolyzer performance requires considering power source fluctuations and conducting specialized computational optimizations based on specific scenarios. In summary, this paper proposes optimization strategies of size design for scaling up electrolysis stacks to improve stack performance, with the goal of driving the advancement of green hydrogen. [ABSTRACT FROM AUTHOR]

Rational planning for remote integrated energy systems is of great importance for demand guarantee and sustainable development of remote areas. A critical challenge is that comprehensive factors, e.g., technical, economic, environmental, and social, which are inherently intertwined, should be properly evaluated and balanced in terms of social benefits. In addition, the feasibility of each planning scheme can only be verified based on their local electrical conditions by employing multi-energy flow calculations. Thus, this paper develops a comprehensive methodology for the optimal planning of remote integrated energy systems taking into account the uncertainty of renewable energy, where a multi-criteria decision-making procedure is designed, including the candidate set construction, remote integrated energy systems modeling, multi-energy flow calculation of the candidates, index definition and selection, and the optimal scheme determination. The methodology is applied to a case study in Lankao County, China. The optimal scheme, selected by the proposed multi-criteria decision-making procedure from 432 candidates, shows the best comprehensive performance, which is verified by different ranking methods. The optimal scheme can rank in the top 13% of each aspect on average, with economic and environmental aspects ranking in the top 2%. The methodology can help decision-makers find reasonable solutions for energy system planning in remote areas. • A comprehensive methodology for planning remote integrated energy systems is proposed. • Refined multi-energy flow calculation fitting with remote area characteristics is presented. • Multi-criteria decision-making procedure realizes high social benefits. • A real case in Lankao is performed to verify the rationality of the proposed method. [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 multiphysics model of Power-to-Hydrogen electrolyzer has been proposed with outperforming mathematical properties for fast calculation. • A one-dimensional linear diphasic model is established to evaluate the bubble effect in the electrolyzer. • The proposed model can be computed over 10 times faster than the traditional diphasic-flow models, at the expense of the accuracy loss of the i-V characteristics within 3%. • The linear PDE defined model can be applied to stack-, plant-, or system- level simulations since it is independent of the CFD algorithm. This paper proposes a multiphysics model of Power-to-Hydrogen electrolyzer that possesses outperforming mathematical properties for fast calculation. The proposed model features a one-dimensional linear diphasic-flow model to describe the bubble effect in low-temperature water electrolysis, which is a crucial physical process affecting cell performance. According to the characteristics of the multiphysics in electrolysis cells, the bubble layer is introduced to only solve the bubble distributions near the electrodes, decreasing the computing scale compared with traditional Calculating Fluid Dynamics (CFD) models. Besides, by the homogeneous flow assumptions and the liquid-phase steady parallel flow, the analytical solution of the momentum conservation is derived to eliminate the nonlinearity of the Naiver-Stokes equation. Once the thickness of the bubble layer is determined, the proposed model can be computed over 10 times faster than the traditional diphasic-flow models, at the expense of the accuracy loss of the i-V characteristics within 0.5%. As a result, the model can be applied to the integrated simulation in the energy system to efficiently acquire both operating characteristics and the multiphysics of the PtH electrolyzer. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies in the integration of the EVs' fast regulation speed and CGs' large regulation capacity. The whole optimization model is formulated under a two-stage Markov Decision Process (MDP) to correspond to the real-time RRD process. The user behavior characteristic and EV battery degradation are taken into account to stimulate the users to participate in RRD. Based on the formulated MDP, a piece-wise linear (PWL) function based scalable approximate dynamic programming (ADP) algorithm for different EV clusters is constructed to solve the real-time RRD model under uncertainties. A separable slope update method is proposed to update the slopes of PLF for different EV clusters with different user behaviors. The proposed ADP based real-time RRD algorithm (ADP-RTRRD) provides the approximate optimal real-time RRD policy with the empirical knowledge embedded through off-line training. Numerical simulation on a modified IEEE-39 bus system and Henan power grid in China verify the superiority of the proposed joint RRD model and the advantages of the proposed ADP-RTRRD algorithm. • Proposing the joint RRD model of EVs and CGs considering EV battery degradation. • Taking advantage of EVs' fast regulation speed and CGs' large regulation capacity. • Applying EV aggregation methods to simplify optimization complexity. • Reformulating the real-time RRD process under a two-stage MDP. • Proposing a scalable ADP algorithm to obtain approximate optimal solution. [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]

• An overview of state-of-the-art commutation failure (CF) is provided. • A review of the impact of multi-infeed structure on CF is undertaken. • The advantages and limitations of different CF prevention methods are examined. • Suggestions and potentially important future research directions are put forward. [Display omitted] Commutation failure (CF) is the main fault type in high voltage direct current (HVDC) systems with line commutated converters. To mitigate CFs, considerable researches have been done either in modelling or control methods. However, with the operation of more and more large-capacity HVDC projects, the coupling characteristics between HVDC and power grid are more complex. The existing reviews tend to focus only on the CFs of a single HVDC itself. This paper provides a more systematic overview of CFs, covering fifteen specific aspects in six areas, including four types of system structures, such as multi-infeed HVDC, and various types of CFs. The mechanism, discrimination, modeling, and prevention of CFs are reviewed extensively in combination with the latest literature. In particular, the advantages and limits of various CFs prevention methods are analyzed and compared in order to reveal the latest research results. Finally, it points out that the analysis and control of multi-infeed HVDC and hierarchical infeed HVDC, the application of artificial intelligence (AI) technology, the improvement of communication-based on 5G technology, harmonic problems, and control and coordination strategies of new auxiliary equipment are challenges and research directions of HVDC CFs in the future. [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]

• The evolution process of subsequent commutation failures (SCFs) is analyzed. • The influence of electrochemical energy storage (EES) power on SCFs is analyzed. • A coordinated power control strategy of EES to mitigate SCFs is proposed. • The influence of active power amplitude of EES on SCFs is discussed. Commutation failure is one of the most common faults in high voltage direct current (HVDC) transmission system. Subsequent commutation failures (SCFs) caused by commutation failure will cause large voltage and current swing. Several SCFs may cause DC transmission power interruption, which seriously threatens the safe and stable operation of AC/DC hybrid power grid. With the construction and commissioning of grid-side electrochemical energy storage (EES), it is possible to mitigate SCFs of adjacent HVDC transmission lines using EES with fast power response characteristics. In this paper, the influence mechanism of active and reactive power output of EES on commutation conditions is studied by combining the evolution of cascading outages and SCFs. It is found that reactive power mainly affects AC voltage and active power mainly affects DC current. Based on the mechanism analysis, a coordinated power control strategy for EES is presented. This strategy, combined with EES capacity constraints, can control EES active and reactive power output instructions adaptively according to the degree of AC failure at different stages of SCFs to fully release the transient support capacity of EES. Case studies are undertaken on the Henan multi-infeed UHVDC system and CIGRE-HVDC standard test system, respectively. The simulation results verify the correctness of the proposed EES power control strategy and the effectiveness of the strategy for SCFs mitigation in different systems, different fault types, and different fault degrees. [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]

With increasing wind farm scale, dynamic modeling and simulating the low-voltage ride-through (LVRT) process of wind farms are time- and source-consuming. Thus, a dynamic equivalent model of a wind farm considering the LVRT process is necessary and vital for power system analysis. However, the crowbar status of each doubly-fed induction generator (DFIG), voltage, and wind speed differences among will influence the LVRT process and equivalent results. To consider these three factors comprehensively and eliminate the error, this paper proposes a three-stage dynamic equivalent modeling approach, using accurate crowbar identification and voltage differences among DFIGs. Stage I: DFIGs are divided by crowbar status. A new accurate crowbar identification method is proposed to consider the interference from other DFIGs, using the experimental two-machine system and several dynamic simulations to correct fault voltage. Stage II: The often-overlooked voltage differences are considered, and DFIGs are divided into different clusters for accurate fault dynamics. Stage III: To eliminate errors resulting from wind speed differences, the DFIG active power reference is corrected based on the analytical function of the wind farm LVRT process. The proposed model can accurately identify crowbar status and consider the voltage and wind speed differences without detailed model simulation results, improving the accuracy. Case studies are undertaken on a wind farm with fifty DFIGs. Simulation results indicated that the proposed model can match the detailed model under different wind scenarios and fault voltages, and it also increases simulation efficiency, which is suitable for studying the effect of LVRT on power systems. [Display omitted] • A three-stage dynamic equivalent method is proposed to model the wind farm. • Fault voltage is corrected to identify crowbar status given DFIGs' mutual impact. • Voltage differences among wind turbines are considered to reduce equivalent error. • The simulation results demonstrate the effectiveness of proposed equivalent method. • The proposed model can capture the LVRT process of large-scale wind farm. [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]

Versatile catalyst systems with large current density under industrial conditions are pivotal to give impetus to hydrogen energy from fundamental to practical applications. Herein, a Schottky heterojunction nanosheet array composed of dispersed NiFe hydroxide nanoparticles and ultrathin NiS nanosheets (NiFe LDH/NiS) is proposed to regulate cooperatively mass transport and electronic structure for triggering oxygen evolution reaction (OER) activity at high current. In catalytic systems, the rich porosity of the NiS nanosheet array contributes abundant catalytic sites and good infiltration of the electrolyte for fast mass transfer. Furthermore, theoretical calculations reveal the coupling of NiFe LDH onto the NiS could tune the d‐band center of Ni(Fe) atoms and the binding strength of oxygen intermediates for favorable OER kinetics. Therefore, the NiFe LDH/NiS Schottky heterojunction exhibits a remarkable OER activity, delivering a current density of 1000 mA cm–2 at the ultralow overpotential of 325 mV. Meanwhile, scaled‐up NiFe LDH/NiS electrodes are implemented in an industrial water splitting electrolyzer and exhibit a stable cell voltage of 2.01 V to deliver a constant catalytic current of 8000 mA over 80 h, saving 0.215 kWh of electricity to generate more hydrogen per cubic meter than commercial Raney Ni electrodes. [ABSTRACT FROM AUTHOR]

This paper proposes a general optimal design method of probabilistic robust damping controllers (PRDCs) to suppress multiband oscillations in the power system integrated with wind farm. The proposed optimal design method consists of the following two steps. In the first step, owing to the high efficiency of the probabilistic collocation method (PCM), it is adopted to investigate the probabilistic small signal stability analysis (PSSSA) of power system integrated with wind farm. In the second step, a novel adaptive compass search (ACS) with an adaptive sequence of exploration directions is proposed to enhance the global searching ability through the previous searching results, and the proposed ACS is used to obtain the parameters of the PRDCs via solving an optimization problem based on the results of PSSSA obtained from the first step. Case studies are conducted on 16-machine 68-bus system to verify the accuracy and computational efficiency of the PCM. Moreover, simulation studies are also conducted to verify the advantages of the control performances of ACS in the design of damping controllers compared with that of the traditional residue method, particle swarm optimization (PSO), grey wolf optimizer (GWO), and teaching learning-based optimization (TLBO), respectively. Finally, the effectiveness of the proposed method is validated by the time-domain simulation. • Probabilistic robust damping controllers is proposed to suppress oscillations. • Probabilistic small signal stability analysis of power system with wind is studied. • Probabilistic collocation method is employed to improve analysis efficiency. • Adaptive compass search is proposed to obtain parameters of the damping controller. • Simulation results verify the effectiveness of the proposed damping controller. [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]

Graphical abstract Highlights • A data-driven wide-area damping controller (D-WADC) of BTB-VSC-HVDC is proposed. • The D-WADC makes use of the active and reactive power control of BTB-VSC-HVDC. • The D-WADC can adapt to the change of system operating condition. • The D-WADC can adaptively compensate the stochastic time delay. • Both simulation and hardware-in-loop experiment results verify the control effectiveness. Abstract This paper proposes a data-driven adaptive wide-area damping controller (D-WADC) for back-to-back VSC-HVDC to suppress the low frequency oscillation in a large-scale interconnected power system. The proposed D-WADC adopts a dual-loop control structure to make full use of the active and reactive power control of VSC-HVDC to improve the damping of the power system. A data-driven algorithm named the goal representation heuristic dynamic programming is employed to design the proposed D-WADC, which means the design procedure only requires the input and output data rather than the mathematic model of the concerned power system. Thus, the D-WADC can adapt to the change of operating condition through online weight modification. Besides, the adaptive delay compensator (ADC) is added to effectively compensate the stochastic delay involved in the wide-area feedback signal. Case studies are conducted based on the simplified model of a practical power system and the 16-machine system with a back-to-back VSC-HVDC. Both the simulation and hardware-in-loop experiment results verify that the proposed D-WADC can effectively suppress the low-frequency oscillation under a wide range of operating conditions, disturbances, and stochastic communication delays. [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]

Different from conventional power plants, a wind farm usually consists of hundreds of wind generators and collector systems with arbitrary topology. The numerous devices increase modeling efforts and computational burden. This paper proposes a vector-variable based method for dynamic modeling of the large-scale wind farm considering collector system dynamics. Using the vector variables, the modeling method converts the complex wind farm containing many components to a model consisting of two typical submodules, the wind generator module and the collector system module. These submodules can establish any number of wind generators and any topology of the collection systems considering dynamics freely, with good scalability. The correctness and high simulation efficiency of the established model are validated by the electromagnetic model. Furthermore, a detailed wind farm containing twenty-five wind generators is built via the proposed method. Compared with the aggregation model, the results show that the established model can reflect the wind farm stability accurately. Also, by comparing with the model neglecting the collector system dynamics, the results reveal the relative error caused by neglecting the collector system dynamics can lead to misjudgment of instability mode when analyzing the sub-synchronous modes. [Display omitted] • Based on vector variables, two typical modules can model whole wind farm in detail. • The model has better scalability and higher efficiency than electromagnetic model. • The model can reveal dynamics of wind farm more precise than aggregation model. • Neglecting collector network dynamics will cause misjudgement of instability mode. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-level coordinated control (MLCC) scheme for symmetrical bipolar modular multilevel converter based high voltage direct current (MMC-HVDC) integrated offshore wind farms. MLCC makes full use of the energy control capability of MMCs to effectively suppress DC overcurrent triggered by submarine cable breakage. This MLCC scheme consists of four control levels. The first level is for the offshore fault pole MMC. Once a DC submarine cable is broken, the fault pole MMC of the offshore converter station passively absorbs power from wind farms using the storage capacity of internal sub-module capacitors. This buys time for the rest of the control to start. The second level is for the onshore non-faulty pole MMC, which can actively raise the DC voltage to suppress DC overcurrent. The third level is for offshore non-fault pole MMC, which balances the power delivered to the DC system in real-time based on the power received from wind farms. The fourth level is for offshore wind farms, which reduce power to restore the non-fault pole submarine cable transmission power and current to the allowed state. The correctness and effectiveness of the proposed MLCC in different scenarios are verified using the PSCAD/EMTDC. [Display omitted] • The fault characteristics of the symmetrical bipolar MMC-HVDC are analyzed. • A sub-module capacitor energy control scheme of offshore MMC is designed. • A multi-level coordinated control (MLCC) is proposed to suppress DC overcurrent. • MLCC can combine safety and economy at the same time. [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]

In this paper, we design a distributed model predictive compensator (DMPC) in the cyber layer based on the mechanism of rolling optimization to compensate the bias of the nominal voltage in the droop control. We then present the structure of cyber–physical system (CPS) to combine the DMPC with the droop control in the physical layer to form a uniform framework as well as accomplish high-quality voltage regulation and power allocation. We also deal with the case of the cyber-layer constraints since the states of cyber-layer among the distributed generators (DGs) of the microgrid are generally constrained due to their great impacts on the stability of the physical layer. Especially, the presented CPS-based DMPC is developed in a fully distributed manner, namely, only the compensated parameters of the neighbors can be accessible for each DG and no global information is employed in the whole control process. Besides, the stability of the presented DMPC can be guaranteed if the initial communication states satisfy the consistency constraint. The effectiveness and feasibility of the presented CPS-based DMPC is verified numerically in the presence of the motor faulty. It is demonstrated from the theoretical analysis and simulation results that the settling time for compensating the droop parameter is acceptable and satisfies the principle of "plug-and-play". • A distributed model predictive compensator (DMPC) based on the framework of the cyber physical system (CPS) is proposed. • The proposed algorithm considers the state constraints of cyber layer in solving the control problem of DC microgrid. • It is the first attempt to design the DMPC at the cyber layer such that the inherent constraints of the communication states can be guaranteed. • Both the problems of voltage deviation adjustment and power distribution for the DC microgrid can be simultaneously solved by using the presented algorithm. [ABSTRACT FROM AUTHOR]

Solar energy loss of the large-scale PV power plant (LPVPP) caused by partial shading can be mitigated by unit-level DC optimizers (DCO). This paper presents a novel DCO-based decentralized control (DBDC) scheme for an LPVPP to provide active frequency support without equipping energy storage. The proposed DBDC scheme consists of two levels: the plant-level modified DC-link voltage control for the PV inverter and the unit-level improved virtual inertia control and lookup table-based droop control deployed in the DCO controller for each PV generation unit. The stored energy of the DC-link capacitor can be adjusted for inertia emulation based on grid frequency fluctuation with plant-level voltage control. Meanwhile, the frequency deviation is converted into the changes of DCO output voltages. Then, according to the voltage deviations of DCOs, the adaptive power adjustment for PV units is realized through unit-level virtual inertia and droop control scheme. Finally, the maximum use of the PV reserve power and the proposed DBDC scheme without using any communication network can be achieved for LPVPPs, even under frequent partial shading conditions. Case studies are undertaken on a PV-integrated two-area system. Comparison simulation results verify the effectiveness and universality of the proposed DBDC scheme under different scenarios. [Display omitted] • DCO-based frequency support scheme is proposed for large-scale PV systems. • Communication-free decentralized inertia and droop controls are employed in DCOs. • High solar energy utilization for PV arrays even under partial shading conditions. • Superior frequency regulation effect compared with conventional methods. [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]

• Develop a dispatching scheme for DSUC incorporating multi-function demand response. • Design the LS and RC demand response to improve flexible ramping capability. • Accelerating solution method is proposed to reduce the computational burden. This paper proposes a two-stage data-driven unit commitment dispatching scheme, which coordinates the multi-function demand response of commercial air conditioning aggregators with thermal units to encounter the variations of load and wind power. In the proposed dispatching scheme, the flexible ramping capability of the thermal units is reflected in the coupling constraints between the ramping capacity and reserve capacity. To improve the flexibility of the whole system, multi-function demand response of commercial air conditioning aggregators is incorporated into the scheduling in both stages. In the day-ahead stage, load-shifting demand response reduces the ramping requirements by reshaping the load profile. In the real-time stage, reserve-capacity demand response releases the ramping capability by providing reserve for the rebalance of wind power fluctuation, which is represented by a data-driven uncertainty set. Since the dispatching scheme is formulated as a min–max-min problem, an accelerating solution method is developed to improve the computational efficiency, which combines the column-and-constraints generation algorithm and the specific property of the model. The simulation results tested on the modified IEEE 118-bus system and Henan power grid in China validate the effectiveness of the dispatching scheme and solution method in increasing flexible ramping capability, reducing wind power curtailment and load shedding, improving economic efficiency, and accelerating computation. [ABSTRACT FROM AUTHOR]

• Wide-area power oscillation damper (WPOD) is used to damp interarea oscillation. • Networked predictive control-based WPOD is proposed for DFIG-based wind farm. • The data transmission and packet dropouts of WAMS are modeled in detail. • The proposed WPOD can compensate the time delay and data dropout actively. • Case studies are conducted to verify the effectiveness of the proposed WPOD. Data dropouts and time delays introduced in communication network would degrade the damping performance of the wide-area power oscillation dampers (WPODs) or may even cause instability of the closed-loop system. This paper establishes a data transmission model in the network control system (NCS) and proposes a networked predictive control (NPC) based WPOD of doubly-fed induction generator (DFIG)-based wind farm to damp inter-area oscillations in a large-scale interconnected power system. The major advantage of the proposed WPOD is that it can actively compensate packet dropouts and communication delays involved in wide-area control channels. Additionally, online model identification is employed to handle the parameter uncertainties and operating condition variations of the power system. Case studies are undertaken on the 16-machine 68-bus power system. Simulation results show that the proposed WPOD can provide better damping performances than those of the conventional WPOD over a wide range of operating conditions and different communication delays and packet dropouts. [ABSTRACT FROM AUTHOR]