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Utility-scale renewable power-to-hydrogen (ReP2H) production typically uses thyristor rectifiers (TRs) to supply power to multiple electrolyzers (ELZs). They exhibit a nonlinear and non-decouplable relation between active and reactive power. The on-off scheduling and load allocation of multiple ELZs simultaneously impact energy conversion efficiency and AC-side active and reactive power flow. Improper scheduling may result in excessive reactive power demand, causing voltage violations and increased network losses, compromising safety and economy. To address these challenges, this paper first explores trade-offs between the efficiency and the reactive load of the electrolyzers. Subsequently, we propose a coordinated approach for scheduling the active and reactive power in the ReP2H system. A mixed-integer second-order cone programming (MISOCP) is established to jointly optimize active and reactive power by coordinating the ELZs, renewable energy sources, energy storage (ES), and var compensations. Case studies demonstrate that the proposed method reduces losses by 3.06% in an off-grid ReP2H system while increasing hydrogen production by 5.27% in average.

Large-scale centralized development of wind and solar energy and peer-to-grid transmission of renewable energy source (RES) via high voltage direct current (HVDC) has been regarded as one of the most promising ways to achieve goals of peak carbon and carbon neutrality in China. Traditionally, large-scale thermal generation is needed to economically support the load demand of HVDC with a given profile, which in turn raises concerns about carbon emissions. To address the issues above, hydrogen energy storage system (HESS) and ammonia energy storage system (AESS) are introduced to gradually replace thermal generation, which is represented as a multi-stage expansion planning (MSEP) problem. Specifically, first, HESS and AESS are established in the MSEP model with carbon emission reduction constraints, and yearly data with hourly time resolution are utilized for each stage to well describe the intermittence of RES. Then, a combined Dantzig-Wolfe decomposition (DWD) and column generation (CG) solution approach is proposed to efficiently solve the large-scale MSEP model. Finally, a real-life system in China is studied. The results indicate that HESS and AESS have the potential to handle the intermittence of RES, as well as the monthly imbalance between RES and load demand. Especially under the goal of carbon neutrality, the contribution of HESS and AESS in reducing levelized cost of energy (LCOE) reaches 12.28% and 14.59%, respectively, which finally leads to a LCOE of 0.4324 RMB/kWh., Comment: 10 pages, 8 figures

Isolated renewable power to ammonia (IRePtA) has been recognized as a promising way to decarbonize the chemical industry. Optimal sizing of the renewable power system is significant to improve the techno-economic of IRePtA since the investment of power sources exceeds 80\% of the total investment. However, multi-timescale electricity, hydrogen, and ammonia storages, minimum power supply for system safety, and the multi-year uncertainty of renewable generation lead to difficulties in planning. To address the issues above, an IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is directly formulated as the objective, rendering a mixed integer linear fractional programming (MILFP) problem. Information gap decision theory (IGDT) is utilized to handle the multi-year uncertainty of renewable generation. Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound (B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model, giving robust and opportunistic planning results. Then, Markov Chain Monte Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the long-run performance. Finally, a real-life system in Inner Mongolia, China, is studied. The results indicate that the proposed methods could reduce the computational burden by orders of magnitude for solving a large-scale MILFP problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to obtain an optimal capacity allocation with the lowest expected LCOA (3610 RMB/t) in long-run simulations., Comment: 10 pages, 8 figures

Renewable power to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on the electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified framework. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC\&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of an RePtA VPP joining multiple markets over conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor's flexibility.

In the conversion toward a clear and sustainable energy system, the flexibility of power-to-hydrogen (P2H) production enables the admittance of volatile renewable energies on a utility scale and provides the connected electrical power system with ancillary services. To extend the load flexibility and thus improve the profitability of green hydrogen production, this paper presents an optimal production scheduling approach for utility-scale P2H plants composed of multiple alkaline electrolyzers. Unlike existing works, this work discards the conservative constant steady-state constraints and first leverages the dynamic thermal and hydrogen-to-oxygen (HTO) impurity crossover processes of electrolyzers. Doing this optimizes their effects on the loading range and energy conversion efficiency, therefore improving the load flexibility of P2H production. The proposed multiphysics-aware scheduling model is formulated as mixed-integer linear programming (MILP). It coordinates the electrolyzers' operation state transitions and load allocation subject to comprehensive thermodynamic and mass transfer constraints. A decomposition-based solution method, SDM-GS-ALM, is followingly adopted to address the scalability issue for scheduling large-scale P2H plants composed of tens of electrolyzers. With an experiment-verified dynamic electrolyzer model, case studies up to 22 electrolyzers show that the proposed method remarkably improves the hydrogen output and profit of P2H production powered by either solar or wind energy compared to the existing scheduling approach., Comment: 27 pages, 16 figures

Large-scale power-to-hydrogen (P2H) systems formed by multi-stack are potentially powerful peak-shaving resources of power systems. However, due to the research gap in connecting the grid-side performance with the inherent operation control, the continuous operation of P2H loads is limited by the PF assessment under volatile conditions when integrating into the grid. This paper first fills the gap in proposing the analytical models of active and reactive power of P2H loads with a typical power converter interface topology. On this basis, the all-condition PF characteristics of multi-stack P2H loads are captured as functions of unified current and temperature control variables. Then, a PF-constrained multi-timescale control framework is constructed to evaluate flexibility, PF, production, and security comprehensively. A two-level nexus, including a model-based hour-ahead robust model predictive controller and a rule-based real-time increment correction algorithm, is proposed to guarantee the control accuracy and tractability. Case studies verify an intrinsic control tradeoff between PF and production, resulting in an unequal-split allocation strategy compared to the traditional production-oriented control. The significance of the extended PF and security dimensions is verified to improve the flexibility. Furthermore, five typical operating modes respectively corresponding to low, medium, and high load levels at the cluster level are concluded for industrial application.

Converting renewable energy into ammonia has been recognized as a promising way to realize ``green hydrogen substitution" in the chemical industry. However, renewable power to ammonia (RePtA) requires an essential investment in facilities to provide a buffer against the strong volatility of renewable energy and the limited flexibility of ammonia synthesis, which involves the three main stakeholders, namely, power, hydrogen, and ammonia. Therefore, the sizing and pricing of RePtA play a core role in balancing the interest demands of investors. This paper proposes an optimal sizing and pricing method for RePtA system planning. First, power to ammonia (P2A) is modeled as a flexible load, especially considering the limited flexibility of ammonia synthesis, which has been verified using real dynamic regulation data. Second, the multi-investor economic (MIE) model is established considering both external and internal trading modes. Then, a two-stage decomposed sizing and pricing method is proposed to solve the problem caused by the strong coupling of planning, operation, and trading, and information gap decision theory (IGDT) method is utilized to handle the uncertainty of renewable generation. Finally, real data from a real-life system in Inner Mongolia are utilized to verify the proposed approach. The results show that the system proposed has a yield of 8.15%., Comment: 10 pages, 10 figures

Electrolysis systems use proportional-integral-derivative (PID) temperature controllers to maintain stack temperatures around set points. However, heat transfer delays in electrolysis systems cause manual tuning of PID temperature controllers to be time-consuming, and temperature oscillations often occur. This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control. A thermal dynamic model of an electrolysis system is established in the frequency-domain for controller designs. Based on this model, the temperature stability is analysed by the root distribution, and the PID parameters are optimized considering both the temperature overshoot and the settling time. The performance of the optimal PID controllers is verified through experiments. Furthermore, the simulation results show that the before-stack temperature should be used as the feedback variable for small lab-scale systems to suppress stack temperature fluctuations, and the after-stack temperature should be used for larger systems to improve the economy. This study is helpful in ensuring the temperature stability and control of electrolysis systems.

Thermal management is vital for the efficient and safe operation of alkaline electrolysis systems. Traditional alkaline electrolysis systems use simple proportional-integral-differentiation (PID) controllers to maintain the stack temperature near the rated value. However, in renewable-to-hydrogen scenarios, the stack temperature is disturbed by load fluctuations, and the temperature overshoot phenomenon occurs which can exceed the upper limit and harm the stack. This paper focuses on the thermal modelling and controller design of an alkaline electrolysis system under dynamic operating conditions. A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design. Based on this model, we propose two novel controllers to reduce temperature overshoot: one is a current feed-forward PID controller (PID-I), the other is a model predictive controller (MPC). Their performances are tested on a lab-scale system and the experimental results are satisfying: the temperature overshoot is reduced by 2.2 degree with the PID-I controller, and no obvious overshoot is observed with the MPC controller. Furthermore, the thermal dynamic performance of an MW-scale alkaline electrolysis system is analyzed by simulation, which shows that the temperature overshoot phenomenon is more general in large systems. The proposed method allows for higher temperature set points which can improve system efficiency by 1%.

Order flow imbalance can explain short-term changes in stock price. This paper considers the change of non-minimum quotation units in real transactions, and proposes a generalized order flow imbalance construction method to improve Order Flow Imbalance (OFI) and Stationarized Order Flow Imbalance (log-OFI). Based on the high-frequency order book snapshot data, we conducted an empirical analysis of the CSI 500 constituent stocks. In order to facilitate the presentation, we selected 10 stocks for comparison. The two indicators after the improvement of the generalized order flow imbalance construction method both show a better ability to explain changes in stock prices. Especially Generalized Stationarized Order Flow Imbalance (log-GOFI), using a linear regression model, on the time scales of 30 seconds, 1 minute, and 5 minutes, the average R-squared out of sample compared with Order Flow Imbalance (OFI) 32.89%, 38.13% and 42.57%, respectively increased to 83.57%, 85.37% and 86.01%. In addition, we found that the interpretability of Generalized Stationarized Order Flow Imbalance (log-GOFI) showed stronger stability on all three time scales., Comment: 9 pages,5 figures

Under the downward tendency of prices of renewable energy generators and upward trend of hydrogen demand, this paper studies the technoeconomic supplement of P2G clusters with hydrogen pipeline for HVDC to jointly consume renewable energy. First, the planning and operation constraints of large-capacity P2G clusters is established. On this basis, the multistage coordinated planning model of renewable energy, HVDCs, P2Gs and hydrogen pipelines is proposed considering both variability and uncertainty, rendering a distributionally robust chance-constrained (DRCC) program. Then this model is applied in the case study based on the real Inner Mongolia-Shandong system. Compared with energy transmission via HVDC only, P2G can provide operation supplement with its operational flexibility and long term economic supplement with increasing demand in high-valued transportation sector, which stimulates an extra 24 GW renewable energy exploration. Sensitivity analysis for both technical and economic factors further verifies the advantages of P2G in the presence of high variability due to renewable energy and downward tendency of prices of renewable energy generators. However, since the additional levelized cost of the P2G (0.04 RMB/kWh) is approximately twice the HVDC (0.02 RMB/kWh), P2G is more sensitive to uncertainty from both renewable energy and hydrogen demand.

Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to enhance the usage of wind generation. This paper proposes a generic optimal configuration model of WtA at the network level to minimize the ammonia production cost by optimizing capacities and locations of WtA facilities including wind turbines, electrolyzers, hydrogen tanks and optimizing supply modes among regions. Specifically, the temporal fluctuation characteristics of wind resources, the operation flexibility of the ammonia synthesis reactor and the transport distances are considered. Three typical supply modes, i.e., the Local WtA, the EN (electric network)-based WtA and the HSC (hydrogen supply chain)-based WtA, combined with two energy transport modes including EN and HT (Hydrogen truck trailers) are included with the consideration of the maximal energy transport capacity of EN and transport distance per day of HT (500km). Real data of Inner Mongolia (a typical province in China with rich wind resources and existing ammonia industries) is employed to verify the effectiveness and significance of proposed model. The effect of above significant factors on optimal planning capacity of WtA facilities and optimal energy transport modes is analyzed, which provides guidelines for WtA configuration. The economic analysis shows that the average LCOA (levelized cost of ammonia) for WtA is approximately 0.57 euro/kg in Inner Mongolia and comparable to that for CtA (coal-to-ammonia, 0.41 euro/kg) with a reduction of 30% in capacity cost of the facilities.