Yiwei Qiu

Yangjun Zeng, Yiwei Qiu, Xiaocong Sun et al.

Integrating renewable generation, hydrogen production, and renewable ammonia (RA) synthesis into power-to-ammonia (P2A) systems creates interactions across electricity and hydrogen markets. Limited operational flexibility, however, places RA at a disadvantage at the Nash equilibrium (NE). Recent advances in ammonia synthesis reactor design enable hot standby (HSB) operation, improving flexibility but introducing integer decision variables that complicate market equilibrium analysis. To address this challenge, we develop a potential game model and derive a convergent ε-approximate equilibrium via an iterative best-response approach. Case studies show that HSB reduces RA's reliance on hydrogen purchases and increases its profit by 20.14%. More importantly, HSB shifts the market equilibrium toward a more mutually beneficial outcome.

Yangjun Zeng, Huayan Geng, Yiwei Qiu et al.

Renewable power-to-ammonia (ReP2A) production offers a viable pathway to decarbonize the power and chemical sectors and is increasingly supported by carbon-emission policies. However, a carbon-related mechanism that links ReP2A producers with fossil-based gray ammonia (GA) competitors while aligning the interests of renewable power, green hydrogen, and green ammonia producers in the ReP2A process chain remains unexplored. To fill this gap, we propose a hierarchical carbon-driven incentive mechanism (PCIM) to improve the market competitiveness of green ammonia. We first construct a trading framework in which ReP2A and GA participate in both the carbon allowance (CA) and ammonia markets, which forms the outer layer. These interactions, together with electricity and hydrogen transactions in the ReP2A chain, which form the inner layer, are modeled as a hierarchical game. For tractability, the inner layer is characterized via decomposable equivalent optimization, and the outer layer is solved as a mixed-integer linear program (MILP) derived from Karush-Kuhn-Tucker conditions. Based on the resulting equilibrium, we identify the carbon-related revenue of ReP2A and propose an incentive-compatible CA allocation mechanism (PCAM) %to ensure equitable benefit sharing across the ReP2A chain. Simulations show that the PCIM reduces carbon emissions by 12.9\% at a cost of only a 1.8% decrease in sectorwide revenue, and results from the PCIM provide guidance for carbon pricing. Furthermore, the application of the PCAM increases stakeholders' willingness to participate in ReP2A production.

Yiwei Qiu, Jiatong Li, Yangjun Zeng et al.

An emerging approach for large-scale renewable hydrogen production is integrating multiple alkaline water electrolysis (AWE) stacks into one balance-of-plant (BoP) system, sharing gas-lye separation and lye circulation components. While this configuration, termed $N$-in-1, reduces cost and complexity, its dynamic performance under fluctuating power remains unclear compared with conventional 1-in-1 systems. This paper develops a state-space model of the multi-stack AWE system, capturing lye circulation, temperature, and hydrogen-to-oxygen (HTO) dynamics, calibrated via experiments on a 4,000 Nm$^3$/h-rated 4-in-1 system. A nonlinear model predictive controller (NMPC) is then designed to coordinate inter-stack current distribution, lye flow, and cooling for load tracking and operational stability. Simulations on the experimental-validated model show that a $4$-in-1 system can achieve very similar performance compared to four parallel 1-in-1 systems. Differences in load-tracking error, temperature stabilization, and specific energy consumption remain below 0.015 MW, 0.346 K, and 0.001 kWh/Nm$^3$ under wind power supply.

Huayan Geng, Yangjun Zeng, Yiwei Qiu

Renewable power-to-ammonia (ReP2A), which uses hydrogen produced from renewable electricity as feedstock, is a promising pathway for decarbonizing the energy, transportation, and chemical sectors. However, variability in renewable generation causes fluctuations in hydrogen supply and ammonia production, leading to revenue instability for both ReP2A producers and conventional fossil-based gray ammonia (GA) producers in the market. Existing studies mainly rely on engineering measures, such as production scheduling, to manage this risk, but their effectiveness is constrained by physical system limits. To address this challenge, this paper proposes a financial instrument termed \emph{renewable ammonia futures} and integrates it with production decisions to hedge ammonia output risk. Production and trading models are developed for both ReP2A and GA producers, with conditional value-at-risk (CVaR) used to represent risk preferences under uncertainty. A game-theoretic framework is established in which the two producers interact in coupled ammonia spot and futures markets, and a Nash bargaining mechanism coordinates their production and trading strategies. Case studies based on a real-world system show that introducing renewable ammonia futures increases the CVaR utilities of ReP2A and GA producers by 5.103% and 10.14%, respectively, improving profit stability under renewable uncertainty. Sensitivity analysis further confirms the effectiveness of the mechanism under different levels of renewable variability and capacity configurations.

Yiwei Qiu, Jin Lin, Zhipeng Zhou et al.

The stochastic differential equation (SDE)-based random process models of volatile renewable energy sources (RESs) jointly capture the evolving probability distribution and temporal correlation in continuous time. It has enabled recent studies to remarkably improve the performance of power system dynamic uncertainty quantification and optimization. However, considering the non-homogeneous random process nature of PV, there still remains a challenging question: how can a realistic and accurate SDE model for PV power be obtained that reflects its weather-dependent uncertainty in online operation, especially when high-resolution numerical weather prediction (NWP) is unavailable for many distributed plants? To fill this gap, this article finds that an accurate SDE model for PV power can be constructed by only using the cheap data from low-resolution public weather reports. Specifically, an hourly parameterized Jacobi diffusion process is constructed to recreate the temporal patterns of PV volatility during a day. Its parameters are mapped from the public weather report using an ensemble of extreme learning machines (ELMs) to reflect the varying weather conditions. The SDE model jointly captures intraday and intrahour volatility. Statistical examination based on real-world data collected in Macau shows the proposed approach outperforms a selection of state-of-the-art deep learning-based time-series forecast methods.