Flexible design and operation of off-grid green ammonia systems with gravity energy storage under long-term renewable power uncertainty

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Flexible design and operation of off-grid green ammonia systems with gravity energy storage under long-term renewable power uncertainty

ReferencesShowing 10 of 55 papers
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Optimal capacity and multi-stable flexible operation strategy of green ammonia systems: Adapting to fluctuations in renewable energy
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Optimising renewable generation configurations of off-grid green ammonia production systems considering Haber-Bosch flexibility
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Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and decomposition algorithm (CC-DA) approach
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Ammonia to power: Forecasting the levelized cost of electricity from green ammonia in large-scale power plants
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Uncertainty analysis of an integrated energy system based on information theory
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Experimental investigation of explosion hazard from lithium-ion battery thermal runaway effluent gas
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Development of an integrated wind and PV system for ammonia and power production for a sustainable community
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Optimal scheduling for wind-powered ammonia generation: Effects of key design parameters
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A novel system for ammonia-based sustainable energy and agriculture: Concept and design optimization
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Real-Time Dynamic Simulation and Ai-Assisted Hierarchical Control of an Integrated Concentrated Solar Plant, Steam Rankine Cycle, and High-Temperature Steam Electrolysis Systems
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Real-Time Dynamic Simulation and Ai-Assisted Hierarchical Control of an Integrated Concentrated Solar Plant, Steam Rankine Cycle, and High-Temperature Steam Electrolysis Systems

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Optimal Sizing of a Wind-Powered Green Ammonia Plant for Maritime Fuel Supply—A Case in the Greater Bay Area
  • Sep 28, 2025
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  • Yimiao Gu + 1 more

Green ammonia has emerged as a promising alternative fuel for maritime decarbonization, owing to its carbon-free combustion, favorable volumetric energy density, and well-established logistics infrastructure compared to other alternatives. However, critical gaps persist in the development of an integrated fuel supply framework, which hinders the large-scale adoption of ammonia-fueled vessels. Therefore, this paper proposes an onshore wind-powered green ammonia plant located along the Gaolan–Yangpu feeder route. The plant comprises PEM electrolysis, nitrogen separation, Haber–Bosch synthesis, and storage facilities. An optimal plant configuration is subsequently derived through hourly simulations based on wind power generation and a priority-based capacity expansion algorithm. Key findings indicate that a stable ammonia supply—synchronized with monsoon wind patterns and capable of fueling vessels with 10 MW propulsion systems consuming around 680 tons per fortnight—requires a 72 MW onshore wind farm, a 63 MW PEM electrolyzer, 3.6 MW of synthesis facility, and 3205 tons of storage. This configuration yields a levelized cost of ammonia (LCOA) of approximately USD 700/ton, with wind turbines and electrolyzers (including replacement costs) accounting for over 70% of the total cost. Sensitivity analysis further shows that wind turbine and electrolyzer prices are the primary factors affecting ammonia costs. Although variations in operational parameters may significantly alter final configuration, they cause only minor (±1%) fluctuations in the levelized cost without significantly altering its overall trend.

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An integrative review of dual-fuel strategies, Nano-additives, and emission control in compression ignition engines fueled by renewable energy sources
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Day-Ahead Planning and Scheduling of Wind/Storage Systems Based on Multi-Scenario Generation and Conditional Value-at-Risk
  • May 12, 2025
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  • Jianhong Zhu + 2 more

The volatility and uncertainty of wind power output pose significant challenges to the safe and stable operation of power systems. To enhance the economic efficiency and reliability of day-ahead scheduling in wind farms, this paper proposes a day-ahead planning and scheduling method for wind/storage systems based on multi-scenario generation and Conditional Value-at-Risk (CVaR). First, based on the statistical characteristics of historical wind power forecasting errors, a kernel density estimation method is used to fit the error distribution. A Copula-based correlation model is then constructed to generate multi-scenario wind power output sequences that account for spatial correlation, from which representative scenarios are selected via K-means clustering. An objective function is subsequently formulated, incorporating electricity sales revenue, energy storage operation and maintenance cost, initial state-of-charge (SOC) cost, peak–valley arbitrage income, and penalties for schedule deviations. The initial SOC of the storage system is introduced as a decision variable to enable flexible and efficient coordinated scheduling of the wind/storage system. The storage system is implemented using a 1500 kWh/700 kW lithium iron phosphate (LiFePO4) battery to enhance operational flexibility and reliability. To mitigate severe profit fluctuations under extreme scenarios, the model incorporates a CVaR-based risk constraint, thereby enhancing the reliability of the day-ahead plan. Finally, simulation experiments under various initial SOC levels and confidence levels are conducted to validate the effectiveness of the proposed method in improving economic performance and risk management capability.

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