Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

Alabi, Tobi Michael; Lawrence, Nathan P.; Lü, Lin; Yang, Zaiyue; Gopaluni, R. Bhushan

doi:10.1016/j.apenergy.2022.120633

Cited by 23 publications

(2 citation statements)

References 39 publications

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“…The results showed that the proposed method can effectively address the increasingly critical need for solving the unit commitment problem in a computationally efficient manner under high penetrations of renewable energy. Alabi et al [71] proposed DRL with an automated hyperparameter selection feature to dispatch a real-time multi-energy system, which achieved great success compared with rule-based scheduling. These works in the literature mainly focus on solving power scheduling problems in HECESSs.…”

Section: Deep Reinforcement Learning Applied For Hecessmentioning

confidence: 99%

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Jiehui,

Su,

Wang

et al. 2024

Clean Energy Sci. Technol.

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With the maturity of hydrogen storage technologies, hydrogen-electricity coupling energy storage in green electricity and green hydrogen modes is an ideal energy system. The construction of hydrogen-electricity coupling energy storage systems (HECESSs) is one of the important technological pathways for energy supply and deep decarbonization. In a HECESS, hydrogen storage can maintain the energy balance between supply and demand and increase the utilization efficiency of energy. However, its scenario models in power system establishment and the corresponding solution methods still need to be studied in depth. For accelerating the construction of HECESSs, firstly, this paper describes the current applications of hydrogen storage technologies from three aspects: hydrogen production, hydrogen power generation, and hydrogen storage. Secondly, based on the complementary synergistic mechanism of hydrogen energy and electric energy, the structure of the HECESS and its operation mode are described. To study the engineering applications of HECESSs more deeply, the recent progress of HECESS application at the source, grid, and load sides is reviewed. For the application of the models of hydrogen storage at the source/grid/load side, the selection of the solution method will affect the optimal solution of the model and solution efficiency. As solving complex multi-energy coupling models using traditional optimization methods is difficult, the paper therefore explored the advantages of deep reinforcement learning (DRL) algorithms and their applications in HECESSs. Finally, the technical application in the construction of new power systems supported by HECESSs is prospected. The study aims to provide a reference for the research on hydrogen storage in power systems.

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Section: Deep Reinforcement Learning Applied For Hecessmentioning

confidence: 99%

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Jiehui,

Su,

Wang

et al. 2024

Clean Energy Sci. Technol.

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“…Ref. [16] proposed a deep reinforcement learning framework integrated with an automated hyperparameter selection feature to study the real‐time scheduling of a multi‐energy system coupled with CCS. Ref.…”

Section: Introductionmentioning

confidence: 99%

Multi‐stage stochastic dual dynamic programming to low‐carbon economic dispatch for power systems with flexible carbon capture and storage devices

Zhang,

Liu,

Ding

et al. 2023

IET Generation Trans & Dist

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The carbon capture and storage (CCS) technique gains much attention due to its role in reducing CO2 emissions. By introducing flexible CCS devices into conventional power plants, the low‐carbon economic operation of the power system can be achieved. However, the uncertainty of renewable energy makes it hard to obtain an optimal operation policy. First, the detailed model of CCS consisting of bypass venting stacks, solvent storage tanks, absorber, and stripper is described. Then, a low‐carbon economic dispatch model of the power system with CCS is proposed to minimize the total operation and renewable energy abandonment penalty costs. Next, the problem is reformulated as multi‐stage stochastic programming, and the stochastic dual dynamic programming (SDDP) method is applied to relieve the computation burden. To control the probability of stopping the algorithm prematurely, a new stopping criterion based on a two‐sided hypothesis test is proposed. Finally, the effectiveness of the proposed model and the new stopping criterion is demonstrated by case studies on a practical power system.

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Batteries or silos: Optimizing storage capacity in direct air capture plants to maximize renewable energy use

Arwa,

Schell

2024

Applied Energy

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Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

Cited by 23 publications

References 39 publications

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Hydrogen-electricity coupling energy storage systems: Models, applications, and deep reinforcement learning algorithms

Multi‐stage stochastic dual dynamic programming to low‐carbon economic dispatch for power systems with flexible carbon capture and storage devices

Batteries or silos: Optimizing storage capacity in direct air capture plants to maximize renewable energy use

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