Optimal energy management of multiple electricity-hydrogen integrated charging stations

Fang, Xiaolun; Wang, Yubin; Dong, Wei; Yang, Qiang; Sun, Siyang

doi:10.1016/j.energy.2022.125624

Cited by 49 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current research on HIES has focused on renewable energy to hydrogen (RE2H) [9][10][11][12]. On the hydrogen production front, electrolyzers, powered by renewable energy sources such as wind power and photovoltaics, are employed to generate hydrogen, commonly referred to as green hydrogen (GH) [10].…”

Section: Introductionmentioning

confidence: 99%

“…In [11], the authors delved into the planning of hydrogen refuelling stations for hydrogen-fuelled vehicles associated with photovoltaic-driven hydrogen production. Additionally, the authors in [12] proposed a multi-stage and multi-time-scale energy management approach for HIES. Commencing with the objective of minimizing the operational costs of HIES, the aforementioned literature introduces a novel approach to optimizing the operation of integrated energy systems within the nexus of electricity and hydrogen.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A coordinated green hydrogen and blue hydrogen trading strategy between virtual hydrogen plant and electro‐hydrogen energy system

Li,

Zhao,

2024

IET Generation Trans & Dist

View full text Add to dashboard Cite

In the hydrogen‐based integrated energy system (HIES), there exists a hydrogen trading market where hydrogen producers and consumers are distinct stakeholders. Current research in hydrogen trading predominantly focuses on high‐cost green hydrogen (GH), which is not aligned with the current trend of utilizing hydrogen from multiple sources. To address this, this paper proposes a hydrogen trading strategy between the virtual hydrogen plant (VHP) and electro‐hydrogen energy system (EHES) based on a bi‐level model, considering the synergy of GH produced from electrolyzers and blue hydrogen (BH) derived from natural gas in the HIES. In the VHP level, the objective is to maximize profit from hydrogen sales, allowing for the determination of hydrogen prices. In the EHES level, the goal is to minimize the cost of energy supply, leading to the formulation of GH and BH purchasing plans based on hydrogen prices. Additionally, this paper incorporates a risk‐averse model from the information gap decision theory (IGDT) to account for the impact of wind power output uncertainties in the VHP level. Subsequently, leveraging the Karush–Kuhn–Tucker (KKT) conditions of the EHES level, the bi‐level problem is transformed into a solvable single‐level mathematical program with equilibrium constraints (MPEC), with the non‐linear equilibrium constraints linearized. The proposed bi‐level optimization model is validated through case studies encompassing industrial and residential hydrogen utilization within the HIES. The outcomes confirm the rationality of the proposed model, demonstrating that, in comparison to exclusively trading GH, the coordinated GH and BH trading can increase the profit of the VHP by 2.7% and reduce the costs of the EHES by 8.5%.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A coordinated green hydrogen and blue hydrogen trading strategy between virtual hydrogen plant and electro‐hydrogen energy system

Li,

Zhao,

2024

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…To further improve the processing results, some researchers have proposed and researched multiple time scales. Fang et al 15 proposed a multistage and multitime scale energy management for hydrogen‐based integrated energy system considering the electricity‐heat‐hydrogen supply‐demand balance and demand uncertainties. The proposed solution consists of three stages, the day‐ahead scheduling stage, MPC based intraday rolling dispatch stage, and intraday real‐time adjustment stage to participate in the electricity and hydrogen market.…”

Section: Introductionmentioning

confidence: 99%

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Zheng,

Hou,

et al. 2024

Energy Science & Engineering

View full text Add to dashboard Cite

In the context of the dual carbon goal strategy, the proportion of new energy generation has increased annually, large‐scale renewable energy integration has been achieved, and the intermittent and uncertain operating characteristics pose an enormous challenge to the complete and stable operation of an integrated energy system (IES), promoting the complexity of IES optimization models. To increase the stability and accuracy of the system and improve the operation efficiency of the system, a Gaussian mixture model is used to fit the probability distribution of wind power and the prediction errors of the photovoltaic output. In addition, the expected maximization method is used to solve a model with hidden variables, the results show that the expectation maximization algorithm can improve the fitting accuracy, reduce the error caused by the subjective setting of the initial value, and make the fitting accuracy of the wind‐solar power prediction error greater. Then, to solve the problem in which the low resolution of the day‐ahead scheduling time leads to large errors, day‐ahead, day‐to‐day multitime scale operation optimization model takes into account the comprehensive demand response according to the differences in wind and solar output and load forecasting accuracy. Finally, simulation validation is conducted in multiple scenarios, and a comparative analysis is performed for single and multitime scale comprehensive demand response scenarios. The simulation results show that compared with the optimization results of no demand response day, the optimization results of this model reduce the total cost by 23.21%, carbon emissions by 13.98%, purchased electricity by 13.87%, and purchased gas by 19.31%, effectively improving the use of gas turbines in the system. The multitime scale scheduling strategy increases the overall operating cost of the IES, modifies the scheduling results, and agrees with real operating scenarios.

show abstract

“…For example, based on hydrogen production by electrolytic water, Fang et al (2022) established an optimal scheduling model of integrated energy microgrids including multiple subsystems of electricity and hydrogen that can be traded with each other. Fang et al (2023) established a two-stage scheduling model of an IES based on green hydrogen considering the electro-hydrogen hybrid replenishment station. However, under the current background of high cost and low energy conversion efficiency of green hydrogen and the prominent price and low carbon emission advantage of blue hydrogen (Zhao et al, 2022), the production of blue hydrogen from natural gas has research value.…”

Section: Introductionmentioning

confidence: 99%

“…In HIES based on green hydrogen, the hydrogen load is completely supplied by green hydrogen. For example, Fang et al (2023) used green hydrogen to supply hydrogen load of a hydrogenation station. Li et al (2017) used green hydrogen to supply the overall hydrogen load of a microgrid system.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy management of an integrated energy system with multiple hydrogen sources

Li,

Zhao,

2023

Front. Energy Res.

View full text Add to dashboard Cite

Hydrogen is considered a promising alternative to fossil fuels in an integrated energy system (IES). In order to reduce the cost of hydrogen energy utilization and the carbon emissions of the IES, this paper proposes a low-carbon dispatching strategy for a coordinated integrated energy system using green hydrogen and blue hydrogen. The strategy takes into account the economic and low-carbon complementarity between hydrogen production by water electrolysis and hydrogen production from natural gas. It introduces the green hydrogen production–storage–use module (GH-PSUM) and the blue hydrogen production–storage–use module (BH-PSUM) to facilitate the refined utilization of different types of hydrogen energy. Additionally, the flexibility in hydrogen load supply is analyzed, and the dynamic response mechanism of the hydrogen load supply structure (DRM-HLSS) is proposed to further reduce operating costs and carbon emissions. Furthermore, a carbon trading mechanism (CTM) is introduced to constrain the carbon emissions of the integrated energy system. By comprehensively considering the constraints of each equipment, the proposed model aims to minimize the total economic cost, which includes wind power operation and curtailment penalty costs, energy purchase costs, blue hydrogen purification costs, and carbon transaction costs. The rationality of the established scheduling model is verified through a comparative analysis of the scheduling results across multiple operating scenarios.

show abstract

Optimal energy management of multiple electricity-hydrogen integrated charging stations

Cited by 49 publications

References 41 publications

A coordinated green hydrogen and blue hydrogen trading strategy between virtual hydrogen plant and electro‐hydrogen energy system

A coordinated green hydrogen and blue hydrogen trading strategy between virtual hydrogen plant and electro‐hydrogen energy system

Strategic optimization operations in the integrated energy system through multitime scale comprehensive demand response

Optimal energy management of an integrated energy system with multiple hydrogen sources

Contact Info

Product

Resources

About