GTCC-SOEC制氢储能耦合深度调峰系统建模研究

王一丰; 肖俊峰; 胡孟起; 夏林; 连小龙; 史宗历

doi:10.19666/j.rlfd.202506112

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GTCC-SOEC制氢储能耦合深度调峰系统建模研究

热力发电 2026年55卷第2期页码：158-169

多类型储能辅助调峰调频技术 | 更新时间：2026-08-07

- GTCC-SOEC制氢储能耦合深度调峰系统建模研究
- GTCC-SOEC制氢储能耦合深度调峰系统建模研究
- 作者机构：
  
  西安热工研究院有限公司
- 作者简介：
- 基金信息：
  
  基于数字孪生的重型燃机健康管理关键技术研究（配套）（T1-25-TYK38）~~
- DOI：10.19666/j.rlfd.202506112
  中图分类号： TM611.31
- 纸质出版：2026
- 稿件说明：
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[1]王一丰,肖俊峰,胡孟起,等.GTCC-SOEC制氢储能耦合深度调峰系统建模研究[J].热力发电,2026,55(02):158-169.
[1]王一丰,肖俊峰,胡孟起,等.GTCC-SOEC制氢储能耦合深度调峰系统建模研究[J].热力发电,2026,55(02):158-169. DOI： 10.19666/j.rlfd.202506112.

DOI：10.19666/j.rlfd.202506112.

摘要

构建了燃气-蒸汽联合循环机组(gas turbine combined cycle

GTCC)耦合固体氧化物电解槽(solid oxide electrolysis cell

SOEC)的制氢储能深度调峰(小于30%额定容量)模型，论证了制氢储能消纳可再生能源的系统内电热资源高效匹配的可行性。通过机器学习预测GTCC变工况发电功率，利用余热锅炉模型计算蒸汽参数，并结合SOEC热化学模型计算制氢电耗和热耗。结果表明：SOEC电解电压和制氢量可快速响应输入电能的变化，生成氢气的热惯性温差能稳定在25～27℃；当调峰深度(可再生能源消纳量和额定容量的比值)从50%增至100%时，储能调峰系统整体效率从47.8%升高至55.3%;GTCC-SOEC消纳可再生能源使制氢总能耗从6.7 k J/m

降低到5.8 k J/m

，降幅达13.4%;SOEC制氢热耗约为制氢电耗的80%，耦合制氢系统效率比GTCC单独调峰发电效率仅低3.15%～3.34%；调峰深度每提升1%，制氢量可增加约0.1 t/h，天然气燃烧排放CO

可减少0.64 t/h。考虑储能成本和天气影响后，机组日运行8 h，当风、光大发时段从0增长到1.5 h，掺氢体积比增至30%，而储氢-释氢循环内平均效率从基准值56.7%升高到62.5%；当可再生能源每日上网时长达到2.5 h，储氢-释氢循环内平均效率能达到67.1%；当储能调峰补贴与电价之比小于0.2时，成本产值率随储-网电量之比的增大呈先降后升趋势，当补贴与电价之比大于0.2时，成本产值率随储-网电量之比的增大而持续升高。

Abstract

A model for hydrogen production and storage during deep peak-shaving(less than 30% of rated capacity) was established by coupling a gas turbine combined cycle(GTCC) unit with a solid oxide electrolysis cell(SOEC)

demonstrating the feasibility of efficiently matching electrothermal resources within the system to accommodate renewable energy. Machine learning was used to predict GTCC variable-load power output

heat recovery boiler models were used to calculate steam parameters

and SOEC thermochemical models were applied to determine hydrogen production electricity and heat consumption. Results show that SOEC electrolysis voltage and hydrogen production can quickly respond to changes in input electrical energy

with the thermal inertia temperature difference of hydrogen production stabilizing at 25～27 ℃. When the peak shaving depth(the ratio of accommodated renewable energy to rated capacity) increased from 50% to 100%

the overall efficiency of the energy storage peak shaving system rose from 47.8% to 55.3%. Using GTCC-SOEC to accommodate renewable energy reduced the total energy consumption of hydrogen production from 6.7 kJ/m

to 5.8 kJ/m

with a reduction of 13.4%. SOEC hydrogen production heat consumption is about 80% of the electricity consumption

and the efficiency of the coupled hydrogen production system is only approximately 3.15%～3.34% lower than the efficiency of GTCC standalone peak-shaving power generation. For every 1% increase in peak shaving depth

hydrogen production increases by about 0.1 t/h

and CO

emissions from natural gas combustion decrease by 0.64 t/h. Considering storage costs and weather impacts

when the unit operates for 8 hours per day

as the peak periods for wind and solar generation increase from 0 to 1.5 hours

the hydrogen blending volume ratio increases to 30%

and the average efficiency of the hydrogen storage-release cycle increases from the baseline 56.7% to 62.5%; When the daily online duration of renewable energy reaches 2.5 hours

the average efficiency within the cycle can reach 67.1%. When the ratio of peak-shaving subsidy to electricity price is less than 0.2

the cost-to-output ratio initially decreases and then increases with the ratio of stored to grid electricity. When the subsidy-to-electricity price ratio is greater than 0.2

the cost-to-output ratio continuously increases with the stored-to-grid electricity ratio.

关键词

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