Curtailed power forecasting based on GRU and operation optimization of electric-hydrogen hybrid energy storage system

doi:10.19799/j.cnki.2095-4239.2023.0828

Abstract

Abstract:

The high penetration of renewable energy sources such as wind and solar will enhance the randomness and volatility of power output, leading to frequent occurrences of wind/solar curtailment. Hydrogen production from wind/solar curtailment is an effective means to handle the deep consumption of renewable energy. In this study, we propose a gated recurrent unit algorithm based on Adam optimization for predicting wind/solar curtailment using Latin hypercube and synchronous backpropagation reduction algorithms to generate typical wind/light generation uncertainty scenarios. A double-layer objective function is constructed to minimize system construction and operating costs. The first layer determines the capacity configuration of the alkaline electrolysis cell (AWE) and battery energy storage system (BESS), and the second layer ensures the system's optimal operation in the generated scenario. In addition, a penalty term is introduced to maximize the abandoned power use. Considering the wind/solar curtailment data from a certain region in northwest China as an example, different structures of neural network algorithms were used to predict the amount of wind/light curtailment. The accuracy of the proposed algorithm was verified by comparing the root mean square errors of the various algorithms. Finally, an optimization configuration analysis was performed on three energy storage schemes. The results showed that using only the BESS system resulted in negative annual profits and the highest annual investment cost. Using the BESS-AWE hybrid energy storage system increased the annual investment cost by 15.66% compared with using only the AWE system, but the annual profit increased by 255%, and the abandoned power usage rate was 92%, effectively improving the system's economy.

Key words: renewable energy curtailment, gated recurrent unit, electric hydrogen hybrid energy storage, two-stage objective function

CLC Number:

TM 73

Ting HE, Junqiang QIAO, Guodong WU. Curtailed power forecasting based on GRU and operation optimization of electric-hydrogen hybrid energy storage system[J]. Energy Storage Science and Technology, 2024, 13(5): 1731-1740.

Figures/Tables 12

Fig. 1

Fig. 2

Table 1

Table 2

Capacity optimization configuration related parameters for AWE and BESS"

参数	数值	参数	数值
C_AWE/(元/MW)	1788000	π_p/(元/MWh)	6000
C_BESS/(元/MW)	2280000	π_H2/(元/kg)	27
π $B E S S F O C$ /(元/MWh)	60000	π_dis/(元/MWh)	540
π $B E S S O & M$ /(元/MWh)	180	R_BESS/%	80
LHV_H2/(MWh/kg)	0.033	NC_BESS/周期	3500

Table 2

Table 3

Fig. 3

Fig. 4

Fig. 5

Table 4

Fig. 6

Fig. 7

Fig. 8

References 22

1	舒印彪, 张丽英, 张运洲, 等. 我国电力碳达峰、碳中和路径研究[J]. 中国工程科学, 2021, 23(6): 1-14.
	SHU Y B, ZHANG L Y, ZHANG Y Z, et al. Carbon peak and carbon neutrality path for China's power industry[J]. Strategic Study of CAE, 2021, 23(6): 1-14.
2	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
	ZHANG Z G, KANG C Q. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
3	HEPTONSTALL P J, GROSS R J K. A systematic review of the costs and impacts of integrating variable renewables into power grids[J]. Nature Energy, 2021, 6: 72-83.
4	霍现旭, 王靖, 蒋菱, 等. 氢储能系统关键技术及应用综述[J]. 储能科学与技术, 2016, 5(2): 197-203.
	HUO X X, WANG J, JIANG L, et al. Review on key technologies and applications of hydrogen energy storage system[J]. Energy Storage Science and Technology, 2016, 5(2): 197-203.
5	刘坚. 适应可再生能源消纳的储能技术经济性分析[J]. 储能科学与技术, 2022, 11(1): 397-404.
	LIU J. Economic assessment for energy storage technologies adaptive to variable renewable energy[J]. Energy Storage Science and Technology, 2022, 11(1): 397-404.
6	邓浩, 陈洁, 腾扬新, 等. 风氢耦合系统能量管理策略研究[J]. 太阳能学报, 2021, 42(1): 256-263.
	DENG H, CHEN J, TENG Y X, et al. Energy management strategy of wind power coupled with hydrogen system[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 256-263.
7	孔令国, 蔡国伟, 李龙飞, 等. 风光氢综合能源系统在线能量调控策略与实验平台搭建[J]. 电工技术学报, 2018, 33(14): 3371-3384.
	KONG L G, CAI G W, LI L F, et al. Online energy control strategy and experimental platform of integrated energy system of wind, photovoltaic and hydrogen[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3371-3384.
8	GONZÁLEZ A, MCKEOGH E, GALLACHÓIR B Ó. The role of hydrogen in high wind energy penetration electricity systems: The Irish case[J]. Renewable Energy, 2004, 29(4): 471-489.
9	LIU J H, XU Z B, WU J, et al. Optimal planning of distributed hydrogen-based multi-energy systems[J]. Applied Energy, 2021, 281: 116107.
10	倪鸾, 王育飞, 薛花, 等. 计及多时间尺度不确定性的电-氢一体化储能站随机-鲁棒混合规划[J]. 储能科学与技术, 2023, 12(3): 846-856.
	NI L, WANG Y F, XUE H, et al. Hybrid stochastic-robust planning of an electricity-hydrogen integrated energy storage station considering multi-timescale uncertainty[J]. Energy Storage Science and Technology, 2023, 12(3): 846-856.
11	袁铁江, 郭建华, 杨紫娟, 等. 平抑风电波动的电-氢混合储能容量优化配置[J]. 中国电机工程学报, 2024, 44(4): 1397-1406.
	YUAN T J, GUO J H, YANG Z J, et al. Optimal allocation of power electric-hydrogen hybrid energy storage of stabilizing wind power fluctuation[J]. Proceedings of the CSEE, 2024, 44(4): 1397-1406.
12	郭梦婕, 严正, 周云, 等. 含风电制氢装置的综合能源系统优化运行[J]. 中国电力, 2020, 53(1): 115-123, 161.
	GUO M J, YAN Z, ZHOU Y, et al. Optimized operation design of integrated energy system with wind power hydrogen production[J]. Electric Power, 2020, 53(1): 115-123, 161.
13	李奇, 赵淑丹, 蒲雨辰, 等. 考虑电氢耦合的混合储能微电网容量配置优化[J]. 电工技术学报, 2021, 36(3): 486-495.
	LI Q, ZHAO S D, PU Y C, et al. Capacity optimization of hybrid energy storage microgrid considering electricity-hydrogen coupling[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 486-495.
14	LI J R, LIN J, ZHANG H C, et al. Optimal investment of electrolyzers and seasonal storages in hydrogen supply chains incorporated with renewable electric networks[J]. IEEE Transactions on Sustainable Energy, 2020, 11(3): 1773-1784.
15	PAN G S, GU W, QIU H F, et al. Bi-level mixed-integer planning for electricity-hydrogen integrated energy system considering levelized cost of hydrogen[J]. Applied Energy, 2020, 270: 115176.
16	滕云, 王泽镝, 金红洋, 等. 用于电网调节能力提升的电热氢多源协调储能系统模型[J]. 中国电机工程学报, 2019, 39(24): 7209-7217, 7494.
	TENG Y, WANG Z D, JIN H Y, et al. A model and coordinated optimization for the multi-energy storage system of electricity heat hydrogen to regulation enhancement of power grid[J]. Proceedings of the CSEE, 2019, 39(24): 7209-7217, 7494.
17	GAO W, WAI R J. A novel fault identification method for photovoltaic array via convolutional neural network and residual gated recurrent unit[J]. IEEE Access, 2020, 8: 159493-159510.
18	高翱, 李国玉, 撖奥洋, 等. 基于Adam算法优化GRU神经网络的短期负荷预测模型[J]. 电子设计工程, 2022, 30(9): 180-183, 188.
	GAO A, LI G Y, HAN A Y, et al. Short-term load forecasting model based on Adam algorithm to optimize GRU neural network[J]. Electronic Design Engineering, 2022, 30(9): 180-183, 188.
19	KUCKSHINRICHS W, KETELAER T, KOJ J C. Economic analysis of improved alkaline water electrolysis[J]. Frontiers in Energy Research, 2017, 5(1): 1-21.
20	MONGIRD K, VISWANATHAN V, BALDUCCI P, et al. Energy storage technology and cost characterization report[R]. U.S.DEPARTMENT of ENERGY, 2019.
21	BERTUCCIOLI L, CHAN A, ELEMENT E, et al. Study on development of water electrolysis in the EU-Final report[R]. Fuel Cells and Hydrogen Joint Undertaking, 2014.
22	NIAZ H, BRIGLJEVIc B, PARK Y B, et al. Comprehensive feasibility assessment of combined heat, hydrogen, and power production via hydrothermal liquefaction of Saccharina japonica[J]. ACS Sustain. Chem. Eng, 2020, 8(22): 8305-8317.

参数	风能	太阳能
总弃电量/MWh	94967	1543464
最大弃电量/MWh	1799	6275
弃电小时数/h	1584	4044
每年弃电小时数占比/%	18	46
标准差	51	521

算法	优化器	RMSE/MW
LSTM	ADAM	219.83
GRU	ADAM	198.64
BILSTM	ADAM	287.04

参数	方案1	方案2	方案3
年综合成本/亿元	44.8	16.34	18.9
基建成本/亿元	16.8	9.1	6.58
固定运维成本/亿元	2.69	0.29	0.27
惩罚成本/亿元	24.64	3.39	9.52
产生的电量/MWh	1042032	—	—
产生的氢气/(t/a)	—	32304	30600
利润/亿元	-12.92	0.78	2.77
AWE规模/MW	—	3 462	2500
BESS规模/MWh	5000	—	2500
弃电利用率/%	79%	97%	92%

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