碳流管控下的配电网储能优化运行

doi:10.19799/j.cnki.2095-4239.2024.0431

摘要/Abstract

摘要：

随着“碳达峰”与“碳中和”目标和新型电力系统不断推进，碳市场和储能系统是其不可忽略的重要助力，因此分析储能系统和碳交易机制提高配电网系统的经济性和低碳性具有重要意义。本文建立一个考虑碳交易市场的配电网储能优化运行模型，应用电力系统碳排放流理论来分析电力拓扑网络的节点碳势，构建考虑充放电效率的储能系统的内部碳流量和碳势计算模型，并运用考虑个体边际效益的Shapley值法来分摊用户侧的碳排放责任。根据IEEE-33节点配电网的仿真结果可知，用户侧碳排放责任与负荷规模及位置密切相关，Shapley值法能够公平合理地处理碳责任分摊，在确保电网运行可靠性的前提下，配置储能系统与阶梯式碳价交易机制的配电网系统使得区域整体碳排放量减少10.7%、配电网运营商收益提高8.2%、用户碳成本降低5.7%。本文展示了储能系统在高比例可再生能源配电网中优化运行和在减排中的关键作用，为未来电网的低碳化和经济化运营提供了理论和实践依据。

关键词: 储能系统, 低碳电网, 碳排放流, 合作博弈, 碳排放责任分摊

Abstract:

With the advancement of carbon peaking and carbon neutrality goals and the evolution of new power systems, the carbon market and energy storage systems have become essential components in enhancing the economic and low-carbon performance of distribution networks. This study establishes an optimized operation model for distribution networks integrated with energy storage, considering the dynamics of the carbon trading market. The model employs carbon emission flow theory to analyze node carbon potential within the power topology network, constructing a framework for calculating the internal carbon flow and carbon potential of storage systems, taking into account charging and discharging efficiencies. The Shapley value method, which evaluates individual marginal benefits, is utilized to allocate carbon emission responsibilities on the user side. Simulation results from an IEEE-33 node distribution network reveal that user-side carbon emission responsibilities are significantly influenced by load scale and location. The Shapley value method proves effective and equitable in managing carbon responsibility allocation. Under conditions ensuring reliable grid operation, a distribution network system equipped with energy storage and a tiered carbon pricing mechanism can achieve a 10.7% reduction in overall regional carbon emissions, an 8.2% increase in profits for distribution network operators, and a 5.7% reduction in user carbon costs. This research highlights the pivotal role of energy storage systems in optimizing operations and reducing emissions in high-renewable energy distribution networks, offering both theoretical and practical support for the future low-carbon and economically efficient operation of power grids.

Key words: energy storage system, low-carbon grid, carbon emission flow, cooperative games, carbon emissions liability sharing

中图分类号:

TM 732

施婕, 彭英智, 孙伟卿. 碳流管控下的配电网储能优化运行[J]. 储能科学与技术, 2024, 13(11): 3971-3980.

Jie SHI, Yingzhi PENG, Weiqing SUN. Optimized operation of energy storage in distribution networks under carbon flow control[J]. Energy Storage Science and Technology, 2024, 13(11): 3971-3980.

图/表 12

图1

图2

表1

参数取值"

参数	取值	参数	取值
T	24 h	$τ$	1 h
SOC_max	0.95	SOC_min	0.1
SOC₀	0.35	e^ESS	1 MWh
S^ESS	1.2 MVA	$η R D G$	80 USD/MWh
p_Deg	90 USD/MWh	$P m a x E S S$	0.4 MW
$Q m a x E S S$	0.3 Mvar	$η c h$	0.9
$η d i s$	0.9	y	10
r	0.05	$I E S S$	323 USD/kWh
$ρ i R D G$	0

表1

图3

图4

表2

图5

表3

图6

图7

图8

图9

参考文献 14

1	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. DOI: 10.13334/j.0258-8013.pcsee.220467.
	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. DOI: 10.13334/j.0258-8013.pcsee.220467.
2	康重庆, 杜尔顺, 郭鸿业, 等. 新型电力系统的六要素分析[J]. 电网技术, 2023, 47(5): 1741-1750. DOI: 10.13335/j.1000-3673.pst. 2023.0535.
	KANG C Q, DU E S, GUO H Y, et al. Primary exploration of six essential factors in new power system[J]. Power System Technology, 2023, 47(5): 1741-1750. DOI: 10.13335/j.1000-3673.pst.2023.0535.
3	陈海东, 蒙飞, 王庆, 等. 储能系统和新能源发电装机容量对电力系统性能的影响[J]. 储能科学与技术, 2023, 12(2): 477-485. DOI: 10.19799/j.cnki.2095-4239.2022.0439.
	CHEN H D, MENG F, WANG Q, et al. Influence of installed capacity of energy storage system and renewable energy power generation on power system performance[J]. Energy Storage Science and Technology, 2023, 12(2): 477-485. DOI: 10.19799/j.cnki.2095-4239.2022.0439.
4	王运, 蒙飞, 张超, 等. 氨分解制氢储能系统容量对电力系统性能的影响[J]. 储能科学与技术, 2024, 13(2): 589-597. DOI: 10.19799/j.cnki.2095-4239.2023.0362.
	WANG Y, MENG F, ZHANG C, et al. Effect of ammonia decomposition hydrogen production and energy storage system capacity on performance of power system[J]. Energy Storage Science and Technology, 2024, 13(2): 589-597. DOI: 10.19799/j.cnki.2095-4239.2023.0362.
5	刘洋恺, 孙伟卿, 刘唯. 基于AHP和TOPSIS-模糊综合评价的多场景储能选型方法[J]. 储能科学与技术, 2024, 13(2): 691-701. DOI: 10.19799/j.cnki.2095-4239.2023.0624.
	LIU Y K, SUN W Q, LIU W. Multiscenario energy storage selection method based on AHP and TOPSIS-fuzzy comprehensive evaluation[J]. Energy Storage Science and Technology, 2024, 13(2): 691-701. DOI: 10.19799/j.cnki.2095-4239.2023.0624.
6	吴笑民, 郭雨, 郑景文, 等. 多能互补智慧园区能源系统优化运行方法[J]. 高电压技术, 2022, 48(7): 2545-2553. DOI: 10.13336/j.1003-6520.hve.20220129.
	WU X M, GUO Y, ZHENG J W, et al. Optimal operation method for power system of multi-energy complement smart park[J]. High Voltage Engineering, 2022, 48(7): 2545-2553. DOI: 10.13336/j.1003-6520.hve.20220129.
7	侯慧, 刘鹏, 刘志刚, 等. 电热氢多元储能系统优化调度方法[J]. 高电压技术, 2022, 48(2): 536-545. DOI: 10.13336/j.1003-6520.hve.20210009.
	HOU H, LIU P, LIU Z G, et al. Optimal dispatch method for multi-energy storage system of electricity heat hydrogen[J]. High Voltage Engineering, 2022, 48(2): 536-545. DOI: 10.13336/j.1003-6520.hve.20210009.
8	王秀丽, 赵凤江, 刘豹, 等. 计及碳减排收益的光储联合电站参与电力市场投标策略研究[J]. 电网技术, 2022, 46(11): 4208-4216. DOI: 10.13335/j.1000-3673.pst.2022.0300.
	WANG X L, ZHAO F J, LIU B, et al. Bidding strategy of photovoltaic storage union power station considering benefits of carbon emission reduction[J]. Power System Technology, 2022, 46(11): 4208-4216. DOI: 10.13335/j.1000-3673.pst.2022.0300.
9	韩莹, 于三川, 李荦一, 等. 计及阶梯式碳交易的风光氢储微电网低碳经济配置方法[J]. 高电压技术, 2022, 48(7): 2523-2533. DOI: 10.13336/j.1003-6520.hve.20220873.
	HAN Y, YU S C, LI L Y, et al. Low-carbon and economic configuration method for solar hydrogen storage microgrid including stepped carbon trading[J]. High Voltage Engineering, 2022, 48(7): 2523-2533. DOI: 10.13336/j.1003-6520.hve. 20220873.
10	周天睿, 康重庆, 徐乾耀, 等. 电力系统碳排放流分析理论初探[J]. 电力系统自动化, 2012, 36(7): 38-43, 85.
	ZHOU T R, KANG C Q, XU Q Y, et al. Preliminary theoretical investigation on power system carbon emission flow[J]. Automation of Electric Power Systems, 2012, 36(7): 38-43, 85.
11	周天睿, 康重庆. 基于碳排放流的配电系统低碳优化运行方法研究[J]. 全球能源互联网, 2019, 2(3): 241-247. DOI: 10.19705/j.cnki.issn2096-5125.2019.03.005.
	ZHOU T R, KANG C Q. Research on low-carbon oriented optimal operation of distribution networks based on carbon emission flow theory[J]. Journal of Global Energy Interconnection, 2019, 2(3): 241-247. DOI: 10.19705/j.cnki.issn2096-5125.2019.03.005.
12	KANG C Q, ZHOU T R, CHEN Q X, et al. Carbon emission flow from generation to demand: A network-based model[J]. IEEE Transactions on Smart Grid, 2015, 6(5): 2386-2394. DOI: 10.1109/TSG.2015.2388695.
13	周天睿, 康重庆, 徐乾耀, 等. 碳排放流在电力网络中分布的特性与机理分析[J]. 电力系统自动化, 2012, 36(15): 39-44.
	ZHOU T R, KANG C Q, XU Q Y, et al. Analysis on distribution characteristics and mechanisms of carbon emission flow in electric power network[J]. Automation of Electric Power Systems, 2012, 36(15): 39-44.
14	周全, 冯冬涵, 徐长宝, 等. 负荷侧碳排放责任直接分摊方法的比较研究[J]. 电力系统自动化, 2015, 39(17): 153-159. DOI: 10.7500/AEPS20150331003.
	ZHOU Q, FENG D H, XU C B, et al. Methods for allocating carbon obligation in demand side: A comparative study[J]. Automation of Electric Power Systems, 2015, 39(17): 153-159. DOI: 10.7500/AEPS20150331003.

子联盟	碳排放量/(kg CO₂/h)	子联盟	碳排放量/(kg CO₂/h)
{A}	938.462	{B}	243.351
{C}	76.755	{AB}	1125.632
{AC}	1008.527	{BC}	320.563
{ABC}	1230.956	—	—

案例	运营商利润 /USD	用户总成本 /USD	碳排放总量/kg	用户碳成本 /USD
1	14084.76	18180.27	22240	0
2	14990.88	18689.62	20374	407.4809
3	15246.38	18564.52	19863	384.2556

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