Research on Unit Commitment Problem Taking Into Account Carbon Trading Under Wind Power Integration

Yawei KONG; Yakun CHEN; Haoyong ZHANG; Yuhua GAO; Jie GUO; Xinyu CHEN

doi:10.16513/j.2096-2185.DE.2409410

TSINGHUA JOURNALS HOME

Source Journal for Chinese Scientific and Technical Papers and Citations

RCCSE Chinese Core Academic Journals

Classification Catalogue of High Quality Scientific Journals in the field of Energy and Power

Classification Catalogue of High Quality Scientific Journals in Coal Field

PDF(1108 KB)

Distributed Energy ›› 2024, Vol. 9 ›› Issue (4) : 86-94. DOI: 10.16513/j.2096-2185.DE.2409410

Application Technology

Research on Unit Commitment Problem Taking Into Account Carbon Trading Under Wind Power Integration

Author information +

History +

Abstract

In order to solve the problem of environmental pollution, particle swarm optimization algorithm is proposed to solve the unit commitment problem taking into account carbon trading under wind power integration. In the aspect of model, For the CO₂ produced by thermal power units, the carbon trading mechanism is introduced, and the form of limited emission and balance trading is adopted. For other pollutants such as SO₂, nitrogen oxides (NO_x), total suspended particulates (TSP), the emission cost is calculated as a penalty cost by establishing a functional relationship with the output of thermal power units. In terms of algorithm, this paper presents an improved binary particle swarm optimization (BPSO) algorithm, which turns the unit commitment problem with pollution cost into a two-layer optimization problem. The improved BPSO is used to determine the start-stop state of the unit in the outer layer, and the improved λ iterative algorithm is used to calculate the unit commitment and wind power output in the inner layer. In terms of calculation examples, the effectiveness of the algorithm is demonstrated by comparing it with other algorithms, the impact of changing carbon emission quotas and carbon trading costs on the unit commitment results is analyzed, and the results that balance environmental protection and economy are obtained.

Key words

wind power integration / carbon trading / unit commitment / particle swarm optimization algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Yawei KONG , Yakun CHEN , Haoyong ZHANG , et al . Research on Unit Commitment Problem Taking Into Account Carbon Trading Under Wind Power Integration[J]. Distributed Energy Resources. 2024, 9(4): 86-94 https://doi.org/10.16513/j.2096-2185.DE.2409410

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	康重庆，杜尔顺，李姚旺，等. 新型电力系统的“碳视角”：科学问题与研究框架[J]. 电网技术，2022, 46(3): 821-833. KANG Chongqing, DU Ershun, LI Yaowang, et al. Key scientific problems and research framework for carbon perspective research of new power systems[J]. Power System Technology, 2022, 46(3): 821-833. Cited in this article [1]

[2]

冯伟忠，李励. “双碳”目标下煤电机组低碳、零碳和负碳化转型发展路径研究与实践[J]. 发电技术，2022, 43(3): 452-461.

FENG

Weizhong

, LI

. Research and practice on development path of low-carbon, zero-carbon and negative carbon transformation of coal-fired power units under “Double Carbon” targets[J]. Power Generation Technology, 2022, 43(3): 452-461.

Cited in this article [1]

[3]

朱静慧，高佳，余欣梅，等. 碳中和背景下我国生态碳汇发展形势及建议[J]. 内蒙古电力技术，2022, 40(6): 1-8.

ZHU

Jinghui

, GAO

Jia

, YU

Xinmei

, et al. Developmengt situation and suggestions for ecological carbon sink in China under background of carbon neutralization[J]. Inner Mongolia Electric Power, 2022, 40(6): 1-8.

Cited in this article [1]

[4]	贺旭辉，王灿，李欣然，等. 计及CLHG-SOFC碳捕集的多能源系统低碳优化调度[J]. 智慧电力，2023, 51(5): 57-64. HE Xuhui, WANG Can, LI Xinran, et al. Low carbon optimal scheduling of multi-energy system considering CLHG-SOFC carbon capture[J]. Smart Power, 2023, 51(5): 57-64. Cited in this article [1]

[5]	郝伟韬，蔡国田，卢俊曈，等. 源网荷储互动减碳研究综述[J]. 广东电力，2023, 36(11): 64-74. HAO Weitao, CAI Guotian, LU Juntong, et al. Review of source-grid-load-storage interactive carbon reduction research[J]. Guangdong Electric Power, 2023, 36(11): 64-74. Cited in this article [1]

[6]

车泉辉，吴耀武，祝志刚，等. 基于碳交易的含大规模光伏发电系统复合储能优化调度[J]. 电力系统自动化，2019, 43(3): 76-82.

CHE

Quanhui

, WU

Yaowu

, ZHU

Zhigang

, et al. Carbon trading based optimimal scheduling of hybrid energy storage system in power systems with large-scale photovoltaic power generation[J]. Automation of Electric Power Systems, 2019, 43(3): 76-82.

Cited in this article [1]

[7]

张晓辉，闫柯柯，卢志刚，等. 基于场景概率的含风电系统多目标低碳经济调度[J]. 电网技术，2014, 38(7): 1835-1841.

ZHANG

Xiaohui

, YAN

Keke

, LU

Zhigang

, et al. Scenario probability based multi-objective optimized low-carbon economic dispatching for power grid integrated with wind farms[J]. Power System Technology, 2014, 38(7): 1835-1841.

Cited in this article [1]

[8]

张程飞，袁越，张新松，等. 考虑碳排放配额影响的含风电系统日前调度计划模型[J]. 电网技术，2014, 38(8): 2114-2120.

ZHANG

Chengfei

, YUAN

Yue

, ZHANG

Xinsong

, et al. Day-ahead dispatching scheduling for power grid integrated with wind farm considering influence of carbon emission quota[J]. Power System Technology, 2014, 38(8): 2114-2120.

Cited in this article [1]

[9]

陈锦鹏，胡志坚，陈嘉滨，等. 考虑阶梯式碳交易与供需灵活双响应的综合能源系统优化调度[J]. 高电压技术，2021, 47(9): 3094-3106.

CHEN

Jinpeng

, HU

Zhijian

, ZHANG

Jiabin

, et al. Optimal dispatch of integrated energy system considering ladder-type carbon trading and flexible double response of supply and demand[J]. High Voltage Engineering, 2021, 47(9): 3094-3106.

Cited in this article [1]

[10]

米阳，赵海辉，付起欣，等. 考虑风光不确定与碳交易的区域综合能源系统双层博弈优化运行[J]. 电网技术，2023, 47(6): 2174-2184.

Yang

, ZHAO

Haihui

, FU

Qixin

, et al. Two-level game optimal operation of regional integrated energy system considering wind and solar uncertainty and carbon trading[J]. Power System Technology, 2023, 47(6): 2174-2184.

Cited in this article [1]

[11]

卫志农，张思德，孙国强，等. 基于碳交易机制的电-气互联综合能源系统低碳经济运行[J]. 电力系统自动化，2016, 40(15): 9-16.

WEI

Zhinong

, ZHANG

Side

, SUN

Guoqiang

, et al. Carbon trading based low-carbon economic operation for integrated electricity and natural gas energy system[J]. Automation of Electric Power Systems, 2016, 40(15): 9-16.

Cited in this article [1]

[12]	张沈习，程浩忠，邢海军，等. 配电网中考虑不确定性的分布式电源规划研究综述[J]. 电力自动化设备，2016, 36(8): 1-9. ZHANG Shenxi, CHENG Haozhong, XING Haijun, et al. Review of DG planning considering uncertainties for distribution network[J]. Electric Power Automation Equipment, 2016, 36(8): 1-9. Cited in this article [1]

[13]	陈皇森，石立宝. 考虑多风电场出力预测误差分布特征的随机机组组合[J]. 电网技术，2023, 47(12): 5026-5035. CHEN Huangsen, SHI Libao. Stochastic unit commitment considering output forecast error distribution characteristics of multiple wind farms[J]. Power System Technology, 2023, 47(12): 5026-5035. Cited in this article [1]

[14]

张旭，王洪涛. 高比例可再生能源电力系统的输配协同优化调度方法[J]. 电力系统自动化，2019, 43(3): 67-75, 115.

ZHANG

, WANG

Hongtao

. Optimal dispatch method of transmission and distribution coordination for power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2019, 43(3): 67-75, 115.

Cited in this article [1]

[15]	秦博宇，周星月，丁涛，等. 全球碳市场发展现状综述及中国碳市场建设展望[J]. 电力系统自动化，2022, 46(21): 186-199. QIN Boyu, ZHOU Xingyue, DING Tao. Review on development of global carbon market and prospect of China's carbon market construction[J]. Automation of Electric Power Systems, 2022, 46(21): 186-199. Cited in this article [1]

[16]	刘思东. 低碳环境下发电优化调度模型与方法研究[D]. 南宁：广西大学，2014. LIU Sidong. Research on models and methods of generation schedluing optimization incorporating low-carbon factor[D]. Nanning: Guangxi University, 2014. Cited in this article [1]

[17]	LUJANO-ROJAS J M, OSÓRIO G J, CATALÃO J P S. New probabilistic method for solving economic dispatch and unit commitment problems incorporating uncertainty due to renewable energy integration[J]. International Journal of Electrical Power & Energy Systems, 2016, 78: 61-71. Cited in this article [1]

[18]	WU Z, ZENG P, ZHANG X P, et al. A solution to the chance-constrained two-stage stochastic program for unit commitment with wind energy integration[J]. IEEE Transactions on Power Systems, 2016, 31(6): 4185-4196. Cited in this article [1]

[19]	SABER A Y, VENAYAGAMOORTHY G K. Plug-in vehicles and renewable energy sources for cost and emission reductions[J]. IEEE Transactions on Industrial electronics, 2010, 58(4): 1229-1238. Cited in this article [1]

[20]	吴小珊. 含风电场的电力系统机组组合问题研究[D]. 武汉：华中科技大学，2013. WU Xiaoshan. Power system unit commitment problem with wind farms[D]. Wuhan: Huazhong University of Science and Technology, 2013. Cited in this article [1]

[21]	JUSTE K A, KITA H, TANAKA E, et al. An evolutionary programming solution to the unit commitment problem[J]. IEEE Transactions on Power systems, 1999, 14(4): 1452-1459. Cited in this article [1]

[22]	SENJYU T, SHIMABUKURO K, UEZATO K, et al. A fast technique for unit commitment problem by extended priority list[J]. IEEE Transactions on Power Systems, 2003, 18(2): 882-888. Cited in this article [1]

[23]	MENG K, YANG H, DONG Z Y, et al. Flexible operational planning framework considering multiple wind energy forecasting service providers[J]. IEEE Transactions on Sustainable Energy, 2015, 7(2): 708-717. Cited in this article [1]

[24]	KIRAN B D H, KUMARI M S. Demand response and pumped hydro storage scheduling for balancing wind power uncertainties: A probabilistic unit commitment approach[J]. International Journal of Electrical Power & Energy Systems, 2016, 81: 114-122. Cited in this article [1]