考虑减排对能源需求潜在影响的沿海城市碳减排路径动态优化

doi:10.16183/j.cnki.jsjtu.2022.437

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (5): 600-609.doi: 10.16183/j.cnki.jsjtu.2022.437

• 新型电力系统与综合能源 • 上一篇下一篇

考虑减排对能源需求潜在影响的沿海城市碳减排路径动态优化

肖银璟¹, 张迪², 魏娟²(), 葛睿³, 陈达伟¹, 杨桂兴⁴, 叶志亮¹

1.上海交通大学电子信息与电气工程学院, 上海 200240
2.湖南大学电气与信息工程学院, 长沙 410082
3.国家电力调度控制中心, 北京 100031
4.国家电网新疆电力有限公司,乌鲁木齐 830000

收稿日期:2022-11-01 修回日期:2023-01-03 接受日期:2023-01-04 出版日期:2024-05-28 发布日期:2024-06-17
通讯作者: 魏娟,助理研究员;E-mail:weijuanba@hnu.edu.cn.
作者简介:肖银璟(1998-),硕士生,从事电力系统规划研究.
基金资助:
国家自然科学基金(51977062)

Dynamic Optimization of Carbon Reduction Pathways in Coastal Metropolises Considering Hidden Influence of Decarbonization on Energy Demand

XIAO Yinjing¹, ZHANG Di², WEI Juan²(), GE Rui³, CHEN Dawei¹, YANG Guixing⁴, YE Zhiliang¹

1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
3. National Electric Power Dispatching and Control Center, Beijing 100031, China
4. State Grid Xinjiang Electric Power Co.,Ltd., Urumqi 830000, China

Received:2022-11-01 Revised:2023-01-03 Accepted:2023-01-04 Online:2024-05-28 Published:2024-06-17

1. 600.pdf(278KB)

摘要/Abstract

摘要：

制定合理的沿海城市碳减排规划是实现全球碳目标的关键环节.碳减排将改变城市气候并影响能源需求,这两者都会影响碳减排路径的优化结果.现有扩容规划模型考虑了直接减排贡献并能解决大部分能源系统长期碳减排路径规划问题,但新型电力系统的建设也会通过改变热岛强度等微气候因素间接影响碳排放.考虑碳排放和热排放变化对空调等负荷需求的潜在影响机理,结合扩容规划与碳排放峰值预测,给出沿海城市碳减排路径动态优化方法.以上海浦东地区作为算例,证实所提方法能有效降低碳减排估计成本,并根据仿真结果对沿海城市碳减排提出建议.

关键词: 动态优化, 碳减排, 扩容规划, 微气象, 负荷预测

Abstract:

Setting a reasonable carbon reduction plan in coastal metropolises is the key part to reach the global carbon target. Carbon reduction will change urban climate and influence energy demand, both of which affect the optimization results of carbon reduction pathways. Current generation expansion optimization models consider direct abatement contribution and solve most problems of planning for long-term carbon emission reduction in energy systems. However, the construction of new type power systems also indirectly impacts carbon emissions by changing microclimate factors such as heat island intensity. By combining generation expansion with carbon emission prediction model, the proposed approach in this paper considers the hidden mechanism of carbon and heat emission change on air-conditioning loads and dynamically optimizes the carbon reduction pathways in coastal metropolises. Taking Pudong Area in Shanghai as an example, the estimated cost of carbon reduction is reduced by the proposed approach. Some suggestions for the carbon reduction in coastal metropolises are made according to the simulation results.

Key words: dynamic optimization, carbon emission reduction, power system planning, microclimate, load forecasting

中图分类号:

TM715
X321

肖银璟, 张迪, 魏娟, 葛睿, 陈达伟, 杨桂兴, 叶志亮. 考虑减排对能源需求潜在影响的沿海城市碳减排路径动态优化[J]. 上海交通大学学报, 2024, 58(5): 600-609.

XIAO Yinjing, ZHANG Di, WEI Juan, GE Rui, CHEN Dawei, YANG Guixing, YE Zhiliang. Dynamic Optimization of Carbon Reduction Pathways in Coastal Metropolises Considering Hidden Influence of Decarbonization on Energy Demand[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 600-609.

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参考文献 29

[1]	CIARLI T, SAVONA M. Modelling the evolution of economic structure and climate change: A review[J]. Ecological Economics, 2019, 158: 51-64.
[2]	LIN B Q, JIA Z J. Impacts of carbon price level in carbon emission trading market[J]. Applied Energy, 2019, 239: 157-170. doi: 10.1016/j.apenergy.2019.01.194
[3]	WANG M, YU H, YANG Y K, et al. Unlocking emerging impacts of carbon tax on integrated energy systems through supply and demand co-optimization[J]. Applied Energy, 2021, 302: 117579.
[4]	SHAHID M, ULLAH K, IMRAN K, et al. LEAP simulated economic evaluation of sustainable scenarios to fulfill the regional electricity demand in Pakistan[J]. Sustainable Energy Technologies & Assessments, 2021, 46: 101292.
[5]	胡春璇, 黄杰, 薛禹胜, 等. 能源转型碳减排效益的货币价值化评估[J]. 电力系统自动化, 2020, 44(1): 29-34.
	HU Chunxuan, HUANG Jie, XUE Yusheng, et al. Monetary value evaluation for carbon emission reduction benefit of energy transition[J]. Automation of Electric Power Systems, 2020, 44(1): 29-34.
[6]	PAN X F, GUO S C, XU H T, et al. China’s carbon intensity factor decomposition and carbon emission decoupling analysis[J]. Energy, 2022, 239: 122175.
[7]	The Intergovernmental Panel on Climate Change. Climate change 2022: Mitigation of climate change[R/OL]. (2022-01-31) [2022-10-31]. https://www.ipcc.ch/report/ar6/wg3/.
[8]	KHANNA N Z, ZHOU N, FRIDLEY D, et al. Quantifying the potential impacts of China’s power-sector policies on coal input and CO₂ emissions through 2050: A bottom-up perspective[J]. Utilities Policy, 2016, 41: 128-138.
[9]	LI N, CHEN W Y, ZHANG Q. Development of China TIMES-30P model and its application to model China’s provincial low carbon transformation[J]. Energy Economics, 2020, 92: 104955.
[10]	FRICKO O, HAVLIK P, ROGELJ J, et al. The marker qualification of the shared socieeconomic pathway 2: A middle-of-the-road scenario for the 21st century[J]. Global Environmental Change, 2017, 42: 251-267.
[11]	BARRON-GAFFORD G A, MINOR R L, ALLEN N A, et al. The photovoltaic heat island effect: Larger solar power plants increase local temperatures[J]. Scientific Reports, 2016, 6: 35070.
[12]	LI C B, CAO Y J, ZHANG M, et al. Hidden benefits of electric vehicles for addressing climate change[J]. Scientific Reports, 2015, 5: 9213. doi: 10.1038/srep09213 pmid: 25790439
[13]	LI C B, YANG H Y, SHAHIDEHPOUR M, et al. Optimal planning of islanded integrated energy system with solar-biogas energy supply[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2437-2448.
[14]	MA Z Y, ZHANG S N, HOU F X, et al. Exploring the driving factors and their mitigation potential in global energy-related CO₂ emission[J]. Global Energy Interconnection, 2020, 3(5): 413-422.
[15]	YANG H Y, LI C B, SHAHIDEHPOUR M, et al. Multistage expansion planning of integrated biogas and electric power delivery system considering the regional availability of biomass[J]. IEEE Transactions on Sustainable Energy, 2021, 12(2): 920-930.
[16]	SONG Q J, LIU T L, QI Y. Policy innovation in low carbon pilot cities: Lessons learned from China[J]. Urban Climate, 2021, 39: 100936.
[17]	WANG S J, WANG J Y, LI S J, et al. Socioeconomic driving forces and scenario simulation of CO₂ emissions for a fast-developing region in China[J]. Journal of Cleaner Production, 2019, 216: 217-229.
[18]	THATCHER M J. Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia[J]. Energy, 2007, 32(9): 1647-1659.
[19]	CHRENG K, LEE H S, TUY S. Electricity demand prediction for sustainable development in Cambodia using recurrent neural networks with ERA5 reanalysis climate variables[J]. Energy Reports, 2022, 8: 76-81.
[20]	HERSBACH H, BELL B, BERRISFORD P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730): 1999-2049.
[21]	LI H, WU Z X, YUAN X, et al. The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth[J]. Energy, 2022, 257: 124801.
[22]	DENG J L. Control problems of grey systems[J]. Systems & Control Letters, 1982, 1(5): 288-294.
[23]	IPCC. Global warming of 1.5 ℃[R/OL]. (2018-10-16) [2022-10-31]. https://www.ipcc.ch/sr15/.
[24]	HUPPMANN D, GIDDEN M, FRICKO O, et al. The MESSAGE_ix integrated assessment model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development[J]. Environmental Modelling & Software, 2019, 112: 143-156.
[25]	上海市浦东新区统计局. 上海浦东新区统计年鉴(2010—2021)[DB/OL]. (2022-06-16) [2022-10-31]. https://www.pudong.gov.cn/014004002002/index.html.
	Shanghai Pudong New Area Municipal Bureau of Statistics. Statistical yearbook of Shanghai Pudong new area (2010—2021)[DB/OL]. (2022-06-16) [2022-10-31]. https://www.pudong.gov.cn/014004002002/index.html.
[26]	CHEN J D, GAO M, CHENG S L, et al. County-level CO₂ emissions and sequestration in China during 1997—2017[J]. Scientific Data, 2020, 7(1): 391.
[27]	International Renewable Energy Agency. Renewable power generation costs in 2020[R/OL]. (2021-06-30) [2022-10-31]. https://www.irena.org/publications/2021/Jun/Renewable-Power-Costs-in-2020.
[28]	MACDONALD A E, CLACK C T M, ALEXANDER A, et al. Future cost-competitive electricity systems and their impact on US CO₂ emissions[J]. Nature Climate Change, 2016, 6(5): 526-531.
[29]	ARCILA A, BAKER J D. Evaluating carbon tax policy: A methodological reassessment of a natural experiment[J]. Energy Economics, 2022, 111: 106053.

考虑减排对能源需求潜在影响的沿海城市碳减排路径动态优化

Dynamic Optimization of Carbon Reduction Pathways in Coastal Metropolises Considering Hidden Influence of Decarbonization on Energy Demand

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