Electric power and energy rebalancing method for new power systems at receiving ends of industrial parks

doi:10.3969/j.issn.2097-0706.2024.02.009

Abstract

Abstract:

Electricity consumption accounts for a high proportion in the total energy consumption of domestic industrial parks. In response to green and low-carbon development initiative of industrial parks, an electric power and energy rebalancing method for new power systems at the receiving ends of industrial parks is proposed. Firstly, electric power and energy demands are forecasted, and their initial balances are struck. Then, taking the coordination of source-grid-load-storage ends of a distribution network， and the energy conservation, electricity substitution, various distributed power sources, energy storage and demand response capability of industrial parks into consideration, electric power and energy are rebalanced. The proportions of electricity purchased from external power grid and electricity generated in the industrial park are annually adjusted according to the rebalancing results. Then, an evaluation system with quantitative indicators including carbon reduction effect and the size of power systems is established， which can evaluate the reduction effect on substation and distribution capacities and carbon emissions brought by electric power and energy rebalance. Finally, the proposed method is validated by a case of a new power system in an industrial park in southern China. The results indicate that: by 2030, the designed substation and distribution capacity of this park can be reduced by 10.1%, and the comprehensive carbon emission factor of electricity consumption can be reduced from 0.60 kg/（kW·h） to 0.54 kg/（kW·h）; by 2060, the designed substation and distribution capacity can be reduced by 9.57%, and electricity substitution will reduce the carbon emissions by 58 500 t per year. The rebalance method provides a strong theoretical support for the planning of new power systems at receiving ends.

Key words: new power system, source-grid-load-storage, electric power and energy rebalance, load forecasting, electricity substitution, energy storage, low carbon emissions, industrial park

CLC Number:

TK018

KONG Huichao, WANG Wenzhong, LEI Yi, PENG Jing, LI Haibo. Electric power and energy rebalancing method for new power systems at receiving ends of industrial parks[J]. Integrated Intelligent Energy, 2024, 46(2): 68-74.

Figures/Tables 6

Fig.1

Table 1

Fig.2

Fig.3

Table 2

Table 3

References 29

[1]	代心芸, 陈皓勇, 肖东亮, 等. 电力市场环境下工业需求响应技术的应用与研究综述[J]. 电网技术, 2022, 46(11):4169-4185.
	DAI Xinyun, CHEN Haoyong, XIAO Dongliang, et al. Review of applications and researches of industrial demand response technology under electricity market environment[J]. Power System Technology, 2022, 46(11):4169-4185.
[2]	COSSI A M, SILVA L WDA, LA Z R, et al. Primary power distribution systems planning taking into account reliability,operation and expansion costs[J]. IET Generation,Transmission & Distribution, 2012, 6(3):270-274.
[3]	肖先勇, 郑子萱. “双碳”目标下新能源为主体的新型电力系统:贡献、关键技术与挑战[J]. 工程科学与技术, 2022, 54(1):47-59.
	XIAO Xianyong, ZHENG Zixuan. New power systems dominated by renewable energy towards the goal of emission peak & carbon neutrality:Contribution, key techniques, and challenges[J]. Advanced Engineering Sciences, 2022, 54(1):47-59.
[4]	ASENSIO M, PILAR M D Q, MUNOZ-DELGADO G, et al. Joint distribution network and renewable energy expansion planning considering demand response and energy storage—Part Ⅰ: Stochastic programming model[J]. IEEE Transactions on Smart Grid, 2018, 9(2):655-666. doi: 10.1109/TSG.2016.2560339
[5]	DYSON M E, BORGESON S D, TABONE M D, et al. Using smart meter data to estimate demand response potential with application to solar energy integration[J]. Energy Policy, 2014, 73:607-619. doi: 10.1016/j.enpol.2014.05.053
[6]	曾鸣, 杨雍琦, 刘敦楠, 等. 能源互联网“源-网-荷-储”协调优化运营模式及关键技术[J]. 电网技术, 2016, 40(1):114-124.
	ZENG Ming, YANG Yongqi, LIU Dunnan, et al. "Generation-grid-load-storage" coordinative optimal operation mode of Energy Internet and key technologies[J]. Power System Technology, 2016, 40(1):114-124.
[7]	HONG T, FAN S. Probabilistic electric load forecasting: A tutorial review[J]. International Journal of Forecasting, 2016, 32(3):914-938. doi: 10.1016/j.ijforecast.2015.11.011
[8]	ACOSTA J S, JUAN C L, RIDER M J. Optimal multi-scenario, multi-objective allocation of fault indicators in electrical distribution systems using a mixed-integer linear programming model[J]. IEEE Transactions on Smart Grid, 2019, 10(4):4508-4519. doi: 10.1109/TSG.5165411
[9]	刘文彬, 刘永刚, 文祥宇, 等. 基于需求响应的居民侧柔性负荷多目标优化研究[J]. 山东电力技术, 2022, 49(8):42-49.
	LIU Wenbin, LIU Yonggang, WEN Xiangyu, et al. Research on multi-objective optimization of residential flexible loads based on demand response[J]. Shandong Electric Power, 2022, 49(8):42-49.
[10]	刘自发, 谭雅之, 李炯, 等. 区域综合能源系统规划关键问题研究综述[J]. 综合智慧能源, 2022, 44(6):12-24. doi: 10.3969/j.issn.2097-0706.2022.06.002
	LIU Zifa, TAN Yazhi, LI Jiong, et al. Review on key points in the planning for a district-level integrated energy system[J]. Integrated Intelligent Energy, 2022, 44(6):12-24. doi: 10.3969/j.issn.2097-0706.2022.06.002
[11]	BATASBJELIĆ I R, ŠKOKLJEV I A, PUKŠEC T, et al. Integrating the flexibility of the average Serbian consumer as a virtual storage option into the planning of energy systems[J]. Thermal Science, 2014, 18(3):743-754. doi: 10.2298/TSCI1403743B
[12]	陈涛, 邢金晶, 刘闯, 等. 基于改进PSO-DE融合算法的风电场储能容量优化配置[J]. 山东电力技术, 2023, 50(1):8-13.
	CHEN Tao, XING Jinjing, LIU Chuang, et al. Optimal allocation of wind farm energy storage capacity based on improved PSO-DE fusion algorithm[J]. Shandong Electric Power, 2023, 50(1):8-13.
[13]	侯鲁洋, 葛磊蛟, 王飚, 等. 面向新型产消者的综合能源系统和电力市场研究[J]. 综合智慧能源, 2022, 44(12):40-48. doi: 10.3969/j.issn.2097-0706.2022.12.006
	HOU Luyang, GE Leijiao, WANG Biao, et al. Research on the integrated energy system and the electricity market towards new presumes[J]. Integrated Intelligent Energy, 2022, 44(12):40-48. doi: 10.3969/j.issn.2097-0706.2022.12.006
[14]	刘自发, 于普洋, 高建宇, 等. 考虑电能替代效果的综合能源系统效益评价方法研究[J]. 供用电, 2022, 39(5):3-12.
	LIU Zifa, YU Puyang, GAO Jianyu, et al. Research on the evaluation method of integrated energy system efficiency considering the effect of electric energy substitution[J]. Distribution & Utilization, 2022, 39(5):3-12
[15]	屈博, 刘畅, 卜凡鹏, 等. 能源结构转型背景下的电能替代发展路径探索[J]. 电力需求侧管理, 2022, 24(6):1-5.
	QU Bo, LIU Chang, BU Fanpeng, et al. Exploration of development path of electric energy substitution under energy structure transformation[J]. Power Demand Side Management, 2022, 24(6):1-5.
[16]	温港成, 石鑫, 张怡, 等. 考虑设备变工况特性的园区综合能源系统两阶段规划优化方法研究[J]. 综合智慧能源, 2022, 44(10):1-11. doi: 10.3969/j.issn.2097-0706.2022.10.001
	WEN Gangcheng, SHI Xin, ZHANG Yi, et al. Research on two-stage planning optimization approach for community integrated energy systems considering off-design conditions[J]. Integrated Intelligent Energy, 2022, 44(10):1-11. doi: 10.3969/j.issn.2097-0706.2022.10.001
[17]	李明. 适应能源电力新形势的“供电+能效服务”模式创新发展研究[J]. 电力需求侧管理, 2022, 24(1):1-6.
	LI Ming. Study on innovation and development of "power+energy efficiency service" mode adapting to new energy and power situation[J]. Power Demand Side Management, 2022, 24(1):1-6.
[18]	VIRAL R, KHATOD D K. Optimal planning of distributed generation systems in distribution system: A review[J]. Renewable & Sustainable Energy Reviews, 2012, 16(7):5146-5165.
[19]	TANCREDO BORGES C L, MARTINS V F. Multistage expansion planning for active distribution networks under demand and distributed generation uncertainties[J]. International Journal of Electrical Power & Energy Systems, 2012, 36(1):107-116. doi: 10.1016/j.ijepes.2011.10.031
[20]	魏国民, 何俊, 熊凤龙. 面向新型电力系统的源网荷储一体化电力平衡方法研究[J]. 电工技术, 2022(18): 34-38.
	WEI Guomin, HE Jun, XIONG Fenglong. Researches on integrated power balance method of source-network-load-storage for new power system[J]. Electrical Technology, 2022(18): 34-38.
[21]	韩凝晖, 周颖, 石坤, 等. 面向新型电力系统电量平衡的可调节负荷互动潜力分析[J]. 电力需求侧管理, 2022, 24(6):70-76.
	HAN Ninghui, ZHOU Ying, SHI Kun, et al. Adjustable load interaction potential oriented to power balance of new power system[J]. Power Demand Side Management, 2022, 24(6):70-76.
[22]	霍佳丽, 叶辛頔, 何卫斌, 等. 考虑广义负荷的配电网弹性电力电量平衡策略研究[J]. 浙江电力, 2022, 41(1):19-25.
	HUO Jiali, YE Xindi, HE Weibin, et al. Study of an elastic electric power and energy balance strategy for distribution networks considering generalized loads[J]. Zhejiang Electric Power, 2022, 41(1):19-25.
[23]	STEVANONI C, DE GRÈVE Z, VALLÉE F, et al. Long-term planning of connected industrial microgrids: A game theoretical approach including daily peer-to-microgrid exchanges[J]. IEEE Transactions on Smart Grid, 2019, 10(2):2245-2256. doi: 10.1109/TSG.5165411
[24]	张运洲, 张宁, 代红才, 等. 中国电力系统低碳发展分析模型构建与转型路径比较[J]. 中国电力, 2021, 54(3):1-11.
	ZHANG Yunzhou, ZHANG Ning, DAI Hongcai, et al. Model construction and pathways of low-carbon transition of China's power system[J]. Electric Power, 2021, 54(3):1-11.
[25]	张子阳, 张聂鹏, 王满商, 等. 面向可再生能源高比例消纳的综合能源系统优化规划模型研究[J]. 可再生能源, 2020, 38(8):1085-1091.
	ZHANG Ziyang, ZHANG Niepeng, WANG Manshang, et al. Research on optimal planning model of integrated energy system for high proportion of renewable energy utilization[J]. Renewable Energy Resources, 2020, 38(8):1085-1091.
[26]	WEI W, LIU F, MEI S W. Nash bargain and complementarily approach based environmental/economic dispatch[J]. IEEE Transactions on Power Systems, 2015, 30(3):1548-1549. doi: 10.1109/TPWRS.2014.2346928
[27]	SINGH R K, MURTY H R, GUPTA S K, et al. An overview of sustainability assessment methodologies[J]. Ecological Indicators, 2009, 9(2):189-212. doi: 10.1016/j.ecolind.2008.05.011
[28]	WEI W, GAO H, XU T, et al. Active distribution network sustainability assessment: A system dynamic approach[C]//2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2017.
[29]	CALIF P A. Research into load forecasting and distribution[M]. Palo Alto: Electric Power Research Institute, 1979.

年份	电量/(TW·h)	最大负荷/MW	所需变配电容量/ （MV·A）	220 kV变电站提供的10 kV供电容量/（MV·A）	所需110 kV变电容量/（MV·A）
2020	3.532	542.1	1 192.7	155	1 037.7
2021	4.062	614.5	1 351.9	155	1 196.9
2022	4.614	705.5	1 552.2	155	1 397.2
2023	5.177	831.0	1 828.2	155	1 673.2
2024	5.736	927.4	2 040.4	155	1 885.4
2025	6.195	1 015.6	2 234.3	155	2 079.3
2026	6.629	1 094.2	2 407.3	258	2 149.3
2027	7.046	1 165.7	2 564.5	258	2 306.5
2028	7.441	1 234.0	2 714.8	258	2 456.8
2029	7.806	1 297.7	2 854.9	258	2 596.9
2030	8.079	1 346.5	2 962.2	258	2 704.2
2035	9.160	1 526.6	3 358.5	360	2 998.5
2040	10.063	1 677.2	3 689.9	360	3 329.9
2045	10.712	1 785.3	3 927.7	360	3 567.7
2050	11.243	1 873.9	4 122.6	480	3 642.6
2060	12.263	2 043.9	4 496.5	480	4 016.5

年份	光伏		燃气
年份	新增电量/ （GW·h）	新增装机/MW	新增电量/ （GW·h）	新增装机/MW
2021	20	20	0	0
2022	40	40	0	0
2023	60	60	0	0
2024	80	80	100	100
2025	100	100	300	100
2026	120	120	500	100
2027	140	140	700	200
2028	160	160	1 200	200
2029	180	180	1 500	300
2030	200	200	2 000	300
2035	220	220	2 500	400
2040	230	230	2 500	400
2045	240	240	2 500	400
2050	260	260	2 500	400
2055	270	270	2 500	400
2060	280	280	2 500	400

年份	所需电量/（TW·h）	起始最大负荷预测/MW	110 kV及以下配电网						220 kV及以上输电网			最终网供电量/（TW·h）
年份	所需电量/（TW·h）	起始最大负荷预测/MW	新增光伏电量/（GW·h）	新增光伏装机/MW	柔性负荷与储能削峰/MW	所需变电容量/（MV·A）	220 kV变电站提供的10 kV供电容量/（MV·A）	所需110 kV变电容量/（MV·A）	新增燃气电量/（TW·h）	新增燃气装机/MW	输变电负荷缩减幅度/MW	最终网供电量/（TW·h）
2020	3.532	542.1	0	0	32.5	1 121.1	155	966.1	0	0	32.5	3.532
2021	4.021	608.3	20	20	36.5	1 251.5	155	1 096.5	0	0	39.5	4.001
2022	4.568	698.5	40	40	41.9	1 431.3	155	1 276.3	0	0	47.9	4.528
2023	5.125	822.7	60	60	49.4	1 681.5	155	1 526.5	0	0	58.4	5.065
2024	5.679	918.2	80	80	55.1	1 872.4	155	1 717.4	0.1	100	117.1	5.499
2025	6.133	1 005.4	100	100	60.3	2 046.3	155	1 891.3	0.3	100	125.3	5.733
2026	6.496	1 072.3	120	120	64.3	2 178.0	258	1 920.0	0.5	100	132.3	5.876
2027	6.906	1 142.4	140	140	68.5	2 316.2	258	2 058.2	0.7	200	189.5	6.066
2028	7.292	1 209.3	160	160	72.6	2 448.1	258	2 190.1	1.2	200	196.6	5.932
2029	7.650	1 271.7	180	180	76.3	2 570.6	258	2 312.6	1.5	300	253.3	5.970
2030	7.917	1 319.5	200	200	79.2	2 662.8	258	2 404.8	2.0	300	259.2	5.717
2035	8.976	1 496.1	220	220	89.8	3 021.2	360	2 661.2	2.5	400	322.8	6.256
2040	9.761	1 626.9	230	230	97.6	3 288.5	360	2 928.5	2.5	400	332.1	7.031
2045	10.449	1 741.4	240	240	104.5	3 522.1	360	3 162.1	2.5	400	340.5	7.709
2050	11.008	1 834.7	260	260	110.1	3 708.3	480	3 228.3	2.5	400	349.1	8.248
2055	11.552	1 925.3	270	270	115.5	3 892.4	480	3 412.4	2.5	400	356.0	8.782
2060	12.065	2 010.9	280	280	120.7	4 066.1	480	3 586.1	2.5	400	362.7	9.285