Operational Decision Model for Demand Response Considering Carbon Reduction Value of Adjustable Loads

doi:10.11930/j.issn.1004-9649.202406091

Abstract

Abstract:

Demand response is one of the important adjustable load resources, which plays an important role in promoting carbon reduction and new energy consumption in new power systems. This article integrated carbon flow theory, intelligent optimization algorithm, and multi-attribute evaluation methods into adjustable load operation strategy decision-making for demand response. It constructed a demand response strategy optimization model with the objective function of minimizing user electricity costs and constraints covering power supply and demand balance and unit output limitations. A genetic algorithm was used to determine the various chromosomes of the advantageous population that are conducive to achieving optimization goals. By combining the economic benefits of each chromosome, the consumption of new energy, and the calculation results of user carbon emission intensity, the improved entropy weight method and weight sum method were combined to comprehensively evaluate and rank all chromosomes. As a result, the demand response strategy that ensures the global best economic benefits, consumption of new energy, and carbon reduction effects of the system was obtained, which maximized the value of adjustable load resources. The feasibility and effectiveness of the method were finally verified through examples.

Key words: adjustable load, carbon flow calculation, demand response, intelligent optimization, multiple values

Xiaoxuan ZHANG, Song XUE, Ye XU, Yi XU, Zeyu DING, Qingkai SUN. Operational Decision Model for Demand Response Considering Carbon Reduction Value of Adjustable Loads[J]. Electric Power, 2024, 57(11): 151-160.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202406091

https://www.electricpower.com.cn/EN/Y2024/V57/I11/151

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References 25

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参数名		参数值
种群大小		100
迭代次数		500
交叉率		0.8
变异率		0.2
选择方式		轮盘赌
目标函数容差		1×10^–8
约束条件容差		1×10^–8

参数名		参数值
种群大小		100
迭代次数		500
交叉率		0.8
变异率		0.2
选择方式		轮盘赌
目标函数容差		1×10^–8
约束条件容差		1×10^–8

染色体编号	经济效益/ 元	新能源消纳量/ (kW·h)	碳排放强度/ (tCO₂·MW^–1)
3	3538.020223	22985.663504	0.600079
62	3538.021444	22985.663205	0.600081
95	3538.023447	22985.757260	0.596880
36	3538.024562	22985.663801	0.600080
60	3538.030573	22985.663811	0.600080
34	3538.040900	22985.740674	0.594854
43	3538.051068	22985.668392	0.626476
8	3538.041506	22985.659628	0.626443
30	3538.041510	22985.661761	0.626476
20	3538.051947	22985.799057	0.621452

染色体编号	经济效益/ 元	新能源消纳量/ (kW·h)	碳排放强度/ (tCO₂·MW^–1)
3	3538.020223	22985.663504	0.600079
62	3538.021444	22985.663205	0.600081
95	3538.023447	22985.757260	0.596880
36	3538.024562	22985.663801	0.600080
60	3538.030573	22985.663811	0.600080
34	3538.040900	22985.740674	0.594854
43	3538.051068	22985.668392	0.626476
8	3538.041506	22985.659628	0.626443
30	3538.041510	22985.661761	0.626476
20	3538.051947	22985.799057	0.621452

负荷编号	需求响应策略实施前	需求响应策略实施后
L1	0.6367	0.6051
L2	0.6782	0.6030
L3	0.6554	0.6013
L4	0.6352	0.5957
L5	0.6243	0.5828
L6	0.6694	0.6149
L9	0.6579	0.6067
L10	0.6706	0.5968
L11	0.6714	0.5941
L12	0.6525	0.6003
平均	0.6552	0.6001