Evaluation of Grid Investment Effectiveness and Investment Simulation for New-Type Power Systems Based on Machine Learning Algorithm

doi:10.11930/j.issn.1004-9649.202411008

Abstract

Abstract:

The access of emerging elecments such as distributed power generation, energy storage, and microgrids has a great impact on the operation characteristics of the power system. Power grid, as a critical component of new-type power systems, requires that its planning and investment decisions fully account for the emerging elements' impact on investment effectiveness, so as to ensure that the grid investment scale and allocation align with the construction objectives of new-type power systems. At present, evaluation of the power grid investment effectiveness mostly focuses on cost input and economic benefits. The construction of new-type power systems, however, requires that the power grid investment effectiveness be guided by the overall benefits, and the key influencing factors be extracted to provide directional guidance for the power grid investment simulation. This paper proposes a power grid investment effectiveness evaluation and investment simulation method for new-type power systems based on machine learning algorithm, and constructs a power grid investment effectiveness evaluation model based on the machine learning algorithm of the least squares support vector machine (LSSVM), and uses the particle swarm optimization algorithm (PSO) to optimize the parameters of LSSVM. The distributed power generation and energy storage scenarios are used for case study. Based on the quantitative mapping relationship between the physical indicators of the power grid, the power grid investment indicators and the power grid investment effectiveness indicators under the new-type power systems, the power grid investment simulation method and model are established, and case study is carried out using differentiated scenarios to verify the feasibility of the proposed power grid investment simulation method. This study can provide a theoretical and technical support for the power grid investment decision-making for the new-type power systems.

Key words: grid investment, machine learning, least squares support vector machines, particle swarm optimization, typical scenarios, investment effectiveness evaluation, strategy simulation

TIAN Xin, JIN Xiaoling, HAN Xinyang, YANG Junwei, ZHANG Xinsheng. Evaluation of Grid Investment Effectiveness and Investment Simulation for New-Type Power Systems Based on Machine Learning Algorithm[J]. Electric Power, 2025, 58(7): 197-206.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202411008

https://www.electricpower.com.cn/EN/Y2025/V58/I7/197

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References 28

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序号	一级指标	二级指标
1	低碳性	新能源并网发电量占地区全社会用电量的比例
2		终端电气化率
3		新能源利用率
4	安全性	断面输电能力提升
5		N–1通过率
6		地区供电可靠率
7		防灾抗灾能力
8	经济性	网损率
9		单位投资增供电量
10		单位固定资产售电量
11	智能性	配电自动化覆盖率
12		采集终端覆盖率
13		低压分布式光伏用户“四可”能力

序号	一级指标	二级指标
1	低碳性	新能源并网发电量占地区全社会用电量的比例
2		终端电气化率
3		新能源利用率
4	安全性	断面输电能力提升
5		N–1通过率
6		地区供电可靠率
7		防灾抗灾能力
8	经济性	网损率
9		单位投资增供电量
10		单位固定资产售电量
11	智能性	配电自动化覆盖率
12		采集终端覆盖率
13		低压分布式光伏用户“四可”能力

场景	新增指标	所属维度	关键影响因素	与电网投资相关性
分布式电源	分布式电源平准化能源成本	经济性	运营维护成本	负相关
	分布式绿电占比	低碳性	电源侧灵活性与稳定性	正相关
	分布式电源供电保障能力	安全性	备用供电和应急响应能力	负相关
微电网	主网投资需求削减能力	经济性	负荷量与主网稳定性	负相关
	微电网备用容量	安全性	主网稳定性	负相关
	微电网自平衡可靠性	安全性	负荷量、负荷成本	负相关
	微电网平准化能源成本	经济性	能源成本	负相关
	微电网节能率	经济性	负荷成本	负相关
	微电网孤岛形成失败概率	安全性	微电网稳定性	正相关
储能	储能调峰效益	经济性	调峰能力	负相关
	储能节煤效益	低碳性	调节能力	负相关
	储能电力应急应用	安全性	应急损失	负相关
	储能响应延迟性	安全性	调节能力	正相关

场景	新增指标	所属维度	关键影响因素	与电网投资相关性
分布式电源	分布式电源平准化能源成本	经济性	运营维护成本	负相关
	分布式绿电占比	低碳性	电源侧灵活性与稳定性	正相关
	分布式电源供电保障能力	安全性	备用供电和应急响应能力	负相关
微电网	主网投资需求削减能力	经济性	负荷量与主网稳定性	负相关
	微电网备用容量	安全性	主网稳定性	负相关
	微电网自平衡可靠性	安全性	负荷量、负荷成本	负相关
	微电网平准化能源成本	经济性	能源成本	负相关
	微电网节能率	经济性	负荷成本	负相关
	微电网孤岛形成失败概率	安全性	微电网稳定性	正相关
储能	储能调峰效益	经济性	调峰能力	负相关
	储能节煤效益	低碳性	调节能力	负相关
	储能电力应急应用	安全性	应急损失	负相关
	储能响应延迟性	安全性	调节能力	正相关

二级指标	–20%	–10%	0	+10%	+20%
新能源并网发电量占地区全社会用电量的比例	47.14	53.23	64.41	72.28	79.30
终端电气化率	52.05	58.90	64.41	69.11	75.47
新能源利用率	50.49	56.97	64.41	66.54	72.78
断面输电能力提升	52.50	59.18	64.41	68.83	75.43
N–1通过率	48.32	54.57	64.41	72.96	79.86
地区供电可靠率	47.88	53.92	64.41	71.72	78.52
防灾抗灾能力	50.12	56.53	64.41	65.23	71.23
网损率	52.25	58.93	64.41	67.78	74.13
单位投资增供电量	47.43	53.45	64.41	71.45	78.37
单位固定资产售电量	49.28	55.53	64.41	67.00	73.18
配电自动化覆盖率	47.37	53.56	64.41	71.96	78.80
采集终端覆盖率	49.24	55.57	64.41	68.87	75.46
低压分布式光伏用户 “四可”能力	51.58	58.12	64.41	66.14	72.24
主网投资需求削减能力	50.90	57.35	64.41	69.29	75.75
微电网备用容量	52.33	59.17	64.41	66.46	72.70
微电网自平衡可靠性	51.32	58.01	64.41	66.98	73.17
微电网平准化能源成本	52.97	59.93	64.41	67.98	74.32
微电网节能率	52.60	59.46	64.41	67.14	73.47
微电网孤岛形成失败概率	49.73	56.27	64.41	66.79	72.95