考虑用户舒适度的蓄热式电采暖参与风电消纳的多目标优化

doi:10.12204/j.issn.1000-7229.2022.03.002

电力建设 ›› 2022, Vol. 43 ›› Issue (3): 12-21.doi: 10.12204/j.issn.1000-7229.2022.03.002

• 新型电力系统下配电网规划与运行优化关键技术研究及应用·栏目主持王守相教授、赵倩宇博士· • 上一篇下一篇

考虑用户舒适度的蓄热式电采暖参与风电消纳的多目标优化

金国锋¹, 邢敬舒¹, 张林¹, 张秀路¹, 赵立军¹, 刘自发²(), 丛彪²

1.国网内蒙古东部电力有限公司经济技术研究院,呼和浩特市 010011
2.华北电力大学电气与电子工程学院,北京市 102206

收稿日期:2021-09-14 出版日期:2022-03-01 发布日期:2022-03-24
作者简介:金国锋(1980),男,硕士,高级工程师,研究方向为新能源并网优化调度;
邢敬舒(1990),女,工程师,研究方向为智能算法在电力系统中的应用;
张林(1988),女,硕士,工程师,研究方向为电网规划与评估;
张秀路(1990),男,工程师,研究方向为电网优化运行;
赵立军(1981),男,高级工程师,研究方向为新能源消纳;
刘自发(1973),男,博士,教授,研究方向为电力系统规划与评估、综合能源系统运行规划,E-mail: tjubluesky@163.com;
丛彪(1996),男,硕士研究生,研究方向为电力系统规划、运行与评估。
基金资助:
国家电网蒙东电力科技项目(SGMDJY00GPJS2000061);国家自然科学基金项目(51977074)

Multi-objective Optimization of Wind Power Accommodation withRegenerative Electric Heating Considering User Comfort

JIN Guofeng¹, XING Jingshu¹, ZHANG Lin¹, ZHANG Xiulu¹, ZHAO Lijun¹, LIU Zifa²(), CONG Biao²

1. State Grid East Inner Mongolia Economic Research Institute, Hohhot 010011, China
2. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Received:2021-09-14 Online:2022-03-01 Published:2022-03-24
Supported by:
State Grid Mengdong Electric Power Technology Project(SGMDJY00GPJS2000061);National Natural Science Foundation of China(51977074)

摘要/Abstract

摘要：

随着双碳目标的提出,大力发展新能源、降低碳排放将是未来能源发展的主线。由于新能源装机容量快速增长,电网消纳形势日趋严峻。为了消纳大规模风电的弃风功率来提升电网的调峰能力,文章提出了基于NSGA-III算法和考虑用户舒适度的蓄热式电采暖参与风电消纳的多目标优化模型。首先,分析了蓄热式电采暖的工作特性及参与风电消纳的运行机理,考虑用户舒适度的电、热需求响应,在满足用户舒适度前提下充分挖掘电、热需求响应潜力。其次,以风电消纳最大、运行成本最小、碳排放量最低作为优化目标,结合功率平衡、电采暖等约束条件,建立蓄热式电采暖参与风电消纳的多目标优化模型,并采用NSGA-III算法对模型进行求解。最后,基于某电网的实际数据进行仿真,结果表明所提方法提高了风电消纳的能力,降低了设备的运行成本,并有效减少了碳排放,且验证了所提算法的有效性。

关键词: 风电消纳, 蓄热式电采暖, 用户舒适度, NSGA-III算法, 多目标优化

Abstract:

With the introduction of the double-carbon target, vigorously developing new energy and reducing carbon emissions will be the main line of future energy development. Due to the rapid growth of new energy installed capacity, the grid accommodation situation is becoming increasingly severe. In order to accommodate the curtailed power of large-scale wind power to improve the peak-shaving capacity of the power grid, a multi-objective optimization model based on the NSGA-III algorithm and considering user comfort for regenerative electric heating to participate in wind power accommodation is proposed. This paper analyzes the working characteristics of regenerative electric heating and the operation mechanism of participating in wind power accommodation. It proposes the electricity and heat demand response that considers user comfort, and fully taps the potential of demand response to electricity and heat under the premise of satisfying user comfort. The maximum wind power accommodation, the minimum operating cost, and the lowest carbon emissions are the optimization objectives. Combined with the constraint conditions of power balance and electric heating, a multi-objective optimization model for regenerative electric heating to participate in wind power accommodation is established, and the NSGA-III algorithm is used to solve the model. Finally, a simulation based on the actual data of a certain power grid shows that the proposed method can improve the ability of wind power accommodation, reduce the operating cost of the equipment, and effectively reduce carbon emissions, and verifies the effectiveness of the proposed algorithm.

Key words: wind power accommodation, regenerative electric heating, user comfort, NSGA-III algorithm, multi-objective optimization

中图分类号:

TM73

金国锋, 邢敬舒, 张林, 张秀路, 赵立军, 刘自发, 丛彪. 考虑用户舒适度的蓄热式电采暖参与风电消纳的多目标优化[J]. 电力建设, 2022, 43(3): 12-21.

JIN Guofeng, XING Jingshu, ZHANG Lin, ZHANG Xiulu, ZHAO Lijun, LIU Zifa, CONG Biao. Multi-objective Optimization of Wind Power Accommodation withRegenerative Electric Heating Considering User Comfort[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 12-21.

图/表 17

图1 蓄热电锅炉运行系统图

Fig.1 Operation system diagram ofregenerative electric boiler

图2 蓄热式电采暖参与风电消纳的运行机理

Fig.2 Operation mechanism of regenerative electricheating participating in wind power accommodation

图3 PMV指标与用户舒适度的关系

Fig.3 Relationship between PMV indicators and user comfort

图4 NSGA-III算法求解流程

Fig.4 Flowchart of NSGA-III algorithm solving

表1 机组信息

Table 1 Power unit information

图5 热负荷与风电预测功率

Fig.5 Heat load and wind power forecast power

表2 多目标算法评价指标世代距离对比表

Table 2 Comparison table of generation distance,one of evaluation indices for multi-objective algorithm

表3 多目标算法评价指标反转世代距离对比表

Table 3 Comparison table of reversal generation distance,one of evaluation indices for multi-objective algorithm

图6 M * = 2时算法的收敛曲线

Fig.6 Convergence curve of thealgorithm when M * = 2

图7 M * = 2时Pareto的解集

Fig.7 Pareto solution set when M * = 2

图8 M * = 3时算法的收敛曲线

Fig.8 Convergence curve of the algorithm when M * = 3

图9 M * = 3时Pareto的解集

Fig.9 Pareto solution set when M * = 3

图10 优化调度的三维帕累托解集

Fig.10 Three-dimensional Pareto solution set foroptimal scheduling

表4 多目标函数的最优权重

Table 4 Optimal weight of multi-objective function

表5 多目标优化调度结果比较

Table 5 Comparison of multi-objective optimizationscheduling results

图11 考虑热舒适度前后室内温度变化曲线

Fig.11 Indoor temperature change curve before andafter considering thermal comfort

表6 不同舒适度对蓄热式电采暖参与风电消纳的多目标优化影响分析

Table 6 Analysis of the influence of different comfortlevels on the multi-objective optimization of regenerativeelectric heating participation in wind power accommodation

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考虑用户舒适度的蓄热式电采暖参与风电消纳的多目标优化

Multi-objective Optimization of Wind Power Accommodation withRegenerative Electric Heating Considering User Comfort

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