面向光伏消纳和冰蓄冷空调群低碳需求响应的新型配电系统多时间尺度优化策略

doi:10.12096/j.2096-4528.pgt.24096

发电技术 ›› 2025, Vol. 46 ›› Issue (2): 284-295.DOI: 10.12096/j.2096-4528.pgt.24096

• 基于群体智能的综合能源系统建模仿真及优化运行 • 上一篇

面向光伏消纳和冰蓄冷空调群低碳需求响应的新型配电系统多时间尺度优化策略

王奎¹^,², 余梦¹^,², 张海静³^,⁴, 李妍¹^,², 刘哲¹^,², 郭军红⁵

^1.国网电力科学研究院有限公司，江苏省南京市 210000
^2.国网电力科学研究院武汉能效测评有限公司，湖北省武汉市 430074
^3.国网山东省电力公司，山东省济南市 250001
^4.国网山东省电力公司营销服务中心(计量中心)，山东省济南市 250001
^5.华北电力大学环境科学与工程学院，北京市昌平区 102206

收稿日期:2024-05-31 修回日期:2024-07-15 出版日期:2025-04-30 发布日期:2025-04-23
作者简介:王奎(1984)，男，硕士，工程师，研究方向为双碳、综合能源系统、储能技术，2352220838@qq.com；
郭军红(1984)，男，博士，副教授，研究方向为气象变化预测，guohongjun@ncepu.edu.cn。
基金资助:
国家重点研发计划项目(2018YFEO208400);国家电网公司总部科技项目(5400-202216165A-1-1-ZN)

Multi-Time Scale Optimization Strategy for New Distribution System Oriented to Photovoltaic Consumption and Low Carbon Demand Response of Ice Storage Air Conditioning Groups

Kui WANG¹^,², Meng YU¹^,², Haijing ZHANG³^,⁴, Yan LI¹^,², Zhe LIU¹^,², Junhong GUO⁵

^1.State Grid Electric Power Research Institute, Nanjing 210000, Jiangsu Province, China
^2.State Grid Electric Power Research Institute Wuhan Energy Efficiency Evaluation, Wuhan 430074, Hubei Province, China
^3.State Grid Shandong Electric Power Company, Jinan 250001, Shandong Province, China
^4.State Grid Shandong Electric Power Company Marketing Service Center (Metering Center), Jinan 250001, Shandong Province, China
^5.School of Environmental Science and Engineering, North China Electric Power University, Changping District, Beijing 102206, China

Received:2024-05-31 Revised:2024-07-15 Published:2025-04-30 Online:2025-04-23
Supported by:
National Key Research & Development Program of China(2018YFEO208400);Science and Technology Project of SGCC(5400-202216165A-1-1-ZN)

摘要/Abstract

摘要：

目的鉴于传统配电网需求响应模型在调控负荷侧灵活可控资源方面存在局限性，特别是忽视了冰蓄冷空调这类独特且高效的资源，因此，致力于通过深度优化冰蓄冷空调这一负荷侧调控对象，以显著提升新型配电系统中可再生能源的利用率，并深入挖掘其低碳运行潜力。方法引入具有虚拟储能特征的冰蓄冷空调作为调控对象，面向光伏消纳和冰蓄冷空调群低碳需求响应，提出了一种新型配电系统日前-日内多时间尺度、多目标优化策略。首先，建立了冰蓄冷空调运行特性及低碳需求响应模型；其次，以供电公司低碳需求响应激励成本最小、光伏生产商利润额最大、空调用户用电费用最小为优化目标，构建了日前多目标优化模型，采用非支配排序遗传Ⅱ型算法(non-dominated sorting genetic algorithm Ⅱ，NSGA-Ⅱ)实现了模型的高效求解，并基于多维偏好分析线性规划法筛选出多目标优化模型的最优解；再次，为消除日前预测误差对模型结果的影响，进一步构建了日内滚动修正优化模型；最后，采用测试算例分析了所建模型的有效性。结果基于日内滚动修正后所得最优方案使得供电公司及空调用户的成本降低约20%，光伏生产商的利润额提升约3%。结论基于多时间尺度的多目标优化调控方法既保障了各方主体的利益，又消除了预测误差对模型结果的影响，为新型配电系统低碳运行提供了重要技术支持。

关键词: 新型配电系统, 光伏发电, 配电网, 新能源, 冰蓄冷空调, 低碳需求响应, 多时间尺度, 多目标优化

Abstract:

Objectives In view of the limitations of the traditional distribution network demand response model in regulating the flexible and controllable resources on the load side, especially ignoring the unique and efficient resources such as ice storage air conditioning systems. Therefore, this paper aims to deeply optimize the load-side control object such as ice storage air conditioning, so as to significantly improve the utilization rate of renewable energy in the new distribution system and deeply explore its low carbon operation potential. Methods Ice storage air conditioning with virtual energy storage characteristics is introduced as the control object. A new distribution system multi-time scale optimization strategy is proposed for photovoltaic consumption and low carbon demand response of ice storage air conditioning groups. Firstly, the operating characteristics and low carbon demand response model of ice storage air conditioning are established. Secondly, with the minimum incentive cost for low carbon demand response in power supply companies, the maximum profit for photovoltaic manufacturers, and the minimum electricity consumption cost for air conditioning users as optimization objectives, a multi-objective optimization model for the day ahead is constructed. The non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) is used to efficiently solve the model, and the optimal solution for the multi-objective optimization model is selected based on the linear programming method with multi-dimensional preference analysis. Subsequently, in order to eliminate the impact of prediction errors on the model results, a daily rolling correction optimization model is further constructed. Finally, the effectiveness of the proposed model is analyzed using test cases. Results The optimal solution obtained after intra-day rolling correction results in a cost reduction of approximately 20% for both power supply companies and air conditioning users, as well as an increase in profit margins of about 3% for photovoltaic producers. Conclusions The multi-objective optimization and control method based on multiple time scales not only ensures the interests of all parties, but also eliminates the impact of prediction errors on model results, providing important technical support for low carbon operation of new distribution systems.

Key words: new distribution system, photovoltaic power generation, distribution network, renewable energy, ice storage air conditioning, low carbon demand response, multi-time scale, multi-objective optimization

中图分类号:

TK 011

王奎, 余梦, 张海静, 李妍, 刘哲, 郭军红. 面向光伏消纳和冰蓄冷空调群低碳需求响应的新型配电系统多时间尺度优化策略[J]. 发电技术, 2025, 46(2): 284-295.

Kui WANG, Meng YU, Haijing ZHANG, Yan LI, Zhe LIU, Junhong GUO. Multi-Time Scale Optimization Strategy for New Distribution System Oriented to Photovoltaic Consumption and Low Carbon Demand Response of Ice Storage Air Conditioning Groups[J]. Power Generation Technology, 2025, 46(2): 284-295.

图/表 17

图1 冰蓄冷空调系统日前多目标优化模型求解流程图

Fig. 1 Flow chart for solving multi-objective optimization model of ice storage air conditioning system

图2 多时间尺度冰蓄冷空调优化策略对比

Fig. 2 Comparison of multi-time scale optimization strategies for ice storage air conditioning

图3 改进的IEEE 33节点新型配电系统结构

Fig. 3 Structure of improved IEEE 33-node new distribution system

图4 空调用户冷负荷需求多时间尺度基本数据

Fig. 4 Multi-time scale basic data of cooling load demand of air conditioning users

图5 光伏发电功率多时间尺度基本数据

Fig. 5 Multi-time scale basic data of photovoltaic power generation

表1 冰蓄冷空调基本参数

Tab. 1 Basic parameters of ice storage air conditioning

参数	数值
蓄冰功率上限/kW	1 500
融冰功率上限/kW	2 500
空调机组直接制冷功率上限/kW	4 800
蓄冰槽蓄冰量上限容量/(kW⋅h)	16 000
供水温度/℃	15
制冷机组容量变化率	0.67
取冷效率	0.95
保温效率	0.98
蓄冰槽传热率	0.85

表2 低碳需求响应模型中价格相关参数

Tab. 2 Price related parameters in low carbon demand response models

参数	数值
可调节负荷电量需求响应补偿单价/[元/(kW⋅h)]	0.1
消纳光伏电量补贴单价/[元/(kW⋅h)]	0.3
光伏生产商售电单价/[元/(kW⋅h)]	0.4
弃光电量惩罚单价/[元/(kW⋅h)]	0.85
碳排放碳税单价/[元/(kW⋅h)]	0.35
机组频繁调节的单位功率差惩罚单价/[元/(kW⋅h)]	0.5
尖时(08:00—11:00，20:00—22:00)电价/[元/(kW⋅h)]	1.2
峰时(13:00—19:00)电价/[元/(kW⋅h)]	0.8
谷时(00:00—07:00，22:00—24:00)电价/[元/(kW⋅h)]	0.4

图6 日前多目标优化调度Pareto前沿解

Fig. 6 Multi-objective optimization scheduling Pareto frontier solutions for the day-ahead period

表3 Pareto解中4个特殊解对应优化结果special solutions in Pareto solutions (元)

Tab. 3 Optimization results corresponding to four

最优解	供电公司激励费用	光伏生产商利润	空调用户用电费用
A	29 488.75	31 635.19	51 252.78
B	54 528.09	32 893.95	41 455.76
C	56 875.49	30 715.04	25 862.51
D	44 163.17	31 502.02	39 947.94

图7 供电公司激励费用最优解对应调度方案

Fig. 7 Dispatch scheme corresponding to the optimal incentive cost for power supply company

图8 光伏生产商利润最优解对应调度方案

Fig. 8 Dispatch scheme corresponding to the optimal profit for photovoltaic producer

图9 空调用户用电费用最优解对应调度方案

Fig. 9 Dispatch scheme corresponding to the optimal electricity cost for air conditioning users

图10 LINMAP法筛选最优解对应调度方案

Fig. 10 Dispatch scheme selected by LINMAP method for screening optimal solution

图11 多时间尺度下空调直接制冷功率变化

Fig. 11 Changes in direct cooling power of air conditioning under multi-time scale

图12 多时间尺度下向上级电网购电功率变化

Fig. 12 Changes in purchasing power from superior power grid under multi-time scale

图13 多时间尺度下空调融冰制冷功率变化

Fig. 13 Changes in deicing and ice storage power of air conditioning under multi-time scale

图14 多时间尺度下蓄冰槽蓄冰量变化

Fig. 14 Changes in ice storage capacity of ice storage tanks under multi-time scale

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面向光伏消纳和冰蓄冷空调群低碳需求响应的新型配电系统多时间尺度优化策略

Multi-Time Scale Optimization Strategy for New Distribution System Oriented to Photovoltaic Consumption and Low Carbon Demand Response of Ice Storage Air Conditioning Groups

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