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

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

Abstract

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

CLC Number:

TK 011

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.

Figures/Tables 17

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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