|Table of Contents|

Research on Energy Consumption Optimization Considering Elastic Constraints of Air-Conditioning Loads(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年02期
Page:
8-15
Research Field:
电气工程
Publishing date:

Info

Title:
Research on Energy Consumption Optimization Considering Elastic Constraints of Air-Conditioning Loads
Author(s):
Pei MinxiBao YuqingLiu Qiliang
(NARI School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China)
Keywords:
air conditioning loadsoptimization strategymixed linear integer programmingcomfort variableselastic constraints
PACS:
TM73
DOI:
10.3969/j.issn.1672-1292.2023.02.002
Abstract:
With the power imbalance problem on the supply and demand side,air conditioning loads(ACLs)can participate in the optimization of the power system as flexible load resources. Making full use of the thermodynamic characteristics of ACLs can not only exert optimization effect of ACLs,but also ensure the comfort of users. Existing optimization methods define the thermodynamic constraints of the ACLs as rigid constraints. In this paper,the rigid comfort variables are relaxed,and an optimization strategy considering elastic constraints is proposed to meet the serious load shedding demand. By introducing auxiliary variables,the relationships between comfort variables and auxiliary variables are redefined as elastic constraints,and penalty costs are added to the objective function. Finally,the optimal solution is obtained by mixed linear integer programming. The optimization results show that the load optimization results based on elastic constraints have more peak loads. Both simulation results of inverter ACLs and fixed frequency ACLs show the effectiveness of the proposed method.

References:

[1]WU H,SHAHIDEHPOUR M,ALABDULWAHAB A,et al. Thermal generation flexibility with ramping costs and hourly demand response in stochastic security-constrained scheduling of variable energy sources[J]. IEEE Transactions on Power Systems,2015,30(6):2955-2964.
[2]鲁宗相,李海波,乔颖. 含高比例可再生能源电力系统灵活性规划及挑战[J]. 电力系统自动化,2016,40(13):147-158.
[3]HAO H,SANANDAJI B M,POOLLA K,et al. Aggregate flexibility of thermostatically controlled loads[J]. IEEE Transactions on Power Systems,2015,30(1):189-198.
[4]周明,李琰,李庚银.基于随机生产模拟的日前发电——备用双层决策模型[J]. 电网技术,2019,43(5):1606-1613.
[5]陈伟伟,张增强,张高航,等. 计及需求响应及抽水蓄能的含风电系统鲁棒机组组合[J]. 电力工程技术,2022,41(2):75-82.
[6]宋梦,高赐威,苏卫华. 面向需求响应应用的空调负荷建模及控制[J]. 电力系统自动化,2016,40(14):158-167.
[7]杨济如,石坤,崔秀清,等. 需求响应下的变频空调群组削峰方法[J]. 电力系统自动化,2018,42(24):44-52.
[8]MATHIEU J,KAMGARPOUR M,LYGEROS J,et al. Arbitraging intraday wholesale energy market prices with aggregations of thermostatic loads[J]. IEEE Transactions on Power Systems,2015,30(2):763-772.
[9]LU N. An evaluation of the HVAC Load Potential for Providing Load Balancing Service[J]. IEEE Transactions Smart Grid,2012,3(3):1263-1270.
[10]CALLAWAY D S. Tapping the energy storage potential in electric loads to deliver load following and regulation with application to wind energy[J]. Energy Conversion and Management,2009,50(5):1389-1400.
[11]JEROME H K,DARREN R. A simplified thermal model to support analysis of urban resource flows[J]. Energy and Buildings,2007,39(4):445-453.
[12]王栋,徐青山,陈亮,等. 参与调峰控制的空调负荷建模仿真研究[J]. 电力工程技术,2018,37(6):80-86.
[13]ZHANG W,LIAN J,CHANG C,et al. Aggregated modeling and control of air conditioning loads for demand response[J]. IEEE Transactions on Power Systems,2013,28(4):4655-4664.
[14]SANANDAJI B M,VINCENT T,POOLLA K. Ramping rate flexibility of residential HVAC loads[J]. IEEE Trans Sustainable Energy,2016,7(2):865-874.
[15]BASHASH S,FATHY H. Modeling and control of aggregate air conditioning loads for robust renewable power management[J]. IEEE Transactions Control System Technology,2013,21(4):1318-1327.
[16]MAHDAVI N,BRASLAVSKY J,PERFUMO C. Mapping the effect of ambient temperature on the power demand of populations of air conditioners[J]. IEEE Transactions on Smart Grid,2018,9(3):1540-1550.
[17]MAHDAVI N,BRASLAVSKY J,SERON M,et al. Model predictive control of distributed air conditioning loads to compensate fluctuations in solar power[J]. IEEE Transactions on Smart Grid,2017,8(6):3055-3065.
[18]HUI H,DING Y,CHEN T,et al. Dynamic and stability analysis of the power system with the control loop of inverter air conditioners[J]. IEEE Transactions on Industrial Electronics,2021,68(3):2725-2736.
[19]JIANG A,WEI H,DENG J,et al. Cloud-edge cooperative model and closed-loop control strategy for the price response of large-scale air conditioners considering data packet dropouts[J]. IEEE Transactions Smart Grid,2020,11(5):4201-4211.
[20]宋爽,李中伟,刘勇,等. 住宅小区负荷群用电优化策略研究[J]. 电测与仪表,2021,58(8):10.
[21]LI Y W,SHEN Y W,ZHOU L,et al. Progressive time-differentiated peak pricing(PTPP)for aggregated air-conditioning load in demand response programs[J]. International Transactions on Electrical Energy Systems,2019,29(1):e2664.
[22]HONG Y,CHANG W,CHANG Y,et al. Optimal scheduling of energy consumptions for air conditioners in a smart community with renewables[J]. 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference(APPEEC),2016:385-391.
[23]WANG J Y,CHEN X Y,YU K,et al. Optimal scheduling of air conditioning loads by aggregator under dynamic price[J]. IEEE Conference on Energy Internet and Energy System Integration,2019:191-195.
[24]王蓓蓓,亢丽君,苗曦云,等. 考虑可信度的新能源及需求响应参与英美容量市场分析及思考[J]. 电网技术,2022,46(4):1-16.

Memo

Memo:
-
Last Update: 2023-06-15