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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (9): 1832-1842    DOI: 10.3785/j.issn.1008-973X.2023.09.015
机械工程、能源工程     
基于热湿负荷与自适应预测时域微网优化调度
林俊光1,2(),周雅敏2,冯彦皓2,马聪1,吴凡1,2(),郑梦莲2,*(),俞自涛2
1. 浙江浙能技术研究院有限公司，浙江 杭州 311100
2. 浙江大学 热工与动力系统研究所，浙江 杭州 310027
Optimal scheduling of microgrid based on heat and humidity load with adaptive prediction horizon length
Jun-guang LIN1,2(),Ya-min ZHOU2,Yan-hao FENG2,Cong MA1,Fan WU1,2(),Meng-lian ZHENG2,*(),Zi-tao YU2
1. Zhejiang Energy Group Research Institute Limited Company, Hangzhou 311100, China
2. Institute of Thermal Science and Power Systems, Zhejiang University, Hangzhou 310027, China
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摘要：

为了提升建筑冷热电联供（CCHP）微网灵活性并减少负荷波动，开展建筑湿负荷参与日前需求响应的效果和日内预测时域的自适应调节方法研究. 在日前阶段，构建包含建筑湿负荷的冷负荷需求响应模型；在日内阶段，提出基于预测负荷方差的自适应预测时域模型预测控制（MPC）方法. 在日前阶段，分析分时电价下包含湿负荷的冷负荷需求响应对总经济成本和蓄能水罐蓄冷量的影响；在日内阶段，分析采用自适应预测时域MPC方法对计算时间、成本和各类设备工作状态的影响. 结果表明，考虑湿负荷的冷负荷需求响应降低了日前阶段成本7.75%；在日内阶段，自适应预测时域MPC方法不仅能够平衡计算时间和成本，还能够增加蓄能量和平滑燃气内燃机出力.
关键词： 冷热电联供(CCHP);  热湿负荷;  优化调度;  模型预测控制(MPC);  自适应预测时域    
Abstract:

In order to improve the flexibility of combined cooling, heating and power (CCHP) microgrid and to reduce load fluctuation, the effect of building humidity load participation in day-ahead demand response and the adaptive intra-day prediction horizon length were studied. In the day-ahead time domain, a cooling load demand response model including the building humidity load was constructed. In the intra-day time domain, a model prediction control (MPC) method with adaptive prediction horizon lengths based on the variance of the predicted load was proposed. The effects of the demand response of the cold load containing humidity load on the total economic cost and the storage capacity of the storage tank under the time-of-day tariff were analyzed during the day-ahead time domain. The effects of the impact of using the MPC method with adaptive prediction horizon lengths on the calculation time, the cost and the working status of various types of equipment were analyzed during the intra-day time domain. Results showed that the cooling load demand response considering the humidity load reduced the cost of the day-ahead scheduling by 7.75%. The MPC method with adaptive prediction horizon lengths not only balances the calculation time and the cost, but also increases the storage and smoothes the output of the gas-fired internal combustion engine in the intra-day time domain.
Key words: combined cooling, heating and power (CCHP)    heat and humidity load    optimal scheduling    model predictive control (MPC)    adaptive prediction horizon length
收稿日期: 2022-11-09 出版日期: 2023-10-16
CLC:  TM 73  
基金资助: 国家重点研发计划项目(2019YFE0126000)；浙江浙能技术研究院有限公司科技项目(No. ZNKJ-2019-087)
通讯作者: 郑梦莲     E-mail: linjg_008@163.com;danfan@zju.edu.cn;menglian_zheng@zju.edu.cn
作者简介: 林俊光（1984—），男，高级工程师，从事综合能源系统优化研究. orcid.org/0000-0002-9792-7659. E-mail： linjg_008@163.com
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引用本文:

林俊光,周雅敏,冯彦皓,马聪,吴凡,郑梦莲,俞自涛. 基于热湿负荷与自适应预测时域微网优化调度[J]. 浙江大学学报(工学版), 2023, 57(9): 1832-1842.

Jun-guang LIN,Ya-min ZHOU,Yan-hao FENG,Cong MA,Fan WU,Meng-lian ZHENG,Zi-tao YU. Optimal scheduling of microgrid based on heat and humidity load with adaptive prediction horizon length. Journal of ZheJiang University (Engineering Science), 2023, 57(9): 1832-1842.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.09.015    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I9/1832

图 1  冷热电联供微网系统结构
图 2  自适应预测时域模型预测控制示意图
图 3  日前阶段电、冷（显热部分）、湿负荷及分时电价
参数 数值 参数 数值 参数 数值
$ P_{{\text{gt,e}}}^{\max } $/kW 1 000 $ P_{{\text{ac,c}}}^{\max } $/kW 1 300 $ P_{{\text{ec,c}}}^{\max } $/kW 600
$ P_{{\text{bt}}}^{\max } $/kW 500 $ P_{{\text{bt}}}^{\min } $/kW 0 $ P_{{\text{wt}}}^{\max } $/kW 500
$ P_{{\text{wt}}}^{\min } $/kW 0 $ W_{{\text{bt}}}^{\max } $/kW·h 2 000 eac 1.2
$ W_{{\text{wt}}}^{\max } $/kW·h 2 000 ηbt_c 0.8 ηbt_d 0.8
ηwt_c 0.8 ηwt_d 0.8 — —
表 1  微网系统各设备的相关参数设置
参数 数值 参数 数值
$\theta_{\text{R} }^{\min }$/℃ 24 $\theta_{\text{R} }^{\max }$/℃ 28
$ \phi _{{\text{RH,R}}}^{\min } $ 0.4 $ \phi _{{\text{RH,R}}}^{\max } $ 0.7
Rgas / (元?m?3) 2.8 Rrm_gt/ (元?kW?1?h?1) 0.03
Rrm_ac/ (元?kW?1?h?1) 0.025 Rrm_ec/(元?kW?1?h?1) 0.01
Rrm_bt/ (元?kW?1?h?1) 0.02 Rrm_wt/ (元?kW?1?h?1) 0.13
表 2  湿负荷参与需求响应和经济成本的相关参数设置
图 4  不同粒子群优化算法的日前阶段优化结果比较
图 5  日前阶段各需求响应方案的冷负荷转移情况
图 6  日前阶段各需求响应方案的室内温湿度情况
图 7  日前各需求响应方案的电网负荷和蓄能水罐蓄冷量
图 8  日前/日内负荷和不同固定预测时域结果
图 9  自适应预测时域方案在日内的预测时域长度变化
预测时域方案 C2/ (元?d?1) ta/s ψ
自适应 11 777 4 481 4.76
固定3 h 12 360 2 725 5.08
固定7 h 11 623 6 688 4.92
表 3  自适应预测时域与不同固定预测时域的结果对比
图 10  各设备结果在自适应与固定预测时域中的对比
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