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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (9): 1832-1842    DOI: 10.3785/j.issn.1008-973X.2023.09.015
    
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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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     
Received: 09 November 2022      Published: 16 October 2023
CLC:  TM 73  
Fund:  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)
Corresponding Authors: Meng-lian ZHENG     E-mail: linjg_008@163.com;danfan@zju.edu.cn;menglian_zheng@zju.edu.cn
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Jun-guang LIN
Ya-min ZHOU
Yan-hao FENG
Cong MA
Fan WU
Meng-lian ZHENG
Zi-tao YU
Cite this article:

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.

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


基于热湿负荷与自适应预测时域微网优化调度

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 
Fig.1 Schematic of combined cooling, heating and power microgrid
Fig.2 Schematic of adaptive prediction horizon for model predictive control
Fig.3 Electricity, cold (sensible heat part), humidity load and time-of-use tariff during day-ahead scheduling
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 — —
Tab.1 Techno-economic parameters for each device in microgrid
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
Tab.2 Parameter settings for humidity load demand response and costs
Fig.4 Comparisons between optimization results obtained by different particle swarm optimization algorithms for day-ahead scheduling
Fig.5 Cooling load shifting of each case during day-ahead scheduling
Fig.6 Indoor temperature and relative humidity of each case during day-ahead scheduling
Fig.7 Grid electricity load and stored cooling energy in water tank of each case during day-ahead scheduling
Fig.8 Day-ahead/intra-day loads and results of different constant prediction horizons
Fig.9 Intra-day change of prediction horizon with adaptive prediction horizon length scheme
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
Tab.3 Comparison of results obtained by constant and adaptive prediction horizons
Fig.10 Comparison of results for each device by adaptive and constant prediction horizons
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