中国电力 ›› 2024, Vol. 57 ›› Issue (2): 171-182.DOI: 10.11930/j.issn.1004-9649.202212079
陈苏豪1(), 吴越2, 曾伟3, 杨晓辉1(
), 王晓鹏1, 伍云飞1
收稿日期:
2022-12-15
出版日期:
2024-02-28
发布日期:
2024-02-28
作者简介:
陈苏豪(1995—),男,硕士研究生,从事分布式能源、综合能源系统规划研究,E-mail:2048676779@qq.com基金资助:
Suhao CHEN1(), Yue WU2, Wei ZENG3, Xiaohui YANG1(
), Xiaopeng WANG1, Yunfei WU1
Received:
2022-12-15
Online:
2024-02-28
Published:
2024-02-28
Supported by:
摘要:
冷热电联产(combined cooling, heating and power,CCHP)系统与微电网的结合有利于促进消纳可再生能源,为了提升CCHP型微电网的经济性、环保性和稳定性,提出了两阶段优化调度模型。离线优化阶段基于需求侧响应策略,建立了基于归一化法向约束法的多目标规划模型,并用熵权-TOPSIS法筛选最优结果。在线优化阶段建立了基于动态矩阵控制算法的有限时域优化模型,对离线优化结果进行跟踪优化和反馈校正,以降低不确定性因素的影响。最后,设计对比方案进行分析,验证了所提优化模型的有效性。
陈苏豪, 吴越, 曾伟, 杨晓辉, 王晓鹏, 伍云飞. 基于NNC法和DMC算法的CCHP型微电网两阶段调度[J]. 中国电力, 2024, 57(2): 171-182.
Suhao CHEN, Yue WU, Wei ZENG, Xiaohui YANG, Xiaopeng WANG, Yunfei WU. Two-Stage Dispatch of CCHP Microgrid Based on NNC and DMC[J]. Electric Power, 2024, 57(2): 171-182.
时段 | 外购电价/(元·(kW·h)–1) | |
01:00—09:00 | 0.471 | |
09:00—19:00 | 1.195 | |
19:00—23:00 | 0.876 | |
23:00—24:00 | 0.471 |
表 1 购电分时电价信息
Table 1 Time-of-use electricity price information
时段 | 外购电价/(元·(kW·h)–1) | |
01:00—09:00 | 0.471 | |
09:00—19:00 | 1.195 | |
19:00—23:00 | 0.876 | |
23:00—24:00 | 0.471 |
参数 | 数值 | 参数 | 数值 | |||
2.350 | 0.017 | |||||
0.034 | 0.00216 | |||||
0.018 | 0.0016 | |||||
0.025 | 4 | |||||
0.020 | 0.7 | |||||
0.047 | 0.8 | |||||
0.016 | 0.02 | |||||
0.600 | 0.35 | |||||
9.780 | 0.9 | |||||
200.000 | 0.1 | |||||
40.000 | 0.9 | |||||
40.000 | 0.4 |
表 2 系统参数与单位运行成本
Table 2 System parameters and unit operating cost
参数 | 数值 | 参数 | 数值 | |||
2.350 | 0.017 | |||||
0.034 | 0.00216 | |||||
0.018 | 0.0016 | |||||
0.025 | 4 | |||||
0.020 | 0.7 | |||||
0.047 | 0.8 | |||||
0.016 | 0.02 | |||||
0.600 | 0.35 | |||||
9.780 | 0.9 | |||||
200.000 | 0.1 | |||||
40.000 | 0.9 | |||||
40.000 | 0.4 |
电量削减区间/(kW·h) | 单位补偿价格/(元·(kW·h)–1) | |
表 3 激励型负荷需求响应参数
Table 3 Incentive load demand response parameters
电量削减区间/(kW·h) | 单位补偿价格/(元·(kW·h)–1) | |
算法 | 目标函数F1/元 | 目标函数F2/kW2 | ||
多目标粒子群算法 | 2174.624 | 10154.151 | ||
NSGA-Ⅱ算法 | 2116.705 | 11850.561 | ||
归一化法向约束法 | 2252.477 | 3831.897 |
表 4 对比不同多目标规划算法的决策结果
Table 4 Compare the decision results of different multi-objective programming algorithms
算法 | 目标函数F1/元 | 目标函数F2/kW2 | ||
多目标粒子群算法 | 2174.624 | 10154.151 | ||
NSGA-Ⅱ算法 | 2116.705 | 11850.561 | ||
归一化法向约束法 | 2252.477 | 3831.897 |
方法 | 评价指标 | 信息熵值 | 权重系数/% | |||
熵权-TOPSIS法 | 目标函数F1 | 0.9820 | 25.30 | |||
目标函数F2 | 0.9470 | 74.70 | ||||
传统TOPSIS法 | 目标函数F1 | 50.00 | ||||
目标函数F2 | 50.00 |
表 5 目标函数权重
Table 5 The weight of the objective function
方法 | 评价指标 | 信息熵值 | 权重系数/% | |||
熵权-TOPSIS法 | 目标函数F1 | 0.9820 | 25.30 | |||
目标函数F2 | 0.9470 | 74.70 | ||||
传统TOPSIS法 | 目标函数F1 | 50.00 | ||||
目标函数F2 | 50.00 |
方法 | 经济性/元 | 环保性/元 | 稳定性/kW2 | |||
熵权-TOPSIS法 | 2178.363 | 74.115 | 3831.897 | |||
传统TOPSIS法 | 2094.835 | 75.370 | 6776.448 |
表 6 2种最优方案的效益对比
Table 6 The benefit comparison of two optimal schemes
方法 | 经济性/元 | 环保性/元 | 稳定性/kW2 | |||
熵权-TOPSIS法 | 2178.363 | 74.115 | 3831.897 | |||
传统TOPSIS法 | 2094.835 | 75.370 | 6776.448 |
误差 | 模型1) | 模型2) | 模型3) | |||||||||||||||
经济性/元 | 环保性/元 | 稳定性/kW2 | 经济性/元 | 环保性/元 | 稳定性/kW2 | 经济性/元 | 环保性/元 | 稳定性/kW2 | ||||||||||
离线±3% | 2897.143 | 74.733 | 19345.411 | 2892.897 | 74.733 | 19321.594 | 2887.044 | 73.804 | 16510.674 | |||||||||
离线±6% | 2835.716 | 73.110 | 16853.787 | 2807.606 | 73.030 | 16260.038 | 2775.044 | 71.256 | 11143.871 | |||||||||
离线±9% | 2801.750 | 71.998 | 15950.591 | 2736.450 | 71.396 | 13914.351 | 2659.652 | 68.640 | 6872.820 |
表 7 对比不同预测误差情景下不同在线优化模型的效益
Table 7 Compare the benefits of different online optimizing models under different prediction scenarios
误差 | 模型1) | 模型2) | 模型3) | |||||||||||||||
经济性/元 | 环保性/元 | 稳定性/kW2 | 经济性/元 | 环保性/元 | 稳定性/kW2 | 经济性/元 | 环保性/元 | 稳定性/kW2 | ||||||||||
离线±3% | 2897.143 | 74.733 | 19345.411 | 2892.897 | 74.733 | 19321.594 | 2887.044 | 73.804 | 16510.674 | |||||||||
离线±6% | 2835.716 | 73.110 | 16853.787 | 2807.606 | 73.030 | 16260.038 | 2775.044 | 71.256 | 11143.871 | |||||||||
离线±9% | 2801.750 | 71.998 | 15950.591 | 2736.450 | 71.396 | 13914.351 | 2659.652 | 68.640 | 6872.820 |
在线优化模型 | 平均求解时间/min | 总用时/min | ||
1) | 3.4666 | 3.4666 | ||
2) | 2.6000 | 2.6000 | ||
3) | 2.1500 | 5.2000 |
表 8 对比不同在线优化模型的求解时间
Table 8 Compare the solution time of different online optimization models
在线优化模型 | 平均求解时间/min | 总用时/min | ||
1) | 3.4666 | 3.4666 | ||
2) | 2.6000 | 2.6000 | ||
3) | 2.1500 | 5.2000 |
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