Electric Power ›› 2024, Vol. 57 ›› Issue (2): 171-182.DOI: 10.11930/j.issn.1004-9649.202212079
• New Energy • Previous Articles Next Articles
					
													Suhao CHEN1(
), Yue WU2, Wei ZENG3, Xiaohui YANG1(
), Xiaopeng WANG1, Yunfei WU1
												  
						
						
						
					
				
Received:2022-12-15
															
							
															
							
																	Accepted:2023-03-15
															
							
																	Online:2024-02-23
															
							
							
																	Published:2024-02-28
															
							
						Supported by: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 | 
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 | 
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) | |
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 | 
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 | 
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 | 
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 | |||||||||
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 | 
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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