Economic Dispatch for Power Systems of Multiple Wind Farms Based on MVTV Copula Method
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摘要: 针对多维风电出力数据间相关性对含有多风电场的电力系统经济调度影响问题，提出了MVTV Copula （mix vine time-varying Copula）方法，以此构建多风电场出力数据随机场景，并建立基于机组燃料费用及再调度费用最小为目标的电力系统经济调度模型. 为验证所述方法的有效性，采用IEEE-30节点系统，并以我国某地3个相邻风电场的实际出力为例对所述方法进行了仿真验证. 仿真结果表明：不考虑风电出力相关性的经济调度费用仅为实际调度费用的43.6%，而应用所述MTVT Copula方法建模后的再调度费用则更加接近实际调度费用，为实际调度费用的82%，验证了本文所提方法的有效性.
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关键词:
- 风电出力 /
- 相关性 /
- MTVT Copula方法 /
- 经济调度
Abstract: As the correlation of multiple wind power outputs is of great significance for the economic dispatch analysis of wind farm power systems, the mix vine time-varying Copula (MVTV Copula) is proposed to construct the random scenarios of multiple wind power outputs.The economic dispatch model is built with the objective of minimizing unit fuel cost and dispatch cost. An IEEE-30 node system is referenced and the measured outputs of three neighboring wind farms in China are used to validate the proposed method. The simulation results show that the cost of the economic dispatch that ignores the correlation of wind power output is only 43.6% of the actual scheduling cost, while that of the model based on the MTVT Copula method is much closer to the actual scheduling cost, being 82% of the actual cost. It is proved that theoutput model of multiple wind farms is more consistent with the actual situation, which can well describe the relevant characteristics of multiple windfarm outputs, and the economic dispatch based on this method can yield ideal dispatch results.-
Key words:
- wind power output /
- correlation /
- MVTV Copula method /
- economic dispatch
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图 4 基于MVTV Copula模型的典型随机场景
Figure 4. Typical random scenarios based on MVTV Copula model
图 5 不考虑相关性的典型随机场景
Figure 5. Typical random scenarios ignoring dependence of wind outputs
图 6 负载曲线及各场景下机组和风电总出力曲线
Figure 6. Load curve and total output curve of unit and wind power output under different scenarios
表 1 各场景下最优藤结构及最优参数
Table 1. Optimal vine structure and optimal parameters for different scenarios
场景 藤结构 最优 Copula 函数 AIC 值 1 C {tv-gumbel，tv-SJC，tv-gumbel} 0.384 2 D {tv-gumbel，tv-normal，tv-gumbel} 0.195 3 C { tv-SJC，tv-gumbel，tv-gumbel} 0.119 4 D {tv-SJC，tv-SJC，tv-gumbel} 0.097 5 D {tv-normal，tv-SJC，tv-gumbel} 0.062 注：tv-SJC、tv-gumbel、tv-normal 分别为时变SJC Copula函数、时变gumbel copula函数、时变normal函数. 
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表 2 采用混合藤与孤立藤结构的AIC值对比
Table 2. AIC values of mix vine and isolated vine structures
藤结构 C 藤 D 藤 混合藤 AIC 值 0.197 0.217 0.129
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表 3 两类典型随机场景情况调度费用对比
Table 3. Economic dispatch results of two typical random scenarios
USD 场景 预测场景
煤耗费用再调度费用 总费用 不考虑相关性 9 969.16 1 544.45 11 513.61 考虑相关性 9 969.16 3 540.64 13 509.80 实际出力 9 969.16 4 320.77 14 289.93
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