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Machine intelligent forecasting based penalty cost minimization in hybrid wind-battery farms
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-07-08 , DOI: 10.1002/2050-7038.13010 Harsh S. Dhiman 1 , Dipankar Deb 2 , S. M. Muyeen 3 , Ajith Abraham 4
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2021-07-08 , DOI: 10.1002/2050-7038.13010 Harsh S. Dhiman 1 , Dipankar Deb 2 , S. M. Muyeen 3 , Ajith Abraham 4
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Modern-day hybrid wind farm operation is fundamentally dependent on the accuracy of short-term wind power forecasts. However, the inevitable error in wind power forecasting limits the power transfer capability to the utility grid, which calls for battery energy storage systems to furnish the deficit power. This manuscript addresses a wind forecasting based penalty cost minimization solution for hybrid wind-battery farms. We choose six wind farm sites (three offshore and the other three onshore) to study machine intelligent forecasting based solutions and compare the performance of a wavelet-Twin support vector regression (TSVR) based wind power forecasting model with 
-Twin support vector regression, Random forest, and Gradient boosted machines, for penalty cost minimization. We access the penalties that arise as power imbalances along with the battery system's cost. We find that TSVR based wind power forecasting method results in a minimum global operational cost for all the wind farm sites under study.
中文翻译：
基于机器智能预测的混合风电场惩罚成本最小化
现代混合风电场的运行从根本上取决于短期风电预测的准确性。然而，风电预测不可避免的误差限制了向公用电网的电力传输能力，这就需要电池储能系统提供不足的电力。这份手稿针对混合风电池场的基于风预测的惩罚成本最小化解决方案。我们选择了六个风电场站点（三个海上和另外三个陆上）来研究基于机器智能预测的解决方案，并将基于小波孪生支持向量回归 (TSVR) 的风电预测模型的性能与-双支持向量回归、随机森林和梯度提升机器，用于最小化惩罚成本。我们访问了由于功率不平衡以及电池系统成本而产生的惩罚。我们发现基于 TSVR 的风电预测方法可以使所有正在研究的风电场站点的全球运营成本最小。
更新日期：2021-09-16
-Twin support vector regression, Random forest, and Gradient boosted machines, for penalty cost minimization. We access the penalties that arise as power imbalances along with the battery system's cost. We find that TSVR based wind power forecasting method results in a minimum global operational cost for all the wind farm sites under study.
中文翻译：
基于机器智能预测的混合风电场惩罚成本最小化
现代混合风电场的运行从根本上取决于短期风电预测的准确性。然而，风电预测不可避免的误差限制了向公用电网的电力传输能力，这就需要电池储能系统提供不足的电力。这份手稿针对混合风电池场的基于风预测的惩罚成本最小化解决方案。我们选择了六个风电场站点（三个海上和另外三个陆上）来研究基于机器智能预测的解决方案，并将基于小波孪生支持向量回归 (TSVR) 的风电预测模型的性能与
-双支持向量回归、随机森林和梯度提升机器，用于最小化惩罚成本。我们访问了由于功率不平衡以及电池系统成本而产生的惩罚。我们发现基于 TSVR 的风电预测方法可以使所有正在研究的风电场站点的全球运营成本最小。
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