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AWS IoT analytics platform for microgrid operation management
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DOI：10.1016/j.cie.2022.108331.png)
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摘要
En 中文
Microgrid (MG) represents a promising opportunity for integrating renewable energy systems with the electricpower grid. However, numerous complexities need to be addressed in the process. The electrical grid iscomplex, vulnerable, and centralized. Thus, the integration is challenging owing to the stochastic nature ofrenewable energy generation, which affects the possibility of reliable forecasting. The wastage due to poorestimation of clean energy generation discourages new investments in this area. However, recent advancementsin big data technologies enable processing a large amount of data captured from multiple sources in real-time.It opens the possibility of improving the operational optimization of MGs and the performance of forecastingmodels. The overall MG problem is formulated using a two-stage stochastic mixed-integer linear programmingproblem with recourse. Amazon Web Services (AWS) IoT analytics platform inputs data in real-time and runsa sophisticated wind generation forecast analysis. The stochastic model is solved using the Sample AverageApproximation (SAA) algorithm. The innovative methodology leads to significant improvements in the totalaverage operating cost by integrating AWS IoT Analytics compared to traditional methods that use historicaldata. Computations are performed for different power-grid settings, including a power-outage, and differentpower generators units capacities with total operation average cost savings of 7.6% and 5.9%, respectively.Sensitivity analysis showed that the SSA algorithm could solve all the instances by providing high-qualitysolutions. The AWS IoT strategy outperformed at 7.7 % and 3.6% for optimality gap and CPU time, respectively.We examined an actual case in Peru for an agricultural application to assess the performance of a stochasticoptimization model with a real-time IoT wind generation forecast strategy. The results revealed the followingcapabilities of our novel framework: (1) it can realize higher cost savings from the MG operating systems;(2) improve real-time renewable energy forecasting; (3) facilitate robust decision-making under conditions ofuncertainty.
Keyword:
Microgrid
IoT analytics
Two-stage stochastic optimization
Wind power
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
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