首页> 外文会议>IEEE Congress on Information Science and Technology >Hybridization and energy storage high efficiency and low cost
【24h】

Hybridization and energy storage high efficiency and low cost

机译:杂交和储能储存高效率和低成本

获取原文

摘要

Hybrid energies interest many companies and countries. No form of electricity production is optimal in all situations. The wind and the sun are intermittent but do not consume fuel and do not emit greenhouse gases. Natural gas electricity production emits greenhouse gases but is distributable (i.e. it has a yield that can be easily controlled between maximum values of nominal capacity or reduced to zero) to help balance supply and demand. Hydroelectric power often requires and devotes large areas, but is renewable and distributable. However, the realization of all these projects remains dependent on the development of more efficient and more economical electrical energy storage systems. Hybrid power plants: a solution for the future? To provide energy that is more affordable, more reliable, and more sustainable. AI, smart grid [6] and Energy storage: The dynamics of new energies, that is to say, local and renewable, are indeed launched. To succeed in this revolution, the problem of storing renewable energies, due to their intermittent nature, remains to be resolved. Machine learning and neural networks play an important role in improving forecasts in the energy industry.
机译:混合能量兴趣很多公司和国家。在所有情况下,任何电力生产都不是最佳的。风和阳光是间歇性的,但不会消耗燃料并且不发出温室气体。天然气电力产量发出温室气体,但可分配(即它的产量可以在标称容量的最大值或减少到零之间,以帮助平衡供需。水力发电经常需要和致力于大面积,但可再生和可分配。然而,实现所有这些项目的实现仍然依赖于开发更高效,更经济的电能存储系统。混合动力厂:未来的解决方案?提供更实惠,更可靠,更可持续的能源。 AI,智能电网[6]和储能:新能源的动态,也就是说,本地和可再生,确实发布。为了在这场革命中取得成功,由于他们间歇性的性质,储存可再生能源的问题仍然是解决的。机器学习和神经网络在改善能源产业的预测方面发挥着重要作用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号