• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Energy Conversion & Management >Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles
【24h】

Solving the duck curve in a smart grid environment using a non-cooperative game theory and dynamic pricing profiles

机译：使用非合作博弈论和动态定价简介求解智能电网环境中的鸭曲线

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With the intermittency that comes with electricity generation from renewables, utilizing dynamic pricing will encourage the demand-side to respond in a smart way that would minimize the electricity costs and flatten the net electricity demand curve. Determining the optimal dynamic pricing profile that would leverage distributed storage to flatten the curve is a novel idea that needs to be studied. Moreover, the economic feasibility of utilizing distributed electrical energy storage is still not given in the literature. Therefore, in this paper, a novel way of solving a citywide dynamic model using a bilevel programming algorithm is introduced. The problem is developed as a novel non-cooperative Stackelberg game that utilizes air-conditioning systems and electrical storage through the end-users to determine the optimal dynamic pricing profile. The results show that the combined effect of utilizing demand-side air-conditioning systems and distributed storage together can flatten the curve while employing the optimal dynamic pricing profile. An economic study is performed to determine the economic feasibility of 20 different cases with different battery designs and the level of solar penetration. Three metrics were used to evaluate the economic performance of each case: the levelized cost of storage, the levelized cost of energy, and the simple payback period. Most cases had levelized cost of storage values lower than 0.457 $/kWh, which is the lower bound available in the literature. Seven out of 16 cases have a simple payback period shorter than the lifetime of the system (25 years). The case with a 100 MW PV power plant and a battery storage of size 597 MWh, was found to be the most promising case with a simple payback period of 12.71 years for the photovoltaic plant and 19.86 years for the demand-side investments.
机译：随着可再生能源发电的间歇性，利用动态定价将鼓励需求方以智能方式回应，这将使电力成本最小化，并使净电量需求曲线变平。确定将利用分布式存储来平整曲线的最佳动态定价配置文件是需要研究的新想法。此外，在文献中仍未给出利用分布式电能存储的经济可行性。因此，在本文中，介绍了使用彼得维基编程算法求解全市动态模型的新方法。该问题被开发为一种新的非协作Stackelberg游戏，它利用空调系统和通过最终用户的电存储来确定最佳动态定价轮廓。结果表明，利用需求侧空调系统和分布式存储的综合效果可以在采用最佳动态定价轮廓的同时压平曲线。进行经济研究，以确定不同电池设计和太阳能渗透水平的20种不同案例的经济可行性。三项指标用于评估每种情况的经济性能：储存量的稳定性成本，能源均衡成本和简单的投资回收期。大多数情况下，存储值的尺寸为低于0.457 $ / kWh，这是文献中可用的下限。 16例中的七种回收期短于系统的寿命（25年）。该壳体采用100 MW PV发电厂和电池储存量597兆瓦的电池，被发现是最有前途的案例，POADBOUND PACKET周期为光伏厂，19.86年的需求侧投资。

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