Abstract 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.

中文翻译：

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使用非合作博弈论和动态定价配置文件求解智能电网环境中的鸭子曲线

摘要 由于可再生能源发电具有间歇性，利用动态定价将鼓励需求方以智能方式做出响应，从而最大限度地降低电力成本并拉平净电力需求曲线。确定利用分布式存储来拉平曲线的最佳动态定价配置文件是一个需要研究的新想法。此外，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，介绍了一种使用双层规划算法求解城市动态模型的新方法。该问题被开发为一种新颖的非合作 Stackelberg 博弈，该博弈通过最终用户利用空调系统和电力存储来确定最佳动态定价配置文件。结果表明，使用需求侧空调系统和分布式存储的组合效果可以在采用最优动态定价配置文件的同时使曲线变平。进行了一项经济研究，以确定具有不同电池设计和太阳能渗透水平的 20 个不同案例的经济可行性。使用三个指标来评估每个案例的经济绩效：平准化存储成本、平准化能源成本和简单投资回收期。大多数案例的平准化存储成本值低于 0.457 $/kWh，这是文献中可用的下限。16 个案例中有 7 个的简单投资回收期短于系统的使用寿命（25 年）。一个 100 MW 光伏电站和一个 597 MWh 电池存储的案例被认为是最有希望的案例，光伏电站的简单投资回收期为 12.71 年，需求侧投资的投资回收期为 19.86 年。