A Pricing Strategy Reflecting the Cost of Power Volatility to Facilitate Decentralized Demand Response
A Pricing Strategy Reflecting the Cost of Power Volatility to Facilitate Decentralized Demand Response
复制标题
反映电力波动成本的定价策略，以促进分散的需求响应
DOI：
10.1109/access.2019.2932499
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Heng Shi
中科院分区：
文献类型：
--
作者：
Zhong Zhang;Furong Li;Heng Shi
以往的电价策略包括分时电价和动态电价，它们反映了系统的边际成本，根据用户的用电量来计算用户的电费。很少有人努力了解电力波动对总生产成本的影响。因此，本文提出了一种新的定价策略，反映了电力波动产生的成本。首先，研究波动性对生产成本的影响，量化波动性成本。其次，提出了一种新的定价模型，将波动成本分摊到消费者和可再生能源发电（REG）。它可以揭示单个负载/REG曲线与系统负载曲线之间的耦合关系。第三，在所提出的定价策略下，客户/REG有助于以分散的方式平坦化系统负荷曲线并降低生产成本，这是基于Haar小波变换的理论证明。对居民负荷的验证表明，综合负荷曲线的波动性和峰谷差分别减小了34.07%和19.81%。该策略解决了小时价格策略所面临的客户之间的同步响应问题。在兆瓦级负荷上的测试表明，系统负荷波动性降低了61.95%，生产成本降低了2.21%。它还将峰谷差减小了6.52%。
Previous pricing strategies including time-of-use price and dynamic price reflect system marginal cost and calculate consumers’ bills according to the quantity of their electricity usage. Little effort is made to understand the impact of power volatility on total production costs. This paper thus proposes a novel pricing strategy reflecting the cost arising from power volatility. Firstly, the impact of volatility on the production cost is investigated to quantify volatility cost. Secondly, a novel pricing model is proposed to allocate the volatility cost to consumers and renewable energy generations (REGs). It can reveal the coupling relationship between an individual load/REG curve and the system load curve. Thirdly, under the proposed pricing strategy, customers/REGs help to flatten the system load curve and reduce the production cost in a decentralized manner, which is certificated theoretically based on the Haar wavelet transforms. Validation on residential level loads shows that the volatility and peak-to-valley difference of aggregated load curve is reduced by 34.07% and 19.81%, respectively. The problem of synchronous response among customers faced by hourly price strategies is addressed by the proposed strategy. A test on megawatt-level loads shows a 61.95% reduction in system load volatility and a 2.21% reduction in production cost. It also reduces the peak-to-valley difference by 6.52%.
影响因子：
9.6
作者：
Seung-Jun Kim;G. Giannakis
通讯作者：
Seung-Jun Kim;G. Giannakis