Understanding power prices dynamics is crucial for valuing flexibility assets such as storage or flexible consumption facilities that accommodate fluctuations in power supply from variable renewables. Owners of such assets need to know how extreme power prices can become in order to optimally manage (dis)charging or adjusting consumption volumes. We examine how to predict those high and low prices, being the different quantiles of the power price probability distribution function, and question how supply from variable renewable sources affect different quantile prices.

The first contribution of our paper is that we apply quantile regressions in a panel data framework. This methodology acknowledges that day-ahead power markets’ data is structured as cross-sectional data and, as opposed to previous quantile regression techniques introduced in power markets, allows for simultaneous predictions for all hours during a delivery day. Day-ahead power prices for all 24 h in the next day are determined at the same moment, one day before delivery. The hourly data is therefore not a time-series, but a cross section. The second contribution is that we examine the interaction between demand and supply from variable renewable sources, instead of linear dependencies only.

We find that lower and higher quantile prices are more heavily affected by variations in supply from variable renewable sources than center quantile prices. This enables owners of flexibility assets to better manage their assets in anticipation of excess or scarce supply from renewable sources. By doing so, they increase the flexibility of power systems that face increasing installed capacity of variable renewable energy sources.

了解电价动态对于评估灵活资产（例如适应可变可再生能源电力供应波动的存储或灵活消费设施）至关重要。此类资产的所有者需要了解极端电价会如何变化，以便最佳地管理（放电）或调整消耗量。我们研究了如何预测高低价格，即电价概率分布函数的不同分位数，并质疑可变可再生能源的供应如何影响不同的分位数价格。

我们论文的第一个贡献是我们在面板数据框架中应用分位数回归。这种方法承认日前电力市场的数据被构造为横截面数据，并且与之前电力市场中引入的分位数回归技术相反，允许同时预测交付日的所有时间。次日所有 24 小时的日前电价在同一时刻，即交付前一天确定。因此，每小时数据不是时间序列，而是横截面。第二个贡献是我们研究了可变可再生能源的需求和供应之间的相互作用，而不仅仅是线性相关性。

我们发现，与中心分位数价格相比，较低和较高的分位数价格受可变可再生能源供应变化的影响更大。这使灵活性资产的所有者能够在预期可再生能源供应过剩或稀缺的情况下更好地管理其资产。通过这样做，它们提高了电力系统的灵活性，因为这些电力系统面临着可变可再生能源装机容量的增加。