Efficiently integrating renewable resources into electricity markets is vital
for addressing the challenges of matching real-time supply and demand while
reducing the significant energy wastage resulting from curtailments. To address
this challenge effectively, the incorporation of storage devices can enhance
the reliability and efficiency of the grid, improving market liquidity and
reducing price volatility. In short-term electricity markets, participants
navigate numerous options, each presenting unique challenges and opportunities,
underscoring the critical role of the trading strategy in maximizing profits.
This study delves into the optimization of day-ahead and balancing market
trading, leveraging quantile-based forecasts. Employing three trading
approaches with practical constraints, our research enhances forecast
assessment, increases trading frequency, and employs flexible timestamp orders.
Our findings underscore the profit potential of simultaneous participation in
both day-ahead and balancing markets, especially with larger battery storage
systems; despite increased costs and narrower profit margins associated with
higher-volume trading, the implementation of high-frequency strategies plays a
significant role in maximizing profits and addressing market challenges.
Finally, we modelled four commercial battery storage systems and evaluated
their economic viability through a scenario analysis, with larger batteries
showing a shorter return on investment.

将可再生资源、储能设备、电力市场、交易策略和电池储能系统作为关键词，研究了通过优化交易策略和量化预测来提高电力市场效率，同时评估了商业电池储能系统的经济可行性。

电力套利中基于分位数的交易策略优化

Optimizing Quantile-based Trading Strategies in Electricity Arbitrage

Wind speed is a powerful source of renewable energy, which can be used as an
alternative to the non-renewable resources for production of electricity.
Renewable sources are clean, infinite and do not impact the environment
negatively during production of electrical energy. However, while eliciting
electrical energy from renewable resources viz. solar irradiance, wind speed,
hydro should require special planning failing which may result in huge loss of
labour and money for setting up the system. In this paper, we discuss four deep
recurrent neural networks viz. Stacked Stateless LSTM, Stacked Stateless GRU,
Stacked Stateful LSTM and Statcked Stateful GRU which will be used to predict
wind speed on a short-term basis for the airport sites beside two campuses of
Mississippi State University. The paper does a comprehensive analysis of the
performance of the models used describing their architectures and how
efficiently they elicit the results with the help of RMSE values. A detailed
description of the time and space complexities of the above models has also
been discussed.

本研究通过使用四种深度递归神经网络模型，对 Mississippi State 大学校园附近机场站点的短期风速进行预测，详细描述了模型的体系结构、效率以及通过 RMSE 值获得的结果，并讨论了上述模型的时间和空间复杂度。