This paper studies the use of conformal prediction (CP), an emerging probabilistic forecasting method, for day-ahead photovoltaic power predictions to enhance participation in electricity markets. First, machine learning models are used to construct point predictions. Thereafter, several variants of CP are implemented to quantify the uncertainty of those predictions by creating CP intervals and cumulative distribution functions. Optimal quantity bids for the electricity market are estimated using several bidding strategies under uncertainty, namely: trust-the-forecast, worst-case, Newsvendor and expected utility maximization (EUM). Results show that CP in combination with k-nearest neighbors and/or Mondrian binning outperforms its corresponding linear quantile regressors. Using CP in combination with certain bidding strategies can yield high profit with minimal energy imbalance. In concrete, using conformal predictive systems with k-nearest neighbors and Mondrian binning after random forest regression yields the best profit and imbalance regardless of the decision-making strategy. Combining this uncertainty quantification method with the EUM strategy with conditional value at risk (CVaR) can yield up to 93\% of the potential profit with minimal energy imbalance.
A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
Columbia Data Science Institute（哥伦比亚数据科学研究所）；Red River Clean Energy（红河清洁能源）；King Abdulaziz City for Science and Technology (KACST)（国王阿卜杜勒阿齐兹城市科学技术城）；National Science Foundation（国家科学基金会）
Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning
Department of Electronics, Information and Bioengineering（电子信息与生物工程系）；Department of Management, Economics and Industrial Engineering（管理、经济与工业工程系）；CMCC Foundation- Euro-Mediterranean Center on Climate Change（CMCC基金会—地中海气候变化中心）；RFF-CMCC European Institute on Economics and the Environment（RFF-CMCC欧洲经济与环境研究所）；Centre for Research on Geography, Resources, Environment, Energy and Networks (GREEN)（地理、资源、环境、能源与网络研究中心）
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market
University College Cork（科克大学学院）；International Energy Research Centre（国际能源研究中心）；Tyndall National Institute（廷德尔国家研究所）；University of Edinburgh（爱丁堡大学）；SFI Insight Centre for Data Analytics（SFI数据洞察中心）
