Over the past decade, bidding in power markets has attracted widespread attention. Reinforcement Learning (RL) has been widely used for power market bidding as a powerful AI tool to make decisions under real-world uncertainties. However, current RL methods mostly employ low dimensional bids, which significantly diverge from the N price-power pairs commonly used in the current power markets. The N-pair bidding format is denoted as High Dimensional Bids (HDBs), which has not been fully integrated into the existing RL-based bidding methods. The loss of flexibility in current RL bidding methods could greatly limit the bidding profits and make it difficult to tackle the rising uncertainties brought by renewable energy generations. In this paper, we intend to propose a framework to fully utilize HDBs for RL-based bidding methods. First, we employ a special type of neural network called Neural Network Supply Functions (NNSFs) to generate HDBs in the form of N price-power pairs. Second, we embed the NNSF into a Markov Decision Process (MDP) to make it compatible with most existing RL methods. Finally, experiments on Energy Storage Systems (ESSs) in the PJM Real-Time (RT) power market show that the proposed bidding method with HDBs can significantly improve bidding flexibility, thereby improving the profit of the state-of-the-art RL bidding methods.

SINGAPORE: The Housing and Development Board (HDB) has rolled out the use of artificial intelligence (AI) to enhance worker safety at its construction sites. This AI system will focus on two scenarios which are common causes of worksite accidents, based on data from the Ministry of Manpower, HDB said in a media briefing on Thursday (Sep 12). The system will monitor workers who come within one metre of a non-barricaded edge with a drop of more than two metres and those who are under the path of heavy loads lifted by tower cranes. Currently, construction work sites rely on manual supervision by site supervisors and Workplace, Safety and Health Officers (WSHO) to ensure compliance with safety standards. According to HDB, this is a "resource intensive endeavour", requiring multiple WSHOs and supervisors.