Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Managing informal learning in engineering contexts: the learners' perspective

According to recent studies, informal learning accounts for more than 75% of our continuous learning through life. However, the awareness of this learning, its benefits and its potential is still not very clear. In engineering contexts, informal learning could play an invaluable role helping students or employees to engage with peers and also with more experience colleagues, exchanging ideas and discussing problems. This work presents an initial set of results of the piloting phase of a project (TRAILER) where an innovative service based on Information & Communication Technologies was developed in order to aid the collection and visibility of informal learning. This set of results concerns engineering contexts (academic and business), from the learners' perspective. The major idea that emerged from these piloting trials was that it represented a good way of collecting, recording and sharing informal learning that otherwise could easily be forgotten. Several benefits were reported between the two communities such as being helpful in managing competences and human resources within an institution.