The PowerTAC competition provides a multi-agent simulation platform for electricity markets, in which intelligent agents acting as electricity brokers compete with each other aiming to maximize their profits. Typically, the gains of agents increase as the number of their customers rises, but in parallel, costs also increase as a result of higher transmission fees that need to be paid by the electricity broker. Thus, agents that aim to take over a disproportionately high share of the market, often end up with losses due to being obliged to pay huge transmission capacity fees. In this paper, we present a novel trading strategy that, based on this observation, aims to balance gains against costs; and was utilized by the champion of the PowerTAC-2020 tournament, TUC-TAC. The approach also incorporates a wholesale market strategy that employs Monte Carlo Tree Search to determine TUC-TAC’s best course of action when participating in the market’s double auctions. The strategy is improved by making effective use of a forecasting module that seeks to predict upcoming peaks in demand, since in such intervals incurred costs significantly increase. A post-tournament analysis is also included in this paper, to help draw important lessons regarding the strengths and weaknesses of the various strategies used in the PowerTAC-2020 competition.

Renewable Energy-Supplying cooperatives (REScoops) are cooperatives of renewable energy producers and/or consumers, which are under formulation in the emerging European smart grid. Their emergence highlights the importance of proconsuming green energy and simultaneously puts forward principles such as energy democracy and self-consumption, assists the fight against energy poverty, and helps reduce GHG emissions. To this end, the incorporation of scientific and technological solutions into the REScoops’ everyday business and practices, is key for improving these practices and assessing their potential benefits, and as such for enabling them to deliver the maximum possible gains to their members and society at large. This chapter outlines three key axes of scientific research and solutions that can be used for REScoops, namely, (a) a statistical analysis, (b) an applied behavioural analysis, and (c) an artificial intelligence/machine learning one. Also presented are results and lessons learned from providing such solutions to European REScoops as part of the H2020 REScoop Plus project.

Cooperatives for renewable energy supply (REScoops) provide their members renewably generated energy within a cooperative model that enables members to co-decide on the cooperative’s future. REScoops do not only collectively own renewable energy production facilities and supply this to their members, they also use their specific position as energy suppliers to take several actions to persuade their members to save energy. Although the activities that REScoops undertake to some extent resemble those of other organizations, because of their particular organisational and business model as citizens initiatives, the cooperative model, REScoops are supposed to be very well positioned for activities to influence and help their members to save energy. The paper discusses arguments why the REScoop model in energy supply can be an important contributor to reduce energy use by their members. Further this paper discusses measures that have been undertaken by REScoops studied in the REScoop Plus project. We use some illustrative examples to discuss if REScoops are in a relatively good position to take certain measures and succeed in persuading customers to lower their energy consumption level and elaborate on future experiments to explore the proposition that REScoop members save more energy due to actions of these REScoops towards their members.