The transition to renewable energy sources requires advanced energy storage solutions to manage their intermittent nature. This thesis explores the feasibility of implementing Battery as a Service (BaaS) for a renewable energy community (RECs) setting, aiming to incorporate this model as part of an existing stacked revenue framework utilized by battery owners across various energy markets. By directly linking battery owners with energy communities, the study shows that renting out battery storage can eliminate intermediary overheads, thus providing financial benefits to both parties. The research addresses two main questions: developing a stacked revenue model for grid-connected batteries including energy communities, and comparing different battery sizing and control methods across various tariff schemes. The findings suggest that the proposed revenue model optimizes energy use and reduces costs for the community. This thesis contributes to the research field by presenting a viable economic model for integrating battery storage into decentralized energy communities.

Automated negotiation agents can highly benefit from learning their opponent’s preferences. Multiple algorithms have been developed with the two main categories being: heuristic techniques and machine learning techniques. Historically, heuristic techniques have dominated the field, but with the recent development in the field of machine learning, this is no longer true. The main goal of the paper is to compare these two techniques quantitatively using the Pearson correlation of bids. The models that were chosen as the heuristic and machine learning baseline are the Smith and the Perceptron models, respectively. Our results show that the two baselines have similar performance. This leads us to conclude that machine learning algorithms have caught up with their heuristic counterparts. Furthermore, we have also found a statistically significant correlation between the Perceptron model’s accuracy and the seen bid space.