uGIM: a week with peer-to-peer transactions (03/06/2019 - 09/06/2019)

uGIM is a microgrid intelligent management software that can represent individual microgrid’s players using a multi-agent approach. This dataset has data regarding a week (from 03-06-2019 to 09-06-2019) of a microgrid with five players (all offices). All agents have consumption and generation data and are able to participate in peer-to-peer transactions using an auction model. The dataset presents the data regarding: energy values (consumption and generation); energy forecasting (consumption and generation); auction participations; and peer-to-peer transactions. The transactions are analysed and classified by type: best choice; wrong sale; wrong purchase; sold too much; and bought too much. All five agents are able to sell energy from the peer-to-peer auction. However, only four can buy energy: L.1; L.2; L.3; and R.2. Agent Z.0 is configured only to sell energy. All agents have photovoltaic generation where L.1, L.2, L.3; and R.2 have 1 kW each and Z.0 has 6 kW. In uGIM, agents are deployed in the player’s facilities using single-board computers. All the data in this dataset is read and stored in five single-board computers. Each agent integrates several resources. In this microgrid deployment, all resources use TCP/IP communication. However, uGIM supports more protocols, such as Modbus/RTU and Modbus/TCP. uGIM related publications: - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. https://doi.org/10.1016/j.measurement.2019.107427 - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. https://doi.org/10.1109/ACCESS.2020.2985254 - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. https://doi.org/10.14201/gredos.144238 - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. https://doi.org/10.1016/j.jclepro.2019.118154The present work has received funding from National Funds through FCT under the project UID/EEA/00760/2019 and SFRH/BD/109248/201