This study presents an energy manager (EM) based on reinforcement learning (RL) for low-power wearable IoT devices with solar energy harvesting used in Precision Livestock Farming (PLF). The system utilizes battery level, device orientation, weather forecasts, and time of day to dynamically adjust the device’s energy consumption by modifying the monitoring duty cycle, the number of transmissions, and GPS executions to balance the energy harvested from flexible solar panels with energy consumption. Using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, the EM learns how to perform these adjustments. Model-based simulations over extended periods, along with real-world deployments, demonstrate the system’s ability to achieve Energy Neutral Operation (ENO), outperforming state-of-the-art algorithm in terms of harvested energy utilization. Additionally, a statistical equivalence test (TOST) is also applied to formally validate that the system maintains energy neutrality. The lightweight neural network design ensures compatibility with resource-constrained devices. This work addresses the scarcity of RL-based EM implementations in real-world wearables and PLF applications and demonstrates their potential for use in such scenarios.

Many people across Latin America still do not have access to reliable electricity. Although Chile exhibits a comparatively high electricity coverage, many barriers are still present for the development of sustainable energy supply solutions exploiting local renewable energy sources. To face this challenge, a coconstruction methodology is proposed, which considers a flexible and participatory design with continuous communication between the technical team of the project and the community, thus ensuring informed decision making around the project design. In this context, microgrid-based solutions offer an ideal opportunity to exploit the integration of energy sources adapted to the specific local characteristics. The coconstruction methodology allows the identification of local requirements, less often considered for design procedures based on a traditional approach, in a joint work with the communities so that the technological solution is tailored for it. Consequently, different technical solutions (design adaptations and innovations) have been proposed and developed under this framework, such as: energy management systems, demand response strategies, microgrid applications for Mapuche communities, microformers, a monitoring system that includes social aspects, and vehicle to grid for microgrids. This paper summarizes the experience of several microgrid projects in Chile, identifies risks, impacts, control actions, and discusses their replicability to the Latin American and the Caribbean region.