Optimizing Self-consumption Algorithms for Enhanced Grid Feed-In from Renewable Sources

Managing energy overproduction in renewable energy installations is a significant challenge, particularly in avoiding inverter shutdowns due to excessive grid voltage. This paper investigates three approaches to automating energy consumption using smart plugs, aiming to reduce inverter shutdowns and increase the amount of energy fed into the grid. The first approach employs standard factory plugs, the second involves user-calibrated plugs, and the third utilizes plugs managed by a home automation server. The study aims to compare the effectiveness of these three solutions in lowering average grid voltage, reducing inverter shutdowns, and minimizing unnecessary energy consumption. Data from real PV installations were analyzed under various weather conditions and network loads. Results indicate that while standard factory plugs are easy to implement and cost-effective, they are the least efficient in energy savings and preventing inverter shutdowns. Manually calibrated plugs offer better energy efficiency but require higher costs and programming skills. The best performance was achieved with server-managed plugs, which, despite higher costs, provide the most efficient energy management and minimize inverter shutdowns. The results contribute to both theory and practice by providing insights into the cost-benefit analysis of different self-consumption strategies and their practical implications for grid management.