A novel method for enhancing the accommodation of renewable energy in flexible AC/DC distribution networks based on energy router devices

In the contemporary landscape of complex industrial processes, the efficient utilization of renewable energy has emerged as a crucial concern, captivating the attention of researchers, industries, and policymakers alike. However, integrating these renewable energy sources into traditional AC distribution networks has proven to be a formidable challenge. Against this backdrop, this paper presents an innovative optimal control method tailored for energy routers (ERs) in flexible AC/DC distribution networks. To effectively harness the capabilities of ERs, a Long-Short-Term Memory (LSTM) network augmented with an attention mechanism is employed. The attention mechanism allows the LSTM network to focus on the most relevant information in the time-series data, thereby improving the prediction accuracy. Subsequently, an optimization model is constructed to maximize the utilization of renewable energy by ERs. To validate the effectiveness of the proposed method, a two-week field test was conducted as part of an energy retrofit project in China. When compared with conventional methods, the proposed approach has been shown to enhance the local absorption of PV generation by over 24.7%.