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. The global push towards sustainable development has led to a significant expansion of renewable energy sources, such as solar and wind. 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. ERs, as advanced power-electronic devices, have the potential to revolutionize the way renewable energy is integrated and managed in the power grid. To effectively harness the capabilities of ERs, a Long-Short-Term Memory (LSTM) network augmented with an attention mechanism is employed. This powerful combination is capable of accurately predicting photovoltaic (PV) generation and load consumption. 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 carefully constructed. The primary objective of this model is 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. The results of this field test are highly encouraging. When compared with conventional methods, the proposed approach has been shown to enhance the local absorption of PV generation by over 24.7%.