In the rapidly evolving energy sector, the integration of distributed photovoltaic (PV) systems and energy storage solutions (ESS) into distribution networks is transforming how electricity is managed and delivered. However, this shift has also introduced challenges, particularly in areas with high PV penetration, where managing voltage stability and minimizing network losses can be complex. A recent study published in the journal “IEEE Access” offers a promising solution to these challenges, potentially reshaping the future of autonomous control in distribution networks.
The research, led by Yukai Wei from the Xuchang KETOP Testing Research Institute Company Ltd. in China, introduces a novel “data-model” dual-driven collaborative optimization model. This model combines the Circulatory System Based Optimization (CSBO) algorithm with Long Short-Term Memory (LSTM) networks to enhance the autonomous control capabilities of distribution transformer areas with high PV penetration.
“We aimed to create a system that could not only optimize PV power consumption but also ensure voltage stability and reduce network losses,” Wei explained. The model achieves this by integrating offline optimization, knowledge migration, and online inference. The CSBO algorithm is used to solve multi-objective optimization problems, formulating strategies for PV reactive power regulation and ESS charging/discharging. These strategies are then used to generate optimal operation datasets through multi-scenario simulations.
The LSTM component of the model leverages its strength in processing time-series data to construct a real-time response model. This allows for rapid perception of grid status and dynamic control decisions. “The organic combination of CSBO and LSTM effectively improves data completeness, model complexity control, and real-time decision response,” Wei noted.
The implications of this research for the energy sector are significant. By enhancing the autonomous control of transformer areas with high PV penetration, the model can help utilities manage the increasing complexity of distribution networks more efficiently. This could lead to reduced operational costs, improved grid stability, and better utilization of renewable energy sources.
“We believe this model provides new ideas and implementation solutions for the autonomous control of transformer areas with high PV penetration,” Wei said. The study’s findings could pave the way for more advanced and efficient energy management systems, ultimately benefiting both energy providers and consumers.
Published in the journal “IEEE Access,” which translates to “IEEE Open Access,” the research represents a step forward in the integration of renewable energy sources into existing distribution networks. As the energy sector continues to evolve, such innovations will be crucial in ensuring a stable, efficient, and sustainable energy future.