In the rapidly evolving landscape of energy systems, integrating renewable sources like solar power presents both opportunities and challenges. A recent study published in the journal “IEEE Access” offers a novel approach to optimizing energy storage systems (ESS) in distribution networks, addressing the uncertainties of photovoltaic (PV) output and load demand. Led by Xiaolong Xiao from the State Grid Jiangsu Electric Power Company Ltd. Research Institute in Nanjing, China, the research proposes a method that could significantly enhance the adaptability and efficiency of ESS configurations.
The study focuses on the temporal variations and uncertainties of PV output and load demand, which are critical factors in designing effective ESS configurations. “Our goal was to develop a method that fully considers the actual operating characteristics of PV and load, thereby improving the adaptability of ESS configuration scale,” Xiao explained. To achieve this, the researchers employed a Gaussian kernel function to map the temporal variation characteristics of PV output scenarios, establishing a comprehensive evaluation index scenario for the uncertainty and temporal correlation of PV-load.
One of the key innovations in this research is the use of the iterative self-organizing data analysis algorithm (ISODATA) to optimize clustering of the comprehensive evaluation index scenario, generating PV-load typical scenarios. This clustering process helps in creating a more accurate representation of the real-world conditions, which is crucial for effective ESS configuration.
The researchers then constructed a distribution network ESS configuration model with multiple objectives: minimizing voltage fluctuation indicator, line loss rate, and minimizing ESS investment cost. To tackle the high-dimensional multi-objective functions in the model, they employed a non-dominated sorting genetic algorithm-III (NSGA-III). The optimal solution was selected using the entropy weight method (EWM), ensuring a balanced and efficient configuration.
The proposed method and model were analyzed and verified through case simulations, demonstrating their effectiveness in formulating ESS configuration scales and operation strategies in distribution networks. “The results show that our model and method can comprehensively consider the actual operating characteristics of PV and load, and can effectively formulate the ESS configuration scale and operation strategy in the distribution network,” Xiao noted.
This research has significant implications for the energy sector, particularly in optimizing the integration of renewable energy sources into distribution networks. By improving the adaptability and efficiency of ESS configurations, the method proposed by Xiao and his team could help reduce costs, enhance grid stability, and promote the wider adoption of renewable energy.
The study, published in the journal “IEEE Access” (which translates to “IEEE Open Access”), represents a step forward in the quest for more efficient and sustainable energy systems. As the energy sector continues to evolve, such innovative approaches will be crucial in shaping the future of power distribution and management.