In the rapidly evolving landscape of renewable energy, the integration of large-scale new energy bases into the electricity grid presents both opportunities and challenges. A groundbreaking study published in ‘Dianli jianshe’ (Electric Power Construction) by YI Haiqiong and colleagues from the State Grid Economic and Technological Research Institute and other affiliated institutions, sheds light on a critical aspect of this integration: energy storage planning. The research introduces a sophisticated two-layer economic analysis model designed to optimize energy storage strategies for new energy bases, with a keen eye on maximizing revenue in the electricity spot market.
The study, led by YI Haiqiong, focuses on the intricate dance between new energy bases and the electricity spot market. The lower layer of the model simulates the long-term operation of the electricity spot market, incorporating security-constrained unit commitment and economic dispatch. This layer ensures that the model accurately reflects the real-world constraints and economic factors at play. The upper layer, meanwhile, concentrates on the optimal scheduling of the new energy base, aiming to maximize its revenue. “The complexity of the two-layer model requires an alternating iteration method to solve the problem,” explains YI Haiqiong, highlighting the innovative approach taken by the research team.
One of the most compelling findings of the study is the economic impact of different energy storage types on new energy bases. Through payback period assessments, the researchers determined that compressed air batteries have the most significant impact on enhancing the revenue of large-scale new energy bases. Conversely, 2-hour lithium batteries offer the lowest investment payback cost. This insight is particularly valuable for energy companies looking to invest in energy storage solutions that balance cost and revenue generation.
The study also identified an optimal energy storage capacity of approximately 1,000 MW, providing a clear target for energy providers. This finding is not just a theoretical exercise; it has practical implications for the energy sector. As ZHAO Lang, one of the co-authors, notes, “Our model and algorithm have been validated through a practical grid case study, demonstrating their effectiveness in real-world scenarios.”
The implications of this research are far-reaching. As the world transitions to renewable energy sources, the ability to efficiently store and dispatch energy will be crucial. The model developed by YI Haiqiong and her team offers a roadmap for energy providers to navigate this complex landscape, ensuring that investments in energy storage are both economically viable and strategically sound. This could shape future developments in the field, influencing how energy storage is planned and implemented in large-scale renewable energy projects.
The study, published in ‘Dianli jianshe’ (Electric Power Construction), represents a significant step forward in the field of energy storage planning. By providing a comprehensive model that considers both market dynamics and technical constraints, the research offers valuable insights for energy providers and policymakers alike. As the energy sector continues to evolve, the findings of this study will undoubtedly play a pivotal role in shaping the future of renewable energy integration.
