China is leading the charge in the global energy transition by advancing large-scale renewable energy hubs, integrating wind power, photovoltaic (PV) systems, thermal power, and energy storage. These efforts aim to meet ambitious power transmission targets and contribute to the nation’s “dual carbon” goals. However, the distinct seasonal generation patterns of renewable energy don’t match demands. New research addresses this issue with an innovative solution; a capacity expansion model for multi-temporal energy storage.
What is Multi-Temporal Energy Storage?
Multi-temporal energy storage refers to the use of different types of energy storage systems that operate over various time scales. Short-term energy storage, such as batteries, handles fluctuations within a daily timeframe, from minutes to hours. Long-term energy storage, like pumped hydro storage, compressed air energy storage, and hydrogen storage, manages energy reserves and transfers over weeks, months, or even seasons.
The new capacity expansion model developed in this study incorporates both the output characteristics of renewable energy and the load demands of the receiving end. By using time series decomposition with an adaptive clustering method, the model extracts typical scenarios that balance computational efficiency and solution accuracy.
Key Findings and Implications
- Increased Transmission Utilisation Hours Drive Long-Term Storage Needs:
The study highlights a direct correlation between transmission utilisation hours and the demand for long-term energy storage capacity. When the transmission usage reaches 6000 hours, the need for long-term storage escalates significantly. This finding underscores the importance of planning for long-term storage solutions as renewable energy bases expand. - Cost Reductions Through Coordinated Planning:
Coordinated planning of multi-temporal energy storage can reduce comprehensive generation costs by 1% to 18%. This cost-saving potential is crucial for making renewable energy projects more economically viable and competitive against traditional energy sources. - Adaptive Clustering Enhances Efficiency:
The model introduces an adaptive clustering method to determine the optimal number of clusters for each month. This method significantly reduces the number of variables involved in the planning process, enhancing computational efficiency without sacrificing accuracy. By decoupling the state of charge (SOC) for long-term energy storage into intra-day and inter-day states, the model effectively captures energy exchanges between typical scenarios, addressing discontinuities in energy across different periods. - Policy and Technological Implications:
The study’s insights have far-reaching implications for policy and technological innovation. As low-carbon policies push for greater integration of renewable energy, the role of advanced long-term storage technologies becomes increasingly critical. Policymakers can use these findings to guide decisions on expanding energy storage capacity and optimising the economic benefits of renewable energy bases.
China’s journey towards a sustainable energy future is marked by innovative approaches to managing renewable energy generation and transmission. The capacity expansion model for multi-temporal energy storage offers a promising solution to the challenges of seasonal generation patterns and load demand mismatches. By enhancing the efficiency and cost-effectiveness of renewable energy bases, this research paves the way for a greener, more resilient energy system. As the world watches China’s progress, these advancements are a useful blueprint for other nations striving to achieve their own energy transition goals.
Source
Capacity expansion model for multi-temporal energy storage in renewable energy base considering various transmission utilization rates, Journal of Energy Storage, 2024-09-20
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