Tuesday 04 March 2025
The Shared Energy Storage (SES) aggregation framework, a novel approach to managing energy storage systems in residential areas, has been proposed by researchers. The framework, which combines deposit and withdrawal services (DWS) with a matching mechanism, aims to incentivize high price-tolerant prosumers to participate in SES.
Prosumers, households that generate their own electricity through solar panels or other means, have traditionally been reluctant to engage with energy storage systems due to the complexity of optimizing rental capacity and bidding prices. The proposed framework addresses this issue by eliminating the need for additional actions from prosumers, allowing them to maintain their existing energy storage patterns.
The DWS component enables prosumers to deposit and withdraw electricity while earning benefits. This is achieved through dynamic coefficients that adjust based on factors such as forecasted electricity demand, solar generation, and battery degradation. These coefficients are optimized using an improved deep reinforcement learning (DRL) algorithm, which takes into account the interactions between prosumers, energy storage systems, and the wholesale electricity market.
The matching mechanism, designed to align prosumer electricity consumption behaviors with ESP trading strategies, facilitates the construction of SES by identifying prosumers whose demand patterns are compatible with the ESP’s optimization goals. This approach helps reduce the need for expensive grid infrastructure upgrades, making it more cost-effective for households and energy service providers (ESPs) alike.
Case studies conducted using real-world data from residential areas demonstrate the effectiveness of the proposed framework in improving overall SES profits by up to 42.87%. Ablation experiments revealed that the design of dynamic DWS and the matching mechanism have the most significant impact on profit, with a loss of 15.06% when these components are removed.
The significance of this work lies in its ability to address the challenges faced by high price-tolerant prosumers in participating in SES. By simplifying the process and providing incentives for participation, the proposed framework has the potential to increase adoption rates and reduce energy costs for households. Furthermore, the integration of DRL algorithms and matching mechanisms enables ESPs to optimize their trading strategies and improve overall profitability.
The proposed SES aggregation framework is a step towards creating more efficient and sustainable energy systems. By leveraging advances in AI and machine learning, it is possible to develop solutions that benefit both households and energy providers. As the demand for renewable energy continues to grow, innovations like this will play a crucial role in shaping the future of our energy landscape.
Cite this article: “Enhancing Energy Storage Systems through Prosumer-Friendly Aggregation Frameworks”, The Science Archive, 2025.
Here Are The Keywords: Energy Storage, Prosumers, Solar Panels, Deep Reinforcement Learning, Dynamic Coefficients, Matching Mechanism, Wholesale Electricity Market, Energy Service Providers, Profit Optimization, Renewable Energy
