AI-based consumption and flexibility prediction for local community optimal aggregation and flexibility trading
Local power network congestion management is highly required due to high shares of intermittent RESs in the ASM headquarters area, as well as the need for reducing the reverse power flow power flow.
What is the challenge that is being addressed?
Due to high shares of intermittent RESs in the ASM headquarters area and the need for reducing the reverse power flow power flow, a strong requirement for local power network congestion management takes place.
The main challenge will be on the top-down selection of the prosumers intelligent clustering, within a much larger number of available consumers scattered throughout the selected piloted area, which include flexible loads, battery storage, RES local generation and EVs flexibility.
What is the AI solution the project plans to implement?
ASM will validate decentralised Virtual Power Plant-based community-level aggregation and local flexibility trading by leveraging on H2020 eDREAM P2P DLT and smart contract-based negotiation for flexibility trading along heterogeneous customers types (residential, C&I ones), which offer complementary loads profiles. ASM will undertake the role of the aggregator within multiple flexibility providers-one flexibility procurer scenario (the DSO) and the flexibility marketplace will be operated from intraday to near real time mode. ASM will focus on leveraging on end users and community-level with a view to predict the consumer behaviour of the end user, while matching with external context (es weather) and combining with P2P decentralised flexibility trading along pre-defined flexibility clusters. Based on such prediction, ASM as aggregator will be accordingly selecting those flexible loads and consumers which better fits with the flexibility request conveyed by the market.
Who will help implement this solution?
This pilot is implemented within the framework of the “I-NΕRGΥ: Artificial Intelligence for Next Generation Energy” Project. The I-NERGY Project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement No. 101016508.
The responsible partner for this Use Case is ASM (https://www.asmterni.it/).