Imbalance Reduction of P2P Energy Market by Closed-Loop Clustering and Forecasting

Energy forecasting at the individual level remains a technical barrier for the peer-to-peer (P2P) energy market. Forecasting errors will cause energy imbalances between real-time delivery and contracts. To reduce the economic loss, this paper proposes an imbalance reduction method based on hierarchical energy forecasting. Existing hierarchical forecasting usually cluster prosumers based on the similarities of their energy profiles. However, minimising the variance cannot guarantee reductions in energy imbalances. This paper explores a novel closed-loop clustering (CLC) algorithm, which aligns the mutual objectives of clustering and imbalance reduction through the introduction of a feedback mechanism. The proposed method is applied in a simulated P2P energy market to cluster photovoltaics (PVs) into virtual groups (VG). The output of each VG is predicted and traded with flexible demands as an entity. The results show that CLC could reduce up to 25.70% energy imbalance compared with individual trading. Network constraints have shown minor impact on the trading results.

Automated summary

Energy forecasting at the individual level is a technical barrier for the P2P energy market. This paper proposes a method based on hierarchical energy forecasting to reduce the energy imbalances caused by forecasting errors. The paper introduces a novel closed-loop clustering algorithm that aligns clustering and imbalance reduction through a feedback mechanism. The results show that the proposed method could reduce up to 25.70% energy imbalance compared to individual trading, with minor impact from network constraints.