Flexibility Requirement when Tracking Renewable Power Fluctuation with Peer-to-Peer Energy Sharing

TL;DR

This paper introduces a privacy-preserving peer-to-peer energy sharing mechanism that calculates individual flexibility requirements for managing renewable power fluctuations, using a scalable and robust optimization approach.

Contribution

It develops a novel linearization and approximation method for determining flexibility needs without relying on exact basis characterization, addressing model degeneracy issues.

Findings

The method accurately estimates flexibility requirements in renewable energy contexts.

The approach is scalable and robust against model degeneracy.

Case studies confirm the effectiveness of the proposed algorithm.

Abstract

Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to obtain the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is established to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on the exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable.