Stochastic and deterministic formulations for capacity firming nominations

TL;DR

This paper compares stochastic and deterministic optimization models for managing a PV plant with storage to meet capacity firming requirements, demonstrating the effectiveness of stochastic methods with unbiased PV scenarios.

Contribution

It introduces both stochastic and deterministic quadratic optimization models for energy management under capacity firming constraints, extending prior stochastic approaches with a real microgrid case study.

Findings

Stochastic formulation effectively manages PV variability.

Deterministic model provides comparable performance.

Case study validates the models with real PV data.

Abstract

This paper addresses the energy management of a grid-connected photovoltaic plant coupled with a battery energy storage device, within the capacity firming specifications of the French Energy Regulatory Commission. The paper contributions are positioned in the continuity of the studies adopting stochastic models for optimizing the bids of renewable energy sources in a day-ahead market by considering a storage device. The proposed deterministic and stochastic approaches are optimization problems formulated as quadratic problems with linear constraints. The case study is a real microgrid with PV production monitored on-site. The results demonstrate the validity of the stochastic formulation by using an ideal predictor that produces unbiased PV scenarios.