Optimisation models for the day-ahead energy and reserve scheduling of a hybrid wind-battery virtual power plant

TL;DR

This paper develops two optimisation models for short-term scheduling and redispatch of a wind-battery virtual power plant participating in day-ahead markets, considering battery degradation and reserve forecasting.

Contribution

It introduces a stochastic and a deterministic MILP approach for VPP scheduling, incorporating battery degradation costs and real-time reserve use strategies.

Findings

Models are effective for short-term scheduling and redispatch.

Proposed models are computationally efficient for daily use.

Battery degradation costs significantly impact scheduling decisions.

Abstract

This work presents a suite of two optimisation models for the short-term scheduling and redispatch of a virtual power plant (VPP) composed of a wind farm and a Li-ion battery, that participates in the day-ahead energy and secondary regulation reserve markets of the Iberian electricity market. First, a two-stage stochastic mixed-integer linear programming model is used to obtain the VPP's generation and reserve schedule and the opportunity cost of the energy stored in the battery. The model has an hourly resolution and a look-ahead period beyond the markets' scheduling horizon and considers the hourly battery degradation costs as a function of both the depth of discharge and the discharge rate. Different strategies are evaluated to forecast the real-time use of the committed secondary regulation reserves. Second, a deterministic MILP model is used to determine the redispatch of the VPP using as input the generation and reserve schedule and the VPP's storage opportunity cost provided by the former model and is executed on an hourly rolling basis. The results obtained show that the proposed models are effective for the short-term scheduling and redispatch of the VPP used with a low computational time, making them tractable and practical for their daily use.