**1. Introduction**

Renewable energy systems face uncertainty in resource availability, which can create challenges in participating in electricity markets that require prior commitments. Energy storage systems (ESS) can mitigate this uncertainty by storing energy for later use [1].

The energy commitment for the day-ahead market is made by submitting offers to the market operator the day before delivery. Forecasting techniques are applied to predict electricity prices and resource availability [2], using techniques such as the SARIMA model, which can capture seasonal correlations in historical data. The authors in [3] demonstrate how a SARIMA model can outperform deep-learning techniques. In this work, SARIMA models are used to forecast both electricity prices and wind speeds, and have been shown to outperform deep-learning techniques in previous studies.

An offering strategy for energy commitment is typically formulated as a constrained optimization problem [4]. The decision vector includes offers for each hour of the market, and the objective is to maximize revenue during the session. Constraints include physical parameters of the plant and market rules, which heavily influence the feasible solution space.

Multi-market participation, which includes day-ahead and intraday market sessions, cannot be formulated as a single optimization problem due to the different timeframes of each market. A progressive optimization approach, as proposed by the authors in [5], is used in this work.

Intraday markets can be utilized to increase profits through revenue stacking, which typically involves combining energy and power services. Studies such as [6] demonstrate

**Citation:** Camuñas García-Miguel, P.L.; Alonso-Martínez, J.; Arnaltes Gómez, S.; García Plaza, M.; Peña Asensio, A. Battery Degradation Impact on Long-Term Benefits for Hybrid Farms in Overlapping Markets. *Batteries* **2023**, *9*, 483. https://doi.org/10.3390/ batteries9100483

Academic Editors: Pascal Venet, Luis Hernández-Callejo, Jesús Armando Aguilar Jiménez and Carlos Meza Benavides

Received: 27 July 2023 Revised: 13 September 2023 Accepted: 20 September 2023 Published: 22 September 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

that combining frequency response and arbitrage can increase revenue by up to 25%. Similar results are found in studies such as [7], which consider multiple spot market participation. This is also proposed by the authors in [7]. These approaches are compared in this work.

The aforementioned studies do not model the forecast generation and market offering processes. Generated forecasts at different times of the day can contradict each other and lead to issues when various markets overlap. This work addresses this issue.

Balancing mechanisms in electricity markets, such as penalties for deviations, can be used to address deviations caused by forecasting errors. Intraday markets can be used to correct errors [8], but require prior commitments. An alternative solution is to use energy storage systems for real-time corrections through a service called capacity firming (CF) [9].

Capacity firming has received increasing attention in recent literature as a service provided in real-time as opposed to arbitrage. Studies such as [10] propose energy conservation methods for control schemes of BESSs integrated with a PV system. Other works, such as [11], show how a simple control algorithm can achieve capacity firming in a BESS combined with a wind farm, although the storage system is only used for this service.

The Iberian electricity markets are used as an example in this work since, like many other systems, they have day-ahead and intraday spot markets. Market operator rules are incorporated into the optimization algorithm and the calculation of net benefits. Participation in intraday markets and the use of the CF service are compared, and the revenue stacking of different services is evaluated. This work also considers the effect of degradation on long-term profits, an approach that has not been considered in the previous literature.

A HF model consisting of a Gamesa G128 Wind Turbine Generator (WTG) and a BESS is presented. Unlike a virtual power plant (VPP), the system components are not distributed; thus, they share a point of common coupling (PCC). The SARIMA forecasting model uses wind historical data from the Sotavento experimental wind farm [12] and electricity prices from the Iberian market as inputs for the EMS.

The results show that participating in all markets may be counter-productive due to market overlap. The best results in terms of profits per degradation are obtained by allowing the BESS to participate only in the day-ahead market and performing capacity firming in real time.

The work proposes a new service called SOC Emptying (SE), which involves dividing the BESS into two virtual energy storage systems (VESS). One VESS provides regular services, while the other is used to empty the BESS whenever the combined state of charge (SOC) exceeds a specific threshold. This service aims to reduce upward deviations and give the BESS more maneuverability. The inclusion of this service further improves the results, resulting in increased profits per percentage of capacity loss and higher net present values when extrapolating the results for the entire project.

The contributions of this work are outlined as follows:


The paper is structured as follows: Section 2 provides an overview of the Iberian electricity market rules. Section 3 describes the HF model used in the study. The day-ahead and intraday market offering optimization models are analyzed in Section 4. Section 5 presents the simulation use cases and results. Conclusions and future research directions are discussed in Section 6.

#### **2. Iberian Market's Rules**

Before each market session opening time, the EMS must have forecasted data of prices and wind power generation. It is therefore necessary to know when each market session takes place and which hourly delivery periods are negotiated, both for forecasting and optimization problem definition. To obtain real benefits, this study follows the regulations of the Iberian markets, which take into account deviation costs.

This section introduces the rules of the Iberian markets, starting with the day-ahead market, followed by the intraday markets, and finally, the deviation rules are described, which consider four different deviation costs.

#### *2.1. Day-Ahead Market*

The majority of energy traded in the Iberian wholesale markets is conducted through the day-ahead market. In 2020, it accounted for 74% of the total energy traded [13]. Therefore, it is the most important market for arbitrage operations. The Iberian day-ahead market session takes place every day of the year at 12:00 CET.

The price and volume of energy is determined for each hour of the following day by the intersection of demand and supply. Market agents submit their offers through the market operator OMIE [14]. As a result, the EMS has to submit 24-hourly offers for the following day, using price and generation forecasts generated 12 to 36 hours prior to delivery time.
