## 6 Conclusions  
In this study, we propose an artificial intelligence-based system that predicts day-ahead market prices using deep learning algorithms and the inclusion of SLBs in the market utilizing the MILP method. To predict the electricity market prices, an N-HiTS-based model is trained. Then, the trained model is compared to four deep learning architectures that are common in the literature (Simple RNN, LSTM, GRU, and N-BEATS). The SLB facility consists of 10 different battery types of the best-selling EVs in Turkkiye in 2023. Various case studies are conducted to demonstrate the effectiveness of the system. It should be noted that the proposed system is tested on real market data from the Turkish day-ahead market. According to the test results, our system can generate gains of up to 85.48% of the maximum gainable profit without any loss. Our novel system approach serves as a proof of concept for exploring new research avenues regarding the involvement of SLB facilities in day-ahead markets. Figure 10: Predictions and actual prices for February. Figure 9: State of energy levels in SLBs for June. The necessary regulations regarding incentives, implementation, and policies for the second-life usage of batteries, as well as the establishment and operation of SLB facilities, need to be established. It is worth mentioning that no policies have been implemented by governments on this matter so far. However, as the sector evolves, future policies may emerge to regulate it. In future studies, when long-term scenarios such as weekly, monthly, or yearly operations are considered, the impact of battery health and the effect of weather conditions on batteries can also be assessed and integrated into the analysis. Additionally, beyond the day-ahead market, other market structures such as intraday markets, balancing power markets, ancillary services markets, and futures markets can also be considered for further research. Writing--original draft, O.K. and A.C.; Validation and supervision, T.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study did not require ethical approval. Informed Consent Statement: Informed consent was not required. Publicly available datasets were analyzed in this study. Access requires free registration. The data can be found here: [[https://seffaflik.epias.com.tr/electricity/electricity-markets/day-ahead-market-dam/market-clearing-price-mcp](https://seffaflik.epias.com.tr/electricity/electricity-markets/day-ahead-market-dam/market-clearing-price-mcp)]([https://seffaflik.epias.com.tr/electricity/electricity-markets/day-ahead-market-dam/market-clearing-price-mcp](https://seffaflik.epias.com.tr/electricity/electricity-markets/day-ahead-market-dam/market-clearing-price-mcp)) (accessed on 31 October 2024). The authors declare no conflict of interest. The maximum charging energy of the SLB \(sl\) [kW]. The maximum discharging energy of the SLB \(sl\) [kW]. The maximum discharging energy of the SLB \(sl\) [kWh]. The initial energy level of the SLB \(sl\) at the beginning of the day [kWh]. The maximum state of the energy level of the SLB \(sl\) [kWh]. The binary decision variable used to prevent simultaneous selling and buying at the same hour. If it is 1, energy can be sold; if it is 0, energy can be purchased [0,1]. The binary decision variable used to prevent simultaneous charging and discharging at the same hour. If it is 1, energy can be charged; if it is 0, energy can be discharged [0,1]. The day-ahead electricity market price predicted with deep learning at time \(t\) [5]. The charging efficiency of the SLB \(sl\) [%]. The discharging efficiency of the SLB \(sl\) [%]. The amount of energy purchased from the electricity market by the SLB facility during time \(t\) [kWh]. The charge energy of the SLB \(sl\) during time \(t\) [kWh]. The discharge energy of the SLB \(sl\) during time \(t\) [kWh]. The amount of energy sold from the SLB battery facility to the electricity market during time \(t\) [kWh].