## 1 Introduction
### Literature Review
(2019) evaluated battery energy storage systems as a flexibility tool for the healthcare sector. In their study, they proposed a model for a UK hospital with a PV system for arbitrage in the electricity market and providing ancillary services. [PERSON] et al. (2019) investigated the degradation status of lithium-ion batteries performing arbitrage and providing frequency regulation services. It was determined that the degradation rate is rapid in the initial 1000 cycles during arbitrage and then it stabilizes. [PERSON] et al. (2019) proposed an operation framework for a battery energy storage system in Great Britain providing Great Britain's Firm Frequency Response (FFR) and realizing arbitrage. In their study, machine learning classifiers such as Random Forest, Naive Bayes, Decision Tree, and neural network algorithms to predict market outcomes are utilized. The results indicated that engaging in arbitrage could yield a significant amount of revenue. [PERSON] et al. (2019) empirically evaluated the service of battery energy storage systems providing frequency control ancillary services in the Australian electricity markets. The results indicated that batteries significantly reduce ancillary service market prices. [PERSON] et al. (2019) examined the historical value of electricity storage in the day-ahead electricity market of the EU-28 countries, Norway, Switzerland, and Turkiye. The model was solved considering that storage durations ranged from 1 h to 10 h and round-trip efficiency ranged from 50% to 100%. [PERSON] et al. (2019) presented an operation model for commercial buildings including lithium-ion batteries for peak shaving and price arbitrage and a PV system for self-consumption. The results stated that if the battery price is below 250 USD/kWh, the net present value is positive. [PERSON] et al. (2019) discussed energy arbitrage and the fast frequency response situations for battery energy storage systems. The authors examined this situation with a techno-economic model. In the results, they stated that the proposed structure provides profit and that a battery that can be discharged for 1.5-2 h at full power is suitable for the system. [PERSON] et al. (2019) proposed a framework for a 100 MWp PV system with liquid air energy storage, located in southern Spain, to engage in arbitrage in the Iberian energy market. [PERSON] et al. (2019) examined the arbitrage situation in the day-ahead electricity market for batteries in European countries for 2019. However, herein, it should be stated that a 1-year long-term evaluation was made based on historical electricity prices. [PERSON] and [PERSON] (2019) discussed the suitability of lithium-ion batteries for the arbitrage situation in European electricity markets. The results showed that Romaniaand the United Kingdom could achieve operating profits of 10,000-13,000 EUR\(/1\) MWh of capacity. [PERSON] et al. (2018) explained a structure for renewable energy sources with battery energy storage to perform arbitrage in the Iberian electricity market. Additionally, Long Short-Term Memory (LSTM) was used for price prediction. In the results of their study, it was stated that the profit can be increased by 21% to 36% with the prediction model and also that the most suitable storage system is a lithium-ion battery. [PERSON] et al. (2018) presented a method for the arbitrage of different energy storage technologies in California markets. It was stated that an income of 270 USD/MW was obtained with California electricity market prices. [PERSON] et al. (2018) conducted a long-term economic analysis of battery energy storage systems in the German and Austrian day-ahead power markets. They also realized a scenario by predicting electricity prices with the seasonal autoregressive integrated moving average (SARIMA) model. There are several works that propose methods for predicting electricity demand and/or prices. [PERSON] (2018) used an LSTM network to predict the demand for electricity in Poland. [PERSON] et al.