In recent years, the global energy sector has seen significant transformation, particularly in Europe, with a notable increase in intermittent renewable energy integration. Italy and the European Union (EU) have been among the leaders in this transition, with renewables playing a substantial role in electricity generation as of the mid-2020s. The adoption of Battery Energy Storage Systems (BESS) has become crucial for enhancing grid efficiency, sustainability, and reliability by addressing intermittent renewable sources. This paper investigates the feasibility and economic viability of batteries in wholesale electricity markets as per EU regulation, focusing on the dynamics of very different markets, namely the Day-Ahead Market (DAM) based on system marginal price and the Cross-Border Intra-day Market (XBID) based on continuous trading. A novel model is proposed to enhance BESS operations, leveraging price arbitrage strategies based on zonal price predictions, levelized cost of storage (LCOS), and uncertain bid acceptance in continuous trading. Machine learning and deep learning techniques are applied for price forecasting and bid acceptance prediction, respectively. This study finds that data-driven techniques outperform reference models in price forecasting and bid acceptance prediction (+7–14% accuracy). Regarding market dynamics, this study reveals higher competitiveness in the continuous market compared to the DAM, particularly with increased risk factors in bids leading to higher profits. This research provides insights into compatibility between continuous markets and BESS, showing substantial improvements in economic profitability and the correlation between risk and profits in the bidding strategy (EUR +9 M yearly revenues are obtained with strategic behavior that reduces awarded energy by 60%).

In recent years, the global energy sector has seen significant transformation, particularly in Europe, with a notable increase in intermittent renewable energy integration. Italy and the European Union (EU) have been among the leaders in this transition, with renewables playing a substantial role in electricity generation as of mid-2020s. The adoption of Battery Energy Storage Systems (BESS) has become crucial for enhancing grid efficiency, sustainability, and reliability by addressing the intermittent renewable sources. This paper investigates the feasibility and economic viability of batteries into wholesale electricity markets as per EU regulation, focusing on the dynamics of very different markets, namely the Day-Ahead Market (DAM) based on system marginal price and the Cross-Border Intra-day Market (XBID) based on continuous trading. A novel model is proposed to enhance BESS operations, leveraging price arbitrage strategies based on zonal price predictions, levelized cost of storage (LCOS), and uncertain bid acceptance in continuous trading. Machine learning and deep learning techniques are applied for price forecasting and bid acceptance prediction, respectively. The study finds that data-driven techniques outperform reference models in price forecasting and bid acceptance prediction (+7-14% accuracy). Regarding market dynamics, the study reveals a higher competitiveness of the continuous market compared to the DAM, particularly with increased risk factor in bids leading to higher profits. This research provides insights into compatibility between continuous markets and BESS, showing substantial improvements in economic profitability and the correlation between risk and profits in the bidding strategy (+9 M€ yearly revenues are obtained with a strategic behavior that reduces by 60% awarded energy).

Recent European regulations promote distributed energy resources as alternatives to centralized generation. This study compares utility-scale and distributed photovoltaic (PV) systems coupled with Battery Energy-Storage Systems (BESSs) in the Italian electricity market, analyzing different battery sizes. A multistage stochastic mixed-integer linear programming model, using Monte Carlo PV production scenarios, optimizes day-ahead and intra-day market offers while incorporating PV forecast updates. In real time, battery flexibility reduces imbalances. Here we show that, to ensure dispatchability—defined as keeping annual imbalances below 5% of PV output—a 1 MW PV system requires 220 kWh of storage for utility-scale and 50 kWh for distributed systems, increasing the levelized cost of electricity by +13.1% and +1.94%, respectively. Net present value is negative for BESSs performing imbalance netting only. Therefore, a multiple service strategy, including imbalance netting and energy arbitrage, is introduced. Performing arbitrage while keeping dispatchability reaches an economic optimum with a 1.7 MWh BESS for utility-scale systems and 1.1 MWh BESS for distributed systems. These results show lower PV firming costs than previous studies, and highlight that under a multiple-service strategy, better economic outcomes are obtained with larger storage capacities.

Recent European regulations promote distributed energy resources as alternatives to centralized generation. This study compares utility-scale and distributed photovoltaic (PV) systems coupled with battery energy storage systems (BESS) in the Italian electricity market, analyzing different battery sizes. A multistage stochastic MILP model, using Monte Carlo PV production scenarios, optimizes day-ahead and intraday market offers while incorporating forecast updates. In real time, battery flexibility reduces imbalances. Here we show that, to ensure dispatchability—defined as annual imbalances below 5% of PV output—a 1 MW PV system requires 200 kWh of storage for utility-scale and 100 kWh for distributed systems, increasing LCOE by 12.6% and 3.8% respectively. NPV is negative for BESS performing imbalance netting only. Therefore, a multiple service-strategy, including imbalance netting and energy arbitrage, is introduced. However, maintaining dispatchability while performing arbitrage reaches economic optimum with a 1.7 MWh BESS for utility-scale systems and 1.1 MWh BESS for distributed systems. These results show lower PV firming costs than previous studies, and highlight that under a multiple-service strategy, better economic outcomes are obtained with larger storage capacities.

In recent years, renewable energy production has expanded rapidly, becoming an essential component of the global energy transition. However, the inherent variability and unpredictability of renewable generation require technologies that can provide grid stability and operational flexibility. Battery Energy Storage Systems (BESS) play a central role in addressing this challenge, but their long-term effectiveness depends on a thorough understanding of their degradation mechanisms. This work aims to model and predict the capacity and power degradation of a real-world BESS operating in the electricity market, bridging the gap between laboratory-based ageing studies and field applications. Several degradation indicators, such as available capacity evolution, DC efficiency evolution, conductivity loss, and loss of lithium inventory, were evaluated to determine which models best describe the system’s ageing behaviour. Some estimations were found inaccurate and subsequently excluded, while the remaining analyses enabled a detailed characterisation of BESS performance over time. Using operational data collected between November 2022 and October 2024, results indicate a linear capacity degradation reaching 4.57% over 23 months (490 equivalent cycles), from approximately 9600 kWh to 9150 kWh, with a Mean Absolute Percentage Error (MAPE) of 0.2%. DC efficiency exhibited a two-phase evolution, with an initial rise followed by a slow reduction trend. These findings confirm that ageing effects can be effectively evaluated using operational data, enabling reliable lifetime forecasting for BESS assets.