ETLAB participated in the AEIT International Annual Conference 2026, held at Sapienza University of Rome from 8 to 10 September 2026, where Silvia Zordan presented the research work entitled “A Data-Driven Multi-Scenario Strategy for Optimal Operation of a Battery Storage System in Wholesale Energy Market”.

The work represents a scientific collaboration between the University of Pavia and the University of Trento, bringing together expertise in power systems, electricity markets, optimisation and energy storage. The research was developed by Rouzbeh Shirvani and Alessandro Bosisio from the University of Pavia, in collaboration with Asja Alic, Silvia Zordan and Vincenzo Trovato from the University of Trento.

The research addresses the optimal operation of Battery Energy Storage Systems (BESS) in wholesale electricity markets, focusing on two key challenges: the uncertainty and volatility of electricity prices and the non-linear degradation of battery capacity caused by cyclic operation. These factors play a crucial role in determining how energy storage systems should be operated over time in order to maximise revenues while preserving their long-term performance and operational capabilities.

The proposed strategy combines a data-driven forecasting model with a degradation-aware optimisation methodology. The forecasting approach generates multiple possible scenarios for future wholesale electricity market prices, allowing uncertainty and market volatility to be directly incorporated into the optimisation of BESS operation.

The data-driven forecasting methodology generates 100 wholesale electricity market price scenarios for the Italian electricity market. The approach considers seasonal and intra-annual factors that may influence market prices and combines historical hourly electricity prices with monthly market drivers. Through statistical time-series techniques, including spectral decomposition, SARIMAX regression and Monte Carlo simulation, the methodology produces a range of possible future price trajectories.

The generated price scenarios are subsequently used as input for the Optimal Capacity Revamping Algorithm (OCRA). This optimisation framework maximises revenues from wholesale electricity market operation while accounting for the non-linear cyclic degradation of battery cells. In addition to short-term operational decisions, the methodology evaluates long-term revamping actions aimed at restoring degraded battery capacity and maintaining the system’s ability to meet its operational requirements.

The results highlight the importance of jointly considering market uncertainty and battery degradation. While the simulated net revenues show a moderate degree of variability across the different price scenarios, more significant differences emerge in the optimal long-term revamping strategies, demonstrating how future market conditions can influence both operational and investment decisions.

The study contributes to the development of advanced strategies for the management of energy storage systems in increasingly dynamic electricity markets. By combining data-driven forecasting, multi-scenario analysis and degradation-aware optimisation, the proposed approach supports more informed decisions regarding both the daily operation and long-term management of battery energy storage systems.

The research was supported by the BOOSSTED project, within the framework of the “Bando a Cascata NEST” under PNRR Mission 4, as well as by the FMJH Program PGMO. These collaborations and research initiatives highlight the importance of combining expertise from different institutions to address the technological and economic challenges associated with the energy transition.

Participation in the AEIT International Annual Conference 2026 represented an important opportunity to share the results of this research with the scientific and professional community and to discuss emerging challenges related to energy storage, electricity markets and the future operation of power systems.

ETLAB congratulates Silvia Zordan for presenting the work at this important international conference and thanks all the authors and collaborators involved in the research. The contribution highlights the value of scientific collaboration between the University of Pavia and the University of Trento in addressing emerging challenges related to battery energy storage, electricity markets and the development of increasingly efficient and sustainable power systems.

 

Location

Italy Rome

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