Multi-Objective and Bi-level Optimization for Managing Battery Energy Storage Systems in Energy Flexibility Markets

The integration of Battery Energy Storage Systems (BESS) into fl exibility energy marketsrepresents one of the most complex and consequential challenges of the ongoing energytransition. BESS operators must simultaneously optimize sizing and investment strategies,allocate limited state-of-charge capacity across competing market segments withheterogeneous activation rules, manage electrochemical degradation over multi-yearhorizons, and anticipate the strategic responses of other market participants, all within arapidly evolving regulatory landscape spanning European balancing platforms (PICASSO,MARI) and Italian national mechanisms (TIDE, MACSE, MSD/MB). MOBI-BESS addressesthis challenge through a unifi ed mathematical programming framework that, for the fi rsttime, jointly captures all these dimensions. The project pursues fi ve tightly coupled scientifi cobjectives: (1) the rigorous formalization of the decision problems governing BESSintegration in fl exibility markets; (2) the development of multi-objective mixed-integerprogramming models for the joint optimization of BESS sizing, scheduling, and marketparticipation under confl icting criteria including revenue, investment cost, batterydegradation, and system resilience; (3) the formulation of bilevel optimization modelsrepresenting the hierarchical interactions among Transmission System Operators,aggregators, and BESS operators; (4) the design of exact, heuristic, and matheuristicalgorithms for multi-objective bilevel mixed-integer programs, with provable solution qualityguarantees; and (5) the computational and empirical validation of models and algorithmsthrough hardware-in-the-loop emulation and fi eld testing at the University of Salerno BESTlaboratory, combined with evidence-based policy evaluation via causal inference andmediation analysis. A distinctive feature of MOBI-BESS is the embedding of a dedicatedmeasurement and monitoring layer, providing real-time estimates of State of Charge, Stateof Health, and system availability, directly into the optimization framework. Thismeasurement-informed paradigm reduces the gap between algorithmic predictions andphysical battery behavior, substantially increasing the practical applicability of the proposedmodels. The project is conducted by six Research Units: University of Rome Sapienza,University of Brescia, University of Florence, University of Genova, University of Molise, andUniversity of Salerno. The complementary expertise of the units covers mathematical programming, machine learning, electrical engineering, energy market design, and causalinference. The methodological advances, in particular the multi-objective and bileveloptimization algorithms applicable to networks with possibly stochastic fl ows and storagenodes, are transferable to logistics, water distribution, telecommunications, and supplychain management, amplifying the scientifi c and societal impact of the project well beyondthe energy sector.