Advancements in battery technology, marked by reduced costs and enhanced efficiency, are steadily making electric vehicles (EVs) more accessible to consumers. This trend is fueling global growth in EV fleet sizes, allowing EVs to compete directly with internal combustion engine vehicles. However, this rapid growth in EV numbers is likely to introduce challenges to the power grid, necessitating effective load management strategies. This work proposes an optimization method where EV load management is integrated into the Transmission Constrained Unit Commitment Problem (TCUCP). A Differential Evolution (DE) variant, enhanced with heuristic repair sub-algorithms, is employed to address the TCUCP. The heuristic sub-algorithms, adapted from earlier approaches to the simpler Unit Commitment Problem (UCP), are updated to incorporate power flow constraints and ensure the elimination of transmission line violations. Additionally, new repair mechanisms are introduced that combine priority lists with grid information to minimize violation. The proposed formulation considers EVs as both flexible loads and energy sources in a large urban environment powered by two grid nodes, accounting for the vehicles’ daily movement patterns. The algorithm exhibits exceptionally fast convergence to a feasible solution in fewer than 150 generations, despite the nonlinearity of the problem. Depending on the scenario, the total production cost is reduced by up to 45% within these generations. Moreover, the results of the proposed model, when compared with a MILP algorithm, achieve values with a relative difference of approximately 1%.

Prosumer energy storage behavior alongside national rooftop photovoltaics (RTPV) penetration metrics is essential for decarbonization pathways in buildings. A research gap persists in quantitatively assessing storage strategies under varying regulatory frameworks that integrate both technical and financial dimensions while accounting for behavioral heterogeneity and policy feedback. This study introduces a novel degradation-aware, feedback-preserving framework that optimizes behind-the-meter storage design and operation, enabling realistic modeling of prosumer responses on large-scale RTPV adoption scenarios. Long Short-Term Memory (LSTM) and Compound Annual Growth (CAGR) models applied for the RTPV penetration rates projections in European urban contexts. The increasing rates in the Netherlands, Spain, and Italy respond to second-order regression behavior, with the former to emit signals of saturation and the latter to perform mixed anelastic and reverse elastic curves of elasticities. Accordingly, Germany, France, the United Kingdom (UK), and Greece remain in an inelastic area by 2030. The building RTPV energy storage arbitrage formulation is treated as a linear programming (LP) problem using a convex and piecewise linear cost function, a Model Predictive Control (MPC), Auto Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average (ARIMA) statistical forecasts and rolling horizon in order to address the uncertainty of the load and the ratio κ of the sold to purchased electricity price. Weekly arbitrage gains may drop by up to 9.1% due to stochasticity, with maximized gains achieved at battery capacities between 1C and 2C. The weekly gain per cycle performs elastic, anelastic, and reverse behavior of the prosumer across the range of κ values responding to different regulatory mechanisms of pricing. The variability of economic incentives suggests the necessity of flexible energy management strategies.