Energy flexibility strategies in urban areas: A sustainable energy consumption and production balance under a stochastic framework

Modelling the energy balance in urban areas is significantly influenced by uncertainties arising from energy production, distribution, and consumption. Renewable energy sources and their distribution, throughout the urban environment, are susceptible to unpredictable meteorological conditions, while energy consumption patterns are shaped by the stochastic nature of individual behaviours, thus the evaluation of energy flexibility strategies remains challenging. Despite the longstanding recognition of stochasticity in the energy field, its accounting in urban energy modelling remains largely confined to single buildings. Furthermore, uncertainties have typically been addressed in isolation across the energy lifecycle: stochastic optimisation is predominantly used to define uncertainties on the energy production scale, while individuals' energy consumption behaviours are usually modelled through probabilistic distributions on aggregated averages, introducing biases in simulations. This review provides an overview of existing modelling approaches, highlighting various techniques for further development, and the appropriate degree of stochasticity required for more realistic outcomes in developing energy flexibility. The analysis draws more than 200 articles, examining different stochastic approaches, focusing also on data-driven implications, as the IoT technologies’ influence continues to grow. The main findings of this review emphasise the necessity for more comprehensive stochastic models that integrate all phases of the energy lifecycle. Such models would enable more realistic simulations, support the development of flexible efficient energy strategies, and address the complexities of urban energy systems in a holistic manner. • A comprehensive review on modelling energy systems under uncertainty is presented. • Individual behaviour and occupancy patterns are integrated into the analysis. • The study bridges energy production, distribution, and consumption models. • Literature gaps and future directions for energy flexibility are identified. • Guidelines are proposed to support future modelling practices and policy design.