The large-scale integration of photovoltaic systems into modern distribution networks requires advanced forecasting and optimisation tools to address variability, uncertainty, and increasingly complex operational conditions. This review examines 160 peer-reviewed studies published primarily between 2018 and 2026 and provides a unified, system-level perspective that links photovoltaic power forecasting, photovoltaic optimisation, and energy storage system management within the broader context of Smart Grid operation. The analysis covers forecasting techniques across all temporal horizons, compares deterministic, stochastic, metaheuristic, and hybrid optimisation approaches, and reviews siting, sizing, and operational strategies for both PV units and Energy Storage Systems, including their effects on hosting capacity, reactive power control, and network flexibility. A key contribution of this work is the consolidation of planning- and operation-oriented methods into a coherent framework that clarifies how forecasting accuracy influences Distributed Energy Resources optimisation and system-level performance. The review also highlights emerging trends, such as reinforcement learning for real-time Energy Storage Systems control, surrogate-assisted multi-objective optimisation, data-driven hosting capacity evaluation, and explainable AI for grid transparency, as essential enablers for flexible, resilient, and sustainable distribution networks. Open challenges include uncertainty modelling, real-world validation of optimisation tools, interoperability with flexibility markets, and the development of scalable and adaptive optimisation frameworks for next-generation smart grids.
Conti et al. (Fri,) studied this question.