Power system flexibility is becoming increasingly critical in modern power systems due to the quick switch from fossil fuel-based power generation to renewables, old-fashioned infrastructures, and a sharp rise in demand. If a power system complies with financial restrictions and responds quickly to unforeseen shifts in supply and demand, it can be considered flexible. It can ramp up production during periods of high demand or increase it during unanticipated or scheduled events. The broad use of renewable energy in the power grid can provide environmental and economic benefits; nevertheless, renewables are highly stochastic in nature, with variability and uncertainty. New management with adequate planning and operation in the power system is necessary to address the challenges incorporated with the penetration of renewable energy. The primary aim of this review is to provide a comprehensive overview of power system flexibility, including appropriate definitions, parameters, requirements, resources, and future planning, in a compact way. Moreover, this paper potentially addresses the effects of various renewable penetrations on power system flexibility and how to overcome them. It also presents an emerging assessment and planning of influential flexibility solutions in modern power systems. This review’s scientific and engineering insights provide a clear vision of a smart, flexible power system with promised research direction and advancement.

Phasor measurement unit (PMU) streams in smart grids lose packets in transit, yet compact learning mechanisms for filling these gaps are often compared without separating reconstruction accuracy from the measurements each method may acquire. This study evaluates a finite-memory ordinal mechanism (FMOM) under three explicit measurement contracts: passive packet arrival, paid queries to fixed snapshots and a common downstream generator-coherency tracker. Nine signal families combine public field recordings, generated events and nonlinear generator simulations; four loss regimes and three masks per episode yield 684 matched passive cases across 14 implementations. Under 50% independent loss, the passive ordinal adapter improved on its predecessor, route–offset recalibration (ROR), in seven of nine families but attained no family-level minimum, ranking third of ten primary implementations behind rolling singular-value decomposition and median shift. In the active pilot, five families required at least as many paid measurements as direct reading, and anchored sorting produced comparable errors. In the coherency pipeline, the IEEE 39-bus mean adjusted Rand index rose from 0.8284 (ROR) to 0.8417, while median shift reached 0.8505. Ordinal memory therefore offers useful but restricted gains, and measurement access, fallback state and reference definitions should be reported when smart grid monitoring algorithms are compared.