Power-Quality-Proxy-Guided Storage State Replay for Renewable-Rich Smart Grids Under Decomposed Production Simulation

Smart grids with high renewable penetration are increasingly evaluated through long-horizon production simulation, but conventional decomposed simulation mainly reports energy balance and unit feasibility, while power-quality stress remains weakly quantified in the storage correction layer. This paper presents a power-quality-proxy-guided state-replay framework for renewable-rich smart grids. Instead of claiming feeder-level electromagnetic simulation, the method defines planning-level proxy indicators that can be exported by production-simulation software: a voltage-deviation proxy obtained from net-power sensitivity, a net-load ramp proxy, an inverter/charger harmonic-risk proxy, and a renewable-curtailment exposure proxy. These normalized indicators are combined into a composite score SPQ, which is then used to distinguish two storage values: charge retention during renewable-surplus voltage-rise intervals and discharge support during voltage-dip, ramp-stress, or inverter-stress intervals. A base decomposed production-simulation schedule is first obtained. The proposed layer then constructs storage accounting cycles independent of monthly and rolling-window boundaries, attaches the proxy ledger to each interval, backtracks terminal residual storage energy to low-value charging actions, and reallocates physically feasible discharge to high-SPQ intervals. The corrected storage path is projected onto power and energy limits and replayed before storage and conventional-unit states are inherited by the next monthly solve; cycles outside the replay validity envelope are escalated to full redispatch rather than counted as successful corrections. An eight-interval case reports explicit SPQ values and shows that a trajectory ending 45 MWh above the 30 MWh reference can be corrected by trimming 35 MWh of low-proxy-value charging and adding 10 MWh of discharge in two high-score intervals. A 96-interval experiment further shows that the full method reduces explicitly discarded residual energy from 214 MWh to 31 MWh, provides 128 MWh of proxy-guided support, and lowers weighted PQ-proxy exposure by 46.3%. The framework links smart-grid data analysis, renewable integration, and power-quality improvement within a traceable production-simulation workflow.