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Abstract
A Multi-Objective Operation Strategy
for a PV-Integrated Hybrid UPS–ESS using
Predictive Heuristics and Receding-Horizon Control
In recent industry, the adoption of renewable energy using photovoltaics (PV)
has been promoted as part of efforts to address climate change. In precision
manufacturing facilities, an uninterruptible power supply (UPS) is required to
respond to momentary voltage sags and outages, and an energy storage system
(ESS) is essential to compensate for the intermittent output of PV. Accordingly,
this study investigates a multi-objective control strategy for a hybrid UPS–ESS
(HUE) system integrated with PV.
Short-term forecasts of load and PV generation are performed using a Long
Short-Term Memory (LSTM) model. Based on these forecasts, the minimum
state of charge (SOC) required to secure HUE reserve power and the target
SOC for system operation are calculated at each time step. Subsequently, the
weights of each objective function are derived using Multi-Criteria Decision
Making (MCDM), and a real-time operation strategy is constructed by
combining predictive heuristics and Receding-Horizon Control (RHC).
The predictive heuristic determines charge and discharge actions under
operational constraints, while RHC updates the control decision at each time
step by incorporating both forecasted and actual values. The proposed control
aims to maintain an adequate SOC headroom for UPS readiness, suppress grid
peaks to reduce electricity costs, and limit unnecessary SOC fluctuations that
accelerate battery degradation.
Simulation results demonstrate that the proposed multi-objective operation
strategy combining predictive heuristics and RHC outperforms the comparison
scenarios in terms of SOC stability, grid operation reliability, and cost efficiency.
