Hidden Degradation Costs in Energy-Cost-Only HEMS Optimisation: Study on Battery and PV Sensitivity
arXiv:2606.16051v1 Announce Type: cross Abstract: Residential battery energy storage systems (BESS) are increasingly deployed alongside photovoltaic (PV) generation to reduce household energy costs under volatile time-of-use (TOU) tariffs. Model predictive control (MPC) is a widely adopted optimisation strategy for home energy management systems (HEMS), typically formulated to minimise net energy cost, subject to physical and operational constraints. However, battery degradation is rarely embedded in the optimisation objective, meaning its cost is unquantified and aggressive; high-cycle-count
The increasing deployment of residential battery and PV systems, driven by volatile energy tariffs, necessitates a re-evaluation of current optimization strategies.
This research highlights a critical oversight in current energy management systems which, by ignoring degradation costs, can lead to suboptimal long-term economic outcomes and premature hardware failure.
Energy management system (HEMS) optimization will need to incorporate battery degradation models to provide more accurate and sustainable cost savings for consumers.
- · Battery manufacturers (designing more durable systems)
- · AI/ML companies (providing sophisticated degradation models)
- · Consumers (achieving true long-term energy savings)
- · HEMS providers (failing to adapt optimization algorithms)
- · Consumers (experiencing unexpected battery replacement costs)
Integrations of battery degradation models will become a standard feature in residential energy management systems.
This will drive demand for more robust and long-lasting battery technologies, influencing R&D priorities.
Improved HEMS will accelerate grid decentralization and stability due to more efficient and sustainable localized energy storage.
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