忽略电池老化成本会导致家庭储能系统总成本被严重低估。
Hidden Degradation Costs in Energy-Cost-Only HEMS Optimisation: Study on Battery and PV Sensitivity
- 用滚动时域混合整数线性规划优化家庭能源管理
- 老化成本最高可达节能收益的1060%
- 适合关注储能长期经济性的系统设计者
住宅电池储能系统（BESS）与光伏（PV）发电协同部署，旨在降低在波动电价下的家庭用电成本。模型预测控制（MPC）是家用能源管理系统（HEMS）常用优化策略，通常仅以最小化净能源成本为目标，未考虑电池老化成本。本文基于英国REFIT数据集，构建了针对英国住宅场景的滚动时域混合整数线性规划（MILP）基准模型，对三种电池容量和三种光伏阵列尺寸进行3×3敏感性分析。通过Naumann应力模型与雨流计数法估算老化成本。结果显示，每种电池容量下的老化成本基本恒定，最高可超过节能收益的1060%。结果表明，仅以能源成本优化会系统性低估真实系统成本，亟需引入老化感知的控制机制。
原文摘要 · Abstract (English)
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 strategies could incur significant losses once deployed to physical systems. This paper presents a receding-horizon mixed-integer linear programming (MILP) baseline for a UK residential HEMS, using demand data from the REFIT dataset. A 3 by 3 sensitivity study is conducted across three battery sizes and three PV array sizes, with post-hoc degradation cost estimated using the Naumann stress model and rainflow cycle counting. Results show that degradation remains constant for each battery size and can exceed energy cost savings by up to 1,060 %. These results demonstrate that energy-cost-only optimisation systematically underestimates the true system cost, motivating a degradation-aware control formulation.
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