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<p>The growing retirement of electric vehicle lithium-ion batteries has created significant opportunities for their reuse in stationary energy storage systems. However, integrating second-life batteries (SLBs) into residential photovoltaic (PV)-based prosumer microgrids remains challenging due to heterogeneous initial state-of-health (SoH) conditions, accelerated degradation behavior, and uncertain remaining useful life. This paper proposes a degradation-aware energy management framework for SLB-integrated prosumer microgrids based on the joint use of particle swarm optimization (PSO) and model predictive control (MPC). The proposed framework consists of four coordinated stages: PSO-based capacity sizing, hybrid AI-based forecasting of PV generation, electricity prices, and load demand, SoH-aware adaptive battery management within the MPC layer, and net present value (NPV)-based battery replacement decision-making. Within the control layer, the adaptive battery management mechanism updates state-of-charge limits, C-rate constraints, and degradation penalty coefficients according to real-time SoH evolution, thereby mitigating excessive capacity fade while preventing economically inefficient underutilization. In addition, a three-stage replacement strategy is developed to determine whether continued operation, SLB, or fresh battery replacement yields the highest long-term economic benefit. The proposed method is evaluated through long-horizon simulations using 2024 meteorological, market, and demand data for five residential prosumers in Antalya, Türkiye. The results show that the framework achieves a fleet-average self-consumption ratio of 71.8%, a renewable energy penetration factor of 37.6%, and a cost of energy of 0.0722 $/kWh, while sustaining battery utilization and enabling lifecycle-aware operation under realistic uncertainty. These findings demonstrate that the proposed PSO–MPC framework provides a computationally tractable approach that achieved stable convergence and feasible MPC solve times (2.1–3.4&#xa0;s per 72-hour horizon) across all 20 simulated years under the five-prosumer Antalya configuration for degradation-aware energy management of SLB-based microgrids.</p>

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Degradation-aware PSO–MPC energy management for second-life battery prosumer microgrids

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Musa Terkes,
  • Alpaslan Demirci,
  • Idriss Dagal,
  • Erdin Gokalp,
  • Umit Cali

摘要

The growing retirement of electric vehicle lithium-ion batteries has created significant opportunities for their reuse in stationary energy storage systems. However, integrating second-life batteries (SLBs) into residential photovoltaic (PV)-based prosumer microgrids remains challenging due to heterogeneous initial state-of-health (SoH) conditions, accelerated degradation behavior, and uncertain remaining useful life. This paper proposes a degradation-aware energy management framework for SLB-integrated prosumer microgrids based on the joint use of particle swarm optimization (PSO) and model predictive control (MPC). The proposed framework consists of four coordinated stages: PSO-based capacity sizing, hybrid AI-based forecasting of PV generation, electricity prices, and load demand, SoH-aware adaptive battery management within the MPC layer, and net present value (NPV)-based battery replacement decision-making. Within the control layer, the adaptive battery management mechanism updates state-of-charge limits, C-rate constraints, and degradation penalty coefficients according to real-time SoH evolution, thereby mitigating excessive capacity fade while preventing economically inefficient underutilization. In addition, a three-stage replacement strategy is developed to determine whether continued operation, SLB, or fresh battery replacement yields the highest long-term economic benefit. The proposed method is evaluated through long-horizon simulations using 2024 meteorological, market, and demand data for five residential prosumers in Antalya, Türkiye. The results show that the framework achieves a fleet-average self-consumption ratio of 71.8%, a renewable energy penetration factor of 37.6%, and a cost of energy of 0.0722 $/kWh, while sustaining battery utilization and enabling lifecycle-aware operation under realistic uncertainty. These findings demonstrate that the proposed PSO–MPC framework provides a computationally tractable approach that achieved stable convergence and feasible MPC solve times (2.1–3.4 s per 72-hour horizon) across all 20 simulated years under the five-prosumer Antalya configuration for degradation-aware energy management of SLB-based microgrids.