Two-Layer Co-Optimization of MPPT and Frequency Support for PV-Storage Microgrids Under Uncertainty

Jun Wang, Lijun Lu, Weichuan Zhang, Hao Wang, Xu Fang, Peng Li and Zhengguo Piao ()
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Jun Wang: Henan XJ Metering Co., Ltd., Xuchang 461000, China
Lijun Lu: Henan XJ Metering Co., Ltd., Xuchang 461000, China
Weichuan Zhang: Henan XJ Metering Co., Ltd., Xuchang 461000, China
Hao Wang: Henan XJ Metering Co., Ltd., Xuchang 461000, China
Xu Fang: Henan XJ Metering Co., Ltd., Xuchang 461000, China
Peng Li: Henan XJ Metering Co., Ltd., Xuchang 461000, China
Zhengguo Piao: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China

Energies, 2025, vol. 18, issue 18, 1-26

Abstract: The increasing deployment of photovoltaic-storage systems in distribution-level microgrids introduces a critical control conflict: traditional maximum power point tracking algorithms aim to maximize energy harvest, while grid-forming inverter control demands real-time power flexibility to deliver frequency and inertia support. This paper presents a novel two-layer co-optimization framework that resolves this tension by integrating adaptive traditional maximum power point tracking modulation and virtual synchronous control into a unified, grid-aware inverter strategy. The proposed approach consists of a distributionally robust predictive scheduling layer, formulated using Wasserstein ambiguity sets, and a real-time control layer that dynamically reallocates photovoltaic output and synthetic inertia response based on local frequency conditions. Unlike existing methods that treat traditional maximum power point tracking and grid-forming control in isolation, our architecture redefines traditional maximum power point tracking as a tunable component of system-level stability control, enabling intentional photovoltaic curtailment to create headroom for disturbance mitigation. The mathematical model includes multi-timescale inverter dynamics, frequency-coupled battery dispatch, state-of-charge-constrained response planning, and robust power flow feasibility. The framework is validated on a modified IEEE 33-bus low-voltage feeder with high photovoltaic penetration and battery energy storage system-equipped inverters operating under realistic solar and load variability. Results demonstrate that the proposed method reduces the frequency of lowest frequency point violations by over 30%, maintains battery state-of-charge within safe margins across all nodes, and achieves higher energy utilization than fixed-frequency-power adjustment or decoupled Model Predictive Control schemes. Additional analysis quantifies the trade-off between photovoltaic curtailment and rate of change of frequency resilience, revealing that modest dynamic curtailment yields disproportionately large stability benefits. This study provides a scalable and implementable paradigm for inverter-dominated grids, where resilience, efficiency, and uncertainty-aware decision making must be co-optimized in real time.

Keywords: grid-forming inverters; maximum power point tracking; distributionally robust optimization; photovoltaic-storage systems; frequency resilience; microgrid control architecture (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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