Planning High-PV Penetration Park: Enhancing Local PV Utilization and Economic Efficiency through Multi-Energy Storage
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PUBLIC SUMMARY
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Introduced a bi-level optimization framework for energy planning in high-PV-penetration parks.
Demonstrated that optimized configurations can increase local PV utilization from 33.6% to 73.0%, reducing annualized costs by 30.2%.
Showed that battery storage becomes crucial under high PV penetration, while thermal storage plays a supplementary role, with charging and discharging patterns aligned to daytime and nighttime/off-peak hours.
Explored external resource integration, boosting PV utilization to 81.6% and reducing the levelized cost of consumed electricity (LCOCE) by 6.5%.
Highlighted the need for internal multi-energy flexibility and external resource integration to optimize PV absorption, cost-effectiveness, and carbon reduction in net-zero parks.
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ABSTRACT
Large airports have substantial energy consumption demands while possessing significant spatial resources for photovoltaic (PV) development. However, addressing the mismatch between PV generation and airport electricity demand to achieve efficient local consumption and optimal energy utilization remains a critical research challenge. This study investigates energy planning strategies for high-PV-penetration parks under net-zero development, using a major Chinese airport terminal expansion as a case study. The proposed bi-level optimization framework jointly determines installed capacities and annual hourly operation of multi-energy systems, integrating cooling, heating, and electricity with flexible resources including battery storage, chilled/hot water storage, and uni/bi-directional EV charging. A net-zero carbon constraint through carbon trading ensures environmental alignment while minimizing annualized costs. Results demonstrate local PV utilization improves from 33.6% without flexible storage to 73.0% with optimized configuration, accompanied by 30.2% cost reduction. Battery storage gains greater importance under high PV penetration due to unconstrained flexibility, while thermal storage serves supplementary roles. The system follows daytime charging and nighttime or off-peak discharging patterns. Beyond internal optimization, the study explores strategies for incorporating external resources such as adjacent buildings and surrounding EV fleets. Results show that these measures further raise PV utilization to 81.6% and reduce the levelized cost of consumed electricity (LCOCE) by 6.5%. Integrating surrounding buildings enhances source-load matching and reduces storage demand, while staff vehicles and external vehicle fleets substitute partial battery capacity. The findings highlight that high-PV parks require both internal multi-energy flexibility and external resource integration for efficient PV absorption, cost-effectiveness, and carbon reduction. This research establishes a paradigm for zero-carbon park planning under policy constraints, offering transferable insights for transport hubs and urban districts maximizing local PV consumption while improving economic performance.-
Keywords:
- . Photovoltaic /
- Energy Storage /
- Airport Planning /
- Carbon Neutrality /
- Local Coordination.
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REFERENCES
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Cite this article:
Zhu J., Han L., Yu J., et al. (2026). Planning High-PV Penetration Park: Enhancing Local PV Utilization and Economic Efficiency through Multi-Energy Storage. Energy Use 2:100042. https://doi.org/10.59717/ipj.energy-use.2025.100042
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Zhu J., Han L., Yu J., et al. (2026). Planning High-PV Penetration Park: Enhancing Local PV Utilization and Economic Efficiency through Multi-Energy Storage. Energy Use 2:100042. https://doi.org/10.59717/ipj.energy-use.2025.100042
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Figure 1.
Schematic diagram of energy system planning for the airport
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Figure 2.
Structure of the bi-level planning framework
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Figure 3.
Simulation and Setting of Airside Vehicle Behavior
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Figure 4.
The results of independent planning of the terminal area
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Figure 5.
Hourly energy balance and consumption results
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Figure 6.
Surrounding Resource Settings
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Figure 7.
Optimized scheduling operation results in representative week after integrated planning (Scenario VII)
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Figure 8.
Summary of the planning results of scenarios