Electricity-Purchase Optimization for PV-Storage Microgrids Based on Historical Error Scenarios and Rolling Correction

Authors

  • Baichuan Qin Faculty of Artificial Intelligence, Shanghai University of Electric Power, Shanghai, 201306, China
  • Jiaqiang Yang Faculty of Artificial Intelligence, Shanghai University of Electric Power, Shanghai, 201306, China
  • Machen Du Faculty of Artificial Intelligence, Shanghai University of Electric Power, Shanghai, 201306, China

DOI:

https://doi.org/10.54097/5qrc6s64

Keywords:

PV-storage microgrid, energy storage scheduling, stochastic optimization, rolling correction, time-varying electricity prices

Abstract

This study investigates electricity-purchase optimization for a PV-storage microgrid under changing photovoltaic output, load and prices, with storage coupling adjacent intervals. It establishes a unified ten-minute physical and accounting framework and compares four information settings using deterministic linear programming with an MILP cross-check, historical-error scenario programming, finite-state dynamic programming for rolling correction, and scenario programming under time-varying prices. The deterministic schedule costs 35,126.95 CNY, 26.90% below the no-storage baseline. Over 334 days, the historical-error scenario setting costs 14,731,085.32 CNY. Rolling updates at 06:00, 12:00, and 18:00 reduce cost from 16,143,244.46 to 13,905,968.07 CNY (13.86%) and emergency purchases by 81.55%. Under variable prices, intraday updating cuts emergency purchases by 38.69% but raises total cost slightly (0.06%). These results show that the shared framework supports physically consistent comparisons, rolling feedback can mitigate forecast errors, and price updates should be assessed alongside adjustment costs and storage state.

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References

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Published

2026-11-10

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Section

Articles

How to Cite

Qin, B., Yang, J., & Du, M. (2026). Electricity-Purchase Optimization for PV-Storage Microgrids Based on Historical Error Scenarios and Rolling Correction. International Journal of Advanced Engineering and Technology Research, 4(1), 29-34. https://doi.org/10.54097/5qrc6s64