Abstract

Photovoltaic (PV) power plants face revenue risks in electricity spot markets due to three critical factors: solar power forecast errors, penalty mechanisms stipulated by China's ‘Dual Detailed Rules’ for deviation settlement, and electricity price volatility. To address these challenges, this study proposes a dynamic bidding strategy that systematically integrates solar power forecasts with penalty-aware optimization and price risk management. The strategy utilizes three key operational inputs: 1) solar power forecasts, 2) day-ahead (DA) and real-time (RT) electricity prices, and 3) penalty thresholds defined in the ‘Dual Detailed Rules’. Our methodology establishes a decision framework where hourly bids are optimized through a profit-maximization model that balances energy revenue against penalty risks. Compared to conventional bidding methods, the proposed strategy achieves a 28.1% revenue increase during a certain hour by strategically controlling declared generation. Furthermore, it yields a 19% higher revenue during peak fluctuation periods, demonstrating its applicability across all trading intervals. The core theoretical contribution lies in bridging operational forecasting with market rule compliance—transforming raw prediction data into penalty-adjusted bids through deterministic optimization. This approach provides a systematic framework for PV plants to effectively convert forecasting capabilities into enhanced financial performance under China's specific electricity market regulations.

Original languageEnglish
Pages (from-to)2652-2658
Number of pages7
JournalCSEE Journal of Power and Energy Systems
Volume11
Issue number6
Early online date5 Nov 2025
DOIs
Publication statusPublished - 30 Nov 2025

Funding

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U2166211)

Keywords

  • Electricity bidding
  • forecast uncertainty
  • PV plant
  • spot market

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • General Energy
  • Electrical and Electronic Engineering

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