PV Project Electricity Price Forecasting & Analysis

Through multidimensional modeling and market simulation, this service accurately forecasts electricity price trends and revenue thresholds throughout a project's entire lifecycle. Its core lies in quantifying variables such as policy fluctuations, market bidding, and technological costs into electricity price curves, providing dynamic risk coordinates for investment decisions. As an independent third party, it eliminates subjective corporate expectations by constructing objective prediction models based on global electricity market databases.

Before the market-oriented reform of new energy electricity prices, electricity price forecasting primarily relied on historical data, supply-demand dynamics, and policy interventions. However, with the full integration of renewables into the market, the factors influencing price predictions have grown significantly more complex. The inherent randomness, volatility, and intermittency of renewable generation make market supply-demand relationships harder to predict. Additionally, policy adjustments, climate variability, and market competition exert substantial impacts on electricity prices. In this new electricity market environment, the accuracy of electricity price forecasting and analysis directly impacts the investment returns of power generation enterprises.

Policy Factors: Tracking policy changes such as subsidy phase-out schedules, green certificate trading rules, carbon tax transmission mechanisms, and global policy change databases.

Market Factors: PPA contract structures, real-time monitoring of historical transaction data, spot price volatility, competitor pricing strategies, power market bidding simulation platforms.

Technical Factors: LCOE decline curves, photovoltaic-storage synergy revenue thresholds, sensitivity of electricity prices to curtailment rates, PV-storage coupling economic models.

Ultimately reducing client electricity price forecast deviations, enhancing PPA negotiation leverage, and increasing full-cycle returns.