The New "Job" in Renewable Energy: From Relying on the Weather to "Calculating" the Weather
Summary of Key Points
This news article reveals a profound industry transformation: Meteorological forecasting is no longer just about providing weather forecasts; it has become a crucial tool for renewable energy companies (wind, solar, storage) to make money and ensure their survival.
As the electricity market becomes increasingly complex, similar to the stock market (spot market), generating renewable energy is no longer just about producing a certain amount of electricity and selling it at that price. Instead, companies must accurately predict the amount of electricity that will be generated tomorrow and even in the next 45 days based on weather conditions, and then decide when to charge and discharge energy, as well as at what price.
In the past, renewable energy companies made money through the price difference between peak and off-peak times (buying low and selling high). However, this profit margin is narrowing. The core of future competition will be intelligent operational capabilities. Those companies that can use AI and high-precision meteorological data to protect equipment during extreme weather and to profit from subtle price fluctuations in normal weather will survive in the fierce market and make money. This has given rise to a new blue ocean in the "energy meteorology" industry.
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Detailed Analysis
1. The Rules of the Game Have Changed: Renewable Energy Is No Longer Relying on the Weather, but Trading Based on Daily Conditions
Previously, renewable energy operations were simply about generating electricity when the wind blew or the sun shone, and the amount generated was sold accordingly. But now, the electricity market has transformed into a 24-hour-a-day "auction hall" (the electricity spot market).
In this market, electricity prices fluctuate in real time. If you mispredict the weather—producing more electricity when the wind is strong but the price is low, you lose money. Conversely, if you underestimate the wind strength and miss the opportunity to sell electricity at a high price, you also suffer losses.
The new logic is:
- Previously: Good weather → More electricity generated → More electricity sold.
- Now: Predict the weather → Calculate the amount of electricity that can be generated → Predict tomorrow's electricity prices → Decide whether to charge or discharge energy from storage → Sell at the best price.
It's like farming in the past, where the harvest depended on the weather; now it's like trading stocks, where you need to analyze market trends (electricity supply and demand) using technical tools (AI algorithms) to make a profit. Meteorological forecasting has evolved from an auxiliary tool to a fundamental trading infrastructure, just as trading software is essential for stock trading.
2. The Logic of Making Money Has Changed: From Profiting from Price Differences to Profiting from Intelligence
In recent years, energy storage companies mainly made money through peak-valley arbitrage, charging at night when prices are low and discharging during the day when prices are high. However, the news indicates that this profit margin is shrinking:
- Narrowing price differences: The largest peak-valley price difference nationwide in September was smaller than in August, indicating that the space for profit from buying low and selling high has decreased.
- Rising costs: The operating costs of energy storage systems have nearly doubled year-on-year, eroding profits.
What does this mean?
Relying solely on simple charging and discharging strategies is no longer sufficient. The future profit model will focus on diversified revenue sources:
1. Precise arbitrage: Not only capturing peak and off-peak prices but also more subtle price fluctuations.
2. Ancillary services: Energy storage can stabilize the power grid during instability, providing valuable services that can generate revenue.
3. Risk mitigation: During extreme weather events (typhoons, cold waves), energy storage can prevent grid failures, and this insurance-like function also has a market value.
Therefore, the competition no longer revolves around the size of the energy storage batteries but around the intelligence of the systems. Companies that can use AI to better predict the market and optimize operations will be able to extract more profit from the narrow profit margins.
3. Technological Advancements: AI Meteorological Models Become Powerful Tools
Since meteorological data is so critical, are traditional weather forecasts sufficient? Not at all. Traditional forecasts tell you if it will rain tomorrow, but for electricity trading, you need to know specific details such as wind speed and cloud cover at particular times, which can affect solar power generation. The "Tianji" meteorological model developed by Yuanjing Technology is a prime example of this advancement:
- Long-term prediction: It can forecast weather conditions for the next 45 days.
- High precision: It can predict weather conditions down to 5 kilometers in detail and even for specific wind and solar power plants.
- Accurate predictions: It can predict the path of extreme weather events like typhoons with 72 hours' advance.
What does this mean for companies?
In the past, all wind turbines would be shut down during a typhoon for safety, resulting in significant losses. Now, AI models can inform maintenance teams which areas will be less affected, allowing for targeted operations that ensure safety while minimizing power generation losses and potentially capturing profit from price fluctuations.
4. Dual Drivers from Policy and Market: A New Billion-Dollar Blue Ocean
The sudden emphasis on meteorological forecasting is driven by both policy and market forces:
Policy: The national "15th Five-Year Plan for Renewable Energy Development" sets a goal of ensuring that wind and solar power generation reaches over 8% by 2030. This means that the power grid must be able to reliably rely on renewable energy sources, forcing companies to invest in energy storage and use precise forecasting and scheduling.
Market: According to Guosen Securities, the market for power prediction services alone is expected to grow to 1.69 billion yuan by 2030, with a compound annual growth rate of 24%. This indicates that providing meteorological data services has become a profitable business. Meteorological data, once free or provided for public welfare, is now valued by companies because it directly affects their profitability.
This is a classic example of infrastructure commercialization, similar to how bandwidth and servers evolved from public utilities to the cloud computing industry.
5. Challenges and Barriers
Despite the promising prospects, the industry still faces several challenges:
1. Regional Differences: China's electricity spot markets vary by region, with different grid structures, load characteristics, and trading rules. This makes it difficult to develop universal meteorological products that can be effectively used across all regions.
2. Data Barriers: Data from wind and solar power plants is often proprietary, and there is a lack of cross-disciplinary collaboration between meteorological and power departments, making it hard to share and analyze data effectively.
3. Market Disruption: The market is flooded with homogeneous meteorological products that mainly provide basic weather forecasts, rather than actionable trading insights. Low-price competition can lead to reduced service quality, harming renewable energy companies.
Future Solutions:
- Cross-disciplinary collaboration: Sharing meteorological and power data is essential.
- Specialized Talent: There is a need for professionals who understand meteorology, power market trading, and AI algorithms.
- Standardization: Establishing industry standards to convert meteorological probabilities (e.g., 80% chance of wind speeds exceeding 10 meters per second) into actionable risk management and scheduling instructions for the power system.
Conclusion
The core message of this news is that the renewable energy industry is undergoing a transformation from being resource-driven to being data and algorithm-driven. For investors and companies, the focus should shift from simply looking at battery capacity and installed renewable energy capacity to who possesses the most accurate meteorological forecasting capabilities, the best AI-based scheduling algorithms, and the ability to turn weather uncertainties into market certainty.
This is a race for certainty. In an environment filled with uncertainties, those who can provide the greatest level of predictability will have the upper hand in the future.