Analysis of the Impact of Guangdong Province's Long-Term PPA Price War and New Electricity Retail Regulation on the Power Industry
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In 2025, Guangdong’s market-oriented transaction electricity accounted for 68% of the total social electricity consumption, of which nearly 90% were medium and long-term transactions. Electricity retailers profited from correctly betting on coal price declines for two consecutive years, triggering frenzied competition in the industry. Some companies quoted prices below the cost bottom line of 0.372 yuan per kilowatt-hour, pushing the long-term PPA price down 15.8% year-on-year to 0.3919 yuan per kilowatt-hour. The regulatory authorities issued new regulations limiting the maximum wholesale-retail price difference income of electricity retailers to 0.012 yuan per kilowatt-hour, with the excess part to be shared with users at a ratio of 2:8, aiming to shift electricity retailers’ profit model from ‘price difference betting’ to ‘professional services’.
For thermal power enterprises, taking Shenneng Group A (000027.SZ), a major thermal power enterprise in Guangdong Province, as an example, its stock price trend was relatively stable from January to December 18, 2025, with an increase of 1.70%, which is consistent with the analysis that ‘thermal power enterprises benefit from capacity price growth and cost pass-through’, indicating that the market has relatively stable expectations for their investment value.
- Marketization Degree Amplifies Policy and Price Impact: Guangdong’s power market-oriented transaction accounts for 68% of total consumption. As the core transaction method, changes in long-term PPA prices will directly transmit to the profit structure of the power generation side and retail side, so the impact scope of regulatory policies is significantly expanded.
- Electricity Retail Industry Will Face Differentiation: The new regulatory regulations standardize market competition from the profit model level. Electricity retailers relying on price difference arbitrage will face elimination risks due to limited profit margins, while companies with professional service capabilities (such as load forecasting and energy management) will gain competitive advantages.
- Differentiation in Investment Value on the Power Generation Side: Thermal power enterprises have relatively stable investment value due to benefiting from capacity price growth and cost pass-through; for power sources with lower marginal costs such as nuclear power and hydropower, the marginal impact of long-term PPA price declines on their profits is more significant, and their investment value is under pressure.
- Electricity Retailers: Profit margins are limited by new regulatory regulations, with great transformation pressure. Companies relying on price difference arbitrage may exit the market.
- Nuclear and Hydropower Enterprises: Long-term PPA price declines directly compress profit margins, negatively affecting investment value.
- Thermal Power Enterprises: Benefit from capacity price growth and cost pass-through, with relatively stable investment value.
- Electricity Retailers: Demand for professional services grows, and companies with transformation capabilities can expand new profit points.
This analysis shows that the long-term PPA price war in Guangdong Province was triggered by electricity retailers betting on coal price declines. The new regulatory regulations promote the transformation of electricity retailers’ profit model by limiting the wholesale-retail price difference. Thermal power enterprises have relatively stable investment value, while nuclear and hydropower enterprises and some electricity retailers are under pressure. The new regulations on electricity retailers may promote industry differentiation, but the specific effect needs to be observed in implementation. The investment value of the power industry shows a differentiation trend due to different power generation types and electricity retailers’ capabilities. Marketization degree and policy implementation are key influencing factors.
Insights are generated using AI models and historical data for informational purposes only. They do not constitute investment advice or recommendations. Past performance is not indicative of future results.
About us: Ginlix AI is the AI Investment Copilot powered by real data, bridging advanced AI with professional financial databases to provide verifiable, truth-based answers. Please use the chat box below to ask any financial question.
