1. The abolition of policy-based “time-of-use electricity pricing” is becoming a trend. By the end of 2025, the National Development and Reform Commission and the National Energy Administration successively issued the “Notice on Doing a Good Job in Signing and Fulfilling Medium and Long-term Electricity Contracts for 2026″ and the “Basic Rules for the Medium and Long-term Electricity Market”, both of which clearly stated:
In principle, direct market participants will no longer implement the time-of-use electricity pricing stipulated by the government.
Optimize the medium and long-term electricity pricing mechanism. All regions should ensure the alignment between the peak and valley time-of-use electricity pricing policy and the market transaction electricity pricing. In principle, direct market participants will no longer be subject to the time-of-use electricity pricing stipulated by the government. Both parties to medium and long-term contracts are encouraged to sign agreements that are responsive to market supply
For business entities directly involved in market transactions, there will no longer be artificial stipulations on the level and time period of time-of-use electricity pricing. For grid-agent electricity purchasing users, the government’s price department will coordinate and optimize the division of peak and off-peak time periods and the price fluctuation ratio based on the spot market price level.
Subsequently, in the implementation details of medium and long-term transactions for 2026 in Liaoning, Sichuan, Shaanxi, Yunnan, Henan, and Shanxi provinces, it was also clearly stated without exception that the level and time period of time-of-use electricity prices would no longer be artificially stipulated for business entities directly participating in market transactions. This does not mean that time-of-use electricity prices will disappear, but rather that their formation mechanism has undergone a fundamental change – from “government pricing” to “market pricing”. Under the influence of electricity supply and demand relationships and market-oriented transactions, peak, normal, and off-peak electricity prices will naturally form according to market supply and demand, and electricity price fluctuations will more accurately reflect real-time market conditions.
However, the market-based time-of-use (TOU) electricity pricing has also posed two major challenges to the “peak-valley arbitrage” model of traditional industrial and commercial energy storage systems.
The timing strategy is rigid, making it difficult to match electricity demand. Traditional energy storage systems often perform fixed-time charging and discharging, which makes it difficult to link with the actual electricity consumption curve of users. Especially in the “photovoltaic + energy storage” scenario, the lack of flexible adjustment capabilities can easily lead to low energy storage utilization or failure to meet electricity demand.
2) Electricity price volatility intensifies, making arbitrage more challenging. With the linkage between time-of-use electricity pricing and the spot market, electricity prices may fluctuate on an hourly or even 15-minute basis, with peak electricity prices reaching over 1 yuan/kWh. During periods of high photovoltaic power generation, negative electricity prices may even occur. Traditional fixed strategies struggle to adapt to such a complex electricity pricing environment. So, under the new mechanism of the electricity market, how should industrial and commercial energy storage seek new value propositions?
II. From “Peak-Valley Arbitrage” to “Microgrid Balancer” Experience from mature electricity markets in Europe shows that the higher the degree of marketization, the wider the application scenarios and the more prominent the value of energy storage systems. Their role is evolving from a simple “peak-valley arbitrage tool” to an “intelligent balancer for distributed photovoltaic and energy storage microgrids”.
1. Poland: The average electricity bill for users has decreased by 50%, and photovoltaic (PV) earnings have increased by over 200%! Since 2025, the Polish Electricity Exchange has implemented dynamic electricity pricing, with prices fluctuating in real-time. A residential user in Warsaw, the capital, installed a Siggy energy storage system equipped with AI scheduling strategies, establishing a small intelligent PV-storage microgrid. During the fixed electricity pricing period, the PV feed-in tariff was only 0.22–0.44 zlotys per kWh. After switching to dynamic electricity pricing and integrating the intelligent PV-storage system, through energy storage and intelligent scheduling strategies, it was possible to “store electricity” during low-price windows and “release” during high-price periods, achieving electricity cost savings and earnings enhancement. One-year operational data shows that the user’s electricity purchasing cost has decreased to approximately 0.55 zlotys per kWh, a reduction of nearly 50%; the average PV feed-in tariff has increased to 1.09 zlotys per kWh, earnings have increased by 220%-300%, and the self-consumption rate has increased by over 200%.
2. Sweden: Smart PV-ESS systems reduce users’ electricity bills by an average of 52.7%! While the single project in Poland may be an isolated case, the statistical data from over 2,500 projects in Sweden is more broadly representative. Recently, Siger New Energy conducted a systematic analysis of the operational data from over 2,500 smart PV-ESS projects in Sweden and found that users’ electricity bills have decreased by an average of 70.3%! The Swedish electricity market is divided into four regions (SE1-SE4) based on the grid operation structure, and over 2,500 independent projects are distributed across these four regions. All users are ranked from lowest to highest actual income, as shown in the figure below.
The data shows that the average reduction in electricity bills for users is 70.3%, with a median reduction of 52.7%. This disparity does not stem from the interference of individual outliers, but rather from the fact that some users in regions with greater electricity price volatility have achieved significantly higher savings returns.
1. AI-based intelligent dispatching: Understanding electricity prices and electricity usage to achieve cost reduction and efficiency enhancement in a dynamic electricity pricing environment cannot be achieved without the deep empowerment of AI technology. The intelligent photovoltaic and energy storage system achieves the following core capabilities through multi-model fusion and reinforcement learning:
1) Load Profiling Learning: Continuously learning the electricity usage behavior characteristics of each project, identifying load peaks, nighttime electricity usage patterns, differences between weekdays and holidays, etc., to construct a highly personalized load profile.
2) Photovoltaic power generation forecasting: Combine meteorological data to accurately predict photovoltaic output;
3) Electricity price trend prediction: Predict electricity price fluctuations based on market data and develop optimal charging and discharging strategies;
4) Rolling optimization and flexible settings: Employing a multi-model fusion strategy, combining weather forecasting and load behavior modeling, and constructing optimal scheduling paths through reinforcement learning and physical simulation; simultaneously, supporting daily strategy adjustments and enabling personalized scheduling based on user preferences. In the future, as AI models continue to evolve and algorithm strategies are continuously optimized, photovoltaic and energy storage systems will move from being “usable” to being “well-used”, helping more users achieve true intelligent energy use and long-term benefits.
IV. Smart Photovoltaic and Energy Storage Microgrid, Empowering Zero-Carbon Factories! China’s 15th Five-Year Plan clearly proposes the development of distributed energy resources and the construction of zero-carbon factories and parks. In December 2025, the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the National Energy Administration jointly announced 52 national-level zero-carbon parks. In January 2026, the Ministry of Industry and Information Technology and other four departments issued the “Guiding Opinions on Carrying out the Construction of Zero-Carbon Factories,” proposing that a batch of zero-carbon factories will be cultivated and constructed in seven major industries, including photovoltaics, lithium batteries, and automobiles, by 2027; and will expand to 12 major industries by 2030. It can be predicted that zero-carbon factories and parks centered around “electricity load” will become one of the main scenarios for new energy in the future. Models such as direct green electricity connection and smart microgrids will usher in significant development opportunities and become important development models for new energy. Under these scenarios and models, their core demands are highly consistent with those of users in Poland and Sweden: achieving optimal electricity costs in a fully market-oriented dynamic electricity pricing environment. Experience from overseas markets shows that smart photovoltaic and energy storage microgrids can effectively reduce users’ electricity costs and increase the returns of photovoltaic projects; moreover, the greater the fluctuation in electricity prices, the better the implementation results! Currently, over 17,000 power stations worldwide have adopted AI models, covering dynamic electricity pricing access scenarios in 47 countries, forming a multi-level and implementable AI application system.
Conclusion
Farewell to the fixed policy-based time-of-use electricity pricing, and welcome to the era of market-oriented and dynamic electricity pricing. This is not the end of the value of photovoltaic (PV) and energy storage systems, but rather the true beginning of the intelligent energy era. Through AI scheduling and microgrid coordination, PV and energy storage systems are evolving from mere “energy storage devices” to “intelligent energy balancers”, capturing value amidst fluctuations and reshaping the future through changes.
Post time: Jan-23-2026