Recently, the 1st Artificial Intelligence Innovation R&D Exchange Event for the Energy & Power Industry, hosted by the Big Data and Artificial Intelligence Branch of the China Electricity Council, successfully concluded in Beijing. Competing against 135 top-tier industry teams including national power grid corporations, major power generation groups and high-tech enterprises, Luculent stood out and was awarded the authoritative certification of "Practical Algorithm" for its self-developed power market price prediction algorithm.

Centered on the theme of "AI Empowerment · Intelligent Transformation of Power", this event marks the first industry-grade AI innovation competition organized by the Branch. Oriented toward real-world demands and grounded in practical business scenarios, it serves as an ecological platform to advance industrial innovation and talent cultivation. This award fully recognizes Luculent’s profound technological reserves and outstanding engineering implementation capabilities at the intersection of artificial intelligence and the power market sector.

Addressing Pain Points of the New Energy Power Market: AI Algorithms Tackle Severe Price Volatility

Against the backdrop of a new power system with continuously rising installed capacity of renewable energy, the output of wind power, photovoltaic and other new energy sources is highly intermittent and random due to natural weather conditions. At noon with abundant sunlight, surging photovoltaic generation leads to an oversupply of electricity, driving spot market prices down to floor prices or even zero tariffs. By the evening peak electricity demand, once wind dies down and sunlight fades, electricity prices surge sharply. This extreme duck curve effect has triggered drastic fluctuations in electricity spot prices and widening peak-valley price gaps.

Power generation enterprises will suffer substantial operational losses if they fail to accurately forecast electricity price trends, as their bidding strategies will be misaligned with actual market prices. For industrial and commercial consumers, uncertain electricity prices hinder rational production scheduling and effective electricity cost control.

Targeting these urgent industry pain points, drawing on over two decades of expertise rooted in the power sector, Luculent has deeply integrated industrial mechanism knowledge with cutting-edge AI technologies to develop this practically valuable electricity price prediction algorithm, delivering core support for all market participants to resolve industry challenges.

Powered by Intelligent Optimization & Control Platform: Building an Agile and Efficient Prediction Engine

The award-winning power market price prediction algorithm is fully backed by Luculent’s Intelligent Optimization & Control Platform. Traditional algorithm development relies heavily on manual work by specialists, with human experience restricting overall efficiency. Adopting low-code and Agent-native modeling approaches paired with a highly parallel algorithm engine, the platform enables agile and batch algorithm design and development, greatly boosting R&D efficiency.

Embedded with massive advanced operators including deep learning modules, the platform equips electricity price forecasting with a comprehensive technical toolkit. To adapt to the high noise and non-linear characteristics of power market data, supported by modeling Agents, the R&D team has rolled out strategic innovations and optimizations across key links including feature engineering, algorithm selection and hyperparameter tuning, enabling the algorithm to better fit power market application scenarios.

Furthermore, the platform preprocesses intricate power grid operation data, meteorological data and market transaction data into intuitive, visual and easy-to-understand feature graphs, allowing algorithm engineers to quickly identify the internal correlation between fluctuations in renewable energy output and electricity price movements. Equipped with self-operation, self-learning and self-upgrading capabilities, the model can adapt promptly to drastic changes in data patterns and trading rules to sustain stable forecasting accuracy at all times.

AI Empowering Full-Scenario Power Trading: Cutting Costs and Mitigating Risks

In practical power trading operations, this electricity price prediction algorithm delivers tangible economic benefits to all types of market players.

It acts as an anchor for risk mitigation. After deploying the algorithm, power generators can obtain advance insights into future electricity price trends. In day-ahead trading, they can raise generation output during high-price periods and arrange unit maintenance or deep peak regulation rationally amid low-price windows. In real-time trading, the algorithm precisely curtails deviation risks and prevents losses caused by price swings, ensuring stable business operations.

It serves as a key driver for profit growth. Power retail companies can flexibly allocate positions between medium-and-long-term contracts and the spot market based on predicted electricity price curves to capture arbitrage opportunities. Industrial and commercial users can formulate the most economical power consumption plans by leveraging their flexible loads (such as energy storage systems and controllable production equipment) in response to peak and valley price differences. Through charging during off-peak hours and discharging at peak times, enterprises can slash overall electricity expenditure significantly.

As the green transition of the power industry accelerates steadily, Luculent will keep abreast of power market developments, continuously analyze incremental transaction data, master market operation rules and interpret the latest industrial policies. The company will iteratively upgrade its AI forecasting algorithms and trading models, offering more accurate, efficient and intelligent one-stop digital solutions for power trading. Luculent will collaborate with numerous ecological partners to forge a new blueprint for green energy and contribute to the construction of the new power system.