AI + New Energy Storage: JDEnergy Unlocks New Approaches to Independent Energy Storage Asset Operations
As the independent energy storage market continues to expand rapidly, a practical challenge is becoming increasingly apparent: project revenues often fall short of expectations after grid connection. Even energy storage power stations of the same scale can see their revenue performance differ by several times under the same market conditions. Revenue uncertainty has become a key bottleneck constraining the release of energy storage asset value.
How can this bottleneck be overcome? On September 22, the 2026 Shaanxi Xi’an “AI + Manufacturing” Solutions Matchmaking Conference, hosted by the Xi’an Association for Advanced Manufacturing, was held as scheduled. At the highly anticipated “AI + New Energy Storage” session, Zhao Weiliang, Power Trading Manager at JDEnergy, was invited to attend and share practical experience and insights on AI-enabled market-based operation of independent energy storage.
In response to this widespread industry challenge, JDEnergy has developed a three-in-one intelligent agent matrix consisting of the Zhuque Investment Research Agent, Xuance Trading Agent, and eMind Energy Storage Cloud Platform, integrating AI capabilities throughout the entire value chain from pre-investment assessment and in-market trading to post-investment operations and maintenance.
During the pre-investment assessment stage, the Zhuque Investment Research Agent generates a nationwide investment index decision-making map, providing market ratings and risk assessments for different provinces. It also generates cash flow and financial models covering a 15-year investment cycle, helping identify investment opportunities across different regions while avoiding a common industry pitfall: directly replicating a single charging and discharging strategy across different provinces.
During the trading stage, the Xuance Trading Agent provides 15-minute-interval electricity price forecasts and simultaneously generates multiple trading strategies, including conservative, balanced, and opportunity-oriented options, together with revenue projections and risk alerts. Powered by an RPA automation engine, it enables concurrent bid submissions across multiple power stations with full-process visual monitoring, reducing errors caused by manual operations and truly closing the loop from strategy formulation to execution.
During the post-investment operations and maintenance stage, the eMind Energy Storage Cloud Platform focuses on station-level operation and maintenance management. Powered by edge intelligent agents, it can proactively identify potential equipment abnormalities, shifting from “passive alarms” to “proactive detection.” Through control strategies such as active cell balancing, it helps slow battery SOH degradation and reinforce the baseline of equipment availability and online operation for energy storage power stations.
As of now, JDEnergy’s cumulative operational capacity has exceeded 10 GWh, with business operations covering key electricity market regions including Ningxia, West Inner Mongolia, and Gansu. At the benchmark Taiyaoxin Energy Storage Power Station in Inner Mongolia, with a capacity of 500 MW/2,000 MWh, revenue performance has remained within the top 10% in the region since the project began operation more than six months ago.
Independent energy storage is now entering a new stage in which “operations determine value.” AI is no longer merely a concept, but a core productivity tool for capturing price spreads, managing risks, and unlocking asset value in real-world operations. JDEnergy will continue to deepen its work in “AI + New Energy Storage,” enabling every kilowatt-hour of electricity to unlock greater value and contributing to the high-quality development of the new power system.
