The Energy Storage Market under Policy Fluctuations: How Industrial PCs Unlock Flexible Business Models such as Virtual Power Plants
In the wave of global energy transition, energy storage systems, as the "stabilizers" of the new energy system, are undergoing unprecedented dual transformations in policy and market. From China's "14th Five-Year Plan" setting clear energy storage installation targets to European and American countries promoting the iteration of energy storage technologies through policies such as carbon taxes and subsidies, every adjustment in policy direction profoundly impacts the business models of the energy storage industry chain. Meanwhile, new business models such as virtual power plants (VPPs), peak-valley arbitrage, and demand response are constantly emerging, injecting new momentum into the energy storage market. However, policy uncertainty, fragmentation of technical standards, and lagging market mechanisms also pose challenges for energy storage companies in "balancing compliance and innovation." Against this backdrop, industrial PCs, with their core capabilities of "connection, computation, and decision-making," have become a key technological fulcrum for unlocking the flexibility of energy storage business models.
The rapid growth of the global energy storage market is inseparable from strong policy support. Taking China as an example, the "14th Five-Year Plan" clearly states that by 2025, the installed capacity of new energy storage should exceed 30 million kilowatts, and market penetration should be accelerated through mandatory energy storage allocation policies for "new energy + energy storage." However, policy orientation is shifting from "quantity" to "quality": on one hand, subsidy thresholds are gradually rising, requiring energy storage systems to have higher charge-discharge efficiency, cycle life, and safety performance; on the other hand, market mechanisms are gradually improving, with provinces such as Shandong and Guangdong piloting spot electricity markets, allowing energy storage to participate in ancillary services such as peak shaving and frequency regulation, and guiding resource allocation through price signals.
The European and American markets place greater emphasis on technological neutrality and market liberalization. For instance, the EU's "Clean Energy Package" requires member states to establish transparent rules for energy storage participation in electricity markets, while the U.S. FERC Order 2222 allows distributed resources (such as energy storage) to participate in wholesale markets through aggregators. These policies provide a legal basis for business models such as virtual power plants but also impose higher requirements on the intelligence and coordination capabilities of energy storage systems.
Despite the continuous release of policy dividends, the energy storage market still faces three major challenges:
Fragmentation of Technical Standards: Different regions have significantly varying requirements for grid connection specifications, safety certifications, and data interfaces for energy storage systems, forcing companies to customize products for different markets and increasing R&D costs.
Single Revenue Model: Early energy storage projects primarily relied on peak-valley arbitrage, but large fluctuations in electricity price differentials and limited charge-discharge cycles resulted in investment payback periods of 8-10 years.
Policy Uncertainty: Policy changes such as subsidy reductions and adjustments to mandatory energy storage allocation ratios may expose companies' previous investments to depreciation risks. For example, in 2025, some provinces in China temporarily lowered their energy storage allocation requirements due to an oversupply of new energy installations, leading to the shelving of some signed projects.
Against this backdrop, energy storage companies urgently need to build "policy-resistant" business models through technological innovation, and virtual power plants (VPPs), with their characteristics of "aggregating dispersed resources and participating in market transactions," have become a key focus for industry exploration.
The essence of a virtual power plant is to aggregate distributed energy storage, photovoltaics, interruptible loads, and other resources into a dispatchable "virtual power generation unit" through IoT, big data, and artificial intelligence technologies, enabling participation in electricity market transactions or the provision of ancillary services. Its core value lies in:
Optimized Resource Allocation: By aggregating dispersed energy storage devices, a large-scale response capability is formed, enhancing market bargaining power.
Risk Hedging: Diversified revenue sources (such as peak shaving, frequency regulation, and demand response) reduce dependence on a single policy.
Technological Empowerment: Industrial PCs enable real-time monitoring of equipment status, data interaction, and intelligent decision-making, supporting the efficient operation of virtual power plants.
Take Germany's Next Kraftwerke as an example: its virtual power plant has aggregated over 15,000 distributed units (including energy storage systems), achieving annual transaction volumes exceeding €1 billion by participating in European spot electricity and ancillary service markets. The success of this model relies on industrial PCs' precise control and data integration of underlying equipment.
As a bridge connecting energy storage equipment to cloud platforms, industrial PCs require three core capabilities:
Multi-Protocol Compatibility: Support for industrial protocols such as Modbus, CAN, and IEC 61850 enables seamless integration of energy storage equipment from different brands.
Edge Computing Capability: Local completion of data preprocessing and strategy deployment reduces cloud latency and improves response speed.
Security Protection Mechanisms: Technologies such as data encryption and access control ensure the safe and stable operation of equipment and the power grid.
Take the USR-EG628 controller launched by USR IOT as an example: its design philosophy aligns closely with the needs of virtual power plants:
Hardware Layer: Equipped with an industrial-grade RK3562J chip, it supports wide-temperature operation from -40°C to 75°C, meeting the harsh environmental requirements of outdoor energy storage scenarios; with 1 TOPS of AI computing power, it enables edge intelligence applications such as battery state of health (SOH) prediction and charge-discharge strategy optimization.
Software Layer: Pre-installed with the WukongEdge edge intelligence platform, it is compatible with mainstream platforms such as USR Cloud, Alibaba Cloud, and Huawei Cloud, supporting secondary development environments like Python and Node-RED for easy integration with virtual power plant operation systems.
Communication Layer: Integrated with multi-mode communication (4G/5G/Wi-Fi/Ethernet) and support for primary-secondary network switching, it ensures reliable data transmission; built-in VPN and firewall functions meet grid requirements for data security.
In practical applications, the USR-EG628 achieves three key functions:
Equipment Aggregation Management: Through protocol conversion, it uniformly connects energy storage inverters and BMS systems from different brands, enabling centralized monitoring and strategy deployment.
Real-Time Data Interaction: Millisecond-level collection of battery voltage, current, temperature, and other parameters is uploaded to the virtual power plant platform, providing data support for market transactions.
Local Decision-Making and Response: Based on grid dispatch instructions or electricity price signals, it executes charge-discharge strategies locally, reducing cloud communication latency and improving response speed.
Against the backdrop of deepening electricity market reforms, the peak-valley arbitrage model is upgrading from "fixed electricity price differentials" to "dynamic pricing." For example, in Shandong's spot electricity market pilot, energy storage systems can monitor electricity price fluctuations in real time, charging during low-price periods and discharging during high-price periods to achieve higher revenues. By integrating electricity price prediction models, industrial PCs can automatically optimize charge-discharge strategies, enhancing arbitrage efficiency.
Demand response is another important way for energy storage systems to participate in grid peak shaving. Taking the U.S. PJM market as an example, energy storage aggregators can receive grid dispatch instructions via industrial PCs and rapidly discharge during peak demand periods to earn subsidies. The edge computing capability of the USR-EG628 ensures millisecond-level response to instructions, avoiding penalty fees for delays.
As the proportion of new energy increases, grid demand for ancillary services such as frequency regulation and peak shaving surges. Energy storage systems, with their fast response speeds and high regulation precision, have become a sought-after option in ancillary service markets. For example, China Southern Power Grid's "14th Five-Year Plan" explicitly states that by 2025, the proportion of energy storage participating in frequency regulation markets should increase to 30%.
By integrating AGC (Automatic Generation Control) algorithms, industrial PCs enable precise coordination between energy storage systems and the grid. Taking the USR-EG628 as an example, it supports the IEC 61850 protocol, allowing direct connection to grid dispatch systems for secondary frequency regulation; simultaneously, AI algorithms predict grid frequency fluctuations to proactively adjust charge-discharge power, improving regulation efficiency.
The core of the virtual power plant model lies in achieving a "1+1> 2" scaling effect by aggregating dispersed resources. For example, an aggregator in Jiangsu, China, aggregated 100 MW/200 MWh of distributed energy storage, boosting annual revenues by 40% compared to standalone projects through participation in spot electricity and peak shaving markets.
In this model, industrial PCs act as "resource managers":
Equipment Management: Through the local configuration function of the USR-EG628, the operating status and revenue data of energy storage equipment can be visually displayed, reducing operation and maintenance costs.
Strategy Coordination: Based on dispatch instructions from the virtual power plant platform, charge-discharge strategies for each energy storage unit are dynamically adjusted to ensure overall response compliance with grid requirements.
Data Security: Blockchain technology records equipment operation data, ensuring transparent and traceable transactions while meeting grid requirements for data authenticity.
Looking ahead, two major trends will shape the energy storage market in terms of policy and technology:
Policy Trends: Globally, energy storage will be integrated into electricity market entities, participating in multi-dimensional revenue mechanisms such as spot trading, ancillary services, and capacity markets; simultaneously, the development of carbon trading markets will further highlight the emission reduction value of energy storage, driving the popularization of "energy storage + green power" models.
Technological Trends: Industrial PCs will evolve toward "high computing power, low power consumption, and strong security," supporting more complex AI algorithms (such as reinforcement learning and digital twins) to enable autonomous optimization and fault prediction for energy storage systems; concurrently, the application of communication technologies such as 5G and TSN will enhance equipment coordination efficiency, supporting virtual power plants' upgrade to "minute-level response."
Against this backdrop, energy storage companies must build a "device-platform-market" integrated business model with industrial PCs as the technological foundation: at the device layer, achieve cross-brand compatibility through standardized interfaces; at the platform layer, leverage cloud computing and big data for resource aggregation and strategy optimization; at the market layer, actively participate in diversified markets such as electricity trading and ancillary services to reduce dependence on a single policy.
Policy fluctuations serve as a "catalyst" rather than a "stumbling block" for the development of the energy storage market. The emergence of industrial PCs provides energy storage systems with an intelligent foundation for "connection, computation, and decision-making," enabling them to flexibly adapt to policy changes and unlock new business models such as virtual power plants, demand response, and ancillary services. In the future, as policy mechanisms improve and technologies iterate, the energy storage market will embrace broader development prospects, with industrial PCs playing an even greater role as a core driver of this progress.
