The most foundational technical function of a VPP in the context of BTM storage aggregation is maintaining a coherent, dispatchable state across a heterogeneous fleet of distributed energy storage devices. Patent landscape analysis of the Siemens Aktiengesellschaft portfolio (2020) reveals a virtual storage power plant architecture in which distributed electrical energy storage systems are electrically interconnected via transmission lines, a measuring device detects the state of charge (SOC) of each storage unit, and a control device adjusts each unit's SOC between defined lower and upper limits.

Charge equalization is performed by transmitting electrical equalization charges from high-SOC units to low-SOC units, with a mathematical algorithm and predictive power utilization curves guiding the balancing. Without SOC equilibration across the BTM fleet, individual units may be too depleted or saturated to respond to a frequency deviation event.

Elisa OYJ (2023, WO) extends this by introducing real-time plan adjustment. The VPP operates according to a pre-planned schedule for charging or discharging distributed energy storage (DES) devices over a time period to fulfil a power reserve obligation—but the plan is continuously analyzed against predefined acceptance criteria and real-time operating context data, enabling mid-execution adjustments. A companion patent (2024, WO) groups VPP battery assets into a reserve group and active groups; assets are offered for frequency balancing from active groups, while the reserve group is used exclusively for SOC adjustment—a segregation that directly prevents frequency regulation capability from being compromised by internal rebalancing operations.

The concept of virtual storage—treating flexible loads and controllable demand as equivalent to physical batteries—is a significant extension of BTM aggregation. The China Electric Power Research Institute (2019) describes a three-layer architecture (terminal device layer, communication network layer, coordinated control layer) that aggregates controllable loads, distributed renewable generation, electric vehicles, and physical storage devices into a unified virtual storage entity. Research from Aalto University confirms that resources such as PV systems, batteries, and smart loads are individually too small to meet the minimum controllable power thresholds required for frequency reserve markets, making VPP aggregation architecturally necessary.

VPPs enable BTM storage to cross the market participation threshold. Individual behind-the-meter storage assets are too small to independently qualify for frequency reserve markets. Aggregation through a VPP provides the minimum controllable power block required, as established by Aalto University (2021). This architectural necessity is the foundational rationale for the entire VPP-BTM-frequency regulation innovation stack. The WIPO patent database reflects growing international filings in this domain.

SOC management is the core operational prerequisite. Without active state-of-charge balancing across the distributed fleet, individual assets may be unavailable when frequency events occur. Siemens's Virtual Power Plant (2017) and Elisa OYJ's asset grouping method (2024) both address this, with Elisa segregating SOC adjustment into a dedicated reserve group to avoid compromising active frequency regulation capacity.

Simultaneous multi-market participation multiplies BTM storage value. Restricting BTM storage to a single market forfeits revenue opportunities. Ayesa (2022) demonstrates that combining dynamic storage virtualization with model predictive control enables simultaneous participation in day-ahead energy and frequency reserve markets, improving asset economics and encouraging BTM storage deployment. The PatSnap platform tracks multi-market optimization filings across jurisdictions.

Distributed price-signal optimization reduces central computation burden. As BTM fleet sizes grow, centralized optimization becomes computationally intractable. Xi'an Jiaotong University (2023) shows that parallel resource-level optimization against a shared frequency regulation price signal converges to market-clearing solutions without requiring a central solver to model all assets simultaneously. For regulatory context, see FERC Order 841 on energy storage participation in frequency regulation markets.