The study, published in Frontiers in Energy Research, confronts a subtle problem that has plagued energy storage planners for years. A stationary battery, no matter how well sized and sited, can only shift energy in time at its one connection point. Yet the operational stress caused by high solar penetration does not sit still. Reverse-power-flow violations, voltage rise, and photovoltaic curtailment migrate across different transformer service areas over the course of a day, as load patterns and solar output change from one neighborhood to the next. A battery bolted to one transformer simply cannot follow that moving target.

Mobile shared energy storage, abbreviated MSES, offers an elegant answer: a single storage unit that can drive or be transported between connection interfaces at different distribution transformers. But mobility is not free. Every relocation consumes traction energy drawn from the battery itself, occupies a full scheduling period during which no charging or discharging can occur, and requires connection-preparation time after arrival before the unit can resume grid service. Previous research on mobile storage often glossed over these engineering frictions, and frequently compared mobile units against stationary batteries that were never properly optimized, making the apparent benefits of mobility difficult to trust.

The new framework introduces a two-part solution. The first element is a diagnostic tool called the effective mobility service index, or MSIeff, computed from the location and magnitude of reverse-power-flow exceedances in a network operating without any storage. The index captures two things at once: how much solar stress exists in total, and how far that stress migrates among service areas over time. If violations stay concentrated at a single transformer, a well-placed stationary battery captures nearly all the available benefit, and mobility adds nothing. If the dominant violations hop from one area to another across the day, mobility becomes genuinely valuable. The index therefore acts as a pre-assessment screen, telling planners before any detailed optimization whether a mobile unit is likely to pay off.

The second element is a rigorously fair comparison design. The team optimized stationary storage and mobile storage within an identical finite candidate domain of power and energy ratings, spanning 100 to 200 kilowatts and 200 to 800 kilowatt-hours, subject to a common two-to-four-hour duration rule. The stationary benchmark’s connection location, rating, and daily dispatch schedule were all independently optimized, eliminating the common trap of benchmarking mobility against a deliberately weak fixed alternative. Both schemes shared the same total boundary for connection-infrastructure investment, and the mobile model explicitly represented travel-induced unavailability, traction consumption, interface limits, state-of-charge dynamics, and end-of-day closure constraints on both energy and location.

Solving the coupled problem required a nested optimization architecture. An outer layer enumerated feasible power-energy configurations and, for the stationary case, candidate fixed locations. For each candidate, an inner 24-hour dynamic program minimized daily operating cost, tracking stored energy, current service area, and connection-preparation status in its state space for the mobile unit. The daily costs were annualized and combined with investment and maintenance costs to produce a single equivalent-annual-cost metric for every configuration, from which the best option in each scheme was selected.

The results, drawn from 30 parameterized scenarios varying solar capacity, load levels, and temporal shifts in photovoltaic output, paint a nuanced picture. Mobile storage reduced photovoltaic curtailment in 16 of the 30 scenarios and lowered equivalent annual cost in 15, achieving both benefits simultaneously in 13. In the remaining cases, the mobility premium in travel, startup, and traction costs ate up the physical gains. Critically, the effective mobility service index proved to be a strong predictor of real-world benefit: its Pearson and Spearman correlation coefficients with realized curtailment reduction reached 0.867 and 0.870, respectively, confirming that a simple no-storage diagnostic can reliably flag the scenarios where a traveling battery will actually earn its keep.

The starkest illustration comes from a representative high-adaptability scenario in which reverse-power-flow violations first emerged in one service area and then migrated to two others. Both storage schemes converged on the same 200-kilowatt, 800-kilowatt-hour rating, isolating the effect of location flexibility alone. The mobile unit departed its depot, spent the morning absorbing surplus solar in the hardest-hit area, and returned home by midday. The result was a 14.54 percent reduction in daily photovoltaic curtailment, a 2.02 percentage-point increase in solar accommodation, and a 2.04 percent drop in equivalent annual cost compared with the optimally sited stationary battery. The cost breakdown showed that savings came from slashing curtailment penalties and network losses, not from cheaper equipment.

Sensitivity analyses sharpened the practical guidance. The mobile scheme’s cost advantage eroded as the unit price of mobile connection interfaces rose, breaking even at roughly 280,000 to 300,000 yuan per connection point and collapsing entirely at about 600,000 yuan, beyond which the optimizer chose no storage at all. Longer connection-preparation times also chipped away at the benefit, cutting the annual cost advantage from about 59,800 yuan to roughly 23,000 yuan when preparation stretched to two hours. An idealized mobility model that ignored travel time and traction energy overstated performance, overstating curtailment reduction and understating cost relative to the engineering-constrained design.

The broader lesson is that mobility is a conditional asset, not a universal upgrade. The authors emphasize that their framework rests on an aggregated voltage-sensitivity model and deterministic representative-day profiles, with only three service areas and a single mobile unit, leaving three-phase unbalanced power-flow validation, year-round uncertainty-aware planning, and multi-unit coordination to future work. Yet the message for distribution planners is already actionable: before investing in either fixed or roaming batteries, measure whether your grid’s solar stress actually wanders. If it does, a single battery on wheels can outperform an optimally placed stationary one; if it does not, the open road is just an expensive detour.

Subject of Research: Coordinated planning and operation of mobile shared energy storage in photovoltaic-rich low-voltage distribution networks

Article Title: Service adaptability assessment and coordinated planning and operation of PV-rich low-voltage active distribution network clusters

Article References: Service adaptability assessment and coordinated planning and operation of PV-rich low-voltage active distribution network clusters. (n.d.). https://doi.org/10.3389/fenrg.2026.1963215

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1963215

Keywords: mobile energy storage, photovoltaic curtailment, low-voltage distribution network, reverse power flow, battery storage planning, grid flexibility, voltage rise, energy storage sharing, smart grids, rooftop solar, service adaptability index, distribution transformers