The Economics of
Autonomous Power:
TCO, OPEX and
Infrastructure Cost
Fuel has to be delivered.
Batteries must be replaced.
Operations must be sustained.
These are not engineering problems.
They are cost structures.
VENDOR is designed to change that structure.
VENDOR.Max is an autonomous power node designed for infrastructure deployment in remote and weak-grid environments. It operates as an open electrodynamic architecture requiring external electrical input for sustained operation. At the complete system boundary:
Pin,total = Pload + Plosses + dE/dtThis page explains the economic implications of that architecture — not a claim of energy generation.
All figures are illustrative modeled estimates based on internal analysis and publicly referenced industry cost data. They are not commercial guarantees. Site-specific economics are quantified during pilot assessment. VENDOR.Max is at TRL 5–6 (pre-commercial validation stage).
Patent references: WO2024209235 (PCT), ES2950176 (granted, Spain).
What is the economic model of VENDOR?
VENDOR is a CAPEX-dominant infrastructure system designed to shift energy cost away from fuel dependency and toward predictable lifecycle economics. It does not reduce cost through energy price. It changes cost structure by shifting dependence away from recurring operational inputs and toward infrastructure-based cost.
From Operational Cost
to Infrastructure Investment
Traditional energy systems are built around continuous dependency: fuel supply, logistics chains and maintenance cycles. These dependencies define their economics. They are OPEX-driven systems.
VENDOR introduces a different model. Cost is concentrated upfront. Ongoing operational requirements are reduced. Dependency on consumables is removed. Cost variability becomes more predictable.
This is a CAPEX-dominant infrastructure cost model.
The system does not compete on energy price alone. It changes how cost is structured, how risk is distributed, and how operations are managed. The economic effect emerges from removing fuel dependency, reducing maintenance complexity, and stabilizing long-term cost behaviour.
Why is VENDOR different from diesel or battery systems?
Diesel and battery systems are OPEX-driven. Their lifetime cost is dominated by fuel or replacement cycles. VENDOR is designed to shift cost into upfront infrastructure investment and reduce dependence on ongoing operational inputs.
Where the Money Goes:
CAPEX vs OPEX
In traditional off-grid systems, the purchase price represents only a fraction of what operators actually pay. For diesel systems, 70–90% of total lifetime cost is in operating expenses. For battery-dependent systems, replacement cycles dominate within 3–5 years.
VENDOR is designed to invert this ratio.
Column height is proportional to modeled total lifetime cost.
CAPEX segments are scaled to approximate absolute capital cost.
Illustrative economics framework, not certified site-specific results.
- Fuel ~45%
- Maintenance ~20%
- Logistics ~15%
- Downtime + Compliance ~10%
- Battery Replacement ~50%
- Site Visits ~10%
- Disposal + Downtime ~10%
- Periodic Inspection ~10%
- Remote Monitoring ~5%
Cost Categories:
Traditional vs Autonomous Systems
Diesel vs VENDOR.Max:
Illustrative Multi-Year TCO
Remote infrastructure site. Continuous 24/7 operation. Limited access. Fuel must be transported. Maintenance teams require regular site visits.
Illustrative Multi-Year Cost Behaviour
In high-logistics, high-diesel-dependency scenarios, modeled lifetime cost divergence between diesel and VENDOR.Max may become substantial over multi-year operation. The longer the operating horizon, the greater the modeled divergence — because diesel OPEX accumulates while VENDOR operating costs remain low and predictable.
Indicative scale. No absolute values shown. Diesel cost trajectory driven by fuel, maintenance, and logistics accumulation. VENDOR cost trajectory reflects CAPEX-dominant model with minimal ongoing OPEX.
Network Scale Economics
When this cost divergence is applied across multiple sites, the modeled differential compounds. At portfolio scale, the economic effect of removing fuel logistics from the cost structure becomes a material budget factor.
Figures are based on internal modeling using publicly referenced industry cost data (GSMA, IEA, Fraunhofer ISE). They are not a commercial guarantee and are subject to external validation milestones. Pilot data is used to establish site-specific economics.
Levelized Cost of Energy:
Lifetime Cost per kWh
LCOE measures the total lifetime cost per kilowatt-hour, including all capital expenses, operational expenses, fuel, maintenance, logistics, and disposal costs over system lifetime.
LCOE = (CAPEX + Σ OPEX) / Total Energy Delivered
Energy cost can be interpreted in two fundamentally different ways: as a fuel cost, or as a lifecycle infrastructure cost.
Diesel energy cost is frequently expressed as fuel cost per kWh. This captures immediate operating expense, but excludes the underlying infrastructure required to produce that energy.
A full economic model includes: capital cost of the power system and its deployment, fuel over the operating lifetime, maintenance and overhaul cycles, logistics and site access, and operational constraints including downtime.
This page uses a lifecycle-based approach to reflect the total cost of supplying energy, not only the fuel component.
What determines the cost per kWh in VENDOR systems?
Cost per kWh (LCOE) depends on system lifetime, maintenance frequency, and operating conditions. For VENDOR systems, it is not driven by fuel cost. LCOE is not a fixed value — it varies with deployment environment and operational assumptions.
LCOE Comparison Across Power Technologies
The VENDOR.Max range shown here is an internal modeled scenario, not a certified benchmark and not a universal site-level result.
Sources: Diesel/Solar/Grid ranges from Fraunhofer ISE 2024, IEA, GSMA. VENDOR.Max LCOE from internal financial model (base case, implied from projections). Not independently verified.
VENDOR.Max LCOE from internal model. Not independently verified. Competitor ranges from published industry data.
VENDOR's modeled LCOE advantage grows stronger in remote environments where fuel delivery costs escalate with distance and difficulty. This effect is most relevant where logistics, service access, and uptime constraints dominate total cost.
VENDOR.Max LCOE is derived from internal financial modeling. It has not been independently verified. Competitor data from published industry ranges. All LCOE figures are indicative and scenario-dependent.
The Costs You Do Not See
— Until You Pay Them
The total cost of ownership for conventional power goes beyond line items on a balance sheet. Traditional systems create ongoing operational complexity that consumes management attention, requires dedicated personnel, and introduces recurring points of failure.
- 1 Fuel procurement and transport logistics
- 2 Maintenance cycles (every 500–1,000 operating hours)
- 3 Battery replacement and disposal (2–5 year cycles)
- 4 Site access visits for refueling or component swapping
- 5 Seasonal operation challenges (weather-dependent)
- 6 Emissions compliance and environmental reporting
- 7 Noise management and regulatory compliance
- 8 Safety management for fuel storage
- 1 Remote monitoring and system diagnostics
- 2 Optional periodic inspection (no consumables required)
Physical hardware still requires standard site security — similar to solar panels or telecom equipment. VENDOR reduces operational burden, not security requirements.
Where the CAPEX-Dominant Model
Shows the Greatest Economic Advantage
The CAPEX-dominant model becomes economically relevant where fuel logistics dominate cost, service access is limited, and uptime is critical. In these environments, systems that avoid recurring fuel delivery and reduce maintenance dependency can materially change the lifetime cost structure.
Fuel transport dominates cost. Helicopter delivery to mountaintop sites, ice-road-only access in winter, and seasonal isolation windows create cost multiples. In these conditions, systems structured without fuel logistics can materially reduce the lifetime cost baseline.
Diesel shipping costs to island sites are high and disrupted by weather. In these conditions, an infrastructure system structured without recurring fuel delivery can target a materially lower lifetime cost trajectory. Lifetime assumptions remain subject to the 2-year warranty baseline, the 20+ year engineering target, and scenario-dependent extended operation.
Fuel convoys represent a high-risk logistical vulnerability. Systems structured without recurring fuel dependency can reduce both the operational footprint and the supply-chain exposure of remote installations.
Large-scale deployments of distributed infrastructure assets — monitoring stations, utility outposts, perimeter systems — face compounding maintenance costs at portfolio scale. Reducing per-site service dependency changes the portfolio-level cost structure.
Traditional vs Autonomous:
Key Economic Characteristics
VENDOR is not a "low-cost energy device". The system does not compete by producing cheaper energy per se. It is designed to change how cost is structured — by removing operational dependencies such as fuel and logistics.
The advantage is not cheaper energy.
The advantage is removal of operational uncertainty from energy
supply.
What is the real economic advantage of VENDOR?
The primary advantage is not cheaper electricity in abstract terms. It is the shift from variable operational cost to predictable infrastructure cost.
Current Status and Key Variables
What Is Established
- Three-circuit discharge-resonant architecture (patent ES2950176, PCT WO2024209235)
- Regime formation and stability under real load
- 1,000+ cumulative operational hours
- 532-hour continuous cycle at fixed 4 kW load
- Measured energy delivered during the documented 532-hour cycle: approximately 3.996 MWh
Key Variables and Risks
Economic performance depends on:
- System lifetime (2-year warranty baseline; 20+ year engineering target; extended operation scenario-dependent, not guaranteed)
- Maintenance frequency and environmental conditions
- Certification outcomes (CE / UL — pathway defined, target windows 2026–2028, not yet issued)
- Deployment-specific operating behaviour under varied environmental and load conditions
These factors are part of the ongoing validation process. Final validation requires pilot deployments, certification-stage measurements, and real-world operational data.
This page does not claim energy creation, violation of physical laws, or finalized certified performance metrics. All conclusions are bounded by the current validation stage, the defined measurement scope, and scenario-based engineering assumptions.
Data Sources and Limitations
All economic models on this page are illustrative. They are based on internal financial modeling using publicly referenced industry cost data from GSMA, IEA, Fraunhofer ISE, and comparable infrastructure energy analyses.
VENDOR.Max LCOE and TCO figures are derived from internal base-case projections and have not been independently verified. Competitor cost data reflects published industry ranges and may vary by geography, scale, and site conditions.
Competitor ranges are used for directional comparison, not for claiming universal superiority across all sites and all load conditions.
Site-specific economics — actual fuel cost, delivery complexity, duty cycle, existing infrastructure — are quantified during the pilot assessment process. Pilot data is used to establish the baseline for investment decisions.
VENDOR.Max is at TRL 5–6. Illustrative capital-cost scenarios are used for economic modeling on this page. Final commercial pricing remains subject to validation milestones, certification progress, manufacturing scale, and deployment configuration. Capital-cost assumptions are modeling inputs, not published commercial offers.
Patent references: ES2950176 (granted, Spain) | WO2024209235 (PCT)