Dynamic Administrative Pricing (DAP) Model for Energy in the TRON Network

As CEO of PowerSun.vip, I’ve witnessed firsthand how TRON’s static energy pricing impacts the entire ecosystem. This research presents a mathematical solution that could transform network economics.

Executive Summary

The TRON network utilizes a static administrative price for its key computational resource, “Energy,” which is a suboptimal approach incapable of adapting to changing market conditions and, more importantly, fails to account for empirically observed asymmetric user behavior. Analysis shows that the demand for Energy is highly inelastic to price increases and highly elastic to price decreases. A static price leads to two problems: missed revenue opportunities during periods of high load and suppressed network activity during quiet periods when a lower price could stimulate usage.

This report proposes a Dynamic Administrative Pricing (DAP) Model based on a philosophy of strategic stimulation. Instead of aiming to maximize revenue through price hikes, the model uses the current price of 210 sun as a “Base Price” and offers algorithmic discounts whose depth fluidly depends on the market state. The model classifies the network’s condition in real-time along the axes of “Market Trend” and “Network Load” and calculates the optimal price using a fundamental formula.

Backtesting the model on a relevant period of historical data (starting from September 2024) convincingly demonstrates its effectiveness. The DAP model successfully identifies periods for stimulation by offering flexible discounts, which, according to demand elasticity, should lead to significant growth in network activity. Simultaneously, the total revenue from TRX burning remains comparable to historical levels, proving that it is possible to stimulate the network without sacrificing revenue. The implementation of the DAP model is proposed as a tool for macroeconomic stabilization of the TRON ecosystem, which will maximize useful activity, smooth out peaks and troughs in load, and enhance the long-term attractiveness and sustainability of the network.

1. Model Foundations: TRON’s Resource Economy and Price Elasticity

To build an effective pricing model, a deep understanding of the fundamental economic mechanisms governing resources on the TRON network is necessary. The network’s economy is built on a dual-resource model and offers users two alternative ways to pay for transaction costs, creating a complex system of incentives.

1.1. The Dual-Resource Economy: Energy and Bandwidth

The TRON network replaces the traditional “gas” model used by Ethereum with a more complex system consisting of two separate resources: Bandwidth and Energy.

Bandwidth is a resource that measures the “weight” of a transaction in bytes. Every transaction transmitted over the network has a specific size and requires a corresponding amount of Bandwidth points for processing. The primary purpose of this resource is to handle basic operations, such as transfers of the native TRX token and TRC-10 standard tokens. A key feature is that every active account on the TRON network receives a fixed daily free limit of Bandwidth (currently 600 points). This limit is sufficient for several simple transfers, making basic network usage effectively free for most users and lowering the barrier to entry.

Energy is the primary computational resource required for the TRON Virtual Machine (TVM) to operate. Energy is consumed during the execution and deployment of smart contracts. This includes all operations with TRC-20 standard tokens (such as USDT, a major driver of network activity) and any interaction with decentralized applications (dApps), from DeFi protocols to games. Unlike Bandwidth, Energy is not provided to users for free on a daily basis. This makes Energy the network’s main paid computational resource, the cost of which directly impacts the economics of dApps and the activity of advanced users.

It should be emphasized that complex transactions, such as calling a smart contract function, consume both resources simultaneously: Bandwidth to cover the byte size of the transaction itself and Energy to pay for the computations performed by the TVM.

1.2. Resource Acquisition Mechanisms: Capital vs. Operational Expenditure

TRON provides users with two fundamentally different ways to obtain the necessary resources, which can be classified as capital and operational expenditures.

Staking (Freezing) TRX — “Capital Expenditure” (CapEx)

The primary way to obtain resources is by freezing (staking) the native TRX token. A user can lock a certain amount of TRX, choosing which resource they want to receive: Energy or Bandwidth. The amount of resources received is not fixed but is calculated proportionally to the user’s share of the total pool of TRX frozen for that resource across the entire network. This mechanism can be viewed as a capital investment. A user or dApp invests their capital (temporarily giving up TRX liquidity) to secure a predictable and steady stream of resources, essentially purchasing the “right to compute” for future periods. This approach is advantageous for regular, active network participants, such as large traders, DeFi protocols, or gaming platform operators. An important economic factor is the 14-day unfreezing period, during which the locked funds remain illiquid.

Burning TRX — “Operational Expenditure” (OpEx)

If a user lacks sufficient resources from staking (or free Bandwidth points) to execute a transaction, the system provides an alternative mechanism: automatically burning TRX from the user’s balance to pay for the missing resources. The cost of this operation is determined by an administratively set parameter. For Energy, this parameter is called sun_per_energy_unit (1 TRX = 1,000,000 sun) and represents the price per unit of Energy when paid for by burning. This mechanism can be defined as an operational expenditure. It is intended for infrequent users or those who are unwilling or unable to freeze significant capital. They pay for resources as needed, using a "pay-as-you-go" model.

This dual mechanism creates a fundamental economic choice for every network participant. The decision to stake or burn TRX is a classic “CapEx vs. OpEx” dilemma. This decision is influenced by the user’s activity level, their time horizon, and, most importantly, the administrative price of burning. The sun_per_energy_unit parameter is not just a fee but a critically important lever that determines the economic attractiveness of staking. A high burning price makes staking more profitable, encouraging long-term capital investments and enhancing network security. A low burning price makes one-time network use cheaper, lowering the entry barrier for new or infrequent users. The current static price is unable to dynamically manage this trade-off, which is the key problem.

1.3. The Central Problem: Asymmetric Price Elasticity of Demand

The key element justifying the need for dynamic pricing is empirical data on user reactions to changes in the price of Energy. An analysis of four natural experiments (periods when the administrative price of Energy was changed) revealed a strong asymmetry in the price elasticity of demand.

Analysis of Inelastic Demand for Price Increases: Elasticity coefficients ranging from -0.25 to -0.32 indicate that the demand for Energy is extremely inelastic when the price increases. This means that even a significant price hike (e.g., 100%) leads to a relatively small decrease in its consumption (25–32%). This behavior points to a “core” of users (power users, large dApps, arbitrage bots) for whom network use is critically important and who are willing to pay a higher price. From an economic perspective, this means that when the price increases, the network’s total revenue from TRX burning (calculated as Price × Quantity) grows significantly, as the price increase more than compensates for the slight drop in consumption.

Analysis of Elastic Demand for Price Decreases: The elasticity coefficient of -3.0 observed during a price decrease paints a completely different picture. The demand is highly elastic. This means that a 50% price reduction causes a disproportionately large 150% increase in consumption. This behavior indicates a large pool of “marginal” or price-sensitive users and dApps who only start actively using the network when it becomes economically viable. A price decrease is a powerful tool for stimulating network activity and attracting new users.

Conclusion: The existing policy of a static price is fundamentally suboptimal. It fails to capture maximum revenue from TRX burning during periods of high demand (when it is inelastic) and is unable to effectively stimulate network growth during quiet periods (when demand is elastic). This creates a clear and pressing need for a dynamic model capable of adapting the price to current conditions and leveraging asymmetric user behavior for the benefit of the entire ecosystem.

2. Methodology: Diagnosing the Ecosystem’s State

For a dynamic pricing model to be effective, it must be able to accurately diagnose the current state of the ecosystem. We propose a classification model that translates continuous market data into a set of discrete, understandable, and actionable “states.”

2.1. Defining the Classification Axes

The classification model is based on two key orthogonal indicators that together provide a comprehensive view of the network’s health and dynamics:

• X-Axis: Market Trend: This indicator serves as a proxy for measuring general investor sentiment, speculative activity, and the inflow of new capital into the ecosystem. It is measured as the percentage change in the average monthly closing price of TRX compared to the previous month (Month-over-Month, MoM).
• Y-Axis: Network Load: This indicator directly reflects the real, organic demand for the network’s computational resources from users and dApps. It is measured as the percentage change in total monthly Energy consumption (MoM). This indicator is a key measure of the blockchain’s actual usage.

2.2. Market State Classification Matrix

The combination of these two axes and their threshold values (-10% for TRX price and -15% for Energy consumption) allows for the creation of the following matrix, which is the diagnostic core of our new "strategic stimulation" model. Unlike the initial, more granular model, this matrix defines not a specific action but a general pricing regime.

3. The New Approach: The DAP Stimulation Model

Based on the identified asymmetric elasticity, a new model was developed whose philosophy is not to maximize price, but to optimize network activity through strategic discounts.

3.1. The Concept of a Floating Target Price

The model is anchored to the current Base Price of 210 sun. This level is considered the equilibrium point. The model never proposes a price above this level. Its task is to identify market states where a temporary price reduction is justified and to calculate the depth of that discount.

A key departure from a simplistic approach is the rejection of discrete target levels. Instead, the Target Price is calculated using a continuous formula that responds smoothly to negative changes in the network economy. The depth of the discount is directly dependent on the severity of the market downturn and the reduction in network activity.

3.2. The Final Regulatory Formula

The logic of the DAP model can be distilled into an elegant mathematical formula. The recommended price Pₜ at time t is determined by the price in the previous period P₍ₜ₋₁₎ and the Target Price P_target, which is a function of the market state.

First, normalized downturn indicators are determined for the TRX price (Iₜᵣₓ) and Energy consumption (I_Energy). These indicators are 0 if there is no downturn and reflect the depth of the downturn if there is one.

The formula for each indicator k (where k is TRX or Energy) is:

Where:

• Δₖ is the percentage change of metric k over 30 days

• Tₖᵐⁱⁿ is the threshold minimum (start of downturn)

• Tₖᵐⁱⁿᵐᵃˣ is the maximum acceptable decline (full discount threshold)

Parameter Values:

• For TRX: Tᵐⁱⁿ = -10%, Tᵐⁱⁿᵐᵃˣ = -50%
• For Energy: Tᵐⁱⁿ = -15%, Tᵐⁱⁿᵐᵃˣ = -50%

This normalization ensures that:

• When Δₖ ≥ Tₖᵐⁱⁿ (small changes): Iₖ = 0 (no discount)

• When Δₖ = Tₖᵐⁱⁿᵐᵃˣ (maximum decline): Iₖ = 1 (full discount)

• All intermediate values are properly mapped to the [0,1] range

The fundamental formula for the Target Price :

Where:

• P_base = 210 sun (Base Price)
• P_floor = 70 sun (Price Floor)

This formula ensures a smooth transition:

• If both indicators are 0, then P_target = P_base
• If one of the indicators falls, P_target smoothly decreases from 210 sun
• If the sum of the downturn indicators exceeds 1 (a deep downturn), the discount reaches its maximum, and P_target = P_floor

The final formula for the price in the next period P_t uses a smoothing factor (speed) α (in our backtest, α = 0.5) for a gradual move towards the target:

Where:

• α = 0.5 (smoothing factor)
• P_(t-1) = price in previous period
• P_target = calculated target price

This formula implements an exponential smoothing mechanism, which ensures stability and predictability, preventing sharp price jumps and smoothly adapting to changing conditions within the given stimulation strategy.

4. Backtesting Results and Visual Evidence

Instead of a thousand words, the model’s effectiveness is best demonstrated by the graphs generated from the backtesting results.

Graph 1: Price Comparison This graph compares the historical static price (210 sun) with the dynamic price that the DAP model would have proposed. It is clear how the model reacts to market conditions by offering discounts during downturns.

Graph 2: Revenue Comparison Despite the lower average price, the simulated monthly revenue (green line) is higher than or comparable to the historical revenue (grey line). This proves that stimulating activity effectively compensates for the price reduction.

Graph 3: Demand Stimulation Map This graph is key to understanding the model’s logic. It shows how the actual DAP price (blue line) smoothly follows the calculated Target Price (orange dashed line). The shaded blue area clearly illustrates the “missed opportunities” — periods when the static price of 210 sun was inefficient, and a dynamic price reduction could have (and in our simulation, did) bring the most benefit to the network by stimulating demand.

5. Quantitative Results and Economic Impact

An analysis of the backtesting results provides a quantitative assessment of the economic impact of implementing the DAP model. The figures clearly demonstrate that the model achieves its primary goal: stimulating network activity without compromising revenue from TRX burning.

5.1. Key Performance Indicators

Over the period under review (starting from September 19, 2024), the following aggregate results were obtained:

5.2. Interpretation of Results and Final Conclusion

The backtesting results lead to a conclusion that is, at first glance, paradoxical but is economically sound:

By reducing the average resource price by 6.14%, the DAP model not only avoids losses but increases the total revenue from TRX burning by 6.85%.

This effect is achieved exclusively through the significant stimulation of network activity. The price reduction during quiet periods, dictated by elastic demand, leads to a +16.66% increase in energy consumption. This surge in activity more than compensates for the lower average price, creating a net positive effect for the ecosystem.

The model proves that the static price of 210 sun was suboptimal. It missed opportunities to stimulate the network when needed and failed to extract maximum value during periods of stability. DAP solves both these problems by acting as an intelligent regulator.

Final Conclusion:

The implementation of the Dynamic Administrative Pricing (DAP) model is an economically justified and strategically sound step for TRON. The model allows for the achievement of two key objectives simultaneously:

1. To increase useful network activity, making it more accessible and attractive to users and developers during downturns.
2. To increase the deflationary pressure on TRX through more efficient revenue collection from burning.

Thus, the DAP model is not just a price regulation mechanism but a tool for enhancing the long-term competitiveness, stability, and investment attractiveness of the entire TRON ecosystem.

About the Author:

Anton Erpulev is CEO of PowerSun (powersun.vip), the leading TRON resource marketplace processing over 100M energy units daily.