Bitcoin has recently captured headlines, overshadowing the buzz around AI chips. Despite this, the role of green energy, the underlying force behind both trends, remains understated. This seemingly low-profile asset could become a cornerstone of the financial system in the next two decades.

The conversation about renewable or new energy isn’t new. Issues like the global transition from fossil fuels amid carbon emission concerns, potential shifts away from oil dollar dependencies, subsidies for local renewable energy in Europe and America, and carbon tariffs on Chinese renewable equipment are central to discussions about green energy.

In May, G7 countries pledged to eliminate unabated coal-fired power by 2035. This summer has seen unusual weather events, such as extreme temperatures and flooding, all of which are linked to green energy.

Why the “Green Energy + DePIN + AI” Combination?

As we move towards the Tokenization of Real World Assets (RWA), green energy stands out as a foundational asset. During recent discussions on AI, both Elon Musk and Jensen Huang highlighted the substantial energy consumption of AI computations, suggesting that green energy could be the ultimate power source for AI. There’s an informal hierarchy in the AI world: Green Energy > Electricity > Computing Power > Base Models > Applications. As we transition into the AIGC and carbon-based/silicon-based eras, green energy is crucial. Future assets and applications will likely be tied to AI, including AI computing power and AIGC, as well as green energy through renewable sources like solar, hydro, wind, hydrogen energy, and integrated green energy trading.

Green energy, as a real-world asset, will need DePIN and AI to support its tokenization. Combining “Green Energy + DePIN + AI” is like giving green energy a brain (AI) and a heart with veins (DePIN). Green energy is a primary asset source for both the real world and the crypto world, characterized by its inherent scarcity and leading narratives. DePIN provides the infrastructure and trusted device network for the green energy ecosystem, enabling its distributed network, digitization, and blockchain integration. AI is key to showcasing green energy’s cost-saving and efficiency-enhancing potential, whether through AI-driven green energy arbitrage or home computing centers and edge computing.

The “Green Energy + DePIN + AI” combination can take many forms, depending on the actors and scenarios. For instance, in photovoltaic storage, some focus on distributed solar panels and green energy certificates, while others emphasize blockchain-based green economies and carbon metrics. Some explore battery lifecycle management with BatteryGPT, demand forecasting and price arbitrage with EnergyGPT, integrated investment models for solar storage, and virtual power plants (VPPs), as well as P2P green energy trading. Personally, I am focusing on an energy-centric “SaaS+AI” model for storage.

Green Energy’s DePIN Soft Heart

(1) Distributed Green Energy Smart Storage Network
The main challenge with green energy is its distributed nature. Most projects are small-scale, for personal use, or have limited grid integration. However, with Energy Management Systems (EMS) for storage, combined with DePIN and AI technologies, we can achieve “Green Energy + EMS + DePIN + AI == Green Energy Storage as a Service” (Green Energy SaaS).
In green energy’s photovoltaic storage system, components like photovoltaic modules, storage modules, and charging stations are highly digitized. For instance, storage modules may include Battery Management Systems (BMS), Energy Management Systems (EMS), and Power Conversion Systems (PCS). Even photovoltaic modules involve inverter controls and grid management, indicating full digitization and energy management at the module level.
The key feature of the “soft heart” is that, based on digitized hardware devices, DePIN technology enables distributed networking. DePIN’s IoT capabilities and supporting AI computation, combined with AI bots or agents for green energy, integrate green energy into a distributed network infrastructure.
The next evolution involves VPPs and P2P networks. VPPs can model and link green energy’s DePIN smart boxes and IoT devices (batteries, inverters, or chargers), enabling edge device forecasting and control. This setup can aggregate telemetry data from millions of IoT devices, manage green energy storage services, and establish a green energy cloud.

(2) Green Energy’s “EVM” Ecosystem Loop
From an integrated energy management perspective (source, grid, load, and storage), the green energy ecosystem loop extends beyond individual modules like photovoltaics or charging stations. It includes the entire cycle from generation to storage and consumption. In green energy SaaS storage services, this involves unique timestamps and consumption hash values, creating a complete green energy digital asset cycle.
Just as GPU hours are a unit for computational asset pools, green energy assets might be quantified as GWh of green watt-hours, forming a pool of green energy assets. In distributed green energy infrastructure, a secondary layer (similar to Layer 2) of virtual energy asset pools comes into play. This involves asset pooling, dynamic pricing, and liquidity management, with an emphasis on algorithms for asset rights, location, and entry conditions (e.g., staking or conditional entry).
In this context, storage modules are critical intermediaries in the green energy ecosystem loop, surpassing individual modules like photovoltaics or charging stations. Hence, in my framework, the second layer for green energy is envisioned as an “EVM” for green energy assets. This layer includes smart contracts, algorithm protocols, and basic services, forming an open Layer 2 platform for aggregating distributed green energy assets/resources. For instance, PPA smart contracts on this Layer 2 platform could use a Ricardo-like contract model for smart contract-based asset pooling.

(3) Green Energy Trading Network
With a green energy asset pool and VPPs (as the trading network foundation), P2P green energy trading becomes feasible. Key to this is full marketization, featuring multi-tiered market structures (funding, asset sides, and trading methods), diverse participants (buyers, sellers, liquidity providers, arbitrageurs, and standardized investors), and multi-dimensional liquidity pools.

Green energy trading relies on AI technology for dynamic pricing algorithms, price arbitrage predictions, optimal resource allocation, and price curve optimization. These require deep support from EnergyGPT and time-series AI models.

In the green energy trading liquidity pool, energy sources vary among market participants, with differing demands and timing. This includes standardized 2B solar storage nodes and large modules, as well as numerous distributed home storage modules. Green energy pricing is dynamic and decentralized, governed by a consensus-driven algorithm, which means control over pricing power. Pricing power signifies control over asset pricing, a concept well understood in finance.

Different trading methods and market participants also affect asset pooling coordination. While CEXs, DEXs, and OTC platforms exist, the core of green energy trading is SWAP, or decentralized resource optimization. This implies that the optimal trading configuration involves a decentralized Automated Market Maker (AMM) algorithm. Given the dissipative nature of green energy assets, adjustments to fixed-product algorithms similar to Uniswap may be needed.

Green Energy’s AI Smart Heart

(1) EnergyGPT and Home AI Computing Centers
The intersection of AI and green energy is dynamic and impactful. Our collaboration with American university energy labs on EnergyGPT addresses green energy arbitrage, peak cut, equipment degradation cycles, battery thermal runaway, demand forecasting, power load predictions, and charge-discharge strategies. These represent extensive application scenarios for green energy.
Unlike traditional large language models (LLMs) or time-series forecasting models, EnergyGPT is a hybrid MoE (Mixture of Experts) model. Given that DePIN smart boxes for green energy can be equipped with GPU computing power, they can handle local price demand forecasting, optimal arbitrage strategies, and local rendering for entertainment and gaming. They can also collaborate with regional centers for edge computing tasks. \
Standardized solar storage nodes can be configured as regional AI computing centers, incorporating AI chips for demand forecasting, price arbitrage, peak cut, charge-discharge strategies, and regional AI tasks, thus serving as edge computing nodes.

(2) AI Agents and AI Bots
Green energy’s EnergyGPT and AI computations appear on the user side mainly as AI Agents or AI Bots. Each 2B small and medium-sized enterprise or standardized node, and each 2C household, has distinct needs requiring intelligent green energy AI configurations. More application scenarios may emerge as these models become more accessible.
Distributed AI nodes can expand to become regional AI computing nodes based on VPPs and standardized solar storage nodes, forming distributed AI computing and edge computing nodes.

Due to space constraints, this overview of AI will be explored in more detail at upcoming

The New Financial System for Green Energy

(1) Standard Investment Model

In the green energy sector, a key standardized product is the integrated photovoltaic storage and charging station. Typically, this consists of about 300 KWh of solar panels, around 200 KWh of energy storage, and additional components like charging piles and parking spaces. AI chips are often incorporated into the storage modules to boost computational power, creating a benchmark green energy photovoltaic storage and charging AI node. This node enables precise assessment of total investment, actual output, and investment return cycles, serving as a model investment framework. Revenue streams primarily include green energy charging fees, dynamic grid balancing subsidies, peak cut and valley filling, electricity price arbitrage, and income from regional AI cloud rendering, cloud computing, and edge computing tasks. Consequently, this model node can be structured as a fixed-income RWA product.

In contrast, the green energy household investment model is less standardized due to significant variations in household sizes and electricity consumption patterns across regions like Europe and America. Some households operate more like small-scale industrial setups, while others are purely residential. This variability makes it difficult to develop a uniform green energy household investment model. However, these models will feature diverse charging and electricity use scenarios, such as: “rooftop photovoltaics + home storage + new energy vehicles + home AI computing center + robots/VR/gaming,” offering intriguing possibilities.

(2) Green Energy RWA Product Framework

Designing green energy RWA products involves focusing on the operational cash flow of photovoltaic storage and charging modules, in addition to the standard investment model node discussed earlier. Currently, the charging pile business stands out, where operators can directly earn from charging fees (2C), including both electricity and service charges—essentially representing green energy consumption. In terms of scale and stability, future growth will likely come from energy storage operators. While most current storage systems are OEM, independent operators are not yet widespread. Brand-name storage operators will generate stable, scalable cash flow by collecting monthly service fees from owners (2B institutions or 2C home storage).

Currently, photovoltaic modules are often used as spare parts, with most green energy from photovoltaics consumed on-site, leaving minimal surplus for sale, and therefore not contributing to operational cash flow.

In the green energy RWA framework, DePIN plays a crucial role as one of the infrastructure components for tokenizing green energy assets, enhancing transparency. Since green energy asset pooling and trading utilize DePIN smart boxes, DePIN also integrates RWA token cash flows. This makes RWA a feedback mechanism for the green energy DePIN ecosystem, driving the intrinsic value of the distributed green energy network.

Green energy RWA token cash flows typically involve earnings from green energy price arbitrage, demand response, trading fees, management fees for green energy liquidity pools, and margins on carbon credits and green energy certificates.

(3) Green Energy RWA Product Innovation

To innovate green energy RWA products, it’s essential to base them on core algorithmic protocols. These include three primary algorithms: decentralized green energy asset pooling, decentralized asset pricing, and decentralized trading algorithms.

With these foundational protocols, green energy RWA products can be developed, such as green bonds (fixed-income products) and green ABS (diversified revenue or cash distribution products). These products are fundamental RWA offerings, akin to green energy’s “US Treasury bonds” and high-quality “corporate bonds.”

Building on these assets, further innovation could lead to new RWA products such as green energy-based lending products, futures and options, insurance for electricity prices or computational power, and advanced products like green energy computing stablecoins and ETF index products.

Another area for exploration is integrating Bitcoin mining with green energy. For example, producing green energy-mined Bitcoin or equipping regional photovoltaic storage and charging AI nodes with Bitcoin mining machines to utilize surplus electricity for cost-effective Bitcoin mining. This would create a “Green Energy + Bitcoin Computing Power” RWA product.

Globally, projects like MicroGrid, P2P electric trading, and Renewable Power Coin are already exploring the tokenization and P2P trading of green energy. Even in space-constrained Hong Kong, there is a microgrid with 600,000 photovoltaic units. China also dominates 80% of global manufacturing capacity for photovoltaic storage and charging industries. Thus, green energy presents a critical opportunity for China. However, with US restrictions on AI computing power exports, China’s energy market remains underdeveloped, and higher-level infrastructure for photovoltaic storage and charging is not based in China. We have hardware production capabilities but lack the soft infrastructure, pricing power, trading rights, and computational capabilities.

(ChatGPT drawing)

New green energy financial ecosystem

Green energy represents more than just a prime asset for RWA; it embodies an entirely new ecosystem. The integration of green energy with DePIN, AI, and RWA creates a vast virtual green energy pool, trading platforms, and a novel financial system.

Currently, opportunities remain, as China retains a hardware production advantage. The green energy photovoltaic storage and charging sector lacks appropriate valuation models within traditional financial frameworks. Given the decoupling between China and the US and the surrounding pressure from Europe and the US, valuing green energy photovoltaic storage with traditional methods is challenging. However, within the RWA framework, the blend of green energy with DePIN, AI, and RWA trends, alongside real-world photovoltaic storage nodes and installed capacity, can lead to high-premium advantages in the digital asset space.

Nonetheless, it is crucial to advance the development of soft infrastructure and foundational research for green energy trading, such as SaaS cloud energy management, EnergyGPT, energy blockchain, green energy certificates and carbon credits, P2P exchanges, and relevant protocols like PPA smart contracts, decentralized pricing algorithms, P2P trading algorithms, and green energy financial derivatives DeFi protocols. Europe and the US currently lead in these areas. Bitcoin and AI computing power are also dominated by these regions.

Additionally, foundational elements of the RWA system, including public chains, Layer2 solutions, and green energy stablecoins, are led by Europe and the US. We must recognize that this new green energy ecosystem will introduce a new valuation system, incentive system, trading system, currency system, and financial system.

Green energy is the future core energy, while Bitcoin is the core asset in the crypto world. The narrative linking Bitcoin and green energy computing power is inevitable. If the US and the global community adopt Bitcoin or green Bitcoin as reserve assets, official institutions, financial entities, and large investors are likely to promote Bitcoin, green energy, and AI.

For years, real estate finance has been supported by “land + real estate.” Moving forward, the financial foundation for the next two decades will be “green energy + computing power (Bitcoin and AI computing power).” The future competition between China and the US will center on green energy and computing power. The key to green energy’s future is not just hardware production capacity but its soft computing capabilities, system modelling, pricing power, and derivative products. Therefore, green energy must evolve beyond hardware to encompass software; we need to make innovations and advancements in software, not merely focus on manufacturing.

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