AI-Driven Green Power Trading Boosts China's Energy Market

By Soham NandiReviewed by Susha Cheriyedath, M.Sc.May 22 2024

In an article published in the journal Nature, researchers introduced a green power certificate trading architecture in China, leveraging quality-learning (Q-learning), smart contracts, and a multi-agent Nash strategy to improve quoting efficiency and multi-party collaboration.

The proposed model integrated green certificate (GC), electricity, and carbon asset trading, achieving a 20% increase in trading prices and a 30-fold increase in transaction success rates, demonstrating higher convergence efficiency and consistency compared to models only using smart contracts.

Background

The urgency of addressing climate change has underscored the importance of renewable energy for reducing greenhouse gas emissions and achieving sustainable development. In China, the world's largest energy consumer and carbon emitter, the promotion of renewable energy through GCs is pivotal for meeting energy demands and targeting carbon neutrality by 2060. To foster sustainable energy, China has implemented measures like Renewable Energy Portfolio Standards (RPS) and Tradable Green Electricity Certificates (GEC) systems.

International experiences from the United States, the United Kingdom, Italy, Norway, and Sweden have shown that market mechanisms can effectively promote renewable energy. However, the diverse policies and initiation times for GCs across countries present challenges such as price volatility and regulatory hurdles, which can deter investors and hinder the economic feasibility of renewable projects.

Despite these initiatives, China's GC market faces significant obstacles, including complexities in issuance, verification, and trading, as well as the adverse impacts of policy changes. A major issue is the lack of a robust market-based trading system, which hinders the integration of electricity trading, carbon emission rights trading, and the recognition of green certificates' environmental attributes. This gap results in low transaction volumes due to the absence of effective pricing mechanisms and market incentives.

This study proposed a zero-carbon energy GC trading system (GCTS) architecture, leveraging Q-learning, smart contracts, and multi-agent Nash strategies to enhance transaction efficiency and collaboration. By addressing these gaps, the proposed framework aimed to improve market efficiency, transparency, and sustainability, thereby contributing significantly to China's and the global community's renewable energy goals.

GC Framework

The trading model for GCs in China included two primary components: GCs bundled with green power and standalone GCs. When GCs accompanied green power it ensured traceability and aligned green power with environmental benefits. This framework allowed the power trading center to allocate GCs based on mutually agreed data, enhancing market participation and simplifying trading processes.

Conversely, standalone GCs were traded on a voluntary platform, enabling renewable energy producers to reach buyers through negotiations, listings, or auctions. However, this method struggled to integrate electricity market data and link GCs with carbon credits. The lifecycle of a GC involved issuance, transactions, and verification, fulfilling various stakeholders' roles in green energy and carbon reduction.

Revenue Model for Green Power Manufacturers and Quota Enterprises

Green power manufacturers (GPMs) sold GCs to subsidize their initial investments. The GC price varied with time, region, and supply-demand factors. GPMs' total revenue included electricity sales and GC transaction income. This model considered the on-grid price and the cost of renewable energy generation but excluded government subsidies.

Qualified entities (QEs) also traded GCs, balancing costs with carbon asset conversion rates. The QE's utility function reflected their ability to meet quota requirements, integrating GC prices, transaction volumes, and carbon asset prices. This framework aided in optimizing trading offers and achieving market equilibrium through reinforcement learning.

Game Theory and Smart Contracts in GCTS

The multi-agent trading game involved buyers, QEs, and sellers, GPMs, of GCs employing various strategies in dynamic environments. Agents interacted based on observed states and actions, aiming for Nash equilibrium. Leveraging reinforcement learning algorithms like Q-learning, GPMs refined bidding strategies, optimizing GC trading. Smart contracts further streamlined transactions, ensuring efficient execution and consensus among parties.

A collaborative approach integrated game theory with Q-learning and smart contracts, fostering sustainable energy use. Algorithmic refinement and exploration-exploitation tradeoffs enhanced trading efficacy and market equilibrium. Smart contract deployment, illustrated through the Nash Q-learning-GC process, enhanced transactional integrity and equilibrium. This comprehensive framework offered a systematic solution for GC trading, advancing renewable energy adoption and market efficiency.

Results and Analysis

The researchers focused on GC demand from key industries in China, using a multi-agent simulation involving QEs and GPMs. Through Nash Q-learning and smart contracts, the trading model aimed for efficient market equilibrium. The convergence analysis revealed the stability and speed of the Nash Q-learning algorithm, especially when integrated with smart contracts, indicating potential trading benefits. Price analysis demonstrated how bid prices converged towards equilibrium, facilitated by the Nash Q-learning mechanism.

Comparison of algorithms showcased the efficiency of Nash Q-learning with smart contracts, improving transaction success rates and reducing strategy spending time. Robustness analysis underlined the scalability of the model across diverse agent groups, despite increased complexity.

Finally, comparing simulated and actual transactions highlighted the potential value-added benefits of the Nash Q-learning strategy, suggesting increased transaction volume and price stability. These findings underscored the effectiveness of the proposed trading model in enhancing market efficiency and supporting carbon neutrality goals in China's green energy landscape.

Conclusion

In conclusion, the researchers introduced a GCTS in China, using Q-learning, smart contracts, and multi-agent Nash strategies to improve quoting efficiency and multi-party collaboration. The model integrated GCs, electricity, and carbon asset trading, achieving higher trading prices and success rates. It addressed complexities in GC issuance, verification, and trading, enhancing market efficiency, transparency, and sustainability. The system's scalability and robust framework supported China's renewable energy goals and carbon neutrality by 2060, offering a reference model for future GC trading platforms.