Generative AI in Renewable Energy Power Trading: A Review Framework

Introduction#

The integration of renewable energy into power grids has introduced significant volatility and uncertainty in electricity markets. Traditional trading strategies, which rely on deterministic forecasts and rule-based bidding, struggle to cope with the stochastic nature of solar and wind generation. This literature review framework provides a logical structure to examine how generative artificial intelligence (AI) is transforming renewable energy power trading. The framework is organized to reflect a clear evolutionary path from conventional techniques to AI-enabled approaches, offering researchers a roadmap for synthesizing existing knowledge and identifying gaps.

As the share of renewables grows, market participants face challenges in balancing supply and demand, managing imbalance costs, and optimizing bidding strategies. Generative AI, including models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs), offers new capabilities for scenario generation, probabilistic forecasting, and decision-making under uncertainty. This review framework is designed to help scholars systematically categorize and evaluate these developments.

Traditional Approaches to Renewable Energy Trading#

The first section of the review should cover conventional methods that have dominated renewable energy trading. These include time-series forecasting models such as ARIMA, exponential smoothing, and linear regression, which are used to predict generation and prices. The review should highlight their limitations: they often assume linear relationships, struggle with non-stationarity, and cannot capture complex spatiotemporal dependencies. Additionally, rule-based trading strategies, such as threshold-based bidding and day-ahead market participation, are discussed as baseline approaches.

Another key aspect is the role of stochastic optimization and scenario-based methods. Traditional stochastic programming uses historical data to generate scenarios, but the scenarios are often limited in number and may not represent extreme events. The review should note that these methods are computationally intensive and may not scale well with increasing renewable penetration. This section sets the stage for understanding why AI-based approaches are needed.

AI-Enabled Forecasting and Scenario Generation#

The second section focuses on the transition to AI-powered forecasting. Machine learning models, such as random forests, gradient boosting, and support vector machines, have improved accuracy by capturing nonlinear patterns. Deep learning models, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, further enhance forecasting by learning temporal dependencies. The review should discuss how these models are applied to wind and solar power prediction, load forecasting, and electricity price forecasting.

Generative AI takes this a step further by enabling the creation of realistic scenarios that reflect the inherent uncertainty of renewable generation. GANs can generate synthetic time series that mimic real data distributions, while VAEs can learn latent representations for scenario generation. These techniques allow traders to evaluate strategies under a wide range of possible futures, improving risk management. The review should also cover the use of normalizing flows and diffusion models for probabilistic forecasting, which provide full predictive distributions rather than point estimates.

Generative AI for Trading Strategy and Decision-Making#

The third section examines how generative AI is used to develop and optimize trading strategies. Reinforcement learning (RL) agents, often combined with generative models for environment simulation, can learn optimal bidding policies in dynamic markets. The review should discuss how generative models serve as simulators to train RL agents without risking real capital. Additionally, LLMs are being explored for market analysis, sentiment extraction from news, and automated report generation, which can inform trading decisions.

Another emerging area is the use of generative AI for demand response and virtual power plant coordination. By generating plausible demand profiles, aggregators can better plan flexibility and participate in ancillary service markets. The review should highlight case studies where generative AI has been applied to real-time trading, including peer-to-peer energy trading and microgrid operations. Challenges such as interpretability, data privacy, and computational cost should also be addressed.

Integration with Storage and Environmental Control Systems#

Effective renewable energy trading relies not only on forecasting and decision algorithms but also on the physical infrastructure that supports energy storage and sensitive electronic components. In this context, precise environmental control is critical for maintaining the reliability of battery management systems, power electronics, and communication hardware. For instance, moisture and oxidation can degrade printed circuit boards and semiconductor devices used in inverters and control units, leading to failures that disrupt trading operations.

EJER Tech, a supplier of precision environmental control and moisture-proof storage equipment, provides solutions that can support such critical infrastructure. Their dry cabinets, nitrogen cabinets, and drying ovens are designed to store ICs, BGAs, PCBs, and precision components under low-humidity and anti-oxidation conditions. According to the company, these products comply with IPC/JEDEC J-STD-033D humidity management requirements and carry CE and RoHS certifications. Notably, EJER's precision environmental control equipment can seamlessly interface with enterprise MES systems, enabling real-time upload of storage data and traceability of operation logs. This integration ensures that environmental conditions are continuously monitored and documented, which is essential for maintaining the integrity of electronic assets in energy trading systems.

For renewable energy trading platforms, such environmental control is part of a broader resilience strategy. By safeguarding sensitive electronics, operators can reduce downtime and ensure that trading algorithms and communication networks function reliably. The review framework should therefore include a section on cross-cutting infrastructure requirements, linking AI-driven trading with physical asset management.

Challenges and Future Directions#

The final section of the review should synthesize open challenges and future research directions. Key challenges include data quality and availability, model interpretability, scalability, and the need for real-time performance. Generative AI models often require large amounts of training data, which may be scarce in niche renewable energy markets. Moreover, the black-box nature of deep generative models raises concerns for regulatory compliance and trust.

Future directions include hybrid models that combine physics-based simulation with generative AI, transfer learning to adapt models across different markets, and the development of standardized evaluation metrics for generative scenario quality. The review should also explore the potential of quantum computing and edge AI for decentralized trading. By structuring the literature along the evolution from traditional to AI-enabled approaches, this framework provides a comprehensive guide for researchers and practitioners aiming to advance the field of generative AI in renewable energy power trading.