Fractional-Order Memory-Enhanced Transformer-CVaR Scheduling for Renewable Energy Systems Across Coupled Electricity and Carbon Markets

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Original abstract

Renewable energy producers in coupled electricity and carbon markets face multi-dimensional uncertainties with cross-domain correlations. This paper proposes T-CMRS, an integrated forecasting optimization system for wind-solar-storage portfolios enhanced by fractional-order memory-aware uncertainty characterization. T-CMRS comprises (1) a transformer-based probabilistic forecasting engine capturing long-range temporal dependencies; (2) a fractional-order uncertainty modeling module employing Caputo derivatives to capture memory effects in price dynamics; and (3) a CVaR-based risk-averse optimization module reformulated as a linear program. Case studies on modified IEEE systems demonstrate that T-CMRS achieved 8.7% higher average daily profit than deterministic methods with 33.3% lower volatility, 15% VaR improvement over stochastic programming, and 12.3% scenario generation error reduction via fractional-order modeling. Scalability analysis confirms tractable computation for 500-bus systems, verifying applicability to large-scale deployments.

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