Risk-Aware Data-Driven Cross-Market Trading for Renewable Virtual Power Plants: A Survey
Keywords:
Virtual power plant; cross-market trading; data-driven optimization; distributionally robust optimization; reinforcement learning; settlement risk; market regime shift.Abstract
Renewable-rich virtual power plants (VPPs) are now regularly trading energy, reserve, balancing, and carbon-linked products in coupled markets. Their choices are hard to make as prices and renewable generation vary together, while flexible demand and settlement penalties change concurrently and can potentially fall outside the data used to train the model. This survey summarizes studies on cross-market trading that are based on data and consider risk factors from recent literatures. It categorizes approaches as forecast-then-optimize, stochastic and distributionally robust optimization (DRO), strategic game models, reinforcement learning, and hybrid safety-shielded learning. Based on the synthesis, stochastic and robust models are implementation-ready, whereas deep reinforcement learning (DRL) is supported only by simulation. The main research gap is settlement-consistent risk calibration that accounts for market regimes. An evaluation ladder and research agenda are suggested from a deployment perspective.
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