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The current renewable portfolio standards (RPS) mechanism implemented in China requires electricity consumers to comply with quota obligations by consuming renewable electricity, purchasing tradeable green certificates (TGCs), or engaging in consumption above quota (CAQ) transactions. However, most existing studies on RPS policy seldom consider the CAQ market and ignore the dynamic interactions among microlevel obligated entities, which limits the model from considering the heterogeneity among obligated entities and weakens the capture of macrolevel emergence properties. To fill this research gap, a novel agent-based model for China's RPS policy is established, in which the microlevel obligated subjects' complex interactions and decision-making process in coordinating three compliance approaches, i.e., electricity consumption, TGC consumption and CAQ transactions, are depicted, and the dynamic macrolevel emergence in the electricity market, TGC market and CAQ market in terms of multimarket coupling trading, multimarket price-linkage, renewable energy electricity diffusion and RPS target compliance are captured. Notably, a genetic algorithm-based training stage is structured to calibrate and stabilize the heterogeneous behavioral parameters of obligated entities. The results illustrate that 1) the existence of the CAQ market in RPS policy can significantly stimulate obligated entities to consume more renewable energy electricity earlier, promote individual and aggregate profits, and improve the overall compliance rate and RPS implementation effectiveness and 2) a combined penalty–reward mechanism with an effective reward interval is highly important for promoting RE consumption diffusion and the effective operation of the CAQ market.
Wu et al. (Mon,) studied this question.