System dynamics emerging from compliance behaviors coordination of electricity consumers under renewable portfolio standards in China: An agent-based analysis

The Renewable Portfolio Standards (RPS) policy imposes mandatory obligations on the obligated entities for the consumption of renewable energy electricity (RE), which can be completed through RE consumption in the electricity market, participating in Tradeable Green Certificates (TGC) transactions and consumption above quota (CAQ) transactions in supplementary markets. In this context, how to coordinate the three compliance options and how to design the RPS policy become key issues. This paper proposes a hybrid agent-based model (ABM) for RPS by incorporating an evolutionary training algorithm to depict the micro-level compliance behaviors and decision-making process of obligated entities and evaluate the macro-level impacts of various RPS targets. Our analysis reveals that 1) RPS target can positively promote the RE electricity consumption, but the trend of change in CAQ consumption share and consumers’ profit with the RPS target regulation is non-monotonic; 2) the CAQ market and TGC market exhibit a positive price linkage, tightening RPS constraints can significantly stimulate the vitality of both markets; 3) when RPS target is set at 60%, the total consumption obligation and actual RE consumption exceed the RPS target the most; when set at 90%, the gap between actual RE consumption and RPS target is the largest. • A novel agent-based model integrated with a GA training stage is proposed for China’s RPS policy. • The decision-making process in coordinating three compliance approaches of agents is depicted. • The effects of RPS targets on microlevel behaviors and macrolevel aggregate outcomes are captured. • The heterogenous behavioral parameters are endogenously generated through GA-based training. • The relationship between individual outcomes and behavioral parameters is examined.