Scenario-driven distributionally robust optimization model for a rural virtual power plant considering flexible energy-carbon-green certificate trading

Abstract

With the increased coupling of agriculture and energy, there is a trend to aggregate and manage distributed energy resources in agricultural parks using rural virtual power plants (RVPP). This paper investigates the impact of uncertainties in renewable energy generation and energy usage, as well as the flexibility of energy‑carbon-green certificate (GC) trading, on the planning and operation of RVPP. Firstly, the basic architecture of RVPP is constructed, and a joint trading mechanism for the carbon emission allowance (CEA) and GC is designed. On this basis, a two-stage deterministic optimization model is developed considering capacity configuration in the planning stage and the Stackelberg game in the operation stage of RVPP. Then, several typical scenarios considering the correlation of uncertainties are generated, and the deterministic model is transformed into a distributionally robust optimization (DRO) model in a scenario-driven manner. The confidence intervals of the scenario probability distributions are constrained by a combination of 1-norm and infinity-norm. Finally, the DRO model is decomposed into two problems, solved iteratively using a revised Kriging model and a column-and-constraint generation (C&CG) algorithm. Several cases covering different transaction forms and solution methods are analyzed comparatively to validate the effectiveness of the DRO model. The simulation results indicate that, compared to the energy trading with a fixed price, flexible trading based on the Stackelberg game can reduce the total planning and operating costs by 22.49 %. Compared to the separate trading of GC and CEA, the trading volume of CEA decreases by 44.21 % under the joint trading mechanism, with the increased configuration of renewable energy resources.