Joint Trading Strategy of Green Contracts and Spot Markets for Distributed PV Aggregators

Distributed photovoltaic (PV) aggregators can aggregate large-scale distributed PV resources to participate in green electricity trading and spot market transactions, this paper proposes a joint trading strategy that integrates green electricity medium- and long-term contract decomposition with spot market revenues. First, based on historical price and PV generation data, Latin Hypercube Sampling (LHS) is employed to generate stochastic scenarios for day-ahead prices, real-time prices, and aggregated PV output, followed by K-means clustering for scenario reduction. Conditional Value-at-Risk (CVaR) is introduced to quantify the risk of bidding decisions, and a risk-aversion coefficient is incorporated to capture the aggregator's risk preference. On this basis, a two-stage stochastic programming model is developed to formulate the joint trading strategy of distributed PV aggregators across green electricity medium- and long-term markets and spot markets. Case studies demonstrate that the proposed model can derive trading strategies consistent with aggregators ' risk preferences and effectively promote the coordinated optimization of distributed PV aggregators' participation in green electricity medium- and long-term contracts and spot market trading.