Fairness-Aware Market Clearing for Small- and Medium-Sized Virtual Power Plants Based on GiniConstrained Resource Matching

Abstract

Under conventional economic clearing, market opportunities for small- and medium-sized virtual power plants (VPPs) may be concentrated among a few participants. To address this issue, this paper proposes a centralized clearing model based on a Gini-coefficient constraint and resource-matching adjustment. Each VPP is treated as an integrated market participant; the offer cost is adjusted using a resource matching score, and a Gini-based fairness constraint is imposed on normalized clearing opportunities. The absolute-value terms are further linearized, making the model solvable by standard linear programming solvers. A case study based on CAISO OASIS public day-ahead load forecast and locational marginal price data shows that, compared with conventional economic clearing, the strongfairness case reduces the Gini coefficient by about 73.95\% and the maximum market share by about 18.33\%, while significantly improving the VPP participation rate with only a 5.60\% increase in clearing cost. Additional disturbance tests on load, VPP scale, and price fluctuation show that the proposed method maintains stable participation fairness with a limited cost increase.