Abstract Matching markets are often used in exchange settings (e.g., supply chain) to increase economic efficiency while respecting certain global constraints on outcomes. We investigate their application to pricing and cost sharing in group purchasing of electricity in smart grid settings. The task is complicated by the complexities of producer cost functions due to constraints on generation from different sources (they are sufficiently complex that welfare-optimal matchings are not usually in equilibrium). We develop two novel cost sharing schemes: one based on Shapley values that is “fair,” but computationally intensive; and one that captures many of the essential properties of Shapley pricing, but scales to large numbers of consumers. Empirical results show these schemes achieve a high degree of stability in practice and can be made more stable by sacrificing small amounts (< 2%) of social welfare.