A Two-Stage Optimization Method for Real-Time Pricing in Smart Grid Considering Shared Energy Storage

Abstract

Real-time pricing (RTP) is an effective mechanism for regulating demand-side flexible resources and reducing carbon emissions in smart grids via the large-scale integration of shared energy storage (SES) and distributed photovoltaic (PV) systems on the demand side, as well as the participation of the supply side in electricity, carbon, and green certificate markets. Specifically, the RTP problem needs to account for the intertemporal operation of SES, predefined dispatch priorities, and the benefits of supply-side participation across multiple markets, which increases its computational complexity. To address these challenges, this paper proposes a two-stage optimization method for smart-grid RTP based on social welfare maximization. Exact convex hull relaxation is employed to transform the non-convex problem involving the SES mutual exclusivity constraint into an equivalent convex optimization problem. Subsequently, a two-stage optimization framework is developed. In the first stage, the alternating direction method of multipliers (ADMM) is adopted to solve the convex problem through distributed iterations, yielding one converged globally optimal solution and its corresponding optimal objective value. In the second stage, strict lexicographic optimization is performed while preserving the optimal social welfare obtained in Stage 1, thereby selecting a solution that satisfies the predefined dispatch priorities. Simulation results demonstrate that the proposed method effectively solves the formulated model. Compared with the case without SES participation, the obtained optimal solution increases social welfare by approximately 2.62% and reduces carbon emissions by approximately 3.53% while maintaining supply–demand balance.