Carbon-aware optimal scheduling of a virtual power plant with energy storage degradation

Abstract

With the rapid penetration of distributed photovoltaic generation, wind power, battery energy storage and flexible loads on the distribution side, virtual power plants (VPPs) are becoming an important operational paradigm for aggregating heterogeneous distributed energy resources. Conventional VPP scheduling models mainly minimize electricity-purchase costs or maximize operating revenue, while the temporal variation of grid-related carbon emissions and the life-cycle impact of frequent battery cycling are often simplified. This paper proposes a day-ahead low-carbon scheduling model for a VPP considering time-varying carbon emission factors and energy-storage degradation costs. Wind power, photovoltaic generation, battery energy storage and transferable loads are coordinated under power-balance, state-ofcharge and demand-response constraints. Electricity purchasing cost, demand-response compensation, battery degradation cost and indirect carbon emissions are incorporated into a unified objective. A 24-h case study compares conventional operation, purely economic scheduling and the proposed low-carbon strategy. Compared with conventional operation, the proposed method reduces daily operating cost from CNY 22,704.35 to CNY 15,596.98 (31.30%), decreases CO2 emissions from 17.05 t to 12.92 t (24.23%), and increases renewable-energy utilization from 93.56% to 100%. Relative to purely economic scheduling, an additional carbon reduction of about 10.13% is achieved at an operating-cost increase of about 8.15%, demonstrating a transparent trade-off between economic and environmental objectives. The proposed formulation provides a compact and engineering-oriented framework for low-carbon VPP operation under high penetration of distributed renewable energy.