A major barrier to the deployment of Virtual Power Plants (VPPs) with energy storage under Demand Response (DR) programs is the absence of a clear dynamic pricing (DP) framework that simultaneously addresses the objectives of all stakeholders (prosumers, VPP aggregator, and the grid) under a bidirectional energy flow. This work investigates a DP regime for a VPP integrating battery storage, aimed at optimizing stakeholder objectives in the day-ahead market. The UK national rolling demand data are employed to capture the time-varying nature of wholesale electricity prices. A Cumulative Performance Index (CPI) is used to quantify the VPP’s contribution to dynamic load leveling. A Genetic Algorithm (GA) is utilized to optimize the transaction of prices and energy exchanges for stakeholders’ welfare maximization. Results show that optimal prices and energy transactions within the DR framework depend strongly on stakeholder objective priorities. The price margin significantly influences the amount of financial rewards received by the stakeholders.