With the widespread integration of high-penetration renewable energy, load volatility and spatio-temporal imbalances in power systems have intensified, imposing higher demands on real-time supply-demand balance. Traditional real-time pricing mechanisms often neglect the physical constraints of distribution networks, resulting in dispatch schemes that may be infeasible in practical grid operations. Meanwhile, locational marginal pricing mechanisms typically assume inelastic loads, failing to effectively integrate demand-side response from users. To simultaneously enhance the economic efficiency and physical feasibility of grid operation, this paper designs a real-time locational marginal pricing strategy integrated with demand-side management. A Bi-level optimization framework based on the Stackelberg game is constructed: the upper level aims to maximize the profit of electricity suppliers, employing a power flow model based on the DistFlow equations and utilizing second-order cone relaxation techniques to accurately characterize physical constraints such as grid congestion, losses, and voltage limits. The lower level seeks to maximize consumer welfare, establishing a convex optimization response model that incorporates distributed energy resources, energy storage, and user electricity utility. By applying the Karush-Kuhn-Tucker (KKT) conditions and duality theory, the lower-level problem is transformed into constraints for the upper level, ultimately forming a mixed-integer linear programming model that can be efficiently solved. Simulation results validate the rationality and effectiveness of the proposed pricing mechanism in promoting renewable energy accommodation and enhancing overall system welfare.
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