Adaptive Step-Size ADMM Based Real-Time Pricing Strategy for Smart Grids with Stepwise Carbon Trading Considerations

What the paper says

In the current pursuit of diversified energy complementation and a low-carbon economy, the operation of power systems faces unprecedented complexity.Real-time pricing (RTP) strategies based on demand-side management (DSM) are crucial for maintaining the balance between electricity supply and demand, as well as for peak shaving and valley filling.To support dual-carbon goals and enhance the economic efficiency of smart grids, this paper proposes a social welfare-maximising model that considers the interests of both consumers and suppliers.The model aims to maximise consumer utility while minimising supply-side costs, accounting for spinning reserve constraints in thermal power generation, and integrating a tiered carbon trading mechanism to encourage low-carbon energy transitions.To address the non-smooth and discontinuous characteristics of tiered carbon trading costs, a smoothing transformation is introduced to improve numerical tractability and algorithmic stability.A distributed optimisation dispatch method using an improved Alternating Direction Method of Multipliers (ADMM) is employed for the solution.Finally, the convergence and effectiveness of the proposed algorithm are verified through simulation results.Compared to the traditional ADMM algorithm, the proposed adaptive step-size ADMM significantly improves convergence efficiency, reducing the number of iterations required for primal residual convergence from 83 to 39 and for pairwise residuals from 84 to 45, while enhancing computational stability.