Electric Vehicle Orderly Charging Guidance Strategy Based on Peak-Valley Time-of-Use Tariffs

As electric vehicles (EVs) gain widespread adoption, their unmanaged charging patterns may introduce significant load variations to power systems, potentially compromising grid stability. To tackle this challenge, this study proposes a coordinated EV charging scheduling framework utilizing dynamic peak-valley pricing mechanisms. The research methodology comprises three phases: First, EV mobility patterns and charging demands are statistically modeled through Monte Carlo simulations. Second, a dual-objective optimization model is established to concurrently minimize grid load fluctuations (quantified by mean square deviation) and user charging expenditures. Third, an enhanced particle swarm intelligence algorithm is employed to determine optimal time-of-use tariff parameters. Case study results validate that the proposed pricing strategy effectively aligns EV charging behaviors with grid load regulation requirements, demonstrating practical viability for demand-side management in smart grid operations.