Adaptive Control Strategy for Peak Shaving Based on Real-Time Power Supply-Demand Conditions
The rapid increase in electricity demand and peak load consumption has led to rising energy costs and grid instability. This paper proposes an adaptive control strategy for peak shaving, integrating energy storage (ES) and electric vehicles (EV) with real-time power supply-demand monitoring. The proposed system dynamically estimates household demand, including EVs as a load, and adjusts ES and EV charging and discharging schedules based on energy availability, load conditions, and time-of-use (TOU) pricing. By leveraging photovoltaic (PV) generation, surplus energy is stored in the ES and EV during low-demand periods and discharged during peak-demand hours, thereby reducing grid dependency and electricity costs. A real-time simulation model is developed to validate the effectiveness of the proposed strategy. The results demonstrate significant improvements in load balancing, cost reduction, and peak-shaving efficiency, ensuring optimal utilization of renewable energy sources and storage assets.