Qinglin Lin

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios.