A novel differentiable predictive control (DPC) approach for safe and optimal EV charging and discharging scheduling

Differentiable Predictive Control (DPC) has emerged as a data-driven alternative to Model Predictive Control (MPC), attracting growing interest across various research domains. Unlike traditional MPC, which relies on physics-based models, DPC utilizes black-box models—typically neural networks—to learn control policies. The primary advantage of DPC is its computational efficiency. However, a key challenge lies in ensuring constraint satisfaction, as neural network-based policies often struggle with maintaining feasibility. In conventional DPC frameworks for building-related studies, both equality and inequality constraints are enforced through penalty methods. However, tuning penalty parameters is complex, and constraint violations often persist. To address this issue, we propose a novel DPC approach that strictly enforces constraints. Specifically, we incorporate a projection layer based on Karush-Kuhn-Tucker (KKT) conditions to satisfy equality constraints. For inequality constraints, we replace the traditional penalty method with barrier functions, which provide steeper gradient as the solution approaches constraint boundaries, enforcing stricter constraint compliance. To evaluate the effectiveness of our approach, we apply it to an electric vehicle (EV) charging and discharging optimization problem, considering three objectives: minimizing electricity costs, reducing C O 2 emissions, and load flattening. We compare our method against conventional penalty-based DPC and the augmented Lagrangian method. The results indicated that the proposed approach reduced constraint violations by over 90 % in occurrence, compared to the conventional penalty method. Additionally, objective performance improved, achieving a 3 % greater bill reduction, 12 % more C O 2 reduction, and 29 % greater peak load reduction. • Proposed a novel DPC approach for safety assurance in hard-constrained optimization problems. • Introduced a projection layer derived from KKT conditions to enforce equality constraints within neural network-based control. • Developed a novel barrier function method to effectively handle inequality constraints. • Applied the framework to a multi-objective EV charging and discharging optimization problem, demonstrating practical effectiveness. • Achieved significantly reduced constraint violations and improved objective performance compared to standard DPC approach.