پروژه های آماده پایتون (Python)

موضوع این پروژه آماده، مقاله انگلیسی محدودیت‌های فیزیکی بازار انرژی و شبکه در پایتون می باشد. Abstract— This paper proposes a deep reinforcement learning (DRL)-based approach for real-time demand response (DR) scheduling in energy markets, explicitly accounting for physical network constraints such as voltage limits and line thermal capacities. The proposed framework leverages the Deep Deterministic Policy Gradient (DDPG) algorithm to learn continuous control policies that minimize operational costs while ensuring grid stability. The DR scheduling problem is formulated as a Markov Decision Process (MDP), where state, action, and reward functions are designed to reflect real-time grid dynamics and operational limits. The IEEE 33-bus test system is used to validate the methodology under high-load, uncertainty, and congestion scenarios. Results demonstrate significant improvements in cost efficiency, voltage stability, and load balancing compared to MILP and heuristic approaches, highlighting the model’s adaptability and real-world potential. Index Terms-- Demand Response, Deep Reinforcement Learning, Real-Time Scheduling, Smart Grid, Network Constraints.