Stackelberg Game between Charging Stations and Distribution Networks with Regional Load Forecasting and Intelligent Charging Strategies

In order to cooperate with the research of the paper "Stackelberg game between charging station and distribution network based on regional load forecasting and intelligent charging strategy", we have constructed a comprehensive data set, which includes the following contents:
README File: This document specifies the computational environment (Python 3.8+, TensorFlow 2.5, Gurobi 9.1), outlines the step-by-step workflow (from data preprocessing and LSTM model training to Stackelberg game simulation), lists key parameter settings, and describes the result verification method.
Core Scripts: The bundle includes three Python scripts:
(1)data_preprocess.py (performs min-max normalization and splits the load dataset)
(2)lstm_load_forecast.py (trains the LSTM model and outputs load predictions)
(3)stackelberg_game.py (solves the Stackelberg game using backward induction and outputs DN pricing and CS power purchase decisions)
Tiny Synthetic Sample: A 100-hour synthetic dataset, generated based on the statistical distribution of real load data from Tianjin, is provided. It includes features such as date, load demand (kW), dry-bulb temperature (℃), dew point (℃), and hour of day. This sample supports basic LSTM model training and end-to-end verification of the game-theoretic simulation.
This data set supports game optimization between distribution network and charging station, load forecasting model training, and simulation and evaluation of intelligent charging strategy for electric vehicles, and is suitable for power system optimization, game theory application and related research of smart grid.