Linear Programming-Based Optimization Control of Gas Turbine Output Energy Using Long-Term Load Forecasting

Long-term load forecasting and gas turbine output optimization play critical roles in energy management systems. By integrating historical data with machine learning techniques, long-term load forecasting enables relatively accurate estimation of future energy demands. This forecasting result can be further utilized to optimize the power distribution of gas turbines, ensuring efficient and economical energy supply. In practical implementation, one might develop Python scripts using libraries like scikit-learn or TensorFlow for time-series forecasting models, incorporating seasonal decomposition and regression algorithms.

In this process, Linear Programming (LP) serves as an effective mathematical optimization method. By establishing objective functions (such as minimizing generation costs or maximizing energy utilization) and constraints (including unit output limits and grid stability requirements), linear programming can identify optimal gas turbine output combinations while maintaining system stability. A typical LP implementation might involve using optimization libraries like PuLP or SciPy in Python, where the objective function could be formulated as cost minimization subject to constraints like turbine capacity limits and ramp rate restrictions.

Meanwhile, control strategy development requires comprehensive consideration of factors such as fuel efficiency, emission limitations, and equipment lifespan. The optimization model not only enhances overall generation efficiency but also reduces operational costs and minimizes environmental impact. Algorithm designers might incorporate penalty terms for emissions in the objective function and implement dynamic constraint adjustments based on real-time equipment condition monitoring.

For power system operators, this optimization approach integrating long-term load forecasting with linear programming provides more scientific decision-making foundations, enabling dynamic balance between energy production and demand. The system could be implemented as a modular framework with separate forecasting and optimization modules, possibly using MATLAB's Optimization Toolbox or Python's CVXPY for solving large-scale LP problems with hourly resolution over extended planning horizons.