Optimizing Renewable Energy Utilization and Grid Dispatch: Application of MATLAB Linear Programming in Energy Systems

Optimization of Renewable Energy Utilization and Grid Scheduling: The Application of MATLAB Linear Programming in Energy Systems

# 1. Theoretical Foundations of Renewable Energy Optimization and Grid Scheduling

Renewable energy optimization and grid scheduling are essential technological means to achieve sustainable energy systems. This section will introduce the fundamental theoretical foundations of renewable energy optimization and grid scheduling, providing theoretical support for subsequent chapters of practical applications.

**1.1 Renewable Energy Optimization**

Renewable energy optimization aims to maximize the use of renewable resources and improve their power generation efficiency and economic performance. Its main technologies include:

- Power generation characteristic modeling: Establishing the relationship model between renewable energy power generation and weather conditions to predict its output.
- Optimized scheduling: Based on predicted power generation and grid load, optimizing the scheduling strategy for renewable energy to enhance its utilization rate.

**1.2 Grid Scheduling**

Grid scheduling refers to the coordinated control of power generation, transmission, and consumption in the power grid to ensure the safe and stable operation of the power grid. Its main technologies include:

- Load forecasting: Predicting the trends in power grid load changes to provide a basis for scheduling decisions.
- Power generation cost function modeling: Establishing a relationship model between power generation costs and output to be used in scheduling optimization.
- Power grid stability constraints: Considering the requirements for power grid stability, formulating scheduling strategies to prevent grid accidents.

# 2. Practical Application of MATLAB Linear Programming in Energy Systems

### 2.1 Basic Principles of MATLAB Linear Programming

#### 2.1.1 Establishment of Linear Programming Models

Linear programming is a mathematical optimization technique used to solve optimization problems with linear objective functions and linear constraints. In energy systems, linear programming models are often used to optimize renewable energy power generation and grid scheduling.

The establishment of linear programming models includes the following steps:

1. **Defining decision variables:** Decision variables are the variables to be optimized in the model, such as renewable energy power generation output or grid scheduling plans.
2. **Establishing the objective function:** The objective function is the function to be optimized in the model, such as renewable energy power generation costs or grid scheduling costs.
3. **Establishing constraint conditions:** Constraint conditions limit the range of values for decision variables, such as renewable energy power generation output not exceeding the capacity of generators or grid scheduling plans not violating power grid stability constraints.

#### 2.1.2 Solving Methods for Linear Programming Problems

Linear programming problems can be solved using various methods, including:

1. **Simplex method:** The simplex method is a classical linear programming solving algorithm that gradually approaches the optimal solution through iteration.
2. **Interior-point method:** The interior-point method is a modern linear programming solving algorithm that seeks the optimal solution by moving within the feasible domain.
3. **Branch and bound method:** The branch and bound method is an algorithm for solving mixed-integer linear programming problems by decomposing the problem into subproblems.

### 2.2 Renewable Energy Optimization Modeling

#### 2.2.1 Modeling of Renewable Energy Power Generation Characteristics

Modeling of renewable energy power generation characteristics is an important step in linear programming models. Renewable energy power generation is intermittent and fluctuating, ***

***mon methods for renewable energy power generation characteristic modeling include:

1. **Probability distribution method:** Using probability distributions to describe the randomness of renewable energy power generation, such as using normal or Weibull distributions.
2. **Time series method:** Using historical data to construct time series models for renewable energy power generation, such as using autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models.
3. **Physical model method:** Using physical models to simulate the operating characteristics of renewable energy power generation equipment, such as using photovoltaic cell array models or wind turbine models.

#### 2.2.2 Modeling of Grid Load Forecasting

Grid load forecasting modeling is another important step in linear programming models. Grid load forecasting is the process of predicting future grid load demand, ***

***mon grid load forecasting methods include:

1. **Time series method:** Using historical data to construct time series models for grid load, such as using ARMA or ARIMA models.
2. **Regression method:** Using regression models to predict grid load, such as using linear or nonlinear regression models.
3. **Machine learning method:** Using machine learning algorithms to predict grid load, such as using support vector machines or neural networks.

### 2.3 Grid Scheduling Optimization Modeling

#### 2.3.1 Modeling of Power Generation Cost Function

Modeling of the power generation cost function is an important step in linear programming models for optimizi***

***mon power generation cost function modeling me