Distributed Generation Power Output Constraints and Optimization

Constraints and optimization of distributed generation power output represent critical challenges in power system operations. The output of distributed energy resources (such as photovoltaic systems and wind turbines) is often influenced by environmental factors, exhibiting volatility and uncertainty, thus typically requiring upper and lower power output limits. The optimization objective involves maximizing renewable energy utilization or minimizing operational costs while ensuring secure grid operation.

The program's implementation approach focuses on several key aspects: Power Output Constraint Modeling: Based on distributed generation types, appropriate upper and lower output limits are established to ensure operation within permissible ranges. For instance, photovoltaic generation output depends on solar irradiance, requiring constraint definitions based on historical data or predictive models. Optimization Objectives: Common optimization targets include economic efficiency (e.g., minimizing generation costs), environmental considerations (e.g., reducing carbon emissions), or grid stability (e.g., minimizing voltage fluctuations). The program may employ multi-objective optimization or weighted single-objective approaches to balance different requirements. Efficient Iterative Algorithms: Through appropriate algorithm selection (such as gradient descent, particle swarm optimization, or interior-point methods), the program reduces iteration counts and enhances computational efficiency. Particularly in multi-node systems, rapid convergence is crucial for real-time dispatch. Constraint Handling: During optimization, strict adherence to grid constraints like power flow limits and voltage boundaries is required. The program likely utilizes penalty functions or Lagrangian multiplier methods to handle inequality constraints, ensuring solution feasibility.

The program's standout feature lies in its reduced iteration counts, indicating optimization in algorithm convergence—potentially through adaptive step sizes or heuristic strategies that maintain precision while accelerating computation. For practical power system dispatch, such efficiency is vital, enabling more frequent optimization cycles and responsive handling of sudden fluctuations.