Multi-objective Optimization of Coordinated Control in Distributed Energy Generation Systems
- Control Objective:Effective, stable, and reliable coordination of distributed energy sources.
- Optimization Objective: To reduce costs, increase the efficiency and reliability of the system, and support informed decision-making through AI-driven optimization.
- Optimization Criteria: To minimize operational costs (such as fuel and maintenance expenses), maximize overall energy efficiency, and reduce CO₂ and NOx emissions.
Challenges to System Optimization: Multiple conflicting goals, system complexity and nonlinearity, as well as uncertainty and variability of parameters.
Proposed Software Solution: The software utilizes neural networks for intelligent modeling and forecasting of distributed energy sources’ behavior, optimizing costs, efficiency, and reliability in real time.
Results:
Efficiency: +5.5%;
Energy Savings: +10%.