Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic...

Area of Science:

• Power Systems Engineering
• Optimization Theory
• Renewable Energy Integration

Background:

• Traditional Environmental Economic Power Dispatch (EEPD) lacks network security and struggles with renewable energy integration.
• Increasing renewable energy sources (RES) like wind and solar PV introduces stability challenges due to intermittency and uncertainty.
• Environmental sustainability goals require reduced emissions and higher RES penetration.

Purpose of the Study:

• To formulate and solve the Stable Environmental Economic Power Dispatch (SEEPD) problem, incorporating network security and RES uncertainties.
• To address the challenge of integrating uncertain renewable energy sources while maintaining economic dispatch and grid stability.
• To extend the problem to a multi-period stochastic framework considering intertemporal constraints.

Main Methods:

• Modeling uncertainties in RES and load demand using Gaussian, Weibull, and log-normal probability density functions.
• Applying state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs) to solve the stochastic SEEPD problem.
• Incorporating network security constraints, generator capabilities, and intertemporal reserve requirements.

Main Results:

• The proposed SEEPD formulation effectively balances economic, environmental, and stability objectives.
• A bidirectional coevolutionary-based MOEA with advanced constraint handling demonstrated superior performance.
• Simulation results indicate a better trade-off between conflicting objectives compared to existing MOEAs.

Conclusions:

• The SEEPD problem provides a robust framework for optimizing power systems with high RES penetration.
• The employed MOEA successfully handles complex constraints and uncertainties in stochastic power dispatch.
• This approach enhances grid reliability and economic efficiency while supporting environmental sustainability goals.