Two-Layered Hierarchical Optimization Strategy With Distributed Potential Game for Interconnected Hybrid Energy...

Area of Science:

• Engineering
• Optimization
• Game Theory

Background:

• Hybrid energy systems (HESs) face operational challenges due to multiple stakeholders and the inherent complexity and uncertainty of distributed energy resources.
• The competitive nature among stakeholders in HESs complicates achieving optimal system operation.
• High-dimensional complexity and output uncertainty are significant hurdles in HES optimization.

Purpose of the Study:

• To propose a novel potential game-based two-layered hierarchical optimization strategy for HESs.
• To address the competitive relationships among stakeholders and the uncertainty issues in HES operation.
• To enhance the optimal operation of HESs by reducing computational complexity and improving robustness.

Main Methods:

• A two-layered hierarchical HES model was developed, comprising upper-level and lower-level components.
• A multiagent system and a potential game with a distributed primal-dual perturbed algorithm were employed to manage stakeholder competition in the upper-level model.
• Uncertainty and robustness analysis was performed, coordinating the lower and upper models to define a feasible robust uncertainty interval.
• A gradient descent-based multiobjective differential evolution (GD-MODE) algorithm was utilized for the lower-level optimization.

Main Results:

• The convergence and optimality of the potential game approach were mathematically proven.
• A feasible robust uncertainty interval was deduced for the lower-level model through coordinated analysis.
• The GD-MODE algorithm successfully optimized economic cost and emission simultaneously, yielding Pareto-optimal solutions.
• Simulation results validated the proposed strategy's effectiveness in reducing computational complexity and handling uncertainties.

Conclusions:

• The proposed potential game-based hierarchical optimization strategy effectively addresses the complexities of HES operation.
• The method successfully manages stakeholder competition and system uncertainties, leading to improved optimal operation.
• The approach offers a robust and computationally efficient solution for optimizing hybrid energy systems.