Multi-objective optimal decision for orderly power utilization based on improved ε-constraint method in active...

Area of Science:

• Electrical Engineering
• Operations Research
• Optimization Theory

Background:

• Rising electricity demand in China necessitates efficient power management.
• Peak period electricity shortages require effective load management strategies.
• Active distribution networks (ADNs) are crucial for modern power systems.

Purpose of the Study:

• To develop a multi-objective optimization model for orderly power utilization in ADNs.
• To minimize total operation cost, user cost, and system load fluctuation.
• To address computational challenges of complex optimization models.

Main Methods:

• Formulated a multi-objective optimization model with integer variables and nonlinear constraints.
• Applied convex relaxation to transform the model into a mixed-integer second-order cone programming (MISOCP) problem.
• Proposed an improved ε-constraint method combined with a simplex search algorithm for efficient solution finding.

Main Results:

• The MISOCP model significantly reduced computational complexity compared to the original model.
• The improved ε-constraint method efficiently identified compromise optimal solutions.
• The simplex search algorithm avoided exhaustive grid point traversal, reducing computation time.
• Case studies on the IEEE-33 bus network validated the proposed method's effectiveness.

Conclusions:

• The proposed optimization approach effectively balances cost reduction and load stability in ADNs.
• Convex relaxation and the improved ε-constraint method offer a computationally efficient solution for complex power system optimization.
• The methodology provides a practical tool for managing electricity shortages during peak demand periods.