An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network

Area of Science:

• Electrical Engineering
• Optimization Theory
• Power Systems

Background:

• Intelligent electric parking lots (EPLs) integration into distribution networks presents optimization challenges.
• Minimizing power losses, grid costs, and voltage deviations are critical for efficient grid operation.
• Battery degradation cost (BDC) and network load uncertainty (NLUn) are significant factors impacting EPL optimization.

Purpose of the Study:

• To develop a stochastic multi-objective optimization framework for intelligent electric parking lots (EPLs).
• To minimize annual power losses, grid purchase costs, unsupplied energy, vehicle-to-grid costs, and voltage deviations.
• To incorporate battery degradation cost (BDC) and network load uncertainty (NLUn) into the optimization process.

Main Methods:

• Utilized the unscented transformation method (UTM) for accurate and computationally efficient network load uncertainty (NLUn) modeling.
• Employed an improved fire hawks optimization (IFHO) algorithm, enhanced with Taylor-based neighborhood technique (TBNT), for determining optimal EPL site and size.
• Evaluated the methodology through three simulation scenarios, including considerations for BDC and NLUn.

Main Results:

• The IFHO-based multi-objective optimization significantly reduced annual losses (21.06%), voltage deviations (12.15%), energy not supplied (ENS) cost (70.82%), and substation costs (39.10%).
• Incorporating BDC and NLUn led to increased annual losses, voltage oscillations, ENS cost, and grid costs compared to scenarios without these factors.
• The TBNT-enhanced IFHO demonstrated superior performance across scenarios, achieving better objective values and faster, more accurate convergence.

Conclusions:

• The proposed stochastic multi-objective optimization framework effectively optimizes EPL placement and sizing in distribution networks.
• Considering BDC and NLUn is crucial for realistic and robust EPL integration strategies.
• The IFHO algorithm, enhanced with TBNT, provides a powerful tool for solving complex power system optimization problems.