Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Power Systems

Background:

• Distributed power grids utilize diverse distributed generators (DGs).
• Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) face challenges in DG siting and capacity determination, including slow convergence and local optima.
• Accurate planning for DG integration is crucial for grid stability and economic viability.

Purpose of the Study:

• To propose an improved simulated annealing particle swarm optimization (SA-PSO) algorithm, termed ISA-PSO, for optimal distributed generator (DG) siting and sizing.
• To enhance the global searching and local exploration capabilities for DG planning.
• To minimize the overall economic cost of DG integration in distributed power grids.

Main Methods:

• Introduced genetic algorithm (GA) crossover and mutation operators into SA-PSO, creating the ISA-PSO algorithm.
• Equivalenced diverse DGs to four node types for flow calculation using backward/forward sweep methods.
• Applied reactive power sharing principles for initial value determination and correction to accelerate convergence.
• Established a mathematical model for minimum economic cost, considering investment, operation, grid loss, electricity purchase, and environmental costs.
• Incorporated power flow, bus voltage, conductor current, and DG capacity constraints.

Main Results:

• The proposed ISA-PSO algorithm demonstrated superior performance compared to traditional PSO and SA-PSO.
• Achieved desirable economic efficiency in DG planning.
• Ensured a safer voltage level within the distributed system.
• The method proved effective for siting and sizing DGs in distributed power grids.

Conclusions:

• The ISA-PSO algorithm offers a more effective approach for the siting and sizing of distributed generators (DGs).
• The enhanced algorithm improves convergence speed and avoids local traps, leading to better optimization outcomes.
• The developed planning method balances economic benefits with grid operational safety.