Asymptotically Optimal Scenario-based Multi-objective Optimization for Distributed Generation Allocation and Sizing in Distribution Systems

다국어 초록 (Multilingual Abstract)

Suitable location and optimal sizing are impact on voltage stability margin of the distributed system. It is important to accurately simulate the random output active power of Distributed Generation (DG). In order to model uncertainties of intermittent distributed generation and load, this paper proposes a multi-scenario tree model of wind-photovoltaic-load using multiple scenarios technique based on the Wasserstein distance metrics, which generates asymptotically optimal scenario. And in this paper, a multi-objective optimizes control model with scenario tree is presented, which including objectives that are the total active power losses and the voltage deviations of the bus. Moreover, a new hybrid Honey Bee Mating Optimization and Particle Swarm Optimization (HBMO-PSO) algorithm is proposed to solved the problems. In the HBMO-PSO algorithm, the mating process is corrected, which the PSO algorithm is combined with the HBMO algorithm to improve the performance of HBMO. Finally, a typical IEEE 33-bus distribution test system is used to investigate the feasibility and effectiveness of the proposed method. Simulation results illustrate the correctness and adaptability of the proposed model and the improved algorithm.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Multi-scenario Tree Models of Wind-Photovoltaic-Load
• 2.1 Asymptotically Optimal Scenario Generation based on Wasserstein Distance Metric
• 2.2 Asymptotically Optimal scenario of Wind Power and PV
• 2.3 Multi-Scenario Tree Models of Wind-Photovoltaic-Load
• 3. Multi-Objective Model Based on Asymptotically Optimal Scenario and Its Solution
• 3.1 Objective Functions
• 3.2 Hybrid HBMO-PSO Algorithm for Multi-Objective Optimization
• 4. Simulation Results
• 4.1 Optimal Scenarios Analysis
• 4.2 Multi-Objective Optimized Results
• 5. Conclusions
• Acknowledgements
• References