改良式黏菌演算法應用於微電網之能源管理系統

Abstract

本研究旨在為智慧家庭發展一整合太陽能發電、市電及儲能系統之微電網能量管理系統(Energy Management System, EMS)，透過設計最佳化多能源系統能量管理技術，合理分配不同能源之間的功率流向，同時確保儲能系統進行必要的儲能與釋能，以降低總體用電成本。研究中建構一個包含太陽能、市電和儲能系統的微電網模型，並提出多種控制策略，包括基本規則庫、全域搜索法、黏菌演算法以及本研究所提出的改良式黏菌演算法，該策略將螺旋搜索策略整合至黏菌演算法中，以降低演算法在搜索過程中陷入局部最佳解的機率，同時提高搜索速度。透過本研究提出的控制策略，能夠針對在不同需求功率和儲能電池狀態下，計算出最佳功率分配比例，實現能量管理最佳化。本研究以個人電腦及Matlab/Simulink發展控制策略，透過個人電腦計算最佳能量分配比例後，輸出控制訊號以對各組直流-直流轉換器進行電壓與電流控制，使各模組輸出功率可被主動式分配，實現最佳化能量管理目標。實驗結果表明，改良式黏菌演算法控制策略能夠有效地最佳化分配能源，以達到降低用電成本的目的。以夏日情境為例，使用全域搜索法控制策略能比基本規則庫控制策略之用電成本改善幅度11.826%；使用黏菌演算法控制策略改善幅度10.210%；而使用改良式黏菌演算法控制策略改善幅度10.945%。由上述說明可知，本研究提出的改良式黏菌演算法能實現理想的微電網能量管理目標。

The purpose of this study is to develop an Energy Management System (EMS) for smart homes that integrates solar power generation, utility power, and energy storage systems into a microgrid. By designing of optimized multi-energy system energy management techniques, the objective is to rationally allocate power flows among different energy sources while ensuring necessary energy storage and discharge, ultimately reducing the overall electricity costs. In this study, a microgrid model with multiple energy systems was established, including solar power generation, utility power, and energy storage systems. Several control strategies were proposed, including Rule-Based (RB), Global Search Algorithm (GSA), Slime Mould Algorithm (SMA) and Improved Slime Mould Algorithm (ISMA) introduced in this study. This strategy integrates a spiral search strategy into the slime mould algorithm to reduce the likelihood of the algorithm getting stuck in local optimal solutions during the search process. Simultaneously, it shortens the required search time, addressing the issue of excessive search time in the global search algorithm. Through the controlstrategy proposed in this study, optimal power distribution ratios can be calculated for different power demand scenarios and energy storage battery states, achieving energy management optimization.This study developed control strategies using a personal computer and Matlab/Simulink. After calculating the optimal energy distribution ratios on the personal computer, commands for controlling voltage and current were generated to regulate each group of DC-DC converters. This enables the active allocation of power output from each module, achieving the goal of optimized energy management. The experimental results of the study indicate that the ISMA control strategy effectively optimizes energy distribution to achieve the goal of reducing electricity costs. Specifically, in the case of a summer scenario, the use of the GSA control strategy improves electricity costs by 11.826% compared to the RB control strategy. The SMA control strategy improves costs by 10.210%, and the ISMA control strategy achieves an improvement of 10.945%. Overall, the results suggest that the ISMA proposed in this study approaches the results obtained by the GSA more closely.