太陽能儲電系統的應用研究

Abstract

本研究於國立臺灣師範大學建置一套太陽能儲電系統，核心結構包含太陽能模組、OPTI逆變器、鋰鐵電池、四路Modbus控制模組與多功能監控平台，支援尖峰放電、離峰充電、時間電價控制等調度策略，且依據用電特性進行參數調整，實現能量最佳化與成本控管。為驗證系統效能，本研究針對同一太陽能結合儲電系統於不同負載條件(500W、1000W、1500W)下之運行表現進行實測與效益分析，評估其能源調度能力、經濟性及減碳潛力。結果顯示，該系統具備良好的適應性與運轉穩定性，能依據時間電價與負載需求，透過控制策略降低尖峰電價負擔與市電依賴，並自動於離峰低價時段進行電池充電，產生明顯的節能與環保效益。整體而言，系統於不同負載條件下皆展現出良好的能源調度能力，電費改善率平均降低約40%，具穩定回售潛力與減碳能力。本研究結果驗證太陽能儲電系統於分散式能源應用中之可行性，未來可進一步導入AI預測模型與虛擬電廠架構，對於推動智慧建築、低碳社區與能源轉型具參考價值。

This study involves a solar energy storage system built on the campus of National Taiwan Normal University. The core components of the system in-clude solar panels, an OPTI inverter, LFP batteries, a four-channel Modbus control module, and a multi-functional monitoring platform. The system is capable of setting dispatching strategies such as peak-time discharging, off-peak charging, and time-of-use pricing control. It also allows parameter adjustments based on electricity usage patterns to achieve energy optimization and cost management.To evaluate system performance, this study conducted empirical tests and benefit analyses under various load conditions (500W, 1000W, and 1500W) using the same solar-plus-storage setup. The analysis assessed the system’s energy dispatch capabilities, economic benefits, and carbon reduction potential. The results show that the system possesses excellent adaptability and opera-tional stability. It can reduce peak electricity costs and reliance on grid power through control strategies based on time-of-use pricing and load demand and automatically charge batteries during off-peak and low-price periods, resulting in significant energy-saving and environmental benefits.Overall, the system demonstrated strong energy dispatching performance across different load conditions, achieving an average electricity cost reduction of approximately 40%, with stable potential for grid feed-in and notable carbon reduction capabilities. The results confirm the feasibility of solar energy stor-age systems in distributed energy applications.This system can be further en-hanced in the future integrating AI-based forecasting models and virtual power plant (VPP) frameworks, offering valuable insights for advancing smart buildings, low-carbon communities, and energy transition strategies.