Solar energy, particularly through photovoltaic (PV) power generation, plays a crucial role in transitioning to renewable energy sources, offering an inexhaustible and clean energy solution that significantly reduces carbon dioxide emissions and supports global carbon neutrality goals. Despite the rapid expansion of PV capacity worldwide, optimizing economic returns remains challenging due to market volatility and storage inefficiencies. During PV generation, produced energy is stored in batteries, waiting for favorable sale prices as the market fluctuates. In our model, storage losses for the batteries are represented by a constant, denoted as \(C_{loss}\) , which means that the remaining energy in the next period is reduced to \((1-C_{loss})\) times that of the previous period. This paper proposes a novel online algorithm designed to maximize revenue from selling PV power generated in each period. The algorithm dynamically adjusts selling strategies to effectively balance market price fluctuations and storage losses, achieving a competitive ratio of \(O(\log h)\) , where \(h\) is the highest unit price. By strategically reserving \(\frac{1}{\log h + 1}\) of energy for each expected electricity unit price, the algorithm ensures greater profits at higher prices. Furthermore, the paper establishes that the lower bound of the trading problem is \(\Omega (\log h)\) , demonstrating that the algorithm is tight and performs optimally within the modeled constraints.