This study presents an improved Levelized Cost of Electricity (LCOE) model for evaluating the economic performance of electrochemical energy storage systems (ESSs) in competitive electricity markets, especially those characterized by high intraday price volatility. Traditional LCOE models often fail to capture practical deployment challenges by neglecting critical factors such as end-of-life residual value, performance degradation over time, and real-time market dynamics. To address these limitations, this work introduces three key enhancements: (1) inclusion of the residual value of storage equipment at the end of its service life, (2) degradation modeling based on annual cycle frequency and discharge throughput, and (3) integration of high-resolution electricity price time series to reflect actual dispatch conditions. These improvements enable a more realistic and operationally relevant assessment of lifecycle economics for large-scale ESS projects. A Monte Carlo simulation framework is developed to quantify how LCOE responds to variations in five key parameters: annual cycle frequency, discharge duration, energy conversion efficiency, electricity purchase price, and cycle life. Results show that system utilization frequency is the most dominant factor influencing LCOE, while improvements in conversion efficiency and cycle life yield diminishing economic returns. Additionally, extending discharge duration increases system flexibility but reduces responsiveness to peak prices, requiring trade-offs between stability and arbitrage efficiency. Incorporating residual value is shown to significantly reduce LCOE, particularly for systems with extended calendar life. Despite its importance, this factor remains underrepresented in most engineering and regulatory evaluations. To assess the influence of dispatch strategies, two approaches are proposed and tested: the Forecast-based Time-Series Threshold Strategy (FTSRS), which utilizes day-ahead price forecasts to define fixed charge–discharge pairs, and the Historical Price Quantile-based Real-time Strategy (HPQRS), which dynamically sets thresholds based on historical price distributions. While FTSRS delivers more consistent results under stable market forecasts, HPQRS demonstrates greater adaptability in volatile environments. However, both strategies show that excessively frequent dispatch can reduce net profit by compressing price spreads, while insufficient dispatch leads to underutilization and elevated LCOE. The study emphasizes the need to optimize dispatch frequency for both cost-effectiveness and operational feasibility. Keywords: Further analysis highlights a gap between the planned dispatch frequency and actual executed cycles caused by the sequential nature of storage operations and the misalignment of real-world price sequences. This discrepancy is particularly evident under aggressive dispatch settings such as FT = 2:2 leading to increased charging costs and reduced net profit. Additionally revenue bias between forecasted day-ahead and actual real-time prices increases under short-duration configurations while longer discharge durations help average out price deviations thus stabilizing revenue. These findings suggest that long-duration ESSs offer better protection against market volatility while short-duration systems are more vulnerable to pricing errors and strategy mismatch. In conclusion, this paper offers a comprehensive and practical LCOE modeling framework that accounts for residual asset value, cycle degradation, real-time pricing, and dispatch strategy design. It provides a valuable decision-support tool for developers, investors, and policymakers seeking to assess the lifecycle costs and economic viability of energy storage systems under dynamic market conditions. The model is especially suited for utility-scale ESSs participating in price arbitrage and balancing markets, where accurate dispatch planning and revenue forecasting are critical to project success.