Long-term usage of the off-grid photovoltaic system with lithium-ion battery-based energy storage system on high mountains: A case study in Payiun Lodge on Mt. Jade in Taiwan

TL;DR

The paper addresses the challenge of supplying reliable, green energy at high-altitude, grid-isolated sites by studying a long-running off-grid PV energy storage system based on Li-ion LiFePO4 cells at Paiyun Lodge. It combines a data-driven health assessment from multi-year BMS data, identification of typical daily operation patterns, C-rate and temperature distributions, and a simple cost model comparing Li-ion against lead-acid, followed by a real-world engineering upgrade. Key findings show predominantly low C-rate operation, robust temperature uniformity, and remaining capacity exceeding 95%, indicating slow aging; the PV/ESS upgrade plus a cloud EMS improved reliability and monitoring while enabling capacity planning. The work provides a practical blueprint for durable off-grid PV ESS deployment in alpine settings and informs design choices for aging-off-grid PV ESS deployments, including when Li-ion offers long-term cost advantages despite higher upfront costs.

Abstract

Energy supply on high mountains remains an open issue since grid connection is unavailable. In the past, diesel generators with lead-acid battery energy storage systems (ESSs) are applied in most cases. Recently, photovoltaic (PV) system with lithium-ion (Li-ion) battery ESS is an appropriate method for solving this problem in a greener way. In 2016, an off-grid PV system with Li-ion battery ESS has been installed in Paiyun Lodge on Mt. Jade (the highest lodge in Taiwan). After operation for more than 7 years, the aging problem of the whole electric power system becomes a critical issue for long-term usage. In this work, a method is established for analyzing the massive energy data (over 7 million rows) and estimating the health of the Li-ion battery system, such as daily operation patterns as well as C-rate, temperature, and accumulated energy distributions. The accomplished electric power improvement project dealing with the power system aging is reported. Based on the long-term usage experience, a simple cost analysis model between lead-acid and Li-ion battery systems is built, explaining that the expensive Li-ion batteries can compete with the cheap lead-acid batteries for long-term usage on high mountains. This case study provides engineers and researchers a fundamental understanding of the long-term usage of off-grid PV ESSs and engineering on high mountains.

Figures (14)

• Figure S1: Paiyun Lodge, the highest lodge in Taiwan, is at an altitude of 3402 m (11161 ft), located 2.4 km (1.5 mi) below the west slope of the main peak of Mt. Jade. Photographer: Yu-Chun Lin.
• Figure S2: Methodology scheme of the work. Three parts are contained, i.e., part I: Li-ion battery condition analysis, part II: electric power improvement project for Paiyun Lodge, and part III: simple cost analysis between medium-scale lead-acid and Li-ion battery systems.
• Figure S3: Data processing. (a) Colormap of total voltage obtained from the source data. The oblique red line indicates a time shift (about 15000 seconds) in the source data, causing a serious data shift. It's obvious that the data in the red rectangle should be on the right side of the figure. (b) Colormap of total voltage obtained from the time-adjusted data. After time adjustment, the data exhibits a clear daily pattern. (c) 3D plot of total voltage obtained from the time-adjusted data.
• Figure S4: Four major daily operation patterns of the battery system (corresponding pattern conditions are listed in Table \ref{['tab:ESS_Patterns']}). Patterns 1, 2, 3, and 4 are shown in (a)--(d), (e)--(h), (i)--(l), (m)--(p), respectively. Each pattern contains the total voltage ($V_{tot}$), 16 cell voltages ($V_n, n=1 \sim 16$), current ($I$), and 4 temperatures ($T_n, n=1 \sim 4$). The level of zero current ($I = 0$) is denoted by dashed lines, and the charge (discharge) current is a positive (negative) value. The red circles highlight the instant voltage drop and high current of the battery system. The green circles highlight many instant voltage and current drops under the charge state of the battery system. The blue circles mark the characteristic voltage and current variations caused by the diesel generator. The date is displayed in the lower right corner of each pattern.
• Figure S5: Time distribution of C-rate during battery system operation. The probability of charge and discharge C-rate are marked by blue and red bars, respectively. Inset shows the low-probability region. The battery system operated under a C-rate below $C_R < 0.15$ h$^{-1}$, which lower than the suggested value of $C_R < 0.25$ h$^{-1}$, advancing system stability and prolonging the life of the system. Time range of data: 2016-10-14$\sim$2020-07-20. (Total: 1376 days.)