Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability.

Waste-to-energy (WtE) is national policy. From this view, WtE technology has been promoted. Many WtE projects in Thailand were unsuccessful due to several problems. This research aimed to analyze the key barriers impacting the WtE project development in Thailand. The Interpretive Structural Model (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis tool have been used to evaluate the barriers that significantly in the development of WtE projects. In this study, WtE projects focused on electricity power generation in order to correspond to the Alternative Energy Development Plan (AEDP) target and power purchase agreement constrain of the government. The barriers were obtained from six sections consisting of social issues, environment, national policy, technology, economy, and project management. From six sections, there are 20 barriers that were identified. The ISM and MICMAC analysis showed that the key barriers impacting the WtE projects development were insufficient amount of waste and poor waste management planning. These two barriers correspond with many studies in Thailand and other countries. The project developers or investors must take these two barriers and other barriers with less impact mentioned in this study into account before developing the WtE projects in Thailand.

This research investigated the increases of the voltage profile on the Provincial Electricity Authority (PEA)’s low voltage (LV) network due to the solar photovoltaic (PV) penetration. This study proposed the solution to maintain the voltage profile within the PEA’s standard limitation by using battery energy storage system (BESS) application. The algorithm using bisection method to figure out the optimal size and location of BESS was examined and simulated in different scenarios such as summer/winter and weekend/weekday behaviors. Furthermore, the allocation of a battery in various locations was also considered. DIgSILENT power factory with DPL script and Python are the tools used to cover diverse scenario cases. The results showed that the best practice of how to implement BESS to solve the voltage rise problem was the BESS installation at the distribution transformer and the BESS installation separately at the end of each feeder near the loads. However, the optimal size of BESS installation at the distribution transformer was almost double that of installation at the end of each feeder.

In this paper, we present a model for calculating the State of Health (SOH) of battery energy storage systems (BESSs) and battery capacity percentage, specifically tailored for grid-scale applications in Thailand. Unlike conventional models that rely on controlled laboratory data, the proposed approach uses actual operating temperature data for both development and validation, enabling a more correct assessment of battery performance under the high-temperature conditions typical of tropical climates. A set of coefficients derived from real operating data was incorporated, and the SOH results deviated by only 0.05% from theoretical values, proving high predictive accuracy beyond laboratory settings. Our findings revealed that capacity degradation rates in Thailand are approximately 20–60% higher than under the best conditions. Over a 10-year warranty period, battery capacity declined to approximately 80% at the lowest temperature range, 60% at the average range, and 40% at the highest range. By calculating both SOH and remaining capacity, the model provides a practical tool for lifespan prediction and system planning. Based on these findings, it is recommended that thermal management systems support battery operating temperatures between 25 and 35 °C and limit cooling losses below 10%, thereby mitigating energy yield degradation and ensuring efficient BESS operation. These results highlight the importance of incorporating real environmental data into degradation modeling. Future studies should include long-term monitoring of operating temperatures and cooling demand, economic analyses to enhance operational efficiency, and evaluation of external heat loads, particularly from solar radiation, to further refine predictions for tropical climates.