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.

Bhutan’s hydropower-reliant electricity supply faces seasonal imbalances, with a winter deficit prompting costly imports from India at tariffs of up to $0.09/kWh. Despite the estimated solar potential of 12 GW, PV deployment remains limited. This study presents a demand-driven techno-economic assessment of a 150.8 kWp rooftop PV system for the Ministry of Infrastructure and Transport using high-resolution hourly load data and PVsyst simulation. Three operational configurations are evaluated: self-consumption without export, self-consumption with export, and a battery energy storage system (BESS) introduced to mitigate curtailed energy. The system is expected to generate 252 MWh annually, achieving self-sufficiency and Self-Consumption Ratios of around 60%. Without export, the performance ratio (PR) is reduced to 51% due to significant curtailment, resulting in a negative Net Present Value (NPV) of −$33,687.5 and a Levelized Cost of Electricity (LCOE) of $0.0682/kWh. Enabling export raises the PR to 85.62%, improving the NPV to $27,965.42, the Internal Rate of Return (IRR) to 8.07%, and the LCOE to $0.0405/kWh. A 200 kWh BESS, sized based on surplus energy and nighttime demand, increases self-consumption and self-sufficiency to 75% and 73%, respectively. However, the LCOE rises to $0.0841/kWh, limiting economic viability under current tariff structures. The results reveal a structural mismatch between prosumer-level economics and system-level benefits, underscoring a need for improved compensation and targeted policy support in Bhutan and similar hydropower-dependent systems.