Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Socio-Environmental Evaluation of MV Commercial Time-Shift Application Based on Battery Energy Storage Systems

The urgent need to curb climate change calls for an energy transition to cleaner, more resilient and sustainable solutions. Combined designs of energy storage systems and demand management strategies are becoming more frequent in the literature. However, are these solutions really sustainable from a multi-dimensional approach and in real-world applications? To answer this question, this work performs a local and scaled-up field-based evaluation of the social and environmental impacts of a pilot project in Brazil, which consists of replacing diesel generators with a Battery Energy Storage System (BESS) in a peak power plant of a Medium Voltage (MV) commercial load. For this, the combined RCPA-LCI method is applied, which allows characterizing both energy alternatives jointly considering the Life Cycle Inventory (LCI) and the multi-dimensional evaluation perspective of the Resource Complete Potential Assessment (RCPA). Then, the scalability of this commercial solution at the national level is analyzed through two main lenses: GHG emissions reduction and job generation. The benefits are estimated at a potential 15.4 million tons of CO2 avoided and 113 new job opportunities per year. The results demonstrate the positive socio-environmental performance of BESS-based peak plants for MV commercial applications in Brazil.