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The Problem

Integrating battery energy storage systems facilitates the storage of excess renewable energy for use during high-demand periods. When connected in an electricity system network, these systems enable power producers to swiftly adjust energy supply to meet system needs. At the distribution level, optimizing BESS placement near substations and throughout the network eliminates overload in stressed areas of the distribution network and reduces the need for infrastructure buildout provided its operation can be automated with grid awareness.Efficient management of BESS is required to reduce grid stress, limit costly upgrades, and optimize the necessary capacity for deployment. Basic BESS management software lacks awareness of grid conditions and the current energy mix, energy mix forecast for the next day, and often fails to optimize charge and discharge cycles, missing opportunities to absorb renewable energy in the system, reduce demand charges and capitalize on energy arbitrage. Without automated, real-time optimization, these systems will struggle to capture full value from grid services programs such as demand response and operating reserves. Moreover, these BESS assets are deployed for operation over decades, and will need to adapt their operation to handle changes in the grid conditions and the regulatory environment.

Built on BluWave-ai’s AI-driven energy optimization platform, BluWave-ai’s Energy Storage Autopilot™ meets the need for automated BESS optimization throughout the electrical system. The Energy Storage Autopilot is grid-aware and serves as a communication layer, enabling basic BESS software to operate automatically based on real-time data. It automates the process of submitting energy bids to market operators. By analyzing market conditions and forecasting prices, it can place bids in capacity and demand response markets that maximize revenue and minimize costs, ensuring that the BESS participates effectively in the energy market. The Energy Storage Autopilot also takes care of interpreting and responding to market dispatch signals. It converts these signals into dispatch instructions for the battery energy storage system to optimally charge and discharge in real-time in an automated manner. Embedded in the Energy Storage Autopilot is a BESS Optimization Module, which synthesizes inputs including load metering, energy prices, weather and local renewable and system wide generation mix to develop a comprehensive operational strategy. This module considers factors such as market price fluctuations, operational costs, and revenue opportunities to optimize the charging and discharging schedules of the BESS. Leveraging these AI-driven tools, BluWave-ai provides owners and operators of BESS’s with a powerful solution to the challenges of BESS management.

Such an electrical system operating with various BESS assets managed by the Energy Storage Autopilot is illustrated above. Distributing the intelligent management of energy storage across the grid maximizes the opportunity to manage peaks and surplus renewable generation from where it is generated to where it is consumed, minimizing stress on the infrastructure.