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The Problem
With the accelerating adoption of electric vehicles (EVs) in neighborhoods, distribution utilities are faced with the challenges to support the increased demand from EV chargers in customers’ homes and businesses. Utilities need to better plan the infrastructure required by identifying priority areas where peak demand from charging will overload the system. They need to address capacity shortfalls by either implementing costly capital upgrades or delay and reduce the need by implementing non-wires alternatives.
For comprehensive details on the related problems and solutions, download the Whitepaper from EV INfo.net and BluWave-ai:
"How AI Will Leverage Mass Adoption of EVs to Help, Not Break the Electric Grid."
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Improve visibility into grid and EV penetration to better plan capital infrastructure and operations
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Run test scenarios with various levels of EV and DER penetration behind and in front of the meter.
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Gain real-time insights into local load and peak predictions across the network, considering EV charging demand
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Improve customer engagement and enable incentives for EV charging to assist grid operation
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Enable intelligent management for enrolled customers with behind-the-meter EV chargers to mitigate peak demand
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Optimally dispatch control of energy storage and EV charging schedules, to mitigate peak demand within the capacity of existing network infrastructure
The Solution
BluWave-ai’s EV Everywhere eases the transition to high EV penetration. Building on BluWave-ai’s distributed artificial intelligence (AI) SaaS platform, it uses real-time data to predict demand considering forecasted consumer loads plus behind and front-of-the-meter energy storage and renewable generation. Integrated with utility systems, EV Everywhere provides optimal dispatch for intelligent scheduling of EV charging and available battery energy storage. This provides a non-wires alternative solution mitigating peak loads and hence deferring and reducing the need for capital infrastructure upgrades
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EV Everywhere incorporates load prediction, power flow optimization, IoT connectivity and fast AI execution capability to send load balancing signals to hardware. It learns and continuously optimizes operation, adapting to system changes automatically. With residential and commercial customers opting for charger management from the utility, EV Everywhere will automatically shift or regulate vehicle charging to optimally meet system goals while satisfying customers’ needs. Customers may also use a mobile app to get notifications of the best time to charge, along with incentives, setting of preferences and opting in to programs. EV owners participating with utilities using will be able to save money on their energy bills to reliably charge their vehicles. This will benefit utilities, consumers and system operators
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In operation, EV Everywhere smooths local peaks in the system and manages available distributed energy resources. EV Everywhere makes participation easy for the residential EV driver, and off-loads the task of managing active charging from the utility. Dashboards accessible via web app for utility operators provide visibility into the system status, real-time grid-power flow and predictions using machine learning for both day-ahead and same-day load predictions. Additionally, EV load detection allows utilities to track changes in the overall system with minimal new hardware.
Features
BluWave-ai also provides simulation tools to measure the potential impact of increased EV penetration down to the neighborhood level, and assess the impact with EV Everywhere. This enables targeted planning for capacity upgrades to non-wires alternatives by identifying priority areas of the distribution network to address increased peak demand.
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Phased Onboarding and Deployment
BluWave-ai works with utilities in a phased approach that starts with an initial assessment and follows with AI model training with historical data, running scenarios in simulation and analysis to assist in operational planning through to system integration, live testing and, lastly, operational deployment.
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