Energy optimization and renewables penetration designed for campus microgrid operators including institutional, municipal and commercial facilities, and industrial and manufacturing plants.

Features and Benefits

BluWave-ai SaaS provides attractive OPEX, CAPEX and environmental benefits through a full set of features:

• Provides real-time simulation and dashboarding for current states and future predictions of microgrid assets (for example, 5/15/60 minutes ahead, 6 hours ahead, 1 day ahead)
• Predicts electrical load, including negative load from distributed solar PV assets and battery storage availability
• Predicts renewables generation from distributed solar PV and wind assets
• Formulates AI model to manage the energy mix of the microgrid generation assets (fossil fuel, distributed solar, battery energy storage) to minimize energy cost, minimize greenhouse gas (GHG) emissions and maximize utilization of renewables

• Performs systems integration to aggregate device and sensor data from:
  • Upper grid live electricity market and pricing
  • Distributed solar generation and battery assets
  • Microgrid fossil fuel and thermal power plant generation
  • City-wide and substation load
• Produces near real-time energy dispatch signals
• Provides demand-response signals for commercial and industrial partners
• Helps with load shedding, peak shifting management and planning
• Strategically controls battery energy storage assets for energy arbitrage and grid stability

Like so many responsible energy  producers, large institutional, commercial, and industrial campuses and plants with grid-tied microgrids are considering how to reduce energy costs and greenhouse gas emissions. Increasingly, operators are looking to integrate renewable energy assets, such as solar photovoltaic (PV) ones, and storage systems with dispatchable load and generation, to lower harmful emissions and displace grid import.

Using AI models trained with site operational data to predict load and generation, as well as energy pricing, BluWave-ai optimizes the use of dispatchable assets, energy storage, and import/export of grid power in real-time. By intelligently managing energy storage and local generation, loads can also be shifted to avoid peak demand periods and reduce demand charges. The net result  is a reduction in energy costs and greenhouse gas emissions of up to 20%.

BluWave-ai also helps reduce battery storage requirements and maximizes battery lifetime, to reduce and/or delay future purchases. BluWave-ai leverages industry standard technologies to ensure interoperability with SCADA and IoT devices, and to work with external data sources such as weather, planned schedules, event signals, market pricing, and performance objectives.

BluWave-ai’s software-as-a-service (SaaS) model allows for rapid deployment with a low upfront cost, immediate operating savings, and an improved return on existing infrastructure investments.

BluWave-ai Edge provides IoT and SCADA data collection and pre-processing, AI inference, prediction and optimization.

BluWave-ai Center provides user interface and configuration provisioning and management, and AI model training.

Distributed AI Architecture for Campus Microgrids within a Utility Network

Low-Risk, Multi-Phased Onboarding Process

To help you evaluate and quantify the benefits of using the BluWave-ai solution in your unique operation, we offer an opportunity assessment and model-building service as either a first phase of product deployment or a standalone service. Learn more about our deployment process.

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Smart Campus Application

Smart building energy management systems are already deployed in individual buildings and campuses. Data is collected from meters and other IoT devices connected to building elements such as the HVAC system, elevators, and lighting. Where installed, there are also readings from rooftop solar panels and electric vehicle (EV) charging stations. The BluWave-ai platform aggregates this data and sends intelligent recommendations to the building management system so that it can reduce energy consumption, predict maintenance requirements and improve infrastructure efficiency.