Open Source, next generation grid management as a service.

Camus Energy’s Ritta™ provides customers with unparalleled situational awareness.

  • High quality insights from existing data sources – an integrative system model fuses operations data from multiple sources with a high performance physics model, for advanced real-time analysis
  • Understand what’s happening in front of and behind-the-meter – connect customer systems, local generation models and third-party telemetry to build a complete picture of current and near-future grid conditions

Camus Energy’s Mimo™ provides:

  • Local balancing provides tools for making the most of local resources for energy and grid services, while managing costs and market commitments
  • Policy based automation enables operators to define outcomes and switch between modes – such as optimizing for local resilience during a storm, versus optimizing for system costs under normal operation
  • Intelligent optimization helps make the best choices for managing costs, carbon, and reliability profile

Camus Energy’s Gem™ platform helps electric grid operators:

  • Drive customer and member engagement with public dashboards to help customers understand how and where they can participate
  • Locational pricing – understand the local, time-specific value of distributed resources
  • Markets and settlement – manage local transactions and settle services provided by customers

Why Open Source?

The challenge of transforming our grid landscape is a big one. It requires connecting to every device that’s out there on the current grid – from smart metering systems to smart reclosers to SCADA interfaces – and bringing together data from many sources to create a clear picture of what’s happening. And then, we need to connect the landscape of distributed energy resources, so that they can work together to support the broader grid. 

This is bigger than any one vendor, or any one software platform. Open source software development allows many companies to co-operate, so that each contributes to the broader ecosystem, and everyone can benefit. We’ve seen the power of open source to transform other industries – from the internet, to the web, to Android and 5G – and we believe that by working together, we can create a next-generation grid which unlocks the smart, decentralized energy ecosystem of the future.

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Protecting Against Cyber Threats

One of the biggest concerns as we transition to smart grid management is increased cybersecurity risk. Camus employs a zero-trust cybersecurity model, which requires hardening every interaction point within the system with a differential authentication and authorization model, and ensures that access is verified and data is encrypted at every step. 

With team members from the application security and intrusion response teams at Google and Uber, Camus brings state-of-the-art intrusion prevention, detection and mitigation, and our open source approach allows us to leverage the expertise of industry leaders in multiple security domains. Through our integrated multi-source analytics, the Camus platform can also help identify possible breaches by highlighting anomalous system behavior, as part of a comprehensive system surveillance approach.

Our Technology

Camus Energy’s Ritta and Mimo platforms are cloud-based, open-source, situational awareness and control platforms designed for understanding grid behavior in dynamic environments with high levels of distributed resources. The integrated software suite will also perform optimization and control of distributed energy resources, including rooftop solar, utility scale renewables, and colocated storage. The applications ingest a diverse spectrum of static and time-series data from utility and non-utility sources to describe the current and near-term grid conditions (1 hour, 1 day, and 1 week forward). These data can be explored through a user-defined dashboard and used in downstream applications.

Reliable Data At Any Scale

Existing grid data sources and monitoring methods typically leverage either operational data (e.g. SCADA), customer or billing data (metering), or third party data (e.g. forecasting), but rarely combine all three. Data segmentation reduces the value of the data by limiting real-time, market and planning analyses. Distributed renewable energy resources are generally behind-the-meter, separate from and not synchronized with SCADA data – limiting the opportunity to assess the holistic impact and value to the broader grid. Also, distributed renewables may not be individually monitored, leaving their contribution to power flows on the distribution network unknown – and telemetry across multiple sources is not synchronized. 

The Camus platform leverages an integrated, composite view of interactions across the broader grid landscape – from supply and demand, to network data, to DER telemetry – to provide a deep understanding of what’s happening right now, in the past, and in the near future. Using monitoring techniques derived from high-performance computing applications, we can provide high-fidelity understanding of the energy landscape in front of and behind the meter – even if the source data has gaps, and regardless of data volume.

Camus Energy works with grid operators to address the data pipeline challenges by building a data collection system that ingests, manages, and displays high volumes of fine-scale, static and time-series data from multiple operational and planning sources, including AMI, solar metering, GIS, customer accounts, and interconnections. This pipeline also includes both SCADA and full telemetry for hybrid solar PV-plus-battery systems. External data include current and forecast weather with solar insolation forecasting. These data streams are upgraded, synchronized, and integrated into a composite time series using ML (machine learning) and DL (deep learning) methods. Approaches to address computational scale and complexity are based on Camus’ extensive experience with large-scale monitoring approaches derived from industry-leading work building the monitoring systems for Google’s global frontend infrastructure and for SpaceX’s flight software for vehicle monitoring. 

Simple Control for Complex Environments

Camus’ control architecture is based on experience with control systems managing millions of participants in real time. Leveraging prior work on systems which manage real-time constraint-based resolution of supply and demand balancing, we use a tiered, decomposable system control model, which automates common operations, and provides a simple, high-level control interface to help operators manage the behavior of complex grid systems by assigning goal-oriented, policy-based controls.

Our architecture leverages a design model inspired by the Border Gateway Protocol (BGP) used to control large computer networks such as the internet. Key concepts from BGP – such as standardized AS (autonomous systems) architecture and inter-AS communication protocols that enable replicable implementations – are adapted to advance electrical distribution grids. 

The AS layers are defined based on the hierarchy of subsystems and components of the network. The control signals generated by each AS vary depending on its operational status and risk, control objectives, and even the boundaries between the AS. In contrast, the protocols communicate AS-level information across the interfaces remain consistent, interpretable, and traceable. Uniform protocols reduce system complexity and system data requirements while AS control algorithms provide each AS with freedom and flexibility to adopt optimization and control methods with a varying level of sophistication according to local needs. These properties make our approach adaptable to a wide range of distribution utilities with varying ability to maintain accurate system models. Adapting these BGP properties ensures Camus’ implementation is both scalable and flexible. Our approach achieves reliable and resilient system performance at the transmission-grid interface and within the distribution grid by integrating AS resource estimation algorithms and risk mitigation via optimization. 

Within a given AS, Mimo’s control algorithms provide policy-based control, which leverage machine learning and AI approaches to evaluate the possible solution field for a given set of constraints (for example, managing cost vs respecting per-customer control limits). Use of AI enables the rapid evaluation of very large solution space to efficiently identify optimal and contingency solutions to provide a given system outcome.

Grid-Aware Market Integration

In grid environments which have control objectives beyond utility-managed devices, the Camus platform provides pricing and market integration for distributed behind-the-meter resources. In this model, pricing or market resolution can act as an extended control signal. This can range from simple implementations – such as managing program rates and basic visibility for customers with DERs – to constraint-based economic dispatch. 

By leveraging real-time understanding of the local grid environment, we can model the localized value of individual assets and asset classes across the value stack. Reconciling that value into utility or portfolio level management goals supports system-level objectives such as providing services or supporting load management goals (such as peak shaving) while providing customers with incentives to participate in system level control.

Our proprietary risk mitigation approach uses adjustable robust optimization (ARO) to construct and control optimal portfolios of DERs to manage aggregate grid-service delivery risk. Further information on how customers can reduce the risk to the distribution network is available upon request. Please contact us using the below form. 

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