Elpis Squared (Elpis) is a software company who is primarily focused on offering tools for distribution system asset management, planning, modeling and analysis. Elpis offers a software platform, along with professional services, to help distribution utilities address the limitations and challenges with conducting analyses for an increasingly complex distribution system.

The company is privately owned and was founded in 2013 and subsequently won their first big contract in 2014 with LADWP. Since then, Elpis has gained traction via engagements with several large, investor-owned utilities, including National Grid, PG&E, SCE and SDGE.

Elpis' offers a commercial-off-the-shelf software system that includes a suite of application modules that were built to meet business and technical needs for utility grid modernization efforts. They offer 5 main software solutions for system planning, engineering and operations:

• Model Validator: Ensures accurate representation between digital network model and in-field state. Focus areas include: MV phase relationship, secondary networkhase relationship, transformer-meter connectivity, conductor size continuity and asset attribute data verification.

• System Planner: Unifies critical system planning tasks under one platform. Key capabilities include: 8760 load profile data cleansing and normalization.

• Customer InterconnectDER: Customer portal to streamline DER interconnection process. Includes DER application management, automating data error checking and calculations, workflow customization business logic.

• Service optimizer: Identifies system constraints that impacts Quality of Service: transformer load analysis, voltage analysis, outage events and exception data handling, meter health analysis, energy theft and hot-socket analysis.

• Power Analyzer: Power flow engine targeted to support big data ecosystems and distributed computing architecture. 8760 time-series load flow analysis,overcapacity, voltage and short-circuit analysis, time-series duct thermal analysis and mitigation modeling.

SaaS

It might be challenging to answer basic questions about the status, performance, and condition of many key components of smart grids. While large amounts of raw data are being collected, most utilities are deriving limited value from this trove of source data. Main challenges being:

• Too Much Data being stored from AMI, SCADA, OMS, GIS, and many other sources
• Incomplete Data
• Lack of Expertise - Analytics pipelines are complex, with many moving pieces
• One-size-fits-all Solutions - Many large vendors offer general analytics solutions, not specifically targeted to specific use cases
• Cloud vs On-Premise

The solution offered by Elpis offers:

• Analytics in The Cloud - a fully managed and configurable analytics platform.

• End-to-end Analytics Framework - flexible analytics pipelines on premise in data center, delivering data processing, machine learning, and visualization at scale.

• Outage Management with Machine Learning - a software suite designed to provide a centralized platform for monitoring, analyzing, managing, and optimizing distribution network outages.

• Asset Load Management Leverage interval load data to characterize transformer utilization and identify overloaded transformers. Assess transformer overload impact to customer voltages and system reliability. Provide transformer replacement recommendations.

• Voltage Management Ensure delivered voltage is within ANSI C84.1 specifications. Avoid customer equipment failures due to extreme high or low voltages. Optimize voltage regulating device settings (i.e. cap banks, LTCs, etc.) Identify and mitigate phase voltage imbalance and identify end-of-line voltage issues.

• Outage Management Dynamically monitor real-time power outages and restorations. Live system mapping of active and restored outages. Validate outages programmatically by verifying comm state using ping, as well as other API supported functionality.

• DER Management Understand voltage affects from renewable energy loads. Continuously monitor and characterize distributed generation and EV usage to identify impacted feeders and highlight its impacts to system reliability.

• Distribution Management Quantify reliability modeling of your Distribution Management Infrastructure. Create profiles for planning and engineering by dynamically measuring and characterizing load on each Feeder. Decompose models on each feeder to properly forecast rolling annual profiles.

• Conductor Sizing Analyze all the secondary and service network assets, e.g. smart meter voltage, power consumption data, and distribution design standards. Estimate the type of secondary/service conductor connecting end points to the service transformer.

• Advanced GIS and Mapping Geospatially visualize grid assets, connectivity, and other critical aspects of the infrastructure. Mapping and visualization integrated across the entire Grid Foundry platform.

• Data Validation Correct data quality issues and connectivity models between assets. Record and identify asset relationships (e.g. meter-to-transformer-to-feeder-to-substation.) Geospatially map assets and update real-time statuses to improve overall situational awareness.

• Phase Identification Leverage AMI voltage profiles and GIS connectivity to identify phase at the transformer level. Patented machine learning algorithms use the data you have to produce highly accurate results.