Renewable Energy Strategy and the Analytics That Drive It GridCure - - PowerPoint PPT Presentation

The Macroeconomic Impact of a Distributed and Renewable Energy Strategy and the Analytics That Drive It

GridCure uses data to optimize energy strategy and improve smart grid operations

GridCure translates complex datasets into simple solutions

Produces simple and intuitive dashboards Recommends actionable insights Securely aggregates and cleanses data Performs multilayer big data analysis

Limited Resources: Limited access to both water and

• il, especially compared to its neighbors

Large Refugee Population: 1/3 of the countries current population Geopolitically Sensitive: Civil unrest (Arab Spring) and regional warfare JORDAN: A PRIMER

Of all fuel and power consumption

97%

In 2011, Jordan relied on foreign energy imports for

Located conveniently in the sun belt, Jordan receives between 5-7 kW/m^2. Major projects currently include:

• Shams Ma’an (160MW)
• Rooftop solar heating (30% of all households by 2020)
• Aqaba Proposal for Concentrated Solar (50MW)

With a goal of hitting 12-15% of the countries energy needs supplied by solar within the next few years.

Solar

Jordan also benefits from ideal location for wind power generation, especially in the southern part of the country. The Tafila Wind Farm set many precedents:

• One of the largest wind projects in the region (117MW)
• Was the first PPA with NEPCO, the Jordanian Transmission Operator
• Set documentation and legal precedent for all major feed-in projects since

Wind

To offset instantaneous demand and fuel oil purchase, Jordan has run major initiatives for EV adoption and grid-scale storage

• To solve the chicken-and-egg issue with charging stations vs. vehicles, the

entire Jordanian governmental vehicle fleet has been transferred to EVs

• Vehicle import tariffs removed on EVs
• 40MW battery storage project with AES

EVs and Storage

GRIDCURE SOLUTION

Gridcure Health Monitoring Platform

GridCure’s Platform seamlessly integrates data from disparate data sources to allow for secure, privacy-compliant transfer and analysis

• Full data encryption and NERC-CIP compliance
• Intuitive interface with customizable access privileges, custom

filters, and anonymization features

• Bulk data ingestion, data cleansing, and data processing

Module examples

Balance Loading Storage Capacity Network Loss Blink Analysis C&I Customer Analysis Vegetation Management Transformer Health Electric Vehicle Distribution Feeder Cable Health

GridCure layers stackable modules on top of its Grid Health Monitoring Platform; each module answers a specific question or group of related questions

KEY BENEFITS UTILITY & POWER CLIENT BENEFITS SOCIAL BENEFITS

Save money & increase revenue Save time & engineering resources Improve internal operations Improve reliability & customer satisfaction The specific benefits each customer receives will be contingent upon the module(s) selected. The most common benefits include: The specific benefits each customer receives will be contingent upon the module(s) selected. The most common benefits include: Meet increasing energy demand Utilize renewable resources Increase reliability, safety, and transparency Reduce carbon footprint

THANK YOU hello@gridcure.com

Step 1: GridCure signs up new clients to access to its platform and select the module they would like to test.

SIMPLE ROI EVALUATION

Step 2: Clients will upload the relevant data or integrate existing data streams through the platform interface. Step 3: GridCure will cleanse and prepare the data for analysis. GridCure may also integrate additional 3rd party data sources, like weather information, for better analysis. Module Dashboards GridCure Proprietary Technology Client Data GridCure Data Upload Tool

Data

Step 4: GridCure will tune its algorithms to the partner data. Using the partner’s existing historical data, GridCure can make predictions against a test set and then compare them to actual results. Step 5: GridCure will provide the client access to front-end dashboards to help stakeholders visualize the data and insights in a real-time context.

Tagg Jefferson

• Dr. Kim Montgomery

Zack Milosevich Emily Basileo Brian McKean

TEAM

Rich Landy

GridCure Team Key Partners & Investors

Jonathan Hersch Marcus Melo

Predictive Maintenance

CASE STUDY

The Challenge A US IOU was in the process of replacing 100 miles of feeder cables over 5 years and needed to prioritize the order in which they should be replaced. The Solution GridCure Predictive Maintenance can predict maintenance schedules, forecast equipment expenditures, and increase grid reliability. The Results Of the 5 feeder cables that were predicted to be most likely to fail, all 5 actually experienced a failure. Furthermore, our algorithms yielded a 10X improvement on standard heuristic models for predicting feeder cable failure over a pre-specified period of time. Network Health Monitor Single Asset Investigation Detailed Asset Analysis Asset Comparison

Non-technical Loss

LOSS DETECTION

The Challenge A US utility was interested in identifying and accounting for potential revenue loss. The Solution GridCure Loss Detection can identify non-technical loss on the

• network. Our Partner leveraged our loss detection solution to

identify and understand loss on their network. The Results Working in tandem with existing detection methods to properly size the problem, GridCure Non-Technical Loss Detection accurately predicted 82% of tamper events were actually thefts. Grid Loss Overview Meter-Specific Risk Scoring Meter-Specific Detailed Overview Meter Summary List View

Blink Heat Map Cause Details Meter Details Blink Monitor Overview

Blink Analysis

Case Study

The Challenge A US utility partner needed a way for engineering teams to understand blinks on their network. The Solution GridCure Blink Analysis displays relevant information about meter blinks and connects blinks to outage events. The Results The client was able to diagnose network issues faster, allowing for service prioritization and faster time to resolution. In addition, customer service representatives were able to easily communicate

• utage information to customers.

Load Prediction

CASE STUDY

The Challenge A IOU client needed a way to accurately predict load usage across their network in order to purchase the right amount of energy for a given time interval. The Solution GridCure Load Prediction can predict load usage across a utility’s network. The Results GridCure was able to reduce the client’s overestimation of power purchasing by 50%, resulting in a 3.37% overall reduction of power purchased on their network. Interval Load Data Raw vs. Cleaned Usage Data Load Prediction Load Prediction with Data Override