Energy Storage Capital Expense Optimization
Industry
Use Case Activity
Use Case Business Improvement

Challenge:
Each substation in a transmission and distribution (T&D) system has multiple transformers feeding distribution circuits. Each of these substations has a set capacity, and utilities must report capacity overages to regulators. Generally, the sum of all substation transformer ratings defines a substation's capacity. In this case, a utility could monitor the sum of MVA load for all transformers to total MVA normal and 24-hour emergency ratings.  

In order to avoid capacity overages, T&D operators install batteries. Batteries are expensive, so T&D operators must decide carefully what locations make the most sense. Considerations include:

  • Capability of existing transformers to serve present peak loads
  • Capability of existing transformers to pick up full load of station when other transformer fails
  • Asset health
  • Outage impact
  • Forecasted demand
  • Cost and pricing of battery installation

Solution:
Seeq is used to monitor a signal against thresholds and investigate what would have happened if one transformer had failed during a certain time period. These same analytics are also applied across a field of substations using asset swapping.

Results:
Implementing Seeq advanced analytics reduced Capital expenditures by prioritizing battery installation based on actual equipment condition and risk of overload. It also improved system reliability to avoid $10s of millions in unplanned downtime and capital expenditures for premature replacement.

Seeq’s advanced analytics enable the aggregation of data from multiple data sources, investigation of "what-if" scenarios, and rapid iteration across assets.

Installing batteries at a substation improves grid reliability and the ability to meet demand anytime. Specific benefits include:

  • Solar smoothing: smooth short-term changes in voltage due to intermittent generation
  • Distribution deferral: non-wires alternatives to defer or eliminate the need for traditional utility upgrade
  • Outage management: reduces the cost of deploying mobiles for contingency resources during substation construction
  • Microgrids for critical facilities: allows critical facilities to operate independently of the electrical grid during extended grid outages
  • Peak reduction: help resolve potential overloads, address power quality issues at host sites, reduce bills for public sector customers
  • Energy cost: discharge batteries every day for one to four hours, reduction in the amount of energy to purchase and charge battery at night at a much lower cost

Data Sources:

  • Load data is stored in PI
  • Capacity ratings are stored in a SQL database

Data Cleansing:

  • Use Low Pass Filter to create a smoothed version of the load
  • Signals are cleansed to remove outliers, downtime and abnormal operating data before establishing monitoring boundaries and models

Calculations and Conditions:

  • Periodic condition
  • Formula (splice)
  • Deviation search
  • Low Pass Filter
  • Signal from Condition
  • Scorecard
  • Asset swap

Reporting and Collaboration:

  • Trends, Metric tables, multivariate scatter plots, and Treemap visualizations are combined into an Organizer Topic for quick consumption of the analytics by stakeholders.