© 2015 IBM Corporation

IBM Research

Opus: An IBM Research Energy Analytics and Orchestration PlatformQuantifying and managing uncertainty in utility business and operations

Ron AmbrosioIBM Distinguished EngineerChief Technology Officer, IBM Smarter Energy Research

Chairman Emeritus & Member, U.S. Dept. of Energy GridWise Architecture CouncilChairman Emeritus, Smart Grid Interoperability Panel Architecture Committee

Can the data be used to plan, evolve and orchestrate

energy systems?

Renewablesgetting

economical

Renewableenergy

mandates

Time and place

of energy useis critical anddetermines

cost

Distributed Energy

Resources

Grid is increasingly

instrumented and intelligent

More extreme weather;

aging assets and workforce

Industry trends and propositions1. “Distributed” is the keyword for the new grid

– More resilient– Less losses (today ~7%)– Better asset utilization (today ~48-54%)– New business models

2. Energy cost is based on time and place of use– Energy efficiency has a profound new meaning– Rate payers “prosumers”

3. Renewable energy is getting cost competitive– Economics will accelerate adoption

4. Renewable energy mandates are accelerating adoption– But this injects intermittency

5. The grid is increasingly instrumented and intelligent– We are drowning in data!

6. Other– More extreme weather events– Aging assets and workforce

2

Proposition 7: The energy system is rife with uncertainties

Daily NYISO Average Cost/MWh

3

Weather DemandConsumerBehavior

Energy Price / Fuel Costs

RenewableProduction

Regulatory Policy

TechnologyDisruption

How do we incorporate all sources of uncertainty into a series of informed business and operational decisions?

4

What is Opus

Opus system architecture

5

• IBM will pilot and deploy Opus with industry partners• Opus will

Will support data-intensive planning and real-time use cases Be built on a common, open analytics platform and

uncertainty workbench Be scalable, componentized, and open to enable partners to

contribute to the ecosystem Communicate with existing infrastructure and IT systems

from any vendor Be built on a common data model using industry standards

so that applications can talk to one another

Creating a 21st Century Electric System for New York“…Grid modernization’s long-run and greatest value is the result of leveraging cross-functional capability through system integration where multiple components are brought together to improve reliability and customer service…”

Cyber Security/Data Privacy

Planning and operating the electric system and associated energy services

Uncertainty in energy systems

• A comprehensive system model• A comprehensive probabilistic model of uncertainties (including high-resolution weather prediction) Used to optimize decision variables in real time without leaving performance/value on the table

6

Generation Bulk Trade/Planning Transmission & Distrib.

Retail TradeCustomerWeather

Energy

$$$Energy

Energy

Energy

$$$

Correlation

Correlation

Opus can be applied to a broad range of utility system problems

DeterministicModel

Characterizeand modeluncertainty

Optimize inthe face ofuncertainty

Decisionsupport

automation

Example Opus Use Cases

Real-TimeNear Real-TimeNon-Real-Time

Physical

Operations

Business

Asset Health AssessmentRenewable Forecasting

Demand Forecasting

Energy BalancingAsset Failure Prediction

Customer Intelligence

Market Optimization

Capital Planning

Maintenance Planning

Outage Repair Scheduling

Storage Management

Demand Management

PMU Analytics

Transactive Energy MgtOutage Mitigation

DG Management

Fuel Price Forecasting

Network Health Assessment

Damage Forecasting

Connectivity Model

Renewable Integration Stochastic Engine

Microgrid Management

Existing projectsNext phase projects

Bulk Supply Forecasting

Renewable Site Planning

Bulk Supply Availability

TX Congestion Forecasting

7

Outline

8

What are the benefits?

Reducing uncertainty can reduce excess energy expense

9

EnergyEnergy Gapwc

Pro

babi

lity

dens

ity

Supply

Demand

Energy Gapdet

$Ms of savings Energy Gapopt

Savings from not worst-casing uncertainty

10

Stochastic optimization of DER integration and management

For discussion purposes only

Wind Energy Forecast

Solar Energy Forecast

Demand Forecast

Renewable Integration Stochastic Engine

Opus Platform• Common data model• Shared services• Hybrid event and

service architecture• Distributed agent

framework• Visualization• Big data integration• Open APIs• Analytics toolkits• Uncertainty workbench• Support for multiple

network model stds

Opus Applications

• Weather Data• Grid Topology• Grid Assets• Live sensor Data• Historical sensor Data

Opus System Simulation

Optimized decision-making under uncertainty

Data

DER Management

Asset Health & Planning

Definition of Transactive Control

TransmissionGeneration CustomersDistribution

e - e - e -

Transactive Incentive Signal (TIS): reflects true cost of electricity at any given point

Transactive Feedback Signal (TFS): reflects anticipated consumption in time

z

$

P

Signals forecast several days

“A set of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter.”

– GridWise® Architecture Council Transactive Energy Framework

All business and operational objectives and constraints can be monetized and thereby incorporated in these signals.

Propagation of the incentive and feedback signalsIncentive and feedback signals propagate through an information network (the Transactive Control System) that overlays the electrical network; the signals are modified by Transactive Control Nodes (software agents)

G

G

G

Information Network

PhysicalNetwork

• Respond to system conditions as represented by incoming Transactive Incentive Signals and Transactive Feedback Signals through

– Decisions about behavior of local assets– Incorporation of local asset and other information– Updating both transactive incentive and feedback signals

Role of a Transactive Control Node

Transactive Control Node

Local Asset

Toolkit Function Alogrithm

Local Condition Information

Local AssetSystems

conn conn

Asset Model

Command, control

Statesupdate

conn conn

Basic Design of a Transactive Control Node: Toolkit Function, Asset Model and Local Asset Interface

Inbound TIS signals Modified TIS signals

Inbound TFS signalsModified TFS signals

Transactive Energy Scenario

8:008:45

9:3010:15

11:0011:45

12:3013:15

14:0014:45

15:3016:15

17:0017:45

18:3019:15

20:0020:45

21:30$0.00

$0.10

$0.20

$0.30

0.0

1.0

2.0

3.0

4.0

TIS (cost in $/KWh) TFS (net load in KW) Renewable Supply (KW)

$/KW

h

KW

-2.0

0.0

2.0

Wind generation falls off in morningSun heats up house and solar PV outputSudden solar PV drop-outs due to cloudsA/C load and solar PV fall off in afternoonEvening activity causes second load peakStorm causes temporary outage on gridWind returns and load tails off in late evening

© 2015 IBM CORPORATION

IBM RESEARCH

Ron AmbrosioIBM Distinguished EngineerChief Technology Officer,IBM Smarter Energy Research

Ron Ambrosio/Watson/IBM@IBMUSrfa@us.ibm.com

+1 914-945-3121

IBM T.J. Watson Research CenterP.O. Box 2181101 Kitchawan Rd. / Route 134Yorktown Heights, NY 10598

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