© 2015 ibm corporation ibm research opus: an ibm research energy analytics and orchestration...
DESCRIPTION
Proposition 7: The energy system is rife with uncertainties Daily NYISO Average Cost/MWh 3 Weather Demand Consumer Behavior Energy Price / Fuel Costs Renewable Production Regulatory Policy Technology Disruption How do we incorporate all sources of uncertainty into a series of informed business and operational decisions?TRANSCRIPT
© 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@[email protected]
+1 914-945-3121
IBM T.J. Watson Research CenterP.O. Box 2181101 Kitchawan Rd. / Route 134Yorktown Heights, NY 10598
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