1. © 2015 IBM Corporation IBM Research Opus: An IBM Research Energy Analytics and Orchestration Platform Quantifying and managing uncertainty in utility business and operations Ron Ambrosio IBM Distinguished Engineer Chief Technology Officer, IBM Smarter Energy Research Chairman Emeritus & Member, U.S. Dept. of Energy GridWise Architecture Council Chairman Emeritus, Smart Grid Interoperability Panel Architecture Committee Ron Ambrosio IBM Distinguished Engineer Chief Technology Officer, IBM Smarter Energy Research Chairman Emeritus & Member, U.S. Dept. of Energy GridWise Architecture Council Chairman Emeritus, Smart Grid Interoperability Panel Architecture Committee

2. Can the data be used to plan, evolve and orchestrate energy systems? Renewables getting economical Renewable energy mandates Time and place of energy use is critical and determines cost Distributed Energy Resources Grid is increasingly instrumented and intelligent More extreme weather; aging assets and workforce Industry trends and propositions 1.“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

3. 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?

4. 4 What is Opus

5. 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 21 st 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

6. 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 Trade Customer Weather Energy $$$ Energy $$$ Correlation

7. Opus can be applied to a broad range of utility system problems Deterministic Model Characterize and model uncertainty Optimize in the face of uncertainty Decision support automation Example Opus Use Cases Real-Time Near Real-Time Non-Real-Time Physical Operations Business Asset Health Assessment Renewable Forecasting Demand Forecasting Energy Balancing Asset Failure Prediction Customer Intelligence Market Optimization Capital Planning Maintenance Planning Outage Repair Scheduling Storage Management Demand Management PMU Analytics Transactive Energy Mgt Outage Mitigation DG Management Fuel Price Forecasting Network Health Assessment Damage Forecasting Connectivity Model Renewable Integration Stochastic Engine Microgrid Management Existing projects Next phase projects Bulk Supply Forecasting Renewable Site Planning Bulk Supply Availability TX Congestion Forecasting 7

8. Outline 8 What are the benefits?

9. Reducing uncertainty can reduce excess energy expense 9 Energy Energy Gap wc Probability density Supply Demand Energy Gap det $Ms of savings Energy Gap opt Savings from not worst-casing uncertainty

10. Stochastic optimization of DER integration and management 10 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

11. Definition of Transactive Control TransmissionGenerationCustomersDistribution 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.

12. Propagation of the incentive and feedback signals Incentive 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)

13. 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

14. Basic Design of a Transactive Control Node: Toolkit Function, Asset Model and Local Asset Interface Inbound TIS signals Modified TIS signals Inbound TFS signals Modified TFS signals

15. Transactive Energy Scenario Wind generation falls off in morningSun heats up house and solar PV output Sudden solar PV drop-outs due to clouds A/C load and solar PV fall off in afternoonEvening activity causes second load peak Storm causes temporary outage on grid Wind returns and load tails off in late evening

16. © 2015 IBM CORPORATION IBM RESEARCH Ron Ambrosio IBM Distinguished Engineer Chief Technology Officer, IBM Smarter Energy Research Ron Ambrosio/Watson/IBM@IBMUS rfa@us.ibm.com +1 914-945-3121 IBM T.J. Watson Research Center P.O. Box 218 1101 Kitchawan Rd. / Route 134 Yorktown Heights, NY 10598 Contact