decision-making framework for the future grid (5.1)...decision-making framework for the future grid...
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Decision-Making Framework
for the Future Grid (5.1)
Santiago Grijalva, Tanguy Hubert
Georgia Tech
PSERC Future Grid Initiative
May 29, 2013
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Context
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• The Future Grid = a billion devices and millions of
decision-makers that will pursue their own objectives
• New goals, more stringent requirements, and
increased uncertainty and complexity
• Residential Energy Users = New Decision Makers
“Enabling Informed Participation by Customers…” • What role will they play in the future grid?
• How will they respond to incentives?
• Value of the new capabilities deployed at the residential
level?
• How to accurately and dynamically model their future
behavior?
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Research objectives
1. To develop a decision-making framework:
An electricity industry model that describes who will be
making decisions, what decisions, when, where and how.
a. Scalable to millions of decision-makers
b. Addresses decision complexity through layered abstractions.
c. Covers multiple spatial and temporal decision scales.
2. To develop specific decision-making mechanisms:
a. scheduling algorithm to allow residential users to optimally
schedule their energy use in a dynamic pricing environment.
b. method to optimally design electricity price signals on retail
markets.
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A Shared Representation: Prosumers
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• Need a shared
representation of the future
grid that improves collective
intelligence for decision
making.
• The prosumer abstraction is
flexible enough to be
adaptable across multiple
view points and scales.
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A Scalable Representation
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Decision-Making Framework
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System level: “set rules” applicable to
prosumer functions and services
Prosumers: “set mechanisms”
Our focus
System
Agent
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Mechanism 1: Residential Prosumers
Prosumer: residential electricity user
Mechanism: optimize energy usage in dynamic pricing environment
Approach
Complex Set of Constraints • Physical
• Comfort Preferences
• Modeling
• Market 7
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Results
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T. Hubert, S. Grijalva, “Modeling for Residential Electricity Optimization in Dynamic Pricing Environments”, IEEE Transactions on Smart Grid, Dec 2012
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Mechanism 2: Utility Prosumers
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Prosumer: utility serving electricity prosumers
Mechanism: optimally design price signals
Master problem
Desired prosumer schedules
Price signals
Prosumer problem
Actual prosumer schedules
Inverse optimization
Schedules for grid assets
heuristics
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Results
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Optimization Problem Running time (4-core machine)
Master Problem (initial) 20.49 s
Price generation algorithm (1 prosumer) Average = 1.94 s
Std = 1.95 s
Master Problem (final balancing) <1s
Pilot simulation: 1,000 distinct residential prosumers
Performance
Error Value
Error on individual
desired schedules
Average = 11.4%
Std = 16.5%
Error on aggregated
schedules (daily)
4.9%
Typical prosumer response
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Potential Uses and Benefits
Prosumer concept & general framework
•
• Enable multidisciplinary teams to collaborate on the
future grid research and foster long-term innovation
Energy scheduling algorithms:
• Simulate and analyze impact of: Smart Appliances, DG,
use of dynamic pricing, V2G, H2G, etc.
• Implement home energy controllers:
“Decision Making = Information + Intelligence”
• Potentials:
• Prescriptive: provide concrete guidance on how to act
• Descriptive: illustrate why to make these changes
• Normative: demonstrate how decisions should be made
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Who Prosumers & gouvernance When Tactical and strategic
What Decisions associated with
energy optimization and
goals definition
Where Locally (prosumers),
Centralized (gouvernance)
How Mechanisms and rules
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Potential Uses and Benefits
Pricing design method:
• Enhance dispatch: new control strategies based on
engineered price signals
• Bridge differences between local and system objectives
• Decompose the optimization problem into distributed
problems coordinated by the master problem
• New business models as electricity providers transition to
the future grid and adapt to its new rules and mechanisms.
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Project Publications to Date
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Hubert, Tanguy, and Santiago Grijalva. "Modeling for Residential
Electricity Optimization in Dynamic Pricing Environments." In Smart
Grid, IEEE Transactions on, vol.3, no.4, pp. 2224-2231, Dec. 2012.
Costley, M., and Santiago Grijalva. "Efficient distributed OPF for
decentralized power system operations and electricity markets." In
Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp.1-
6, 16-20 Jan. 2012.
Hubert, Tanguy, and Santiago Grijalva. "Realizing smart grid
benefits requires energy optimization algorithms at residential
level." In Innovative Smart Grid Technologies (ISGT), 2011 IEEE
PES, pp.1-8, 17-19 Jan. 2011.
Grijalva, Santiago, and M. U. Tariq. "Prosumer-based smart grid
architecture enables a flat, sustainable electricity industry." In
Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES, pp.1-
6, 17-19 Jan. 2011.
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