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Robust and Decentralized Operations for
Managing Renewable Generation and
Demand Response in Large-Scale
Distribution Systems
PSERC Industry-University Meeting
December 2-4, 2015
Research Team
Andy Sun, Georgia Tech
Duncan Callaway, UC Berkeley
Industry Advisors
• Tongxin Zheng, ISO-NE
• Hong Chen, PJM
• Jim Price, CAISO
• Masoud Abbaszadeh, GE
Research
• Bahman Darynian, GE
Research
• Santosh S. Veda, GE
Research
• Lei Fan, GE Energy
Management
• Eduard Muljadi, NREL
• Mirrasoul J. Mousavi, ABB
• Harish Suryanarayana,
ABB
• Evangelos Farantatos,
EPRI
• Erik Ela, EPRI
• Jens Boemer, EPRI
• Xing Wang, Alstom Grid
• Ying Xiao, Alstom Grid
• Curtis Roe, ATC
Outline
• Motivation
• Project Description
• Technical Approaches
• Robust optimization for scheduling of demand
response and distributed generators
• Fully decentralized optimal dispatch in distribution
systems
• Potential Applications and Benefits
• Summary
Increasing Renewable Penetration
• Peak demand: 69,621MW (Aug 10, 2015)
• Wind Capacity: ~ 16,000MW
• Wind Generation record: 12,971MW (Nov 25, 2015) ~32%
of load at that time
http://www.ercot.com/content/news/presentations/2015/ERCOT_Quick_Facts_12715.pdf
ERCOT 2015
• ERCOT: 6.6 million advance meters
• 97% ERCOT load in competitive area settled with 15-min interval
• More than 2100 MW in demand response, including
• Load resources (mostly large industrial) ~ 1,390 MW
• Emergency response service (commercial & industrial) 850MW
• Utility load management programs ~220MW
• Demand response provider manages large portfolios of DR
• E.g. Enernoc 24-27GW peak load under management over
14000 sites
• DR resources can be highly uncertain
Demand Response Management
Project Description
• Develop Robust Scheduling Tools for managing
uncertainty in demand response portfolios
• Robust operation of DG and DR in distribution systems
with interface to transmission systems
• Fully decentralized optimal dispatch for active distribution
systems
• Study flexibility and reliability performance of proposed
models in distribution systems
• Develop Simulation platform for large-scale systems
TA 1: Robust Scheduling of DR
• A quick motivation on using robust optimization for
power system management:
• Systematic and practical approach to model uncertainty
in variable resources --- “uncertainty set”
• Dispatch decision is uncertainty-aware and adaptive
• Able to save cost and increase reliability comparing to
deterministic approaches
[BLSZZ, 2013][LS, 2015][TSX, 2014][LSLZ, 2015]
TA 1: Uncertainty Set – A Primer
• Uncertainty set for renewable variation
t hour • This classic uncert set is Static
• We want to develop Dynamic
uncertainty set
TA 1: The Need to Model Correlation
• Modeling temporal and spatial correlation of
renewable resources is crucial for operations
Kennewick
Vansycle
Goodnoe Hill
39 km
146 km
wind
• Spatial and temporal correlation
of 3 wind farms
• Wind and solar production are
also correlated
Source [Xie et. al. 2011]
TA 1: Dynamic Uncertainty Set – A New Proposal
• A dynamic uncertainty set for wind speed:
• Dominates performance of static uncertainty set
Seasonal pattern
Residual
Linear dynamics:
Temporal & Spatial
correlation
Uncertainty in
Estimation with
Budget Constraints
[LS 2015]
TA 1: Demand Response Uncertainty
• A demand response event:
• DR resource ramps up, holds reduction, ramps down
• DR aggregator/scheduler plans for DR events
• Final realization of DR performance can be quite different
• Uncertainty in realization depends on planning decision
• How to model such a correlation?
TA 1: A New Dynamic Uncertainty Set
• We propose to develop a new type of dynamic uncertainty
sets that model this decision dependence:
• Scheduled DR reduction decision:
• Deviation in realized DR reduction:
• Final realized DR reduction:
Total variations controlled
Uncertainty depends
on decision
TA 1: Robust Scheduling of DR Portfolio
• Now imagine a DR portfolio of hundreds of C&I DR
resources
• Managing such a DR portfolio with uncertainty in DR
performance is a challenging task
• No commercial software is available
• We propose the following robust scheduling model
•
• is a set of operational constraints on DR reduction decision
TA 1: Robust Scheduling of DR Portfolio
• Preliminary results
• Type A: Highest profit and highest uncertainty
• Type B: Medium profit and uncertainty
• Type C: Lowest profit and lowest uncertainty
• Type A: most favored in deterministic model
• Types B, C: favored in robust model, balance
btw profitability and operation uncertainty
TA 2: Fully Decentralized Dispatch
• With thousands of distributed generators in the
grid, centralized controlling is challenged
• Can we do decentralized control down to the
device level?
• Yes?...!
TA 2: Fully Decentralized Dispatch
• Literature review:• Parallelization of certain computation steps (matrix factorization) in centralized optimal
power flow (OPF) algorithms [Huang,Ongsakul 94] [Lin, Ness 94] [Oyama et. al. 90]
• Market coordination: dividing high-voltage control area into a few sub-regions, each sub-
region solves a OPF [Kim, Baldick 97] [Baldick et. al. 99][Conejo et. al. 02][Ji, Tong, 15]
• Linearized approximation of OPF and decentralization to sub-systems [Biskas, Bakirtzis 06]
• Convex relaxation formulation of OPF and decentralization to cliques [Jabr 06] [Lavaei, Low
12] [Zhang, Tse 11,12] [Boyd et al. 12][Zhu et. al. 14]
• Mostly on linearized DC OPF, regional coordination,
convexification etc.
• We want to do down to the nodal level decentralized
control
• Full decomposition & AC OPF
TA 2: Fully Decentralized Dispatch
• Centralized AC OPF:
Power Flow
Equations
Nodal power/voltage bounds
Minimize cost or loss
TA 2: Fully Decentralized Dispatch
• Nodal decomposition:
• At each node 𝑖, a set of artificial variables are
introduced:
• 𝑒𝑗𝑖, 𝑓𝑗
𝑖,𝜃𝑗𝑖 are node 𝑖’s estimate of node 𝑗’s variables
i
TA 2: Fully Decentralized Dispatch
• Solve the following problem:
• Augmented Lagrangian:
• ADMM consists of three steps:
TA 2: Fully Decentralized Dispatch
One generator at node 0,
N loads at nodes 1…N
• Convergence to a stationary point
• Linear scaling of computation time vs N
Potential Benefits
• This research will provide the industry partners with a set of new
tools for managing large-scale distribution systems with intrinsic
uncertain and active resources, such as distributed renewable
generation and complicated demand response portfolios.
• The new operational models and solution algorithms are anticipated
to substantially increase the utilization of the DG and DR resources,
therefore, encouraging their further adoption in the distribution
system.
• The proposed models and algorithms will also provide new
computational tools for the solution of fundamental operational
problems in power systems, such as the multi-time scale optimal
power flow problem in distribution systems.
• The proposed methodology is not limited to distribution system, but
can be applicable to other power system analysis functions.
Expected Outcomes
• Robust scheduling tools for managing large DR
portfolios under uncertainty of DR resources.
• Uncertainty modeling techniques for distributed DGs,
DRs, and customer owned resources in the distribution
system.
• A hierarchical and decentralized control scheme for
solving multi-time scale optimal power flow problems in
distribution systems.
• Software platform that implements the proposed
modeling and operation tools with data management
and processing functions.
• A comprehensive evaluation of the proposed methods
and models in a real-world power system.
Potential Applications
• The proposed work can be used by utility companies and demand
response aggregators for managing their operational portfolios and
hedge against significant variations in DR resources and renewable
generations.
• The decentralized control scheme provides a scalable approach for
the distribution system operator to operate a large-scale distribution
network with a significant number of active devices.
• If successful, the proposed models and algorithms can help
distribution system operators to upgrade their operational scheme
to allow much more accurate and robust control of the
heterogeneous devices in the system and to improve the flexibility
and reliability of the entire distribution system.
• The models and algorithms from this project can also be developed
into commercial software packages.
Summary• The distribution system is becoming more complex and active. Distribution
system operators may face a portfolio of an extremely large number of devices
including distributed generators (DG), demand response (DR) resources, storage
devices, and emerging proactive customers with various resources (electric
vehicles, smart appliances, rooftop PVs, TCLs).
• Many of these devices may exhibit stochastic supply or consumption patterns.
• The goal of this project is to develop new operational models and algorithms to
efficiently operate such a large portfolio of controllable but uncertain resources in
an active distribution system with the aim to increase flexibility and reliability of
both distribution and transmission systems.
• The proposed models will provide the industry with computational tools to
manage various types of uncertainties through robust optimization techniques
and a mixture of centralized and decentralized control schemes in order to improve
scalability of the operational algorithms.
• The project will also explore efficient solution methods for incorporating
unbalanced multi-phase power flow models in the proposed scheduling
algorithms in order to accurately model the distribution system.
Work Plan
Number Task Year 1 Year 2
1 Develop robust optimization models and algorithms for the DR portfolio management problem
2 Develop robust operational models including renewable DGs, DRs, and storage
3 Develop decentralized operation model and algorithms for the multiphase AC OPF problem
4 Development of simulation platform with data processing functions
5 Comprehensive evaluation on real-world power systems
6 Project documentation
THANK YOU!
References:
• D. Bertsimas, E. Litvinov, X. A. Sun, J. Zhao, T. Zheng, “Adaptive
robust optimization for the security constrained unit commitment
problem,” IEEE Trans. Power Syst., vol. 28, no. 1, pp. 52-63, 2013.
• A. Lorca, X. A. Sun, “Adaptive robust economic dispatch with
dynamic uncertainty sets for significant wind,” IEEE Trans. Power
Syst., vol. 30, no. 4, pp. 1702-1713, 2015.
• A. Lorca, X. A. Sun, E. Litvinov, T. Zheng, “Multistage robust
optimization for the unit commitment problem,” accepted for
publication at Operations Research, 2015.
• A. Thatte, X. A. Sun, L. Xie, “Robust Optimization Based Economic
Dispatch for Managing System Ramp Requirement”, HICSS 2014.
Multi-period, Multi-phase AC OPF
• min 𝑡=1
𝑇 𝑓𝑡 𝒑𝑡 ∶ 𝒑𝑡 ∈ 𝑃𝑡 , −𝑹𝑡 ≤ 𝒑𝑡 − 𝒑𝑡+1 ≤ 𝑹𝑡 ,∀𝑡 = 1,… , 𝑇
• Time decoupling:
• Lagrangian relaxation of ramping constraints
• Resource decoupling:
• Ramping is resource specific
• Multiphase AC OPF
• 𝑃𝑖𝑠 = 𝑗=1𝑁 𝑡=𝑎
𝑐 𝐺𝑖𝑗𝑠𝑡 𝑒𝑖𝑠𝑒𝑗𝑡 + 𝑓𝑖𝑠𝑓𝑗𝑡 − 𝐵𝑖𝑗𝑠𝑡 𝑒𝑖𝑠𝑓𝑗𝑡 −
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