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2016 Smart Grid R&D Program
Peer Review Meeting
(Fast Response to Major Outages in Secondary
Distribution Networks)
(Chen-Ching Liu)
(Washington State University)
(August 17, 2016)
December 2008
Fast Response to Major Outages in Secondary Distribution Networks
Objectives & Outcomes
Life-cycle Funding Summary
($K)
Prior to
FY 16
FY16,
authorized
FY17,
requested
Out-year(s)
150 280 160 160
Technical Scope
(Note: The life-cycle funding table above should include all FY funds received and to be requested, from the project beginning year to the project ending year)
To enhance the resilience of distribution systems with
respect to extreme events, microgrids and distributed
generators can be utilized to serve critical loads
connected at distribution feeders. Service restoration
strategies have been proposed for both radial distribution
systems and secondary networks. The proposed method
has been applied to the Pullman-Washington State
University Distribution System. A field test is planned and
expected by the end of 2016.
• Small distributed generators have limited ability to absorb
shocks and maintain system stability. Generation
resources within microgrids can be limited and hard to
support after an extreme event. Formulation and
algorithm considering dynamic and generation-resource
constraints are proposed. The proposed method has
been evaluated with the PNNL test system and Pullman
distribution system.
• Specific technical issues associated with secondary
network service restoration have been identified. A new
method has been proposed to address these issues.
Transient simulations are performed with PSCAD for
demonstration.2
December 2008
Progress and Results Since Last Review Meeting
• Microgrids for service restoration to critical load in a resilient (radial) distribution system (FY15)
• What has been done: problem formulation, algorithm, case study, application to Pullman system, IEEE journal paper
• Pullman-WSU distribution system field test (FY16)
• What has been done: partners, test plan
• What will be done: implementation, final report
• DG-critical load restoration in a secondary network distribution system (FY16)
• What has been done: technical issues, problem formulation, algorithm
• What will be done: case study, IEEE paper
3
December 2008
Microgrids for Service Restoration to Critical Loadin a Resilient Distribution System
• Resilience: “..ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions..”*
• When a major outage occurs, microgrids can be controlled to provide an efficient service restoration strategy to restore critical loads in a distribution system and improve the resilience.
• Challenges:
• Distributed generators (DGs) in a microgrid have relatively smallcapacity. They have limited ability to absorb shocks and maintain stability
• Scarcity of generation resources: Fuelsfor generators, e.g., diesel and naturalgas, electric energy in storage devicesare limited and hard to support after an extreme event
4
Restoration schemes considering
DERs and Microgrids
Microgrid
* Office of the Press Secretary of the White House, Presidential Policy Directive
21 – Critical Infrastructure Security and Resilience [Online]. Available:
http://www.whitehouse.gov/the-press-office/2013/02/12/presidential-policy-
directive-critical-infrastructure-security-and-resil
FY15
December 2008
Methodology for Critical Load Restorationin a Radial Distribution System
• Problem Formulation
• Objective: maximizing the cumulative service time of microgrids to loads on the distribution feeders weighted by their priority.
• Dynamic, generation-resource, operational, and topological constraints are considered.
• Algorithm
• By introducing the concepts of restoration tree and load group, restoration of critical loads is transformed into a maximum coverage problem, which is a linear integer program (LIP). The restoration paths and actions are determined for critical loads by solving the LIP.
• Case Study
• The proposed method has been tested on the PNNL 1069-node test system.
• Application
• A strategy using WSU generators to restoration critical loads in the Pullman distribution system, i.e., Pullman Regional Hospital and City Hall, is obtained by applying the proposed method.
• GridLAB-D dynamic simulations are performed for PNNL test system and Pullman-WSU distribution system.
5
FY15
* Y. Xu, C. C. Liu, K. P. Schneider, F. K. Tuffner, and D. T. Ton, “Microgrids for Service Restoration to Critical Load in a Resilient
Distribution System,” Accepted for IEEE Trans. Smart Grid.
December 2008
Pullman-WSU Distribution System Field Test
• Purposes:
• Demonstrate the feasibility of using DGs within microgrids to serve critical loads after a major outage.
• Validate simulations conducted with GridLAB-D. Once validated, we can explore other operational strategies via simulation.
• Partners: PNNL, WSU Facilities Services, SEL, Avista, Enercon, PCE, and Cummins. Feasibility study contract signed with SEL as the lead.
• Progress and Timeline:
• A detailed field test plan has been proposed. A report by SEL is to be completed in August. A draft has been submitted on Aug. 3.
• Proposal to WSU and Avista management for approval of actual field test.
• Field test will be performed by end of November, 2016, if approved.
• A final report including the test plan, results, and analysis will be submitted to DOE by end of December, 2016.
6
FY16
December 2008
SEL Performed Detailed Study on Field Test Plan
7
G3
Natural
Gas
G2
Diesel
EBG3 G2
EP-2 EB-11 EB-M
WSU Loads
T-B
4.16/13.2 kV
SPU122
SPU-XFRM_1
13.2/115 kV
SPU121
Y – Δ
Y – Δ
P1411
Feeder
SPU122
Test Sequence
• De-energize feeder SPU122 from 115kV side of transformer SPU-XFRM_1. Open breakers on the path from
WSU generators to SPU substation, including breakers SPU122, P1411, EB-M, and EB-11.
• Close breaker EP-2. Close G3 breaker. Synchronize G2 with G3. Close EB-11. Energize some WSU loads by
closing EB-10, EB-5, EB-8, EB-3, EB-7, EB-2, EB-1, and EB-4, loading the generators to 1.8MW.
• Energize transformer T-B, feeder SPU122, and transformer SPU-XFRM_1 by closing breakers EB-M, P1411,
and SPU122, respectively.
• Disconnect WSU generators, re-energize feeder SPU122 and WSU loads from Avista side.
FY16
December 2008
Outline of Report from SEL
8
• Description of the test system
• Settings that need to be applied before field test• Settings of protective relays, measurement device upgrade and
adjustment, modifications of Enercon’s and PCE’s SCADA systems
• Test procedures: a complete list of operations included
• Risk analysis• Equipment damage prevention, including electrical protective and
monitoring systems analysis, generation protection system analysis,
breaker EB-M, EB-11, and EP-2 protection system analysis, and in-rush
and load pickup current evaluation (see figure below)
FY16
Data extracted from SEL-300G relay protecting WSU generator
December 2008
Result of Feasibility Study
9
• The proposed field test plan has
been validated by SEL and
partners subject to temporary
modification of relay settings
and automatic functions.
• The target date for the field test
is between September 15 and
October 31.
• The proposed field test plan will
require final approval of WSU
administration and Avista.
FY16
December 2008
Critical Load Restoration in Network Secondaries
10
DG2
DG1
DS
CL2 CL1
CL3
Rooftop
Solar Battery
Backup
DG3
Standalone
• In FY16, we are extending our work from radial systems to Secondary Networks
• Secondary Networks are widely used in metropolis downtown and central business districts.
• Our Goal: Improve resilience of secondary networks using service restoration.
• Progress:
• Identify technical issues and formulate critical load restoration problem (completed)
• Design algorithm for optimal or near-optimal solution (on-going)
• Submit an IEEE conference or journal paper by the end of December, 2016.
CL: Critical Load
* ABB Power Systems Inc., Electrical Transmission and Distribution Reference Book, 4th ed., “Chapter 21: Primary and secondary network distribution systems,” pp. 689-718.
FY16
December 2008
Technical Issues
Load Division among DGs– Depend on locations and control schemes of DGs, as well as
impedances of secondary mains, transformers, and filters
– Ideally, DG ’s share of power is proportional to its capacity
Voltage Regulation– Maintain secondary voltage by DG control
– Avoid inverse power flow through network protector
Cycling of Network Protectors– Undesirable tripping and closing of a network protector,
referred to as cycling of the protector, may occur when there are DGs on the primary feeder and secondary network
Synchronization– Network protector does NOT measure frequency
In-Rush– Transients can occur when line sections, secondary mains, and
transformers are energized
– In-rush may lead to violation of DG capacity limits, deviation of system voltage/frequency, and tripping of protective devices 11
DG1
DG2
FY16
December 2008
Problem Formulation
Objective: maxσ𝑖 𝑐𝑖𝑡𝑖– Maximize cumulative service time to critical loads weighted by their
priority
– Load division among DGs will affect the value of objective function
Constraints– Issues related to network protectors (NPs): operation of NPs,
synchronization
– Dynamic constraints: stability, transient voltages and currents (including in-rush), steady-state and transient frequency
– Limited generation resources: amount of diesel/natural gas, state-of-charge (SOC) of batteries
– Operational constraints: unbalanced three-phase power flow, limits on steady-state voltages and currents, and limits on capacity
12
FY16
December 2008
Algorithm: DG-Critical Load Restoration Strategy
• Assumptions
• A central control system is available for switching operations, DG control, and optimization
• DGs are connected at the primary feeders and secondary network, which can be used to serve critical loads at secondary network
• NPs do not have the ability to synchronize two dynamic systems
• Procedure to determine service restoration strategies
• Step 1: Only one primary feeder with DGs can be used for service restoration. The one with maximum total capacity (kW) of DGs will be selected.
• Step 2: Optimal combination of DGs and critical loads in secondary network can be determined by solving a Linear Integer Program (LIP), where only DG capacity and generation resources constraints are considered.
• Step 3: Determine a sequence in which selected DGs and critical loads are connected to the network. Simulations of the dynamic and operational constrains are performed. Rank DGs by its capacity and critical loads by its priority. Greedy search is used to determine the sequence for DGs to pick up critical loads.
• If a feasible sequence is found to connect all selected DGs and critical loads to the network, solution is found.
• Otherwise, remove the critical load that cannot be restored and go to step 2.
13
FY16
December 2008
An Example of Service Restoration toCritical Loads in a Secondary Network
14
DG4 will start serving CL1 at
the moment when utility
power is unavailable
DG1 is used to pick up CL2
– Close CB1, NP1, and SW1
– Evaluate the effect of in-rush
DG3 is used to restore CL3
– Close CB3 and SW2
– Load evenly divided between
DG1 and DG3
Connect CL1 to the network
and reallocate load among
DGs
– Close CB6
– Shift 100 kW load from DG4 to
DG1 and DG3
Critical load restoration is
completed
DG1
DG2
DG3
CL1, 200 kW
Backup Gen
200kW, 5-h fuel
CB1
CB2
CB3
CB6
NP2 NP1
Feeder 2 Feeder 1
CL2
200 kW
SW1
SW2
CL3
100 kW
DG4
150kW, 8-h fuel
250kW, 10-h fuel
250kW,
10-h fuelSecondary Mains
CB4CB5
100
200
0
200
Outage
Duration:
10 h
1
2
36
4
5
FY16
December 2008
An Example with Transient Simulations
15
DG1:
installed at
primary
feeder
DG2: installed at
secondary network
Restore Critical Load 1
1 DG1 energizes primary feeder and network
transformers. Key issue: in-rush current.
2 2
Network Protectors close.
Secondary Network
energized by DG1.
2 2
5 Restore Critical
Load 2
6 Restore Critical
Load 3
3
4
4 Connect DG2 to
secondary network. Key
issue: synchronization.
DGs are modeled as synchronous generators. AWoodward diesel governor model (DEGOV1) is used to maintain frequency. Asimplified exciter system model (SEXS) is used to regulate the generator’s terminal voltage.
FY16
December 2008
Transient Simulation Results
• In-rush Current when energize the primary feeder and network transformers.
16
• Synchronization between DG2 and the secondary network.
—— DG2 ——DG1
Adjust rotor speed of DG2
Connect DG2 to
the network
Rotor Speed (p.u.) Voltages at the terminals of
Breaker CB2, Phase A (kV)
Connect DG2 to
the network
Current of DG1 (kA)
FY16
December 2008
Collaborations and Technology Transfer
• Collaborations
• PNNL: WSU has been working with PNNL on distribution system service restoration using microgrids and distributed generators for three years. GridLAB-D (with unbalanced three-phase power flow and dynamic simulation capabilities), developed by PNNL, is a powerful tool supporting our research. Several joint papers are published.
• Avista: Avista has been supporting our research by providing model and data of Pullman distribution system and in-kind service for field test.
• Field Test Partners: WSU Facilities, SEL, Avista, Enercon, PCE, Cummins
• Software Development
• Spanning Tree Algorithm for Distribution System Restoration has been integrated into GridLAB-D Version 3.2
• MATLAB software developed for Critical Load Restoration with Microgrids
• MATLAB software developed for Optimal Placement of Remote-Controlled Switches in a distribution system
• MATLAB software developed for Distribution System Reliability Assessment Considering Service Restoration
• Field Test Experience
• A detailed field test plan using WSU generators to energize a Avista feeder and substation transformers has been developed. 17
December 2008
Publications
• Publications after last review meeting:
• Y. Xu, C. C. Liu, K. P. Schneider, F. K. Tuffner, and D. T. Ton, “Microgrids for Service Restoration to Critical Load in a Resilient Distribution System,” Accepted for IEEE Trans. Smart Grid.
• Y. Xu, C. C. Liu, K. P. Schneider, D. T. Ton, “Placement of Remote-Controlled Switches to Enhance Distribution System Restoration Capability,” IEEE Trans. Power Systems, March 2016.
• Y. Xu, C. C. Liu, K. P. Schneider, and D. T. Ton, “Toward a Resilient Distribution Systems,” IEEE PES General Meeting, July 2015.
• Y. Xu, C. C. Liu, H. Gao, “Reliability Analysis of Distribution Systems Considering Service Restoration,” IEEE PES ISGT, Feb. 2015.
• F. D’Agostino, F. Silvestro, Y. Xu, C. C. Liu, K. P. Schneider, and D. T. Ton, “Reliability Assessment of Distribution Systems Incorporating Feeder Restoration Actions,” Power Systems Computation Conference (PSCC), June 2016.
• K. P. Schneider, F. K. Tuffner, M. A. Elizondo, C. C. Liu, Y. Xu, and D. T. Ton, “Evaluating the Feasibility to Use Microgrids as a Resiliency Resources,” Accepted for IEEE Trans. Smart Grid.
• Publications before last review meeting:
• J. Li, X. Y. Ma, C. C. Liu, K. P. Schneider, “Distribution System Restoration with Microgrid Using Spanning Tree Search, “ IEEE Trans. Power Systems, Nov. 2014, pp. 3021-3029.
18
December 2008
Lessons Learned
19
• What Worked Well
• By working with PNNL closely, we are able to simulate switching actions for Pullman-
WSU distribution system using GridLAB-D.
• Performing a field test is much more complicated than performing simulations. Many
practical issues need to be considered, i.e., settings of protective relays, adjustment
of control and management systems, etc. By working with industrial partners, we
have come up with a practical field test plan, which cannot be achieved by
simulations only.
• What Could be Improved
• Avista installed a 1-MW battery in Pullman (Turner 116/117 Feeder) in April, 2015. In
our study of the Pullman-WSU case, the battery has not been taken into account.
• Transient simulations with PSCAD/EMTDC® only serves as a demonstration.
However, it is time-consuming and impractical to use PSCAD for simulation of a
large network. GridLAB-D, which has dynamic simulation capability, will be used for
case study and work with optimization algorithm.
• In our study, communication failures have not been considered.
December 2008
New: Interfacing with DMS in Testbed
TCP/IP
e-terradistributionTM
GE Grid Solutions
Interface Module
DMS
Interface
CSV
Files
System Topologyand DPF Results
Research Applications
(e.g., Spanning Tree)
DMS
Smart City Testbed ... ...
... ...
Data Acquisition and Restoration Actions
e-terra
browser
20
December 2008
Pullman DMS Model
*Pullman data and system model provided by AvistaGE eterradistribution model
1 MW Battery
(Avista)
72 kW Solar Array (WSU)
• Pullman Turner 117 Feeder
21
December 2008
Spanning Tree Restoration Actions
IDs of devices in DMS:
• TUR117_395-2425532_68
• TUR117_395-2425534_69
• TUR117_395-2425536_70
22
December 2008
Plan for FY17
• Resiliency in a complex distribution system environment with microgrids, renewables, and storage
• Method to handle optimal utilization of energy in an extreme condition
• Reconfiguration for service restoration of distribution systems with non-radial topology and multiple sources
• Steady-state and dynamic performance (GridLAB-D simulation)
• Inverters as control devices in a complex system condition
• Development of use cases through industry collaboration
• Validating the proposed method with the WSU DMS testbed with the full model of Pullman distribution feeders
23
December 2008
Contact Information
24
Chen-Ching Liu, Ph.D.
Boeing Distinguished Professor of Electrical Engineering
Washington State University
School of Electrical Engineering and Computer Science
PO Box 642752
Pullman, WA 99164 USA
Tel: 509-335-1150
liu@eecs.wsu.edu
December 2008
Back-up Slides
25
The back-up slides contain details of our work for FY15
December 2008
Problem Formulation:Critical Load Restoration in a Radial Distribution System
• Objective: Maximize cumulative service time to critical loads weighted by their priority
• Constraints:
• Dynamic constraints• Stability and limits on steady-state frequency• Limits on transient frequency• Limits on terminal voltages and currents of DGs
• Generation-resource constraints• Limits on the amount of energy a microgrid can provide to
external critical loads• Operational Constraints• Unbalanced three-phase power flow• Limits on steady-state bus voltages and line currents• Limits on steady-state output power of DGs
• Topological Constraints• Maintain a radial network structure
26
FY15
December 2008
Algorithm to Determine Restoration Strategy
• A four-step graph-theory-based heuristic is proposed:
• Step 1: Feasible restoration paths microgrids to critical loads are identified
• Step 2: Load groups are formed. A load group is a subset of load zones that can be restored as a group by a microgrid through their restoration paths
• Step 3: Formulate and solve a maximum coverage problem
• Step 4: Determine restorative actions
27
Identify Feasible
Restoration Paths
Form Load Groups
Solving a Maximum Coverage Problem
Z1 Z2 Z3 Z4 Z5 Z6
Z8Z7
S2 S3 S4 S5 S6
S10S9
S7 S8
DG
M
S1
S13
Z9
Source 1
Restoration paths
starting from Source 1
Z1 Z2 Z3 Z4 Z5 Z6
Z8Z7
S2 S3 S4 S5 S6
S10S9
S7 S8
DG
M
S1
S13
Z9
Source Load Zones
1 Z3, Z2, Z1 (CL1)
1 Z3, Z7 (CL2)
2 Z8, Z9 (CL3)
Examples of load groups
Restoration
Strategy
CL1
CL2 CL3
CL1
CL2 CL3
FY15
December 2008
Case Study: the PNNL Test System
28
• Utility power unavailable
• 7 faults
• 4 microgrids
• 5 critical loads
• Restoration path identified (green paths)
• Switching operations determined
FY15
December 2008
Case Study: the PNNL Test System (Cont’d)
29
Microgrid 1 restores
critical loads CL1,
CL2, and CL3 in five
switching operations.
Microgrid 3 restores
critical loads CL4 in one
switching operation.
• Transient Frequency of Microgrids 1 and 3
FY15
December 2008
Application: Using WSU Generators to Serve Critical Loads in the Pullman Distribution System
• A diesel and two natural-gas generators on WSU campus are used to serve the Pullman Regional Hospital and Pullman City Hall.
30
Generation Resources• Natural Gas: Pipeline from British Columbia, Canada,
with a back up• Diesel: 250,000-gallon fuel tank; 10,000 gallons per
delivery, 8 deliveries per year• Worst Case: Two pipelines are damaged, a full tank of
diesel can serve WSU critical loads for 5-7 days.
Frequency
DG Terminal Voltages
FY15
December 2008
A Computational Tool for Reliability Assessment Considering Service Restoration
• Evaluate impact of service restoration strategies and distribution automation
• Determine optimal switching sequence to minimize target reliability index
31
Distribution System
Restoration (DSR)
Program
Reliability
Analysis
Program
Switching
Operations
Distribution
System
Information
Set Target
IndexSwitching
Sequence
Values of
Reliability Indices
Reliability Indices considered in the program include:
• SAIDI, SAIFI, CAIDI, ASAI, ASIFI, and ASIDI
FY15
December 2008
Reliability Assessment Considering Service Restoration: the Proposed Methodology
32
• A set of scenarios
• States
• Transition arches
• Levels
• Optimal path
• SAIDI per event
• Calculate Indices
POST FAULT STATE
FINAL RESTORATION STATE
Find optimal
switching sequence
Identify shortest
path
The contribution to reliability indices are
calculated once the shortest path is identified.
FY15
Recommended