Automated System Operating Limit Determination with

Remedial Actions at CAISO using Python

Aftab Alam, Senior Member, IEEE, Dede Subakti, Member, IEEE

Abstract— Determining transfer limits into California over

significant bulk electric system transmission corridors is an

integral part of operations planning, outage management and

real-time operations in the event of contingencies or forced

outages at California ISO (CAISO). These time consuming

analyses are complicated by the need to model contingencies

that change based on various Remedial Action Schemes actions

that could occur and change due to system conditions, interface

or path flows, generating-pumping conditions and also change

while stressing paths to determine SOLs. This paper presents

the design and implementation of a comprehensive framework

using Python and GE Positive Sequence Load Flow (PSLF) to

automate System Operating Limits determination based on

thermal and voltage criterion for offline operations planning

and outage studies and for obtaining path SOLs based on system

conditions.

Index Terms-- SOL, Contingency Analysis, RAS, Transfer

Capability, Python, Path, Interface, Limit, SPS, Automation

I. INTRODUCTION

Various comprehensive Remedial Action Schemes (RAS)

or Special Protection Scheme (SPS) have been in place to

increase import capability over WECC Paths [1] such as the

California-Oregeon Interface (COI) by maximizing the usage

of the transmission system and minimizing congestion.

Determining the import capability into California over bulk

electric system transmission corridors such as COI and other

WECC paths is an integral part of studying and approving

daily, short-term and long-term outages. It is also a very

essential part of real-time operations in the event of forced

outages. These path transfer capabilities essentially establish

System Operating Limits (SOL). SOLs for significant WECC

paths based on transient stability and voltage stability

criterion are currently available to the real-time engineers at

CAISO [2] through separate implementations using Dynamic

Security Assessment Tool (DSAT) [3] and Voltage Stability

Assessment (VSA) [4]. However, there is no existing tool to

determine path SOLs for major WECC paths based on

thermal and voltage criterion (steady state pre-contingency

and post-contingency). Depending on the path to be studied,

these studies can be very time consuming and complicated by

the numerous amount of RAS actions that can occur and

change based on system conditions, interface flows,

generating-pumping conditions etc., that warrant contingency

definitions to continuously change [5]. The process of setting

up contingency definition files and then modifying them

based on system conditions have been traditionally performed

manually consuming a significant time of Operations

Engineers. In addition this manual process is prone to errors

due to the human interventions to modify contingency

definitions based on system conditions. In order to overcome

this tremendous task, improve efficiency and reduce errors, a

framework for an automated SOL determination tool was

designed and implemented in-house using Python [6-10] and

PSLF [11]. The integrated Python-PSLF tool has been

designed to determine SOLs based on steady-state thermal

and voltage criteria. This paper will focus on the details of the

implementation and how the tool has been designed to be

adaptable to changing RAS schemes, thresholds, study

parameters and criteria. It is centered on using open-source

Object-Oriented Programming languages like Python to

develop in-house tools to greatly improve the efficiency of

the study processes to determine offline and real-time SOLs

and maximize the usage of the transmission system [12].

RAS or SPS, Path or Interface and SOL or transfer capability

are used interchangeably.

II. OPERATIONAL STUDIES METHODOLOGY – THE

CONVENTIONAL PROCESS

One of the core responsibilities of Operations Engineers at CAISO is to conduct operations planning studies and outage studies. Fig.1 shows the typical steps involved in running studies to determine path SOLs for major WECC paths.

An essential component of these studies is to determine the applicable SOLs of paths:

• In Seasonal Operations Planning Studies.

• When the respective path is affected by one or more planned outages.

• During real-time forced outages or contingencies.

• In accordance with NERC Standard FAC-011-2 [13].

The iterative process of increasing or decreasing transfers and retesting contingencies is a straightforward implementation when the definitions of the contingencies to be tested do not require any modification. This is usually applicable when:

Aftab Alam and Dede Subakti are with Operations

Engineering and Services, California Independent System

Operator, Folsom, CA, 95630 (aalam@caiso.com,

dsubakti@caiso.com)

978-1-4799-3656-4/14/$31.00 ©2014 IEEE

• No RAS or SPS actions are triggered by these contingencies.

• Or the RAS/SPS actions do not change with different levels of transfers or basecase system conditions.

Set up

basecases

Select list of credible

contingencies

Run Post-Transient loadflows

for each contingency

Analyze Results

Limiting

Condition?

INC/DEC

transfers.

Path SOLs

Determined

Y

N

Figure 1. Typical steps of determining path SOLs in operational studies.

However modifying the contingencies manually during each iteration can be cumbersome when these RAS/SPS actions vary during each iteration depending on path flows, basecase system conditions and generating-pumping conditions . In addition, the process of manual modification is prone to errors especially when a SOL is to be determined during forced outages or contingencies in real-time.

Maximizing transfer capabilities and the requirement to manually check and modify contingency definitions, if required, during this iterative process of determining SOLs is the primary motivation behind developing a framework to automate the repetitive processes, increase efficiency and eliminate errors that can occur due to manual modifications. The following section describes in detail the development and implementation of a ‘Path Study Tool’ developed in-house at CAISO to automate these processes and provide SOLs for planned and forced outages.

III. AUTOMATED PATH SOL DETERMINATION –

COMPREHENSIVE FRAMEWORK

This section describes the framework and implementation of the automated path study tool developed to automatically determine SOLs for planned or forced outages. Fig. 2. depicts a high level framework of the automated study process. In order to determine SOLs in outage studies for planned outages or forced outages during real-time system conditions, the tool was designed to be able to accept cases from both sources.

A. Basecases for System Operating Limit Determination

There are various ways in which cases can be modified either based on planned outages or based on real-time system conditions. It is assumed in this paper that basecases for

planned outages studies are either provided by the user or provided through an automated process that builds cases with planned outages and load forecasts. The development of basecases with real-time system conditions data was built as a separate module as shown in Fig. 3. Real-Time cases can also be provided through EMS. However, mapping real-time system to operations planning cases can reduce gaps between operational planning assumptions and problems observed during real-time system conditions.

Basecase with real-time

system conditions

Path Study Tool

Provide Path SOL and Limiting

Contingencies/Limitations

Planned Outages Study

Basecase

Seasonal All-Lines-In

Basecase

Modify with real-time

data

Modify for planned

outage studies

Figure 2. High-Level framework of the Automated Path Study Tool.

Retrieve real-time

system data

Modify seasonal all-lines-in

basecase with real-time

system data

Retrieve real-time

system data

Any changes

to system

conditions?

Modify real-time

case for detected

changes

YN

Figure 3. Real-Time Case Builder Module.

B. Path Study Tool – Study Parameters

The Path Study Tool has been designed to reflect the thinking process involved in determining path SOLs and also automate the repetitive processes. Towards, this goal, the tool has been designed to accept standardized rules from the users in a study parameters file describing:

• Interface Definitions

• How Interface Flows are to be increased or decreased, Source/Sink definitions.

• How swing bus adjustments are to be made in each area in the case if necessary

• List of areas/zones to be monitored for thermal/voltage violations

• List of voltage deviation criterion for single line contingencies and double line contingencies for each area

• List of branches or buses to be ignored for thermal violations or voltage violations respectively

In the current version, these standardized rules are provided by the user using macro-level commands in a text file that is interpreted by the Path Study Tool and converted to EPCL files that can be called upon by PSLF to execute the required actions on the basecase. These rules are provided for each path to be studied. Many advantages have been observed in this setup:

• Intuitive and easy to modify or add rules.

• Conversion of rules to EPCL commands is performed automatically by a python interpreter instead of user having to write EPCL code.

• Adding new rules and the corresponding conversion to EPCL commands is a simplified one-time process that can be implemented in-house anytime a new requirement is observed during studies.

C. Path Study Tool – Contingency Definitions and the RAS

library

A predefined set of credible contingencies to be tested for each path is provided. Depending on the contingency to be tested, the system conditions in the case during the respective iteration and the status of RAS actions, the Path Study Tool determines the full contingency definition using a separately defined RAS Library. The RAS Library has been setup as a python module that determines the applicable RAS actions and adds any required additional actions on to the contingency definition. This process is repeated for all contingencies to be tested for the respective iteration.

Various advantages have been observed in programming the required RAS actions into a python module:

• Any new RAS actions or modifications can be easily implemented in the module by coding the appropriate control action

• Modifying the RAS status and thresholds for arming of a RAS action or triggering of a RAS action can be easily modified. These are currently provided through a user-controlled text file but can easily be provided from EMS or other implementations.

D. Path Study Tool – Study Process

After the contingency definitions and the applicable contingency definitions have been established during each

iteration of the study process, the contingencies are tested using AC loadflows with applicable redispatch for loss of generation or pumps and the results are analyzed for pre-contingency and post-contingency thermal violations. The results are also analyzed for post-contingency voltage deviations. The list of violations that are considered credible depends on the monitored list defined in the study parameters file. The contingencies that lead to violations of criteria are recorded as the list of limiting contingencies that are to be tested for the next iteration.

During the development of the Path Study Tool, it was recognized that a practical implementation of the study process requires logic to distinguish between violations that are significantly impacted by increasing or decreasing path flows and those are more impacted by local generation or load patterns. Therefore, additional logic was included in the Path Study Tool to identify violations that are not significantly impacted by the Path being studied. The effectiveness of the path flows on the pre-contingency or post-contingency flows on the limiting line or transformer is calculated during each iteration of the study process. If this calculated effectiveness is calculated to be less than a predetermined limit, the limiting line or transformer is added to the list of thermal exceptions for the respective contingency. Practical experience in the implementation of the Path Study tool shows that it may not be possible to identify all non-credible violations automatically. However most can be recognized using such a predefined limit and any additional ones can be added to the list of exceptions in the study parameters file.

The iterative process is stopped when all stopping criterion is met. The Path Study Tool has been setup to end all iterations when pre-contingency and post-contingency flows are within 99.5% and 100% of normal and emergency ratings respectively and when all voltage deviations are within 0.5% of the respective limits for single line contingencies and double line contingencies. These are included in the study parameters file so that they can be adjusted as needed.

Fig.4 shows a detailed flow chart of the comprehensive framework developed to implement an automated path study tool.

IV. RESULTS

With regards to SOL determinations for offline procedure and outage studies:

i) The tool has resulted in hours of time savings by reducing the time required to manually modify contingencies, adjust transfers and other repetitive tasks. It thus allows for more time spent on analysis and other related processes.

ii) It also provides a mechanism to update procedure based limits quickly or provide new limits when needed during forced outage conditions or contingencies in real-time.

Due to the confidentiality of such results, a small snapshot of the results determined using real-time system conditions is being provided here in the form of deviation of the determined

Retrieve real-time

system data

Modify seasonal all-

lines-in basecase with

real-time system data

Retrieve real-

time system data

Any changes

to system

conditions?

Modify real-time

case for detected

changesY

N

Basecase with real-time

system conditions

Simulate all

contingencies

Depending on Path to be

Studied, Obtain applicable

list of credible contingencies

Planned Outages Study

Basecase

Seasonal All-Lines-

In Basecase

Modify for planned outage

studies

All

contingency

definitions

processed?

NRAS Library

Analyze Results

INC/DEC Path

Flows

If Path Flows are to be decreased,

Reduce contingency set to those

that cause violations. If Path Flows

are to be increased and new path

flows are greater than starting

path flows, use starting list of

contingencies.

Limiting

Condition

?

Y

N

YPath SOL

Determined

Figure 4. Comprehensive Framework for Automation of System Operating Limit Determination for major WECC Paths

path SOL for a path from an assumed reference. The results

are shown in Fig. 5. It can be seen in the results that the

calculated limit varies continuously depending on system

conditions. Thus, this provides much needed insight and

situational awareness to system operations on the allowable

transfer capabilities across a path in real-time.

I. CONCLUSIONS – EXPERIENCES AND CHALLENGES

It is well known that a path study limit process that

involves variable contingency definitions due to RAS actions

can consume a considerable amount of time in manual and

repetitive tasks. Experience gained from the development of

such a tool has shown that in-house automation of the manual

and repetitive tasks involved in a path study limit process

allows users to complete studies faster and become familiar

with the system more quickly. Due to the amount of time

taken to complete such studies, conservative limits are

Figure 5. Results showing deviation of calculated SOL of a path from an

assumed reference

usually provided during planned outages or forced real-time outages or contingencies. In addition, the variability of generation dispatch introduced by markets and load conditions in a deregulated environment and the amount of time required to obtain SOLs based on different system conditions, have warranted that conservative path SOLs be used. However, using conservative path SOLs can lead to usage of expensive generation and market congestion where markets exist. During forced outages or real-time contingencies, operators usually provide SOLs based on procedures. The procedures are usually based on offline studies that employ conservative assumptions simply because of the time required to complete such studies. These may be updated based on real-time studies. However, determining path limits in real-time can be time consuming. The development of such an automated process helps in eliminating the time spent to manually test the set of credible contingencies, analyze results and determine the limiting contingencies, increase or decrease path flows and eventually identify the SOL based on a set of stopping criterion. In addition it is observed that setting up a process to modify Operations Planning cases with real-time data can help in identifying issues that are observed in real-time, but not in the offline studies simply because of the gap between the assumptions used in offline studies and real-time system conditions. Most professional power system analysis software packages now have the ability to be invoked in batch modes. Object Oriented Programming languages like Python can be utilized to develop automated processes that can invoke these batch modes to combine the power flow solution engines available in professional packages with automation of iterative processes. This not only allows studies to be completed faster but saves the valuable time of engineers which can be spent more on understanding the system and making decisions. In addition, the engineer has full control of the iterative process which can be adjusted and modified accordingly. The automated process also avoids a prolonged use of procedure-based conservative limits during real-time contingencies and forced outages. The development of the automated process also aids in the development of determining real-time path SOLs based on real-time system conditions. Towards this goal, a module has been developed to extract real-time system conditions and provide real-time cases to the Path Study Tool. However processes can also be setup to provide real-time cases from EMS. The designed framework and its in-house implementation has resulted in significant time-savings and demonstrates the many advantages of automating manual and repetitive tasks performed by the user during a path study process that involves variable contingency definitions during each iteration due to varying RAS actions. It also serves as a mechanism of continuous benchmarking to validate other dedicated online tools that maybe used in its place.

ACKNOWLEDGMENT

The author would like to thank Dede Subakti (Director, Operations Engineering and Services, CAISO) for his input on the functional model and Akshay Shivaram (Graduate Student from Arizona State University in EE), who helped in the development of the module to extract real-time data and develop cases based on real-time data, during his internship at CAISO.

REFERENCES

[1] WECC Path Rating Catalog, “http://www.wecc.biz/library/Pages/Path

Rating Catalog 2013.pdf”.

[2] California Independent System Operator, “http://www.caiso.com”.

[3] DSATools, “http://www.dsatools.com”.

[4] Bigwood Systems Real Time Voltage Stability, “http://www.bigwood-

systems.com”.

[5] N. Varghese, L. Jin, S. Ghosh, G. Lin and P. Bunthath, "The CAISO

experience of implementing automated remedial action schemes in

energy management systems," in Proc. 2009 IEEE Power and Energy

Society General Meeting., pp. 1-5, Jul. 2009.

[6] Python Programming Language, “www.python.org”.

[7] J. Condren, A. Seungwon, "Automation of transmission planning

analysis process using Python and GTK+," in Proc. 2006 IEEE Power

Engineering Society General Meeting, pp.8, 2006.

[8] Y. Zhang, S. Rajagopalan, J. Conto, "Practical Voltage Stability

Analysis," in Proc. 2010 IEEE Power and Energy Society General

Meeting, pp.1-7, 25-29 July 2010.

[9] S. Almeida, N. Machado, R. Pestana, "Voltage Collapse: Real Time

and Preventive Analysis in the Portuguese Transmission System," in

Proc. 2007 IEEE Lusanne Power Tech , pp.1817-1822, 1-5 July 2007.

[10] A. Hernandez, P. Eguia, E. Torres, M. A. Rodriguez, "Dynamic

simulation of a SSSC for power flow control during transmission

network contingencies," in Proc. 2011 IEEE Trondheim PowerTech, ,

pp.1-6, 19-23 June 2011.

[11] PSLF, “http://www.geenergyconsulting.com/practice-area/software-

products/pslf”.

[12] T. O. Seppa, "Increasing transmission capacity by real time

monitoring," in Proc. 2002 IEEE Power Engineering Society Winter

Meeting, vol.2, pp.1208-1211, 2002.

[13] NERC Standard FAC-011-2, “System Operating Limits Methodology

for the Operations Horizon. http://www.nerc.com”.