Risk Identification in Power Generation Projects Due to Trip-Down Instances in Pre-Work Phase
This research analyses the modes by which multiple risks could impact the performance of projects executed by the
client company - a leading power generation company in India. It probes the case of load isolation experienced by one
of the client's power generation units. It traces the instances to similar phenomenon observed in past research and
analyses a wider spectrum of factors influencing the load mismatch of such a project. It also elaborates the multi-
layered impact of factors including forecast of business decisions likely to be taken in the course of execution, as
being
done for IT projects. It generates possible scenarios that could be traced to the likely risk factors, based on analysis
through established research models for project risk assessment. Based on this, it indicates the directions the client
must investigate to construct a comprehensive risk framework for such projects.
Keywords
Power generation failures, Multi-layered risks, business risk mitigation, risk assessment models.
Introduction
The client company Tata Power Limited has a recently emerging concern in the operation of its Jamshedpur division.
The frequent occurrence of load isolation of sale and non-sale bus islands due to system tripping is having an impact
on its group operations, as it is a captive source for the Tata Steel unit and is also impacting its sale revenue vide
export to the Damodar Valley Corporation (DVC) grid. This division is having total power generation capacity of 667.5 MW
(Sale Island generating 240 MW and a Non-sale island with an estimated load of 530-560 MW)
Sale Island (bus) load is categorized as follows:
- Export to Damodar Valley Corporation (DVC) grid 50-60 MW
- Auxiliary power of 20 MW
- Tata Steel consumption of 140 MW (with 60 MW load pulsating type).
Non-Sale Island (Bus) load is categorized as follows:
- Four units of Jojebara (Units 1,2,4& 5) - Load of 370-380 MW
- Tata Steel captive power requirement of 100-110 MW.
- DVC import of 50-60 MW
Sale and Non-Sale Bus Isolation logic has been provided in the system to maintain Tata
- Steel - Tata Power system frequency when the lines from Tata Steel to DVC trip
For captive power generation plants like the Jamshedpur plant, such load isolation impacts the operations of the
recipient industry. Captive power generation is the functional backbone of heavy industry units like Iron & Steel
sector,
chemicals and refineries, having a demand of more than 1 MW. It accounts for 19509 MW of installed power
generation capacity in India (11.67% of total capacity, as per Central Electricity Authority, 2013)
Both power generation units and their customers, especially large organizations are increasingly becoming aware of
the sensitivity of power failures and load isolation. Industries worldwide have begun to analyze the impact of
intermittent power failure on their operations and facility, and capturing these risk scenarios in their Business
Continuity
Management strategies, as pointed by Bruch (2014).
The client company has identified a probable area where the root cause for the tripping could be lying, based on
controlled experimentation in its grid, by installing a cut-off system in the logic. Its observations point that
frequency
variation of operating load is observed in instances of load isolation, indicating that there is a load-to-generated
power mismatch in those instances.
The consultant group is able to deduce that beyond the technical design risks, strategic planning and decision risks
Risk Identification in Power Generation Projects Due to
Trip-Down Instances in Pre-Work Phase
could also be involved in the performance of a system. In this case, the anticipation of future load increase in sale to
the DVC grid and the housing expansion in Jojebara, in the planning phase, also has implications to the robustness of
the system in responding to the load requirement. An analysis by this consultant group on the symptoms shows that this
is
not an isolated symptom –research by Barkans and Zalostiba (2007) identified a similar concern in generation units as
a factor for black-outs. This is also echoed in the findings of Chandrakar et al (2012), which highlighted that impact
of gradual increase in load demands for generation units in the load isolation phenomenon (islanding).
Context
The performance of a power generation unit is quite sensitive to the robustness of variation assessment done during the
design phase (Om Prakash et al, 2011). For the design framework, this marks the importance of a pre-assessment of
possible tripping modes that could occur. This would assist in predicting the failure instances. An example highlighting
the importance of this concern would be that if this behavior is not predicted accurately, the system will have
unplanned
load isolation. When the sale bus is isolated, the immediate impact by load shedding is a cut of 50 MW which is part of
the Export quota to the Damodar Valley Corporation (DVC) grid and a cut of 60 MW pulsating load due for the Tata
Steel plant. When the non-sale bus is isolated, there is an immediate drop in the DVC import of 50-60 MW and Town
load shedding of 40-50 MW, due to the load-cut of Jojobera units. As an aftermath, when one or more units get
tripped, it hits a significant portion of the revenue generation of Tata Power business and its supply to the captive
Tata
Steel units.
The implications of failure risk identification in projects are not only limited to Tata Power application alone.
Jaskowski
and Biruk (2011) have highlighted that on a general framework, all capital projects like construction projects have
multiple risk factors which can affect the execution or cost of the project. The research conducted by them also
presented a framework to assess the risks and simulate the impact on performance in terms of instances of failure.
Recent trends
Project owners across sectors have identified the need to identify risks in the pre-work phase, so as to plan for
mitigation
against possible fallout. A lot of progress in risk assessment is observed in the software industry – Kop et al (2011)
proposed a multi-pronged risk assessment model which was used to assess Technical, Executive and decision-making
risks in an ERP implementation project. Brandas et al (2012) took this a step further by developing a general framework
for IT development projects with multiple methodologies to analyze risk for a project. This has had successful
improvement in new software projects from 2011, especially in SQL (Structured Query Language) application
developers and Industrial Automation Systems, as identified by Biffl et al, 2011. Such a holistic risk assessment in the
planning phase of these development projects has shown a consistent improvement in success rate of project
execution.
Approach to problem / problem definition
It can be deducted from the symptomatic observations of the issue that the problem lies in the prediction of factors
that
could affect the future load demand for the project like strategic business decisions, expanding township loads, etc.,
in
the design phase. This has helped narrow down the following models to approach the problem.
Cascaded approach to collective risk contribution
Research by Barkans and Zalostiba (2007) identified large over-loads of system cross-sections & tripping of lines,
voltage avalanches, ground faults due to sagging of wires and frequency avalanches as the major causes of power
system failure. Isolation logics were thus introduced into the system as a short term restoration against unloading of
cross-sections. They proposed that an ideal self-restoration mechanism should facilitate auto-synchronization along
with automatic re-closing of tripped lines. One of the viable approaches to construct a risk assessment map would be
to take a cascaded model including the above stated multiple factors.
System behavior model
The lag in generator responsiveness to the change in load (as a shortcoming of the system design) was identified as
another key cause for such system tripping by Om Prakash et al (2010). A model including multi-layered risk factors
i.e., both at the design level and at the operating level, as proposed by Sanchez and Rios (2011) would prove to be
effective.
Stochastic framework to identify combined impact
This model will proceed to identify the likely factors at play which might result in tripping of the system and load
isolation. Such a framework could assess the combined impact of these multiple factors towards possible system failure
through a stochastic model, similar to the one used by Adegun et al (2011). This would allow the organizations to
identify and mitigate likely system failures during the design phase of such projects.
Utility distribution model
Research by Paul (2012) indicates that an urban planning model should accommodate for uniform distribution of
services. Load sharing of utility services should follow equity load-shedding in the face of short-fall and system
design
must be modeled to ensure this.
Multi-layered business risk model
Business risks are to be factored in the planning phase including multiple layers like execution, strategic decision-
making risks into the performance of a project (Kop et al, 2011). This is quite relevant for capital projects also as in
this
case, considering that the impact of business decisions of catering to expanding customer segments must be
considered in the pre-plan phase of a project.
Contract outsourcing model for mitigation of business risk
An established method of project risk mitigation in IT projects, especially in public service providers is by segmental
outsourcing. Research by Cox et al (2011) on IT outsourcing in public services by the local governments in UK explored
2 different approaches to outsourcing of contracts namely a sector wise division of utility provision and a scope-wise
outsourcing approach for de-risking project load.
Considering the relevance of the above stated models in approaching the problem with respect to the application at
hand, the consultant group opines that the following approaches are to be pursued further in addressing this
problem:
- Hybrid approach:Combination of the Multi-layered business risk model with the equity distribution model
for utilities.
- Client - Satellite agency model:Business risk mitigation model based on the Client - outsourced contract
agency
model.
The above 2 approaches will be probed in detail in relevance to the specific case faced by the client, for deducing the
methodology to construct a risk framework for this application.
Literature review
The Hybrid approach (Approach 1) offers the advantage of planning for multiple future decisions that would influence the
system
from meeting demand while also identifying a structured mitigation process for a service shortfall. Academic research on
the
avalanche impact of multiple factors leading to execution issues in a project indicates the different combinations of
factors could
result in varying fallouts than that would result in isolation. The model developed by Adegun et al (2011) to analyze
industrial
accidents in oil and gas industries, due to combinations worker-related factors and their different impacts, offers a
perspective into the
system behavior that could be forecasted during the planning phase. The multi-layered approach proposed by Kop et al
(2011) is
significant considering that business opportunities are continuously expanding and there is a perpetual expansion of
scope for such
utility projects. Hence the impact of future strategic decisions also needs to be factored in risk mapping for such a
project.
The second part of the Hybrid approach explores the mitigation decision for a service shortfall. Utility disparity in
various areas of a locality could cause a negative perception of the service provider among consumers as highlighted
by Paul (2012). Research on public utility distribution models, considering growth in consumption and rise in
population, by Yao et al (2012) points that a shortage factor for utility load is to be considered in the model. In this
research work, they analyzed the water shortage in the public utility network of Beijing and proposed that in face of
shortage, there is a need for industrial structure adjustment to balance the load on the distribution system,
considering
widespread disruption of consumers' daily life, if there is a shortfall in essential services.?iegis and Štreimikien????
(2005)
predicted on similar lines that energy distribution patterns would be a key factor in sustainable national development
indicators.
Taking a clue from the work of Kipyegen et al (2012), where the risk scenarios for a software development project
were
identified on different layers in the plan phase, the client can envision similar risk scenarios during the plan
phase of
such power projects by the Hybrid approach like the impact of future power sale deals during execution or expansion
on the system success rate, unit islanding during service distribution on account of shortfall, etc.
The second approach namely the Client- Satellite agency model gives the advantage of mapping the risks of each
division into mapping the risk of the entire system. Going beyond outsourcing of segmental contracts in utility
services
as a method of de-risking, Cox et al (2011) proposed sector wise division of utility provision in which the scope
would
be within the core competency i.e. Utility quota sharing for different wards amongst individual units to be factored
in
planning phase and a scope-wise approach for de-risking namely the main unit retaining the core competency and
distributing services that provide competitive advantage or efficiency improvement to satellite units. Wan et al
(2010)
proposed that such a demarcation of scope between the main entity and satellite entities (similar to a Client-
Contract
agency model) in software projects would require such inter-unit transfer risks also to be captured in the risk
framework
of the whole project.
Parallel deductions from the above mentioned case of risk assessment in Public utility services in the London local
government body (Cox et al, 2011) to the context of the client's problem can be made. It can be inferred that taking
a
Client-Satellite agency model for risk mapping would identify scenarios like likelihood of division-wise performance
against rising demand and the extent of meeting demand for the Sales and Non-sale buses, when the load is
demarcated to the respective divisions.
The secondary research indicates that the context of the underlying problem would require an investigation guided by
a
combination of the above stated frameworks is required for identifying the potential risks for a project. This would
enable the client organization to construct a Risk framework that would be applicable for risk mitigation in the
pre-work
phase for similar projects.
Discussion
The consultant group has analyzed the issue faced by the client namely the load isolation of its sale and non-sale units
during tripping instances of its division. The inference of the client based on symptoms of the occurrences namely
frequency mismatches point towards a load-to-generated power mismatch. Secondary research by the consultant
group indicated that load isolation of generation units could be due to a gradual rise in load demand for the unit
(Chandrakar et al, 2011) and that it is not an isolated phenomenon of the client company. Jaskowski and Biruk (2011)
recognized the impact of such performance risks of capital projects and proposed a framework for assessing the
impact of these risks in the planning phase of construction projects.
This consultant group has analyzed the problem using a broader approach based on the research of Kop et al (2011)
on IT projects, to follow a multi-layered approach considering the impact of strategic risks like potential business
decisions during the execution of the project which could cause scope expansion, along with technical risks. This is
done in conjunction with the Utility distribution model (Paul, 2012 and Yao et al, 2012) which traces impact on load
shedding for utility distribution in the face of load shortfall. Another approach identified by the consultant group
is a
Client-satellite agency framework used by Cox et al, 2011 for business risk mitigation in Public utility services
for the
London local body government. An investigation using the above approaches highlight that the load mismatches
(performance failures) could be caused by multiple risks which have to be captured in the pre-work phase of such a
project.
Managerial implications
The consultant group opines that further investigation by the client, in the direction indicated above would
indicate the
multiple levels of risk that would impact the performance of the project in course of its execution. This will show
the
client multiple areas of impact of business decision driven risks on the project like scope expansion for saleable
load to
DVC, load to the Jojebara units. Thus the client would be able to trace and map such risks for projects and
ultimately
control them as was demonstrated by Biffl et al (2012) in Industrial Automation Systems projects and Kop et al
(2011) in
an ERP implementation project.
On the other hand, an investigation of the project progress in the perspective of the Client-Satellite agency model
would highlight sub-division wise risks and their implications on the whole system in terms of load sharing and
identify
areas of control. This would help the client to isolate the risks that need to be mitigated from each sub-division
of the
project, similar to the approach used by Cox et al (2011).
Research limitations/scope for future research
The consultant group would like to acknowledge that only 2 approaches which were identified closest to the client's
project model. Also, the scope of this consultancy project is only the definition of the problem faced by the
client.
Hence the required solution for the client, namely the risk mapping framework, (for use in planning of such power
projects) will have to be traced by the client by further investigation using one of the above suggested models.
Hence
further research must be focused on choosing the most suited approach and investigating the risks that would have
implications on the performance of the project. This would help the organization construct a generic risk framework
for
similar projects.
Acknowledgements
The project was made under the group of students; Aayush Goyal, Deepak Regunath, Hitesh Gomber, Maneesha
Akshay, Rahul Sarda, SP Jain Institute of Management and Research, Mumbai, India who would like to acknowledge
the valuable insights from Dr. Arup Mazumdar, Faculty in Charge, Business Consulting Projects Group,
SPJIMR,.
They would also like to thank Ms. Vibha Bhilawadikar, Vice-President, Kiesqaure Consulting, who mentored the
group in this project.
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Authored by
Ms. Vibha Bhilawadikar,
Kiesqaure Consulting
vbhilawadikar@yahoo.co.in
Tags: MET Institute of Management