lifecycle optimization for power plants - abb group

Lifecycle Optimization for Power Plants

Marc Antoine, Luis Ruiz-Escribano

ABB Switzerland Ltd, Utility Automation, Baden, CH

Abstract

Due to the deregulation in energy markets there is an increased need for asset

management tools. The essence of asset management is having the decision-support

for finding an optimum between financial performance, operational performance, and

risk exposure. Decisions must be made about operating and maintaining infrastructure

assets. Hence, the primary goal of an asset management tool is informed decision-

making, rather than the pure focus on technical performance.

A central component in achieving these requirements is the calculation of production

costs, taking into account plant depreciation and degradation resulting from this

production. This paper describes a decision support system that is based on the

optimization of an objective function that includes terms for revenues from energy

sales, production costs and plant ageing. Plant ageing is based on lifecycle models

that are directly load dependent. The optimization results in a trade-off between

maximisation of immediate profits and minimisation of lifetime consumption. The

earnings can be achieved by selling energy, or even fuel or emission credits (CO2,

SO2, NOx).

The Lifecycle Optimizer, presented in this paper, addresses the influence of a given

operating mode on the ageing process of the power plant and includes this in the

economic optimization. A Model Predictive Control (MPC) and Mixed Logical

Dynamic (MLD) approach are used to solve the posed optimization problem.

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1 Asset management for power plants

As a consequence of the deregulation in the power industry, utility business units have

been transformed from cost centers into profit centers. Whereas they previously had a

budget and carried out projects, they are now charged with contributing to growth in

corporate earnings. Whereas the justification of their budget was mainly based on

engineering criteria related to Operation and Maintenance (O&M), they have become

increasingly more focused on return-on-investment.

The primary task of asset management is to reduce negative surprises by identifying

performance problems, improving predictive maintenance, extending asset lifecycles,

and most of all, developing solid business plans for investments.

Today the utility business faces the challenge of aligning the management of their

assets with corporate objectives. This requires engineering and economic tools as well

as value-based decision-support. The strategic plan of a company defines the high-

level goals and based on this, the business units setup their operational plans to

achieve their targets. The asset manager sits between these functions and must

therefore have comprehensive tools for decision-making about assets.

The key component in asset management is lifecycle costing, which implies cost

minimization starting with the initial investment, continuing through O&M, and

ending with recycling or phase-out. This approach requires asset plans to be linked to

financial plans. In order to achieve this, the asset manager shall be able to carry out

the following tasks:

• Monitoring the condition and performance of each asset.

• Having the key data of all assets available in real-time across the enterprise.

• Calculating asset lifecycle costs and the impact of asset failure.

• Linking trading decisions to O&M decisions.

This is only possible through informed decision-making. The asset manager shall not

only receive data about specific assets, but shall also be able to translate that data into

knowledge.


1.1 The stakeholders

For complex asset infrastructures such as power plants, the responsibilities can be

divided among the following two key persons:

The asset owner represents corporate strategy, defines the asset costs and risks the

company is willing to accept, and sets the level of expected performance. The asset

owner is also responsible for raising and managing capital.

The asset manager focuses on decision-making and optimizes asset values in line

with corporate objectives. His core competencies are performance analysis, financial

analysis, risk management and economic decisions. The asset manager sets the budget

and the targets for the trader and the O&M manager in the plant.

2 Asset management systems

Information management systems are the backbone of asset management. Such

systems not only handle data archiving and retrieval but need to offer data analysis

tools as well, ranging from condition monitoring, maintenance management, purchase

and inventory control, predictive modeling, and decision-making including risk-

assessment software.

Today, the information retrieved through the automation system from the plant floor

is usually of technical nature. Knowledge of the influence of some technical facts on

the plant floor on the commercial success of the plant lies with the experienced plant

manager. If a plant is run on condition based maintenance, where outages are not

planned in regular intervals, but depending on the state of the plant, the success of this

strategy is highly dependent on the quality of the plant state assessment. But even

with a very precise plant state assessment there is room for optimization in this

approach. Information on how plant operation influences the plant state is needed.

Only with this information, the plant manager has the full picture of the results of his

decisions. If we provide a tool which not only analyses the current plant state, but also

informs the operator about the influence of his actions on the future plant state, a

business decision can be taken on whether to run the plant aggressively and to initiate

maintenance earlier, or to run the plant more smoothly and extend the maintenance

interval.


Planning &Scheduling

Plant Engineering

Maintenance

Operations

Information Centric

Business Systems

Asset Optimization

Field Devices

Design

ProcessEngineerin

g

Figure 1: Seamless integration of plant automation, asset optimization, and collaborative business processes in real-time using IndustrialIT.

IndustrialIT is an architecture which incorporates the features described above:

seamless linking of multiple applications and systems in real-time. This could include

e.g. process automation, asset optimization and collaborative business processes. It

includes functionality ranging from field devices to business systems, focused on

decision-support, from the first phases of design, through installation, commissioning,

operation, maintenance and asset optimization (Fig. 1).

2.1 Aspect Objects

The two central themes in IndustrialIT are information availability and information

integration. This means that information must be available at the right place at the

right time independent of where the information comes from. It must be possible to

seamlessly integrate the right information from any combination of sources.

A well known problem in plant operations as well as asset lifecycle management is

that you need a way to keep together, manage, and have access to information about

all different aspects of a great number of e.g. plant and process entities. These

entities, or real world objects, are of many different kinds. They can be physical

process objects, like a valve, or more complex, like a reactor. Other examples are

products, material, production orders, batches, customer accounts, etc.

Each of these real world objects can be described from several different perspectives.

Each perspective defines a piece of information, and a set of functions to create,

access, and manipulate this information. We call this an Aspect of the object.


Aspects, which can be defined as all the real-time information connected to a

particular Object, including design drawings, control diagrams, maintenance

information, location, quality information and configuration information. In fact, an

Aspect can be composed of data and programs performing specific functions.

Together, these Aspects form a dynamic 'model object' containing links to all the

important information. In IndustrialIT terminology, this is called an Aspect ObjectTM.

Once the physical device (object) is put in place in the plant, the operator can simply

copy and paste the model Aspect Object into the overall system. No matter where

each real object is deployed, one 'click' on the model object provides a link to its

Aspect information.

The approach to introducing an application such as operational optimization into an

existing platform thus centres around one concept – define new aspects specific to

lifecycle modeling and optimization and attach them to power plant objects. In this

way a link between process data and asset management is achieved. Access to the

appropriate information in real-time is now one mouse-click away from the user.

3 The Lifecycle Optimizer

3.1 Complexity grows

Today’s plant manager assumes new responsibilities mostly focused towards the

management of more complex business situations. When talking about decisions for

plants with a complex configuration (multi-unit plants, multi-plants and multi-product

trading, i.e. fuel, power, heat, emissions), finding the operation schedule that provides

the optimal economical results cannot be solved without decision-support tools.

Decisions support systems for operation of power plants in an economic market are

typically based around the concepts of economic commitment and dispatch. The

objective of, what hereafter is referred to as operational optimization, is to schedule or

commit generating units over a certain horizon such that the difference between

operating cost and revenue is minimized. In addition to meeting the expected load, the


operational optimization problem must take into account requirements such as

minimum uptime and downtime of generation units, startup and shutdown times,

spinning reserve, and other constraints such as emissions (e.g. CO2, NOx, SO2) and

minimum or maximum power output.

As indicated above it is important to realize that asset lifetime is a resource that

contributes to the cost of operating the power plant and as such should be included in

the operational optimization problem. The Lifecycle Optimizer addresses this

requirement, i.e. the influence of a given operating mode on the ageing process of the

power plant is included with variables considered in the optimization.

Two elements are at the core of any operational optimization problem. The first is a

model of the plant describing how the plant reacts to various control signals, i.e., an

input-output model. The second is an objective function that uses a combination of

external variables and variables from the plant model to compute a plant performance

measure, e.g., external variables could include the electricity market price and fuel

price, and model variables the plant output and fuel consumption. The objective

function has the generic form [1]:

ττττττττ duuqucuefuJTt

t∫+

= ))()),(,()),(,()),(,((][

where:

T : time optimization horizon; u: power plant control, for example, generated power and heat; e: ageing rate of plant components, or more exactly, its dollar effect; c: cost rates; q: revenue rates.

There are various techniques for solving this class of optimization problem but one

widely adopted by industry to solve control problems of systems subject to input and

output constraints is Model Predictive Control (MPC) [2]. The following components

are thus required:

• Process models to describe how fuel is converted into electrical and thermal

energy. This gives the traditional plant operating costs. The models take static,


dynamic and integral factors into account. These factors include, for example,

fuel heat consumption, auxiliary power consumption, minimum standstill period,

minimum operating time, load gradient limitation, hot start/cold start, limitations

on operating time, on production quantities, on startup frequency, and also on

emissions (e.g. CO2, NOx, SO2).

• Component lifetime models to assess stress loading, which in turn is used to

compute ageing and lifetime cost.

• Operation and constraint models for the plant, including the ageing models in this

plant. This is achieved in a hybrid systems modeling framework [3] known as

Mixed Logic and Dynamic models (MLD).

• An optimization routine that computes the optimal control sequence using the

MPC strategy.

Fig. 2 shows an example of optimization results for generated power and

corresponding price levels for trading.

3.2 Trading

Trading and asset management have much in common with traditional utility

management, such as demand forecasting, lifecycle costing, risk management,

performance monitoring, condition-based maintenance, and other aspects. The power

of the Lifecycle Optimizer is its ability to integrate these components and optimize

the trade-offs between financial performance, operational performance and risk

exposure. This can be achieved by:

• Trading of gas, oil, steam and electricity, including penalties for not matching the objectives.

• Fuel trading: the trader is able to buy and resell fuel.

• Fuel balancing: when a gate-close event occurs and the trader still needs to fulfill a diary or subdiary balance of fuel, this may be done through the storage capacity of the fuel network.

• Power trading: the trader is able to stop power generation in case buying power from other generators and reselling it to the grid is found more profitable.


• Emission trading: the trader is able to stop or reduce power generation in order to comply to emission constraints, or to sell emission permits to others if this is profitable.

• Choosing different types of contracts, e.g. bilateral, fortnight-, week- or day-ahead, block-hour (4h or 6h), hour (1h), half-hour (0.5h) and quarter-hourly (15m) contracts.

Figure 2: Example of optimized plant load profiles and trading prices.

3.3 Ageing

The key stochastic variables for scheduled maintenance are expected life estimations

for repair and replacement of major parts. These are based on manufacturer data and

calculation methods which take into account equipment load factors such as, e.g.

equivalent operation hours, number of startups, etc.

For the purpose of lifecycle or ageing modeling two factors are critical. Firstly, the

models are required to provide a direct relationship between equipment load and

equipment ageing. Ageing in a gas turbine, for example, takes into account effects

like firing temperature, fuel type, fuel switch-over, use of power augmentation, trips,

startups, etc. Secondly, the models must capture the equipment’s operating history.


Finally, for lifecycle optimization purposes, the ageing function is included in the

optimization routine.

3.4 Risk estimation

In order to account for the risk of suffering an unplanned outage or the risk of not

being able to connect to the grid after a plant shutdown, risk estimation and evolution

of the failure probability distribution of individual components are addressed. These

risk considerations are condensed as additional terms in the cost objective function

and add a tuneable term to penalize events through the optimization. Those terms may

depend on factors such as the equipment life already consumed, the time since last

shutdown, the weather conditions, etc. The factors are tuneable by the user because

taking one or the other decision is affected by the market pressure and by the risk the

asset manager or trader is willing to take.

Take as an example the decision to shutdown a plant during the weekends. Even if it

is clear that shutting down is the best economical solution, the operator could choose

not to assume the risk of not being able to provide the committed power to the grid on

monday. This could ruin the earning of the week but can be included as a risk term in

the cost objective function. In this sense, risk considerations include subjective

evaluations by the user but are nevertheless analytically formulated.

3.5 Emissions

Emission trading is playing an increasingly important role in energy markets. If a

plant emits less than the level of its allocations, it can sell the additional permits to

others that may have emitted more. If it emits more, then it needs to purchase

additional permits or pay a penalty. To arrive at an optimal decision the asset manager

or trader needs to consider two important factors:

• Price levels and price volatility.

• The technical characteristics of the plant.

Decision tools which are based on historic data (looking at the past days, weeks or

months) will not be able to capture these factors and therefore disadvantage the

competitive position of the plant. This is true for combined cycle power plants, firing


gas or oil as primary fuel, but is even more important for coal-fired plants because of

the problems of coal storage and the difficulties for the trader to unwind a coal

position. The Lifecycle Optimizer is able to take such effects into account by

including additional terms, related to emission credits and penalties, into the cost

objective function. An example of optimized load dispatch, e.g. shutting down the

plant for a period of time based on NOx emission trading, is shown in Fig. 3. The

influence on profits and losses can be huge, specially in case emission permits have to

be purchased and traded. The strength of this approach is the ability to take such a

variety of factors into the optimization and calculate the impact on profitability, an

impossible task for decision-makers without tool support.

NOx in Flue Gas

Figure 3: Example of optimized load dispatch taking emission constraints into account.

3.6 Mapping Money

The Lifecycle Optimizer approach is intended for decision-makers that economically

quantify the effect of plant operation on plant ageing. The result of the optimization is

a cost per kW (including ageing costs) and a kW production level, characteristics

essential for actions such as bidding into a pool.


A frequent problem is the lack of equipment manufacturer data required for

parametrising the lifetime models. This is overcome by directly mapping the

monetary value of such components, based on O&M data such as replacement cost

and estimated lifecycle. The additional advantage of this method is the possibility for

the asset manager to use his/her preferred accounting method (replacement value,

value in the books, etc.).

The Lifecycle Optimizer has been installed and successfully tested at Enfield Energy

Center Ltd. (UK). The main purpose of this project was to verify the optimization

approach, the system design and configuration, as well as the software

implementation.

4 Examples

4.1 Lifecycle optimum versus Preventive maintenance

In this example we consider the following scenario: the plant outage for inspection

and preventive maintenance is scheduled to take place 100 days from now. The

inspection threshold is set at 1000 EOH (Equivalent Operating Hours) and the plant is

presently at zero EOH. There is a multitude of operating strategies which lead to 1000

EOH in 100 days, e.g. 10 EOH per day, 24 EOH per day for the next 41.6 days, etc. In

order to simplify this example, we allocate extremely high costs to overshooting of

EOH, i.e. this option will not be optimal. The task is to find an operation strategy

resulting in maximum profit over a defined time horizon, and respect the EOH

constraint. Note that profit depends on actual energy prices and that revenues are lost

in case the plant has not “consumed” the available EOH. The following cases are

considered:

• The flat solution running constant plant output to satisfy the EOH constraint.


• The manual solution selecting an operating strategy to match the EOH

constraint, while simultaneously trying to maximize profit. The strategy is to

initially set all hours to minimum production and then increase peak price

hours to maximum production until the EOH constraint is met. Note that this

assumes lifetime models available, which is usually not the case if the

Lifecycle Optimizer is not installed.

• The optimal strategy determined by the Lifecycle Optimizer.

The results (normalized) show the advantage of the optimizer approach in terms of

Net Present Value (NPV) and profit:

Solution method NPV Profit

Flat 100% 100%

Manual 109% 108%

Optimal 111% 109%

4.2 Optimal load scheduling

This example focuses on load scheduling but can also be used for post operation

analysis and monitoring. The task is to find the optimal load dispatch strategy of a

unit resulting in maximum profit over a defined time horizon, again taking lifecycle

costs into account. For the purposes of this analysis three possible operating strategies

are compared:

• Keep the plant on constant base load.

• Allow the plant to peak during high energy price periods.

• Allow the plant to follow load in the range 80 to 100 %.

The results (normalized) indicate that, for this example, a peaking strategy increases

both income from sales and lifecycle costs, resulting in an increase in net revenues. A

load-following strategy reduces both income from sales and lifecycle costs also


resulting in a higher net revenue. The calculated increases in net revenues reflect an

expected upper limit on benefits resulting from the Lifecycle Optimizer.

Strategy Income from sales

Generation cost Lifecycle cost Net revenue

Base load 100% 100% 100% 100%

Peaking 103% 103% 103% 109%

Load-following 98% 96% 96% 124%


5 Conclusions

The use of optimization techniques, risk assessment and information integration are

central components of asset management for today’s power plants. Decisions are

driven by the actual condition and performance of assets. Not meeting corporate

objectives such as operating requirements, financial targets and regulations are among

the risks which shall be addressed by the asset manager.

The objective of Lifecycle Optimization in power plants is to satisfy demand at a

minimum cost or, as is more likely in a deregulated market, selecting the production

levels given predicted energy prices (i.e. prices at which produced energy can be

sold). In either case the requirements are the same — decide when to turn equipment

on and choose the production levels. A key component resulting from this decision is

the information used to formulate bids for submission into a spot market. A Lifecycle

Optimization tool is thus a decision tool for plant asset managers and traders

operating in a competitive market.

The first new element in this approach is the explicit handling of lifecycle models and

their inclusion in the optimization routine. The lifecycle models provide a direct

relationship between plant load and plant ageing, and capture the operating history of

the component.

The other new element is the representation of the lifecycle optimization in a hybrid

systems modeling framework known as Mixed Logic and Dynamic models (MLD).

This enables the economic problem to be solved using Model Predictive Control

(MPC) techniques.



6 References

[1] E. Gallestey, A. Stothert, M. Antoine, S. Morton, “Model predictive control and the optimization of power plant load while considering lifetime consumption”, IEEE PE-803PRS (08-2001).

[2] E. Camacho and C. Bordons, "Model Predictive Control", Springer 1999.

[3] A. Bemporad and M. Morari, "Control of systems integrating logic, dynamics, and constraints", Automatica, Vol. 35, no. 3, pp 407-427, 1999.

Authors: Marc Antoine was born in Belgium 1959, received his M.Sc. in mechanical engineering at

the University of Brussels 1982, research assistant in aerospace & ocean engineering at

Virginia Polytechnic Institute USA, joined Brown Boveri Company as development engineer

for process computer systems in Switzerland 1985. Senior engineer at ABB Power Generation

for plant management systems 1989, development manager for plant monitoring &

optimisation systems at ABB Power Automation 1999. Currently product manager for plant

automation systems at ABB Switzerland.

Luis Ruiz-Escribano was born in Madrid 1974, received his M.Sc. in power engineering at

the Polytechnic University of Madrid, research assistant at the Institute of Thermal Power

Systems, Technical University of Munich. Joined ABB Power Automation Switzerland in

2001. Currently, system engineer at ABB Switzerland for plant monitoring & optimisation

applications.

lifecycle optimization for power plants - abb group

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