Real-time and Intraday Optimization of Multiple Power ... · PDF fileReal-time and Intraday Optimization of Multiple Power ... Real-time and Intraday Optimization of Multiple Power

Paper PowerGen 2013, ID T6S4P3

Real-time and Intraday Optimization of Multiple Power Generation Units

© PSP-I42-, 2013-05-28

Real-time and Intraday Optimization of

Multiple Power Generation Units

Dr. Rüdiger Franke

Hansjürgen Schönung

Marcel Blaumann Dr. Alexander Frick

Stephan Kautsch

ABB AG, Germany

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Introduction 2 / 15

1 INTRODUCTION ...................................................................................... 3

2 BRINGING MATHEMATICAL OPTIMIZATION TO REAL-TIME CONTROL ....................................................................................................... 3

3 INTERNAL OPTIMIZATION OF LARGE MULTI-UNIT PLANTS ... 6

4 COMBINED HEAT AND POWER PRODUCTION ............................... 9

5 REAL-TIME OPTIMIZATION OF POOLS OF BIOGAS PLANTS... 11

6 RENEWABLE SUPPLY MANAGEMENT ............................................ 12

7 INTRADAY OPTIMIZATION OF MUNICIPAL POWER ................. 13

8 CONCLUSIONS ....................................................................................... 15

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Introduction 3 / 15

1 Introduction

Facing increasing penetration of renewable energy, new opportunities arise for the trading of

power. Particularly the increased participation in grid services and intraday trading becomes

crucial for the economical result. This raises the requirement for increased flexibility on the

power generation side:

• Frequent updates during the day complement traditional day-ahead plans for

conventional power generation.

• Combined heat and power generation is changed from heat driven to electricity driven,

exploiting storage capacities on the heat side.

• The controllability of renewable generation units is increased.

This paper introduces a new optimization method that is placed in the real-time control of

power plants. This enables the pooling of individual power generation units and their

management like one large plant. The real-time optimization receives overall set points and

distributes them to each individual power generation unit, considering actual efficiencies,

process constraints and temporary limitations. The introduced hierarchy reduces overall

complexity and increases the flexibility. Moreover, it solves the power generation task at the

best point for the considered units.

The paper introduces the optimization method and lists five different application examples.

2 Bringing mathematical optimization to real-time control

Traditional unit commitment optimization focuses on day-ahead plans for power production

and trading. Well-established optimization tools find on top of plant information management

systems and interface with trading systems.

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Bringing mathematical optimization to real-time control 4 / 15

Unit 2DCS 2

Unit 1DCS 1

Unit …Other DCS/PLC

Engineering & Maintenance

Traditional Optimization

FleetManagement

PIMS

Scanner

Trading, Accounting

Figure 1: Classical system layout for plant optimization, e.g. unit commitment

Figure 1 shows the evolved classical system layout. Multiple production units are connected

through an automation network. A Scanner reads real-time data from the automation network

and provides it for the Plant Information Management Systems (PIMS). The PIMS provides a

process historian database and analysis functionalities. This primarily improved the

engineering & maintenance. Optimization algorithms traditionally run on top of the PIMS

system. Typically, human operators use such traditional optimization tools interactively.

Four new trends arose during the previous years:

1. The number of power production units significantly increases with the use of

renewable energy

2. The power production needs to be re-planned frequently during a day, in order to

account for fluctuations

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Bringing mathematical optimization to real-time control 5 / 15

3. The required optimization cycle times reduces from daily planning cycles down to

seconds, e.g. for pooling of secondary frequency control

4. The role of human operators changes from being part of the loop to supervision

Moreover, the mathematical optimization technology has maturated during the last years.

Modern optimization solvers treat large linear optimization programs, including also integers,

reliably in fractions of seconds. Even quadratic and nonlinear optimization algorithms

perform well in model predictive control applications in real-time.

The new trends require a shift from interactive use of optimization tools to automated

optimization in the real-time control system.

Unit 2DCS 2

Unit 1DCS 1

Unit …Other DCS/PLC

Scanner

Online OptimizationPIMS

Engineering & Maintenance

FleetManagement

Trading, Accounting

Figure 2: New system layout with online optimization

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Internal Optimization of large multi-unit plants 6 / 15

Figure 2 shows the new system layout. The optimization now directly accesses the automation

network through the Scanner. It only communicates data exchanged with the fleet

management, like planning data, through the PIMS.

This system layout provides for significantly improved reliability. For instance, the

optimization can still run if the PIMS goes offline – just updates of planning data would not

work anymore in this case. The direct communication also allows for significantly reduced

optimization cycle times down to about a second.

All communication between the Scanner, the PIMS and the Optimization goes through

TCP/IP sockets. This provides a lot of flexibility for different implementations, depending on

specific needs. In the simplest case, one server PC can run all functions (Scanner, Optimizer,

PIMS). Two redundant server PCs provide for higher availability. Moreover, the Scanner and

the PIMS can run on separate server PCs, in order to increase the throughput of data. Finally

yet importantly, the Optimization can share the use of already existing functions, like an

already installed Scanner or PIMS or of already existing hardware, e.g. for running the

Scanner on servers already existing in the plant control or SCADA system.

Traditionally a private automation network provides the communication with the power

generation units. Alternatively, virtual private networks (VPN) using the Internet or GPRS

enable the communication to small, distributed power generation units.

The new system layout particularly suits for the real-time optimization of actual plant set

points and for the intraday optimization of plant schedules. The following sections give an

overview about important use cases and exemplary installations.

3 Internal Optimization of large multi-unit plants

Two large power plants with an installed capacity of 6x500MW and

2x500MW/1x890MW/1x600MW, respectively, run a real-time optimization for plant set

points and secondary frequency control. The idea is to pool and optimize multiple units per

plant locally. At the same time, the optimization system automates the communication from

Paper PowerGen 2013


© PSP-I42-, 2013-05-28


the load dispatcher to the control systems of the power generation units, in order to enable the

reaction on increasingly frequent updates, see Figure 3.

Figure 3: Principle of the Internal Optimization for a 6x500MW power plant

The optimization model covers efficiency curves and own consumption of the plant units,

besides many constraints ensuring the appropriate provision of primary frequency control,

secondary frequency control and required load ramps. The Modelica technology provides for

graphical structuring of the model equations and generation of efficient executable code, see

Figure 4.

Paper PowerGen 2013


© PSP-I42-, 2013-05-28


Figure 4: Graphical formulation of the optimization model using Modelica

The graphical model editor exports compiled executable models to the online optimization.

In this case, two online optimizations are running in parallel in each plant. One optimization

adjusts the set points for the provision of secondary frequency control automatically in real-

time. The second optimization covers the plant schedule and proposes new set points. The

operator can influence the optimization by adjusting min/max constraints per plant unit. Once

acknowledged, the set points are transferred to all units automatically.

Standard operator graphics hides the optimization details from the operators. For instance,

standard faceplates serve as input for max and min bounds for feasible operating points. The

online optimization automatically determines the overall best point within the ranges specified

by human operators.

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Combined Heat and Power production 9 / 15

Figure 5: Operator graphics visualizing the status of all plant units together with optimiza-

tion results and linking to faceplates for operator inputs

4 Combined Heat and Power production

Combined heat and power production is key to improving energy efficiency. The physical in-

teraction between different components on heat side and on electricity side results in complex

constraints for the plant operation.

Paper PowerGen 2013


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Combined Heat and Power production 10 / 15

Figure 6: Plant model and Operator screen for combined heat and power production

Figure 6 shows the application to real-time optimization of plant set points. The plant model,

shown on the left hand side, considers:

• Efficiency curve and own consumption for each relevant plant component

• Couplings through steam headers

Appropriate assumptions need to be made, in order to enable online optimization in real-time.

The optimization builds on a well-tuned base control system for each unit. This means in par-

ticular that optimal coordination of multiple plant aggregates builds on well-controlled header

pressures and temperatures.

Real-time optimization treats a mathematical optimization program based on the physical

plant model. The optimization constraints and the objective cover:

• Demands for steam and electricity

• Externally given set points for the recovery boilers

• Constraints per plant component (e.g. min and max load)

• Fuel costs for coal and biomass

The real-time optimization of set points integrates with the regular plant operation in a

straightforward way. Standard operator screens visualize optimized set points besides actual

values. The base control systems take over optimization results either automatically or after

confirmation by human operators.

Paper PowerGen 2013


© PSP-I42-, 2013-05-28

Real-time optimization of pools of biogas plants 11 / 15

5 Real-time optimization of pools of biogas plants

Comparable fast load ramps make renewable generation units well suited for the provision of

grid services. This is why the direct marketing of renewable power is becoming increasingly

attractive. Individual renewable power plants are too small though. The pooling of small

renewable power plants is required, in order to achieve an overall capacity that is sufficient

for participation in the electricity market.

Online optimization manages pools of biogas plants for the provision of secondary frequency

control. The real-time optimization implemented on a central redundant server receives set

points from load dispatchers and distributes them to pooled plants. The IEC 60870-5-104

protocol is used in Virtual Private Networks (VPN) for the communication over the Internet.

Figure 7 gives an overview of the system structure.

RedundantProcess Database Real-Time Optimization IEC 60870-5-104 ScannerOperation & Management

Operations Client Engineering ClientImport / Export for

operational management

Service Client

Up to 500 production units,typically 500 kW per unit

4 GridOperators

Figure 7: Pooling of biogas plants for secondary frequency control

Paper PowerGen 2013


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Renewable supply management 12 / 15

6 Renewable supply management

Many renewable production units, like wind and solar, do not participate in the direct trading.

Instead, they are supplying an increasing amount of unmanaged power to the grid. This can

harm the grid operation and stability. Renewable supply management controls the amount of

renewable power fed to particular distribution networks. Online optimization offers proven

technology to break down overall set points to individual power generation units, fulfilling

legal requirements (like EEG Einspeisemanagement) and plant constraints. As a result, the

renewable generation units receive limits for their supply to the grid (see Figure 8).

Figure 8: Overall system layout for renewable supply management

(EEG-Einspeisemanagement)

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Intraday Optimization of municipal power 13 / 15

7 Intraday Optimization of municipal power

Storage capacities enable the temporal decoupling of power production and consumption.

Typical storages are latent heat stores of combined heat and power production and pump

stores. Moreover, the emerging electric mobility offers a huge potential for future storage

capacities.

From a control point of view, storages require a shift from real-time optimization of actual set

points to predictive planning of load trajectories. The planning needs to consider a multitude

of constraints. For instance, heat storages primarily need to ensure the supply of heat and car

batteries primarily have to power the engine.

Intraday optimization enables the optimal use of power generation units and storage

capacities, considering constraints and maximizing the economic result. It reacts on new

conditions by re-planning the power production. This maintains the overall balance, in order

to avoid the purchase of expensive regulating power from the transmission net.

Figure 9 shows the optimization model of multiple producers, consumers and storages that

form one regional balancing zone. Figure 10 shows exemplary intraday optimization results.

Triggered by an update of weather forecast data, a new prediction for the wind power arrives.

The wind power (thick green line) decreases after 12:00 and increases after 14:00, compared

to the original day-ahead plan (thin green line). The intraday optimization can avoid a

misbalance by re-planning the combined electricity and heat production (red lines). The heat

storage buffers excess heat during overproduction of electricity and releases it during

underproduction of electricity (black line).

Paper PowerGen 2013


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Intraday Optimization of municipal power 14 / 15

Figure 9: Graphical formulation of an intraday optimization model

Figure 10: Exemplary results of intraday optimization

Paper PowerGen 2013


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Conclusions 15 / 15

8 Conclusions

Traditional optimization tools run on top of process databases. They are used interactively and

the optimization results are typically transferred manually to the control systems of the opti-

mized power production units (e.g. via email or ftp to the plant and manual input into the con-

trol system). These tools and procedures run into limitations when facing an increasing num-

ber of small renewable power generation units and/or increasingly frequent re-planning cy-

cles.

Online optimization connected directly to automation networks enables the flexible reaction

on frequently changing production tasks. The plant control systems either receive optimized

set points automatically. Alternatively, standard operator graphics may present the optimiza-

tion results to human operators for acknowledgement. In both cases, the online optimization

not only increases flexibility, but also maintains the overall power production at the economic

best point.

Real-time optimization treats current plant set points. The solution time is short (down to frac-

tions of a second) thanks to the availability of mature numerical optimization solvers and

powerful computing hardware. This is why the optimization can even be placed into the

communication and sub-division of set points, e.g. for secondary frequency control or for re-

newable supply management. Moreover, this allows an interactive use. For instance, a human

operator can adjust bounds or constraints using regular process graphics and immediately see

the effect on the optimization results.

Intraday optimization not only covers current set points, but also predicts into the future. This

is required in the case of available storages, like heat buffers, pump storages or batteries. This

decouples the production from the consumption of electricity up to the extent of available

storages.

ABB has implemented the real-time and intraday optimization in OPTIMAX® PowerFit, now

basing on the online platform ABB Dynamic Optimization. The new optimization method has

proven successful in a number of different application projects.

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Real-time and Intraday Optimization of Multiple Power ... · PDF fileReal-time and Intraday Optimization of Multiple Power ... Real-time and Intraday Optimization of Multiple Power