KBC decision making tool optimal planning scheduling utility

Proprietary Information 1
AComprehensive Decision-Making
Tool for the Optimal Planning and
Scheduling of UtilityAssets
VisualMESAMulti-PeriodOptimizer(VM-EMPO)
Juan Ruiz
KBC (a Yokogawa Company)
Research and Development
juan.ruiz@kbcat.com
KBC User’s Conference

Agenda
• Introduction
• Technology Overview
• Case Studies
• Conclusion

Agenda
• Introduction
• Case Studies
• Conclusion

Introduction
Structural
Changes
Real-Time
Operational Changes
Planning-Scheduling of
Utility Assets
Smaller and smaller profit margins have
motivated the utility plants to develop
and implement more and more efficient
ways to use the energy.
Changes in the electricity price contracts (e.g.
real time pricing, time of use pricing, critical
peak pricing), variability of natural gas prices and
the capability of the utility plants to become
electricity providers to the grid, impose
unprecedented challenges to the scheduling
engineers
Improving Energy
Efficiency
VM-EMPO“Optimal Planning and Scheduling of Utility Assets,”
Juan Ruiz and Oscar Santollani, IETC 2016 Proceedings
*(*)

Visual Mesa Energy Management Suite
Visual
Mesa
Suite
VM-
ERTO
VM-
EMPO
VM-EM
VM-ERTO
VM-EM
VM-EMPO
Visual MESA Energy Monitor offers a feature
set specifically built for the monitoring of
energy systems (KPI Calculation, etc)
Visual MESA Real Time Optimizer is the
leading real-time solution for modeling and
optimization of energy systems
Visual MESA Multi-Period Optimizer is the
energy management tool for the optimal
planning and scheduling of utility assets.Systems exposed to minimum up or down
times, start-up and shut-down costs, spinning
reserves, fuel storage, electricity price variability
are typical cases handled by VM-EMPO

Agenda
• Introduction
• Case Studies
• Conclusion

External Utilities
Contracts
Emissions
Regulations
Fuel Steam Water Electricity
Energy and Utilities System
Forecasts
Energy Scheduling
and Planning
Operators
Process (Utilities Consumers and Generators)
Data
Visualizatio
n and
Custom
Reports
Optimal
Allocation
of Utility
Assets
VM-Energy Multi Period Optimizer
Measurements
“Optimal Planning and Scheduling of Utility Assets,” Juan Ruiz and Oscar Santollani, IETC 2016 Proceedings

VM-EMPOArchitecture
Structural
Model
External
Multi-period
Constraints
Forecast
Data
UserInterface
Optimal
Schedule
UserInterface
Calculation
Engine
• Through the user interface, the user
can define the scheduling problem
by providing three major inputs.
Namely, the structural model of the
utility plant, the forecasted data of
future conditions of the plant and
an appropriate set of multi-period
constraints.
Input-output structure
• The calculation engine uses the
inputs to generate the optimal
schedule.
• The user can request different types
of reports via the user interface

VM-EMPO Structural Model
Shared units among all technologies of our suite
M L A$
Boiler
Inlet
Splitter/Mixer
Motor
Gas Turbine HRSG Deaerator Valve Flash
Pump Reboiler Column Condenser
WH Boiler
SW Turbine Turbine Source Sink
Chiller
Electric Load Cost Block Generator Accumulator
Optimizer Constraints
C
Controller
Only available for VM-EMPO
Thermal Energy
Storage
Fuel Tank
Bolted Flange
Furnace
Totalizing
Constraint
Emissions
Condenser
• Typical pieces of equipment for the
steam, electricity, fuel and
condensate subsystems are
supported.
• The structural model can be
defined by dragging and
dropping pre-built equipment
models and interconnecting them
as in the real site.
• Thermodynamic properties of
the utility system are embedded
in the model.
• Specialized pieces of equipment
to account for multi-period
constraints are supported (e.g.
Thermal Energy Storages,
Totalizing constraints)

Multi-Period Constraints
Single-Unit Multi-Period Constraints
ONOFF
MIN/MAXOP
MINDT/UT
Force Off
Max Op 1 period
Forces a unit on or off at any
time in the schedule
Ensures a unit is no turned back
on/off unless a predefined
resting/running time has passed
Sets the minimum/maximum
total time a unit should work
Min Down Time 2 periods
time
time
time
• Multi-period constraints appear
very frequently in scheduling.
Multi-Unit Multi-Period Constraints
CONAC Defines the on-off state of a
unit based on the on-off state
of another unit
PRECE
Ensures a unit starts after
another unit starts plus a
predefined delay
If unit A is ON unit B OFF
Unit B should start 1 period after A
time
A
B
A
B
time
• A library of multi-period
constraints is available.
• Multi-unit multi-period
constraints are supported to
account for required start-up or
shut-down sequences.
• The users can fully define theses
constraints by setting the
required input parameters
according to their needs.

VM-EMPO Forecasting
RTDB
RDBs
REST
EXCEL
OTHER
Data Acquisition
External
Services
VM
EMPO
Built-In
Interfaces
API
VM-EMPO has added a set of features
that enhances its data acquisition and
processing capabilities, suitable for
forecasting.
• VM-EMPO can connect to virtually
any data source.
• It provides built in interfaces for the
most common source (RTDB,
RESTful, Excel, etc.)
• VM-EMPO provides built-in services
to process data series.
• It provides an API to connect to
custom sources and use external
services.
Data Processing
Built-In
Services
API

VM-EMPO User Interface
Same look and feel as all other products from
our technology suite
• Simple scheduling problem
definition. Forecasts, Process
model and Multi-period
constraints are easily defined
through the UI.
• Easy display of results. The
optimal operation of a
particular unit can be fully
visualized by clicking the unit
in the flowsheet.
• Report generation and
distribution can be controlled
through the UI.

• Different formats are
available (e.g. Excel format,
PDF format)
• Any variable in the model
can be reported
Used as the main output file
for distribution
• Excel Add-In allows data
manipulation via Excel
VM-EMPO Reports

Gantt Charts
Bar/Line Charts
Full Solution Embedded
in Model Browser
Different visualization tools facilitate data analysis
VM-EMPO Reports

Agenda
• Introduction
• Case Studies
• Conclusion

Case Study
FuelOilStorageManagement
Natural
Gas
Returning
Water
HPS
Fuel Oil
Tank Farm
Condens.
Petrochemical plants often produce
residue oil or fuel oil as a byproduct
that can be used as an alternative
fuel to generate steam in the boilers.
0
3
6
9
It is common practice to use as much
fuel oil as possible while it is available
since it is virtually free.
$/mmBTUCan we do better?
Natural Gas Price
days

Case Study
FuelOilStorageManagement
0
1
2
3
4
days
FuelOil(ton/h)
0
30
60
90
120
Inventory(ton)
0
2
4
6
FuelOil(ton/h)
0
200
400
600
Typical Operation
Optimal Operation
Fuel is used
as it becomes
available
Stored Fuel is
used when
natural gas
price is high
EMPO was able to propose a better fuel oil
management that led to a 2% decrease in operating cost
days
Inventory(ton)
daysdays

Case Study
CHPOperationwithvariableelectricitypricing
Natural
Gas
HPS
MPS
LPS
Power
Network
In order to improve the efficiency of
steam and power generation,
cogenerating units or CHPs are often
used.
0
0.02
0.04
0.06
0.08
0.1
hours
$/kW
Scheduling engineers should often decide
when to use the CHP and when is more
convenient to shut it down.
In practice, it may be recommended not
to start-up the CHP before a given
amount of time has passed after it was
shut down
This imposes a great challenge
to the schedulers!
Electricity Price

Case Study
CHPOperationwithvariableelectricitypricing
0
20
40
60
80
100
CHP Power Generation Power Purchase
0
20
40
60
80
100
CHP Power Generation Power Purchase
Due to the minimum down time
restriction, the CHP will be down
when the electricity price is high right
after the second peak.
This situation is properly identified by
EMPO which does not propose
shutting down the CHP in the
second peak but rather take the CHP
to the lowest generation capacity
Sub-Optimal Operation EMPO Proposed Schedule
5 % Decrease
in Operating
Cost

Case Study
DistrictEnergySystem
CWS
CWR
Cooling
Water
Power
Network
The main goal of most district cooling
systems is to centrally produce and
distribute chilled water at a minimum cost
while satisfying demand.
0
0.01
0.02
0.03
0
50
100
150
$/kW
hours
hours
To minimize the production cost, thermal energy storages (TES) are often used.
The production cost of chilled water plants that
use electric powered chillers is deeply sensitive
to electricity price variability.
Finding the best
operation of TES
is not trivial!
GPMx1000
Chilled Water Demand Electricity Price

Case Study
DistrictEnergySystem
0
10
20
30
40
50
Chiller-9 Chiller-10 Chiller-8
GPMx1000
hours
-20
-10
0
10
20
0
2
4
6
8
10
TES Flow TES Inventory
MillionGallons
hours
Optimal Chiller Operation Optimal TES operation
EMPO recommends charging the TES when the electricity price is low and
discharging it when the electricity price is high. Also EMPO recommends which
chiller to use and in what capacity based on the performance curves of each of
them and the minimum and maximum capacities.
GPMx1000

Agenda
• Introduction
• Case Studies
• Conclusion

Observations and Conclusions
• Planning and scheduling has a significant impact on the energy
efficiency in utility plants.
• Variability in electricity price, caps on emissions, fuel storage facilities,
predefined maintenance operations on units, etc. impose
unprecedented challenges to the scheduling engineers who try to find
the optimal planning/schedule of the utility plants.
• Visual MESA - EMPO provides a user friendly environment to define,
solve and distribute the solutions of the planning/scheduling problem
particularly suited for complex utility systems.

KBC decision making tool optimal planning scheduling utility

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