Data Centric Load Forecasting and Allocationstaff.utia.cas.cz/kulhavy/powergen00s.pdf ·...
Transcript of Data Centric Load Forecasting and Allocationstaff.utia.cas.cz/kulhavy/powergen00s.pdf ·...
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Zdeněk Schindler, Rudolf KulhavýZdenZdeněěk Schindler, Rudolf Kulhavýk Schindler, Rudolf Kulhavý
Data Centric Load Forecasting
and Allocation
Data Centric Data Centric Load ForecastingLoad Forecasting
and Allocationand Allocation
Honeywell Technology Center Europehttp://nero.htc.honeywell.cz/
June 2000
Honeywell Technology Center EuropeHoneywell Technology Center Europehttp://nero.htc.honeywell.cz/http://nero.htc.honeywell.cz/
June 2000June 2000
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Problem DefinitionProblem Definition
Combined heat Combined heat and powerand powerproduction production
and distributionand distributionsystemsystem
Goal:Goal:Realization of decision support system for the load Realization of decision support system for the load dispatching center with these functions:dispatching center with these functions:
1. Heat demand forecasting2. Hot water distribution enhancement3. Heat & power generation enhancement
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MotivationMotivation
• to avoid unnecessary boiler start-ups and shutdowns
• to improve cogeneration contracting
• to enhance the efficiency of heat production and distribution
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red lines steamblue lines hot water
1 km
Combined cycleCHP
Heating plantMunicipal incinerator
CHP
CHP
• Five heating plants • 96 km of steam pipeline network• 74 km of primary hot water pipelines (five networks )
District Heating System - Pilot Project District Heating System - Pilot Project
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Heat Demand ForecastingHeat Demand Forecasting
Quantities to be forecasted:Quantities to be forecasted:• total supply of steam• total supply of heat• total supply of electricity• heat supply in individual hot water pipelines - sepa rate and
cumulative values• heat supply in hot water• heat supply in steam• total gas consumption• total steam production• total heat production• total heat production in steam
Total Total -- 17 quantities17 quantities
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Heat Demand ForecastingHeat Demand Forecasting
Mode of operation: Mode of operation: 24 x 724 x 7
Forecast time ranges:Forecast time ranges:• 15’ averages one-day ahead … short-term forecast• 1 h averages one week ahead … medium term forecast• daily averages one month ahead … long term forecast
Computer workload:Computer workload:• 1,632 forecasts (of a single value) every 15 minute s• 2,856 forecasts (of a single value) every hour• 1,581 forecasts (of a single value) every midnight
Extra requirement:Extra requirement:• to implement a process history database with nearly
500 variables monitored in 5’ period
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District Heating System ModelDistrict Heating System Model
consumers
distribution
production
Complex stochastic system
First-principle model(s) ?
weatherconditions
time of day
day of week
holidayheat demand
steam demand
hot waterdemand
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District Heating System ModelDistrict Heating System Model
input output
BLACK BOXBLACK BOXBLACK BOXweather conditions
time of dayday of week
holiday
heat demandsteam,
hot water
Data-centric model !
databaserequesteddata
statisticsmonitoring
data
5th Jan 2000, 7:15 h-5°C
...pipeline P2:
150 000 kg/hour,...
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Data-Centric = Memory-Based ForecastingData-Centric = Memory-Based Forecasting
Alldata
Estimation
Globalmodel
Complex, non-linear/non-Gaussian behavior
fit globally witha single model
Adaptation
Recentdata
Localmodel
Complex, non-linear/non-Gaussian behavior fit locally in time with a
simple model
Querydriven
retrieval
Relevantdata
Localmodel
Complex, non-linear/non-Gaussian behavior fit locally in data cubewith a simple model
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Predictedtarget variable(heat supply,
steam production)
ALGORITHM1. Assess the conditions
at the query point (weather conditions)
2. Search database for similar conditions
3. Extrapolate from the past values(heat production)
4. Estimate precision of the forecast
Data-Centric Forecasting Algorithm Data-Centric Forecasting Algorithm
Situation (weather conditions,
time of day, type of day)Query point
Neighborhood
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Output dataOutput data� process variables forecasts� enhanced process variables
Operational Operational datadata
DataData--centric centric forecasting and forecasting and
optimizationoptimization
ActionsActions
Process history databaseProcess history database
Implementation - Honeywell DSSImplementation - Honeywell DSS
Input dataInput data� external weather forecast � process monitoring data
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Hot Water Distribution EnhancementHot Water Distribution Enhancement
Problem:Problem:Define the “best”- pressure difference (set-point for circulating pumps controls)- supply temperature (set-point for heat exchanger controls)for hot water pipelines
Optimal control of complex dynamic system!!!Optimal control of complex dynamic system!!!
Complexity consists in:Complexity consists in:• consumers - stochastic behavior,
- different (unknown) control strategies• network - variable, state dependent time delays• environment - concurrent environment influence
is reflected in load gradually • unknown pipeline characteristics• etc.
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Con
trol
var
iabl
e(p
lant
op.
poi
nts)
ALGORITHM1. Assess the current
conditions (weather conditions)
2. Search database for similar conditions (red points)
3. Estimate profit for retrieved controls under the current conditions
4. Conduct a “fine-trim” search by adjusting the process controls (blue points)
Data-Centric Optimization AlgorithmData-Centric Optimization Algorithm
Situation (weather conditions,
time of day, type of day)Query point
Objective function(profit)
Neighborhood
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Hot Water Distribution EnhancementHot Water Distribution Enhancement
Optimality Criterion:Optimality Criterion:Maximize efficiency of heat distribution
Method:Method:Comparing• available heat supplied to consumers,• unused heat returning back to a heating plant.
The applied algorithm attempts to satisfy the necessary optimality conditions
Motivation:Motivation:Circulating energy (excepting intentional accumulat ion) results in higher heat losses.
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Hot Water Distribution EnhancementHot Water Distribution Enhancement
Solution:Solution:Combination of • data-centric optimization procedures
• simplified model-based objective function
Conditions descriptors:Conditions descriptors:• outdoor temperature• current electricity production• time of day• type of day (day of week, holiday)• age of data
Closed loop safeguard:Closed loop safeguard:• Low level controllers keep critical variables in technology limits autonomously• Operator can increase or decrease the supplied heat
temporarily
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Heat & Power Generation EnhancementHeat & Power Generation Enhancement
Problem:Problem:
Find optimal outputs of boilers that minimizeproduction costs
Solution method:Solution method:Data-centric optimization
Knowledge:Knowledge:operating conditions past actions and costs
implicitheat load line
Boiler 1Boiler 2
Productioncosts
Localcosts Resource Allocation Problem
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DSS Network SchemeDSS Network Scheme
POWERFAULT DATA ALARM
Win NT/98
Modem
Win NT/98
Oracle8i ClientDSS Client
IDC
PHD Server
Win NT 4.0 Webbrowser
Webbrowser
Webbrowser
DSS Thin clients
IDC
Win NT 4.0
Oracle8i ServerOracle Developer Server
Oracle Application ServerDSS Server
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Typical Tangible BenefitsTypical Tangible Benefits
• lower production costs (e.g., elimination of unnece ssary
boiler start-ups, increased efficiency)
• lower distribution costs (reduced heat losses)
• improved product quality (reduced variation of
cogenerated power)
• improved asset management (less boiler start-ups)
• reduced safety margins (owing to lower uncertainty)
• reduced labor costs (less operators required)
• increased productivity (automation of some function s)
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Typical Intangible BenefitsTypical Intangible Benefits
• improved data reliability due to built-in data vali dation
• transparent access to multiple operational data
• schedule-driven 24x7 delivery of information
• access to the analytic results via both clients and Web
• faster more informed decisions
• better customer service
• improved process knowledge
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DSS - General AdvantagesDSS - General Advantages
DSS allows the plant operators, managers, and execu tives toDSS allows the plant operators, managers, and execu tives to
• anticipate changes in the environment;
• respond faster to changing conditions;
• anticipate abnormal situations well ahead;
• detect operating conditions leading to off-spec pro duction;
• operate closer to the optimum or limits;
• reduce time to optimal operating conditions;
• better understand the processes;
• learn continually from the manufacturing experience .
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Data-Centric Method PrinciplesData-Centric Method Principles
Not confined to heat production and distributionNot confined to heat production and distribution
Basic application principle:Basic application principle:• The software mimics the real system behavior
Application policy:Application policy:• Describe the problem carefully• Ask correct questions
Application prerequisites:Application prerequisites:• Enough relevant data for required processing• Correctly specified input-output relations• Well chosen approximating functions