Research of the Decision Model on Capacity and Operation Condition of Energy Systems of the Commercial Building and Urban District Considering Weather Condition of the City

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Research of the Decision Model on Capacity and
Operation Condition of Energy Systems of the
Commercial Building and Urban District
Considering Weather Condition of the City
Annual Meeting of the International Energy
Workshop 2005 5-7 July 2005, Kyoto, Japan

• Takeshi Ishida,
• Doctorate student of Tokyo University of Science
• Shunsuke Mori
• Tokyo University of Science

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Contents

• 1.Background
• - Early studies and technical issues
• 2.Model
• - Integrated Model to evaluate the optimum
  capacity and operation condition of energy
  systems of commercial building
• 3.Results
• - Optimum systems on the regional climate
  conditions and the technological characteristics
  of the energy systems.
• - Case study of Utsunomiya, typical middle city
  of Japan
• 4.Summary and Future Directions

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Background

• 1. Early studies and technical issues
• - Estimate method of the building energy load
• ? Estimation on the standardized demand data
  
• No consideration of weather conditions.
• - No consideration on partial load properties
  of energy systems in optimization models.
• 2.Improvements of our study
• - Estimate model of the building energy load
• ? Consideration of weather conditions of the
  city.
• - Consideration of partial load properties of
  energy systems by mixed integer nonlinear
  programming.
• - Integration of the estimate model of a
  building energy load and optimization model of
  the energy system
• - Evaluate the energy supply network of the
  Urban District

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Estimation Procedure of the Building Energy Load
We can estimate the building energy load which
reflects weather conditions and the usage of the
building.
OUTPUT
INPUT

• - Air conditioning load is estimated from the
  weather conditions of the building outside.
• - Personnel density and human body exothermic
  reaction are considered.
• - Waste heat from the indoor equipment

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Comparison of the Estimated Value and the
Measured Data of the Annual energy Consumption
Fig. Comparison of the annual electricity
consumption (office)
Fig. Comparison of the annual heat consumption
(office)
Our model estimates appear within the actual
energy load statistics.
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Building Energy Load Patterns of the Cities
Cooling load (August)
Heating load (February)
Estimate assumptions Floor Space
10,000m2 Uses of buildingoffice 20,shop 20,
hotel 60
The energy loads vary depending on the regional
climate conditions.
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Outline of the Optimization Model in Our Study
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Modeling of the Air Conditioning System

• Alternatives of air conditioning system
• Heat pump system
• Cogeneration system
• Steam boiler and absorption refrigerator
• Gas hot/cold water service machine and boiler

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Model Equations of Optimization

• Composition of equations
• - Energy demands on the electricity and heat
• - Energy balances of the equipments
• - Model of partial load properties of Heat pump
  system / Cogeneration
• Conditions on operation of cogeneration system
• Constraint on the economic efficiency
• - Investment return year ltX year

Objective function Minimization of Primary
Energy Demand
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Modeling of the Partial Load Properties of the
Heat Pump System

- Efficiency is approximated by a liner function
of the load factor. - Lower limit of the partial
load is formulated with a binary variable.

and
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Preliminarily Calculation ResultsComparison of
CGS and HP for the Office and Commercial Buildings
South
North

• Optimal air-conditioning system varies depending
  on the regional climate conditions.

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Comparison of the Optimum HP Capacityby
Investment-return Year

• Optimal Heat pump Capacity varies depending on
  the constraint in investment-return year.

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Typical Japanese middle city - Utsunomiya
Utsunomiya
Red, Purple commerce building Blue housing

• Central district in Utsunomiya
• - Interminglement of houses and commerce
  buildings.

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Selected Area case study
Case Study Area
Number of building House 66 - Office
26 Shop 32 - Hospital 7 Hotel
1 - School 4 Other 2
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Estimation Procedure of Energy Consumption
Land use and building data for GIS
Bldg A1F xx shop 2F Bistro xx 3F xx
linic -------------------------
GISMapInfo Map dataZenrin Residential Map data
Map BASIC


Floor, area, shape, etc
Building properties
Behavior of residents




Estimation
Map BASIC



Bldg Aoffice xx shop xx hospital
x -----------------
Floor ratio by purpose

Building area


Bldg. A xxxx m2 Bldg. B xxxx m2 Bldg .C
xxxx m2 ---------------
Area and energy demand by purpose and by resident

Simple energy demand estimation model



Bldg Aoffice m2 shop m2 hospital
m2 -----------------
Spatial distribution of energy demand by purpose
and by building


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Extraction of Buildings for the Evaluation

• Number of building
• House 66 - Office 26
• Shop 32 - Hospital 7
• Hotel 1 - School 4
• Other 2

Buildings over the 2000 square meter and 66
housings are chosen.
Building A 3750m2 Office 80 Shop 20
Building B 2665m2 Office 50 Shop 50
Building C 2081m2 Office 100
Building E 1139m2 Office 50 Shop 50
Building D 2048m2 Hospital 100
66 Houses
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Conditions of Case Study

• Case 1 Install Gas hot/cold water service
  machine and boiler in each building.
• Case 2 Install Cogeneration system in each
  building.
• Case 3 Install Heat pump system in each
  building.
• Case 4 Install Cogeneration system in each
  building and energy interchange with houses.
• Case 5 District Cooling and Heating.

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Case Study 1
Case 1 Installed Gas hot/cold water service
machine and boiler in each building and 3kW PV
system in each house.
Gas hot/cold water service machine and boiler
Building A
Gas hot/cold water service machine and boiler
Building B
Gas hot/cold water service machine and boiler
Building C
Gas hot/cold water service machine and boiler
Building D
Gas hot/cold water service machine and boiler
Building E
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Case Study 2
Case 2 Installed Cogeneration system in each
building and 3kW PV system in each house.
Cogeneration System
Building A
Cogeneration System
Building B
Cogeneration System
Building C
Cogeneration System
Building D
Cogeneration System
Building E
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Case Study 3
Case 3 Installed Heat pump system in each
building and 3kW PV system in each house.
Heat Pump System
Building A
Heat Pump System
Building B
Heat Pump System
Building C
Heat Pump System
Building D
Heat Pump System
Building E
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Case Study 4
Case 4 Installed Cogeneration system in each
building and energy interchange with houses.
PV
Cogeneration
Building A
House 1 -66
Electric interchange
PV
PV
Building B
Cogeneration
Building C
Cogeneration
PV
Heat interchange
PV
Building D
Cogeneration
Building E
Cogeneration
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Case Study 5
Case 5 District Cooling and Heating.
District Cooling and Heating
Building A
Heat and Electricity
Building B
Cogeneration
Building C
Building D
Building E
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Result(5 Buildings and 66 houses)
Primary Energy of the Area (Gcal/year) CO2 emission of the Area (t/year)
Case 1 9,109 1,549
Case 2 8,271 1,497
Case 3 8,089 1,307
Case 4 7,648 1,414
Case 5 7,967 1,434
In the case 4( Installed Cogeneration system in
each building and energy interchange with
houses), the energy consumption and CO2 emission
are reduce over 10.
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Summary and Future Directions

• We can construct the model to deduce the optimum
  planning including the installation and operation
  of equipments depends on the regional conditions
  and the technological characteristics of the
  energy systems.
• We can show the selection of the optimum air
  conditioning system of the Japanese cities.
• We carried out the case study in the actual urban
  district and examined the possibility of energy
  saving and CO2 reduction.
• Next step
• - Evaluate the potential of the regional
  energy network.
• - Integration and the assessment of the
  electric power grid with the regional energy
  network.
• - Investigation on the compatibility conditions
  of the quality of electric power supply with the
  energy saving possibility.