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Aristotelis Giannopoulos
The situation:

                           •Between now and 2050 global population
                           expected to grow to 9 billion
                           •80% of the population will live in cities
                           •Buildings consume nearly 60% of the total
                           energy in cities



Problem or opportunity?

•Much new build will be
required-resources
•Buildings currently are
highly innefficient
•High carbon emmiters
•Electricity from grid
•Heat by using boilers

Where is the problem?
•Low efficiency
•High emissions
•Poor reliability and power quality
•Expensive trasmission networks
Building passive design:
                               •Natural ventilation
                               •Enhansed thermal performance
                               •External microclimate
                               •Solar shading
                               •Low energy appliances
                               •Low energy lighting
                               •Green roof

Energy systems optimization:
•PV
•CHP/CCHP
•Biomass
•Wind
•Fuel cells
Sources           Generation           Conversion             Demand

                      Technologies         Technologies

GRID Electricity                                                  Electricity


                                         Lighting
                      Wind
                                                                  Lighting
                                         Technologies
                        PV

                                         VC air cooled VC water
                                         cooled
Natural Gas           CHP/CCHP
Biomass
                                                                  Cooling
                                         Absorption
                   Solar thermal
                                         Cooling
                      Boiler

                                                                  Heating
      IHT
                                                                  Waste Heat


                   Stirling Engine



                                     Electricity
                                     Heat
                                     Cooling
                                     Natural resureces
Which technology to be installed?

    What is the appropriate level of installed

    capacity?
    How should operate the installed capacity?


    With Objective:
    • Minimize the energy cost
    • Minimize the environmental effect


                            +
 For each of the three seasons (summer,
  winter, mid)
 Average and peak day load profile

 Each profile 24 hourly electricity/heat loads
  (kW)



  TAS/IES
Natural Gas Price




    Biomass Price




    Electricity Price




    Trasmission/Distribution

Capicity (KW)

    Lifetime (years)

    Capital cost ($/KW)

    Installation cost ($/KW)

    Operation & Maintenance variable/fixed cost

    ($/KWh, $/KWh)
     Heat rate (Kj fuel/KWh)

    Heat to power ratio (α)

Objective function is to minimize total cost, which consisting of:

    total facilities and customer charges



    total electricity purchases charges



    carbon taxes



    total on-site generation fuel and O&M costs



    total DER investment cost



    and minus the revenues generated by any energy sales to the

    grid
Subject to:

    Equation enforces energy balance

    Equation enforces the on-site generating

    capacity constraint
    Equation limits how much recovered heat can be

    recovered from each technology
    Equation averts the use of the recovered heat for

    meating cooling if no absorption chiller exist
    Equation annualizes the capital cost of owning

    on-site generating equipment etc…
The General Algebraic Modeling System

    (GAMS) is specifically designed for modeling
    linear, nonlinear and mixed integer
    optimization problems. The system is
    especially useful with large, complex
    problems.
    GAMS allows the user to concentrate on the

    modeling problem by making the setup
    simple. The system takes care of the time-
    consuming details of the specific machine
    and system software implementation.
GAMS is especially useful for handling large,

    complex, one-of-a-kind problems which may
    require many revisions to establish an
    accurate model.The user can change the
    formulation quickly and easily, can change
    from one solver to another, and can even
    convert from linear to nonlinear with little
    trouble.
    provides a high-level language for the

    compact representation of large and complex
    models
• allows changes to be made in model

    specifications simply and safely
    • allows unambiguous statements of

    algebraic relationships and permits model
    descriptions that are independent of solution
    algorithms
    While there are some other optimization

    software packages that have these same
    qualities, GAMS is widely used for energy
    optimizations
Three scenarios describe the conditions under which
 the customer purchases electricity

    Fixed price

    Tariff

    Energy Market + revenue neutrality



Sensitivities:

    Base case (real prices, without subsidy)

    High NG prices, Low NG prices

    DER subsidy (e.g. PV, HQ CHP)

    Decrese in DER technology cost

Total customer electricity supply cost ($)

    Energy payments to the distribution company

    during peak/mid/off hours ($)
    Power payments to the distribution company ($)

    Energy sales to the grid ($)

    Self-generation investment/variable costs ($)

    Average paid price (c/kWh)

    Installed capacity (kW) and number of units

    installed
    Hourly electricity production of every DER

    technology
Customer decisions  economic criteria



    Excess/shortage of electricity  grid



    Equipment price and performance are

    accepted without question

    there is any deterioration in output or

    efficiency during the lifetime

    CHP benefits, reliability and power quality

    benefits are not taken into account
Add IHT, Wind and Stirling engine in the

    model
    Add heat/cool/electricity storage option

    Take into account any drop of efficiency for

    for part load opperation of CHP
    Make a real technology database in

    combination with equipment suppliers
    Find details for UK electricity market


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Energy Systems Optimization

  • 2. The situation: •Between now and 2050 global population expected to grow to 9 billion •80% of the population will live in cities •Buildings consume nearly 60% of the total energy in cities Problem or opportunity? •Much new build will be required-resources •Buildings currently are highly innefficient •High carbon emmiters
  • 3. •Electricity from grid •Heat by using boilers Where is the problem? •Low efficiency •High emissions •Poor reliability and power quality •Expensive trasmission networks
  • 4. Building passive design: •Natural ventilation •Enhansed thermal performance •External microclimate •Solar shading •Low energy appliances •Low energy lighting •Green roof Energy systems optimization: •PV •CHP/CCHP •Biomass •Wind •Fuel cells
  • 5. Sources Generation Conversion Demand Technologies Technologies GRID Electricity Electricity Lighting Wind Lighting Technologies PV VC air cooled VC water cooled Natural Gas CHP/CCHP Biomass Cooling Absorption Solar thermal Cooling Boiler Heating IHT Waste Heat Stirling Engine Electricity Heat Cooling Natural resureces
  • 6. Which technology to be installed?  What is the appropriate level of installed  capacity? How should operate the installed capacity?  With Objective: • Minimize the energy cost • Minimize the environmental effect +
  • 7.
  • 8.  For each of the three seasons (summer, winter, mid)  Average and peak day load profile  Each profile 24 hourly electricity/heat loads (kW) TAS/IES
  • 9. Natural Gas Price  Biomass Price  Electricity Price  Trasmission/Distribution 
  • 10. Capicity (KW)  Lifetime (years)  Capital cost ($/KW)  Installation cost ($/KW)  Operation & Maintenance variable/fixed cost  ($/KWh, $/KWh) Heat rate (Kj fuel/KWh)  Heat to power ratio (α) 
  • 11.
  • 12. Objective function is to minimize total cost, which consisting of: total facilities and customer charges  total electricity purchases charges  carbon taxes  total on-site generation fuel and O&M costs  total DER investment cost  and minus the revenues generated by any energy sales to the  grid
  • 13. Subject to: Equation enforces energy balance  Equation enforces the on-site generating  capacity constraint Equation limits how much recovered heat can be  recovered from each technology Equation averts the use of the recovered heat for  meating cooling if no absorption chiller exist Equation annualizes the capital cost of owning  on-site generating equipment etc…
  • 14. The General Algebraic Modeling System  (GAMS) is specifically designed for modeling linear, nonlinear and mixed integer optimization problems. The system is especially useful with large, complex problems. GAMS allows the user to concentrate on the  modeling problem by making the setup simple. The system takes care of the time- consuming details of the specific machine and system software implementation.
  • 15. GAMS is especially useful for handling large,  complex, one-of-a-kind problems which may require many revisions to establish an accurate model.The user can change the formulation quickly and easily, can change from one solver to another, and can even convert from linear to nonlinear with little trouble. provides a high-level language for the  compact representation of large and complex models
  • 16. • allows changes to be made in model  specifications simply and safely • allows unambiguous statements of  algebraic relationships and permits model descriptions that are independent of solution algorithms While there are some other optimization  software packages that have these same qualities, GAMS is widely used for energy optimizations
  • 17. Three scenarios describe the conditions under which the customer purchases electricity Fixed price  Tariff  Energy Market + revenue neutrality  Sensitivities: Base case (real prices, without subsidy)  High NG prices, Low NG prices  DER subsidy (e.g. PV, HQ CHP)  Decrese in DER technology cost 
  • 18. Total customer electricity supply cost ($)  Energy payments to the distribution company  during peak/mid/off hours ($) Power payments to the distribution company ($)  Energy sales to the grid ($)  Self-generation investment/variable costs ($)  Average paid price (c/kWh)  Installed capacity (kW) and number of units  installed Hourly electricity production of every DER  technology
  • 19. Customer decisions  economic criteria  Excess/shortage of electricity  grid  Equipment price and performance are  accepted without question there is any deterioration in output or  efficiency during the lifetime CHP benefits, reliability and power quality  benefits are not taken into account
  • 20. Add IHT, Wind and Stirling engine in the  model Add heat/cool/electricity storage option  Take into account any drop of efficiency for  for part load opperation of CHP Make a real technology database in  combination with equipment suppliers Find details for UK electricity market 