The present study focuses on the development of software (general mathematical optimization model) which has the following characteristics: • It will be able to find the optimal combination of installed equipment (power & heat generation etc) in a Shopping Mall (micro-grid) • With multi-objective to maximize the cost at the same time as minimizing the environmental impacts (i.e. CO2 emissions). • To date, this tool is scarce to the industry (similar to DER-CAM, Homer).

1. Aristotelis Giannopoulos

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
+

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)


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
