1 Energy systems optimization of a Shopping mall Aristotelis Giannopoulos 26/09/08 Supervised by: Prof. David Fisk (Civil and Environmental Engineering) Prof. Stratos Pistikopoulos (Chemical Engineering) A thesis submitted to Imperial College London in partial fulfilment of the requirements for the degree of Master of Science in Sustainable Energy Futures and for the Diploma of Imperial College Faculty of Engineering Imperial College London London SW7 2AZ, UK
Energy Systems Optimization of a Shopping Mall: 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).
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Transcript
1
Energy systems optimization of a Shopping mall
Aristotelis Giannopoulos
26/09/08
Supervised by:
Prof. David Fisk (Civil and Environmental Engineering)
Prof. Stratos Pistikopoulos (Chemical Engineering)
A thesis submitted to Imperial College London in partial fulfilment of the
requirements for the degree of Master of Science in Sustainable Energy Futures and
for the Diploma of Imperial College
Faculty of Engineering
Imperial College London
London SW7 2AZ, UK
2
3
Table of Contents
Table of Contents ...................................................................................................................... 3
List of Figures and Tables ......................................................................................................... 6
conditioning (HVAC) equipment, and finally distributed generation unit performance
parameters and operation strategy.
Concluding, Shopping Malls are large energy consumers, with energy consumption
per 𝑚2larger than the majority of the commercial buildings. The main energy need in
a SM is electricity for cooling and lighting. Especially the cooling requirements are
large due to the high density of people during the working hours and the high thermal
loads from the artificial lighting inside the building. Also and the light requirements
are high due to the special needs of a SM. Until now very little work has been done in
SM as regards the energy optimization and sustainability in adverse with the large
amount of papers existing for other commercial buildings. In the next section will be
introduced the different alternative technologies can be used in SM.
What are the future challenges?
As becomes obvious from the existing analysis buildings due to their increasing
contribution in the global energy consumption and their inefficient way they meet
their demand until now there is a lot of potential to both decrease and meet the
demand in a different more efficient way. In other terms, the objective is the
Sustainable Development of the Buildings and especially in this case Shopping Malls
while the comfort level of these buildings remains constant.
Figure 16, Mall Weekend Load Shape
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2.2 Alternative Technologies and Energy sustainability in SM
2.2.1 Description of the different technical alternatives
In the next figure 17, we can see some of the most important technical alternatives
can be used in a SM to meet the electricity and heat demand. As we can see from the
figure for electricity the alternatives are: Grid, Photovoltaic’s (PV), Combined Heat
and Power (CHP, natural gas). For heat the alternatives are: CHP and boiler. For
cooling we can use both electricity or/and heat in a VC cooled air condition and in an
Absorption cooling system respectively. A short introduction and description of the
above technologies are listed below.
Figure 17, Superstructure with the most important technical alternatives meeting the electricity and heat demand in a SM
Sources
Generation
Technologies
Conversion
Technologies
Demand
GRID
Electricity
Natural Gas
PV
CHP
Boile
r
VC air cooled
VC water cooled
Absorption
Cooling
Electricity-
only
Cooling
Heating
Waste Heat
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2.2.2 Photovoltaic’s
Solar radiation can be converted directly into electricity using photovoltaic (PV)
cells. The electrical efficiency of PV is between 5-15%, and the energy output of such
a system depends from the solar radiation, for UK the radiation range between 800-
1000 kW h (Northern to Southern England). According to the common
technologies the installed cost of a BIPV is about 500 pounds per for roof tile and
900 pounds per for the most expensive facades (F, 2005). One squared meter of
mono-crystalline array will produce roughly 150 kW h per year, and also for each
kW installed will produced about 700 kW h per year. The maintenance and
operation cost of a PV system is too low that is not included, and the lifetime is in
average about 30 years. Finally, PV is almost ‘emission-free’, because there is no
need for fuel or cooling water; it operates silently and is believed to fit in urban
development. One kW panel can save 0.1 to 1 tonne of emitted per year.
However, the manufacture of PV requires a lot of energy and is embodied some
(F, 2005). The above prices summerized in the 18 Figure.
Capacity Cost per in
pounds
Energy output kWh
p a
kWh per year for
each kW installed
1>kW 500 - 900 120 - 150 560 - 700
2.2.3 Co-generation
Co-generation is also called combined heat and power (CHP). CHP in contrast with
conventional power plants uses heat that is normally discarded to produce thermal
energy, which can be provided to district heating systems, with result to reduce𝐶𝑂2
emissions and running costs. The efficiency depends from the type, scale and
operation of the CHP with an average of 70-80% (25-35% electricity and 45-55%
high grade or useful heat 71-82 c) (F, 2005). The different types of CHP are: Micro
turbines, Fuel cells, Reciprocating engines, Gas turbines (simple-cycle cogeneration),
Gas and steam turbines (combined-cycle cogeneration) and gas engines. Some data
about those (capital cost, efficiency, power to-heat ratio, emissions etc) are
represented in the table 4. Interesting issue is the operation of CHP’s, because in
cogeneration it is important to optimize the balance of heat and electricity generation.
This balance depends on the customer loads (electrical and thermal) and is possible
Figure 18, Average costs and productivity of PV’s
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the CHP to follow the thermal or the electrical load. Other option is to produce more
electricity and/or heat and sell it back to the grid/customer in order to have some
profit. One other option is the fuel, natural gas or biomass. All the above options and
other must be taken into account and optimized in the CHP installation to meet the
same demand with less cost and emissions.
Table 4, Characteristics of cogeneration technologies available for use at the scale of individual large buildings (micro turbines, fuel cells, reciprocating engines) and district heating networks (simple- and combined-cycle turbines) (Lemar, 2001)
2.2.4 Tri-generation model
Tri-generation is also known as combined heating, cooling and power generation or
CHCP. CHCP uses the waste heat from CHP not only to meet the heat but also the
cooling demand by applying the heat to absorption chillers. This chiller utilizes the
heat to increase the pressure of refrigerant instead of using compressors which highly
consume electricity. All the facts from co-generation also existing here, with more
complexity because the optimization problem now extended further more. The
advantage of the CHCP compared to co-generation becomes clear in buildings with
high cooling demand like in this case in a Shopping Mall.
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2.2.5 Gas boiler
A boiler is a device for generating steam for power, processing, or heating purposes
or for producing hot water for heating purposes or hot water supply (used until now
for the majority of the buildings). It provides the building with heating and hot water
with efficiencies between 80-90% (Tester W. Jefferson, 2005) and can burn natural
gas or biomass. A great disadvantage of the boiler is the ``bad`` use or degradation of
high quality fuels like natural gas for the production of low grade heat for heating
needs comparing with the CHP which use the same fuel to produce some high quality
energy source (electricity) and some low grade heat.
2.2.6 Grid Electricity and other parameters
The cost of electricity, gas and biomass are given in the table 5 for domestic
commercial and wholesale use and also the 𝐶𝑂2 trading factor. In table 6 depicted the
average 𝐶𝑂2 emissions factor for the total UK grid mix (g/kWh) and in table 7 are
given values about the kg 𝐶𝑂2 emitted per kWh produced for the natural gas, boilers,
renewables, and grid. Also in the table 7 are given prices about the inflation, discount
rate etc.
Table 5, Costs (electricity, gas, and biomass) and also 𝑪𝑶𝟐 trading factor, (SEA/RENUE, 2006).
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Table 6, Proportion of electricity supplied to the national grid from different sources, and associated 𝑪𝑶𝟐 emission factors, 2005.
Table 7, 𝑪𝑶𝟐 factors (grid, boilers, natural gas, and renewables) and other parameters (inflation, discount factor etc), (SEA/RENUE, 2006)
2.2.7 Electric chiller
The majority of the shopping malls use Vapor compression (VC, base scenario) with
air-cooled chiller for air conditioning. The electric chiller is defined by its efficiency
which expressed by the coefficient of performance (COP). The bigger is the COP the
more efficient is the electric chiller with result the decrease of the electricity used (for
the same comfort) and consequently the reduction of the fuel used (to produce
electricity) and the emissions going into the environment. Most of the HVAC systems
used in the shopping malls until now have a COP 3, but there are already existing
vapor compressions with water-cooled chiller systems in the market with COP 5.
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2.2.8 Absorption chiller
The alternative choice of the VC is the absorption cooling (AC) with absorption
chiller (COP 1.2, heating) (Tester W. Jefferson, 2005). Absorption chillers use heat
instead of mechanical energy to provide cooling. A thermal compressor consists of an
absorber, a generator, a pump, and a throttling device, and replaces the mechanical
vapor compressor. The basic cooling cycle is the same for the absorption and electric
chillers, but the basic difference between the electric chillers and absorption chillers
is that an electric chiller uses an electric motor for operating a compressor used for
raising the pressure of refrigerant vapors and an absorption chiller uses heat for
compressing refrigerant vapors to a high-pressure. The rejected heat from the power-
generation equipment (e.g. turbines, micro turbines, and engines) may be used with
an absorption chiller to provide the cooling in a CHP (Combined Heat and Power)
system. The interesting part is to see through the optimization if it is more economic
and environmentally feasible to operate a CHP with higher electric to thermal ratio in
order to produce more electricity which will be used by an electric chiller in order to
meet the cooling demand or is better to operate the CHP in a higher thermal to
electric ratio in order to drive the heat through an absorption chiller and produce in
this way the cooling demand.
2.3 Distributed Energy Resources in Shopping Malls and
Commercial Buildings
Many researchers have been conducted until now as regards the passive design of the
building and the potential for reducing the demand (electricity, heating), but very few
have been done as regards the different ways to meet this demand (e.g. renewable,
CHP etc) in a Commercial building and especially for Shopping Mall less than five.
As regards the Shopping Mall until now there is no paper which use a simulation or
model optimization tool to integrate different distributed energy resources (more than
one e.g. PV & CHP) in it. For other Commercial Building like hospital, big offices
etc, there are studies with the majority of them examine only one energy source (e.g.
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PV) and not a combination of them, and in the case they examine more than one
usually they do an exhaustive case by case simulation (no global optimum guarantee).
One other fact is that most of the studies are not develop an energy optimization
model but they use the existing commercial tools to examine different buildings.
Furthermore from the energy optimization models existing, most of them focused
only in some technologies (e.g. only in photovoltaic’s, or only in micro-turbine CHP,
or only efficiency techniques etc) and in some aspects (e.g. only economic benefits or
only environmental benefits examined but not both etc) of using decentralized energy
resources in buildings. Until now no research has been done in which will examined
different ways of meeting the demand and decreasing at the same time the demand of
a building (without take into account the passive design of the building), with final
objective not only the economic but also and the environmental benefit.
One of the interesting studies was conducted in Japan by Nan Zhou (Nan Zhou a *.
C., 2006). The objective was to find the best distributed energy resource system for
different types of commercial buildings (hospital, big office, hotel sport facilities and
retail) with constraint to meet the energy demands. In order this to be achieved was
used an information base with different distributed technologies, Japanese energy
tariffs and fuel prices, and the buildings needs which have been developed. Three
scenarios were taken for each building type. The first scenario was to take no action
in order to take the baseline (grid, NG boiler) costs, consumption and emissions. The
second scenario made available to purchase a generation technology only for
electricity production (without heat recovery and absorption cooling), and the third
scenario was included everything (generation, recovery and with waste heat cooling).
The results show a significant increase in the efficiency (Figure 19), decrease in
carbon emissions (Figure 20) and finally decrease in annual energy cost (Figure 21).
The results show a great potential and a very promising payoff (between 3 - 6.8
years).
Figure 19, Efficiencies of the overall system, (Nan Zhou a *. C., 2006).
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Figure 20, carbon emissions comparing base and optimal solution for all the buildings, (Nan Zhou a *. C., 2006)
Figure 21, Annual savings, (Nan Zhou a *. C., 2006)
In the next paper Medrano (M. Medrano, 2008) try to investigate the economic,
energy-efficiency, and environmental impacts of the integration of distributed
technologies (high-temperature fuel cells, micro-turbines, and photovoltaic solar
panels) into four representative generic commercial buildings (office building,
medium office building, hospital, and college/school), using as simulation tool the
DOE-2.2- derived user-interface eQUEST program. This tool can calculate the hourly
energy loads and costs of several types of commercial buildings given information
about: building location, construction, operation, utility rate schedule, heating,
ventilating, air-conditioning (HVAC) equipment, and finally distributed generation
unit performance parameters and operation strategy.
The methodology Medrano follow have four steps. First, is the base case where n DG
are included and during this step the electric and gas hourly profiles for days
corresponding to peak electric and gas consumption are analyzed. In the second step
are introduced and implemented different cost effective energy efficiency measures
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(e.g., day lighting, exterior shading, and improved HVAC performance) according to
energy use intensity with objective to reduce energy consumption and emissions. In
the third case different DG technologies integrated in the buildings with the constraint
that the waste heat utilized only for hot water and/or space heating. In the last
approach, the traditional HVAC systems were replaced by heat driven absorption
chillers alternatives, systems which works with hot water loops. In this way the
thermal loads are utilized with result the increase of the overall efficiency of the DG
system. Finally, the influences of utility gas and electric tariffs and weather
conditions are illustrated, comparing the DG economic viability of the same office
building in two U.S. locations.
According to this paper the results gave a promising potential of the DG in these
types of buildings. But I won’t stay in these results but in the methodology and the
tools Medrano used in this report. Using this kind of simulation tools like eQUEST
he investigates case by case combinations of DG in buildings, with the result not to
find the optimum solution for cost reduction and environmental benefits and
efficiency maximization.
36
3. Model Inputs
In this section will be presented and explained all the different inputs to the model.
First will be explained the technology database, then the shopping mall loads and
finally the market inputs.
3.1 Technology database
In this section will be presented and explained the technology database that was used
as input to our model. These dada depicted in figure 22 was initially produced by the
National Renewable Energy Laboratory (NREL) in the study ‘’Gas-Fired Distribution
Energy Resource Technology Characterizations’’ (Goldstein, 2003), and then further
developed by Ernest Orlando Lawrence Berkeley National Laboratory in 2004 report
Distributed Energy Resources Customer Adoption Model Technology Data
(Firestone, 2004).
This technology database contain information for the technologies: fuel cells (FC),
gas turbines (GT), micro-turbines (MC), natural gas engines (NG), and photovoltaic’s
(PV). Each technology described by a number of parameters, parameters which are
inputs to the model and are explained below:
Capacity (maxp): This represents the maximum electrical output of the
machine in KW.
Lifetime (years): is the average life of the machine in years.
Capital cost (capcost): includes the machines cost, the system design and
finally the installation cost. This parameter defined as the cost per KW
electrical output capacity ($/KW). These machines can be purchased:
a) Without heat recovery potential (no CHP)
b) With heat recovery for heating purposes (CHP)
c) With heat recovery for both heating and cooling (CCHP)
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Operation and Maintenance Fixed Costs (OMFix): OMFix includes all the
fixed annual operation and maintenance costs ($/KW per annum) (excludes
fuel costs)
Operation and Maintenance Variable Costs (OMVar): OMVar includes all
variable operation and maintenance costs ($/KWh) (excludes fuel costs)
Heat rate (HeatR): is the equipment heat rate (kJ fuel/KWh). Heat rate is
linked to electrical efficiency, E by the equation:
HeatR = 3600 𝑘𝐽
𝐾𝑊ℎ
𝐸
HeatR in expressed with esteem to the higher heating value (HHV) of natural
gas, due to the fact that the purchase of NG is with respect to the HHV
Heat to power Ratio (α): α is the ratio of recoverable heat per KWh electrical
produced (maxp to maxp).
According to Firestone, α value is based on the waste heat energy content
prior to conversion via a heat exchanger, and here referred as recoverable heat
As soon as the spot market prices calculated, then the annual average price is
calculated and this value is multiplied by 49/51 in order to find the distribution,
transmission and supply payment. This parameter is called DistrPay in the GAMS
and its value is 0.13936531 $/KWh (for 2008). So each time the customer purchases
one KWh, the price will be payed back to the grid consisted of the spot market price
for the exact time of the purchase and the constant DistrPay. Using this method the
final electricity prices for the whole year depicted in figure 31.
Figure29. Contribution of distribution costs to electricity bill (Williams P. a., 2001)
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Figure 30, Spot market electricity prices
Figure 31, Grid electricity price with the distribution company revenue
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0 5 10 15 20 25 30
$ p
er
KW
h
Hour
Spot market electricity priceJanuary
february
March
April
May
June
July
August
September
Octomber
November
December
0
0.1
0.2
0.3
0.4
0.5
0.6
0 5 10 15 20 25 30
$ p
er
KW
h
Hour
Grid electricity price with the distribution company revenue January
february
March
April
May
June
July
August
September
Octomber
November
December
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4. Mathematical Model
4.1 Introduction
In this part, the mathematical model will be presented and explained but also and the
reasons behind this venture. The results which are presented are intended more to
show: the great usage of GAMS in solving difficult optimization problems, the
possible savings that can be achieved by the optimization of the energy systems
(combination of DG and grid) in a shopping mall (extended in a microgrid) and not
the actual numbers of the final energy cost of a shopping mall and the actual carbon
savings. Improvements must be undertaken in the tariffs in order the model to use an
accurate electricity tariff system, in the load profiles which are key input to the model
(must be monitored a real SM for a year and calculated the accurate electricity,
heating and cooling profiles), and also the technology information (more accurate
costs and a more accurate thermodynamic model which will take into account the
efficiency drops etc). Finally we can say, that given all the foresaid inputs (customer
demand, electricity/NG tariffs and technology information) the model can give back
some strategic results about the way DG technologies and Grid must be combined
(which DG must installed) and work (when this capacity will operate during the year)
in order to have some energy, money and carbon savings while we meet the constant
customer demand.
4.2 Mathematical Programming
We use mathematical programming in order to build the energy models. H. Paul
Williams gives a definition for the mathematical programming (Williams H. , 1999):
Mathematical programming has a sense of planning for the purpose of optimization,
it is a mathematical problem regarding to maximizing or minimizing something
which is known as objective function and it has to satisfy the conditions called
constraints. The mathematical programming models are able to be classified as linear
programming models, non-linear programming models and integer programming
models.
Linear Programming Model (LP): Linear programming is the optimization
problem in which the objective functions and the constraints are all linear.
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Non-linear Programming (NLP): Non-linear programming is the optimization
problem in which at least one of the objective functions or the constraints is a
non-linear function.
Mixed-integer Programming (MIP): Mixed-integer programming is the
optimization problem that has both continuous variables together with integer
variables. It can be mixed-integer linear programming (MILP) or mixed-
integer non-linear programming (MINLP).
Mixed-integer linear Programming (MILP): Mixed-Integer Programming
(MIP) methods (L.T. Biegler, 1997) are suitable for modeling and analyzing
buildings energy systems towards design, investment planning and
optimization: this established algorithmic framework fulfills the requirements
and captures the complexities of an investment planning procedure, by
considering the superstructure of all alternatives, representing all possible
choices for a system by binary (0–1) variables, while all the physical and
economic quantities are expressed as continuous variables. All logical and
physical relations are translated into equality or inequality constraints. The
best plan is derived by conducting an optimization for a specific objective
function (Liu Pei, 2007).
4.3 General Algebraic Modeling System (GAMS)
General Algebraic Modeling System (GAMS) is multipurpose optimization software
which is particularly designed for modeling linear, non-linear and mixed integer
optimization (MIP) problems. Most of the researchers use GAMS for solving large
and complex mixed integer linear programming (MILP) problems. Of course GAMS
can do much more than these but is not in the current needs of this subject.
The basic reasons GAMS selected for this optimization are:
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Offer a high level language, for the illustration of large, difficult and
complex models.
Provide easy and safe changes in the specification of the model.
Allows unambiguous statements of algebraic relationships
Allows comments in the model which are independent to the model
solutions.
4.4 Model Description
In this current model, there are two input fuels: natural gas and electricity from the
grid. At the other end of the model there are three end uses that can be met: electrical-
only, cooling and heating loads. The models objective function is to minimize the
cost of meeting the Shopping malls energy demand for one year (while taking into
account the carbon emissions) by optimizing the usage of different distributed
generation technologies and the power from grid. In order to reach this objective the
model must answer the following questions:
If it is economically feasible, which DG technologies must be adopted?
The chosen DG technologies in which capacity will be installed?
How this capacity must be operated during day/year in order to minimize the
energy cost while meeting at all times the customer demand?
It is more economically for the customer to disconnect for the grid or there are
profit opportunities by selling electricity back to the grid (especially the times
of the high demand / high price)?
The model inputs are:
The SM electricity-only, cooling and heating load profiles,
The hourly spot electricity prices for the year 2008/2007 2 (with the payment
of distribution company) and the monthly spot natural gas prices for the same
year,
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DG technology information which includes: capital (include installation),
operation and maintenance, and fuel costs (taken into account the lifetime and
annualized for one year), basic technology physical characteristics, and finally
some general thermodynamic parameters for the efficiency of the combined
heat and power.
Rate of carbon emissions from the microgrid and from the on-site onsite
generation by the usage of natural gas
Carbon taxes by using the price from ETS
The model outputs will be:
The mixture of the DG technologies that will installed to the shopping mall
The capacity level in which these technologies will be installed
Hourly operating schedule of the installed capacity (not 15 minute basis that is
common in spot markets due to the computational constraints but also and the
difficulty to find the load profiles in this study)
The final energy cost to meet the shopping mall demand and also the carbon
emissions through the usage of the DG equipment and/or grid
Some important model’s assumptions:
The optimization based clearly on economic criteria. At the same time while
there is a try to capture the externality of the carbon emissions by taxation
there is no consideration of a detailed environmental model (e.g. maybe there
is a reduction on carbon emission but not local pollution)
The customer can buy and sell electricity back to the grid at any time. Despite
the fact that the spot market electricity prices are real, the distribution
payment (is constant for the year) and the price the customer sells back to the
grid (half of the spot market price and if we take into account the payment for
the distributor is almost the ¼ of the price we buy from the grid) are not
accurate but based more in an average base.
Are not taken into any deterioration in output efficiency of the equipment
during its lifetime, and also there is no penalty in the efficiency for part load
operation (start-up also).
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In this economic report is not taken account the reliability of these equipment
(e.g. if the CHP broke for one reason the power purchase from grid are not in
the same tariffs as usual)
At the same way, CHP/CCHP benefits, power quality and reliability also are
not taken into account.
Equipment price and performance are accepted without question.
2Will be used the prices until July for the 2008, and the prices for the rest of the year from 2007.
4.5 Mathematical Formulation
Customer Data
Name Description
Cdemand m,h,u Customer demand in kW for end-use u during hour h, and month
m (end-use are electric-only, cooling, heating).
Market Data
Name Description
ElectrPricem,h Spot market electricity price during month m and hour h
($/KWh)
CarbonTax Taxes on Carbon emissions ($/Kg)
MCRate Carbon emission rate from the grid (Kg/KWh)
NGCRateu Carbon emission rate from burning onsite natural gas to meet the
end use u (Kg/KWh)
NGpricem Spot market natural gas price during month m ($/Kj)
Technology Information
Name Description
Name Description
DGmaxi Capacity rating of technology i ( kW)
DGlifetimei
Expected lifetime of technology i (a)
DGcapcosti Capital and installation cost of technology i ( $/kW)
DGOMfixi Fixed annual operation and maintenance costs of technology i
($/kW)
DGOMvari Variable operation and maintenance costs of technology i
($/kWh)
DGCostKWhi,m
Generation cost of technology i during month m ($/kWh)
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CarbonRatei Carbon emissions rate from technology i (kg/kWh)
DFCap
Capacity of direct-fired absorption chiller (kW)
DFPrice Capital and installation cost of direct-fired absorption chiller
($/KW)
E(i) Set of end-uses that can be met by technology i
Aditional Parameters
Name Description
InterestRate
Interest rate on DG investments (%)
SolarInsm,h
Average portion of maximum solar insolation received (%)
during hour h and month m and used from photovoltaic cells
DistRev Distribution company revenue1
NGHR
Natural gas heat rate (kJ/kWh)
1This price is added to the spot electricity market when the customer buys from the grid (wholesale market) and pay the cost of
the distribution of the electricity from the producer to the customer. According to Williams and the paper Costing and pricing of electricity distribution services (Williams P. a., 2001) this value is almost the 50% of the total electricity cost. For the simplicity
here the average annual electricity spot price multiplied by 49/51 and this is the value for the whole year.
Variables
Name Description
InvDGi Integer variable which shows if one technology will be installed
and in which quantity
DF
Binary variable which shoes if a direct-fired absorption chiller
will be installed
ai
The amount of heat (in kWh) that can be recovered from every
kWh of electricity is produced by using DG technology i (a has
value 0 for all technologies that are not CHP or CCHP)
bu The amount of heat (in kWh) generated from unit kWh of natural
gas obtained for end-use u (the corresponding value of bu for
electricity-only load is zero due to the fact that never uses NG)
gi,u The quantity of valuable heat (in kWh) that can be allocated to
end-use u from unit kWh of recovered heat from technology i
(given that the electricity-only loads never use recovered heat,the
gi,u equals to zero)
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GenOni,m,u,h Generated electricity by technology i during month m, hour h to
meet the onsite demand u (kWh)
GenSeli,m,h Generated electricity by technology i during month m and hour h
to sell back to the grid (kWh)
PurNGm,u,h Obtained natural gas during month m hour h for end use u (kWh)
(for direct burning)
GridElectrm,u,h
Purchased electricity from the grid by the customer during month
m, hour h to meet the customer demand u (kWh)
RecHeati,m,u,h Recovered heat from technology i that is used to meet the
𝑢 GenOni,m,u,h + GenSeli,m,h ≤ InvDGi ∙ DGmaxi ∀ i, m, h (3)
Annuityi = InterestRate
(1− 1
1+InterestRate 𝐷𝐺𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒𝑖 ) ∀ i (4)
𝑢 GenOnj,m,u,h + GenSelj,m,h ≤ InvDGj ∙ DGmaxj ∙ SolarInsm,h ∀ m, h
if j ∈ {PV} (5)
𝑢 RecHeati,m,u,h ≤ ai ∙ 𝑢 (GenOni,m,u,h ) + ai ∙ GenSeli,m,h)
∀ i, m, h (6)
RecHeati,m,u,h = 0 ∀ i, m, u, h if u ∉ E(i) (7)
GenOni,m,u,h = 0 ∀ i, m, h if u ∈ {heating} (8)
GridElectrm,u,h = 0 ∀ m, h if u ∈ {heating} (9)
PurNGm,u,h ≤ DFCap ∙ DF ∀ m, h if u ∈ {cooling} (10)
Explanation of the equations:
(1) This is the objective function of the model which will try to minimize the total
demand cost / maximize the net present value for the whole year while at the
same time will take into account the carbon emission externality. This cost
consisted from : the cost of buying electricity from the grid (included the
payment to the distribution company), the carbon taxes we have to pay due to
this purchase (internalize the externality), the fuel cost (natural gas) for
producing electricity using the DG technologies for the purpose to meet our
own demand or to sell electricity back to the grid. The variable operation and
58
maintenance cost for producing electricity onsite (meet the demand or sell
back to the grid), the carbon taxes we have to pay for the onsite production,
the fixed operation and maintenance cost for producing electricity onsite, the
annualized capital (included installation) costs for the technologies that will
installed, the Natural gas cost for the direct burning application with also the
equivalent carbon taxes. All these costs will be added and will subtracted
from the revenue we make by selling back electricity to the grid.
(2) The second equation/constraint will make sure that will covered all the
customer demand during the year and also specify the means through which
the load for energy end use u may be satisfied (the demand will be covered
through on-site production and/or purchase from the grid and/or direct
burning of natural gas and/or the recovered heat).
(3) The third equation make sure that the on-site production is limited by the
capacity we initially invest (this balance is for every hour for every day the
whole year).
(4) The fourth equation annualizes the cost of capital investment of DG
technologies.
(5) Fifth equation is responsible to enforce the constraint on how much power
PV’s can produce during the day and this production is in proportion to the
solar insolation.
(6) This equation limits how much heat can be recovered from each type of DG
equipment/technology.
(7) This equation avert the usage of recovered heat by end uses that can not
satisfied by specific DG technology.
(8) This equation not allow the usage of electricity for meeting heating loads
(electrical heaters are extremely un-efficient)
(9) The same with the previous equation, now for the grid
(10) Finally this equation is responsible to prevent the direct burning of
natural gas for meeting cooling demands without the existence of absorption
chiller.
59
5. Results
In this part of the report will be represented the results of the model for different
working scenarios, and different sensitivities analysis (change of the variables by
alteration of the input parameters). The first target of using different scenarios and
sensitivity analysis is the verification of the model. Secondly, by giving different
input values we can see in the lump the great opportunities DG and renewables are
giving for the future for money saving and carbon emissions reductions (regardless
the absence of some details in the model, like extremely accurate tariff system).
5.1 Scenarios and Sensitivities
In this report will be examined two different tariff scenarios (ways SM buy electricity
from the grid). One of the two scenarios will be used furthermore for a detailed
sensitivity analysis. The different scenarios depicted and explained in Table 11.
Table 11, Scenarios examined
The different sensitivities that will be examined are described in Table 12.
Scenarios Description
Constant electricity price In this case the model will take as
electricity tariff input constant prices for
the whole year. That means that the
shopping mall will purchase electricity in
different hours, different months at a fixed
price. The goal behind this scenario is the
verification of the model through the
different selection of the technologies
(grid, CHP etc) under different average
electricity prices.
Spot market prices In this case the shopping mall buys
electricity from the grid in the real spot
market prices (2008) plus the distribution
revenue for each KWh purchased (for
using the lines, for the operator etc). This
scenario will be the bases for the sensitivity
analysis due to the fact that is closer to the
reality.
60
Table 12, Examined sensitivities
Sensitivities Description
Grid plus Boiler In this case the model cannot use any of
the technology to meet the shopping mall
demand. This will be the base for the
comparison with the other results (Net
present value, carbon emissions).
Without CHP/CCHP In this case the model will be constraint
and won’t be able to invest in a CHP
and/or a CCHP system, but will be able to
invest in PV and on-site power generation
only Without CCHP
The shopping mall can invest in
everything except to CCHP (not
absorption cooling). Final case
Here the model will be able to take all the
inputs without any constraint and find the
final solution (include all the
technologies, detailed tariff system,
detailed customer demand). Only PV, Grid and Boiler In this case the model examine the
optimum solution if the only technology
existed from DG is PV (Included boiler
and grid).
At least 7 PV Here will be put in the model constraint to
invest at least 7 PV panels. Then the
model will find the optimum solution
starting from this point
High carbon price with a
20% PV capital reduction Here will be examined a friendly to PV
case with a capital cost reduction for PV
20% and 100$ per tone of CO2 emitted. 50 % cheaper electricity
prices The spot market electricity prices (and the
distribution payment) will be reduced by
50% (all the rest remain the same).
50% more expensive NG The customer will buy natural gas 50%
more expensive prices (without change
the grid electricity prices). High carbon price High price per tone of CO2 emission will
be examined (100$ per tone compared
with the 40$ existing now).
61
5.2 Outline of results
For the bases scenario (spot market prices) and all sensitivities, the below data given
as output:
Technology adopted (name and capacity in KW)
SM total power demand (KWh)
SM electricity-only demand (KWh)
SM cooling demand (KWh)
SM heating demand (KWh)
Electricity met by the grid (KWh)
Electricity me by on-site generation (KWh)
Electricity sales back to the grid (KWh)
Cooling demand met by the grid (electric chiller) (KWh)
Cooling demand met by on-site power generation (electric chiller) (KWh)
Cooling demand met by on-site recovered heat (if any CCHP) by driving
absorption chiller (KWh)
Cooling demand met by on-site direct fire burning of NG (boiler plus
absorption chiller, not CCHP) (KWh)
Heating demand met by recovered heat (CHP or/and CCHP) (KWh)
Heating demand me by direct burning of NG (boiler) (KWh)
NG purchases (KWh)
NG purchases (without those are targeted for selling back to grid) (KWh)
And the annual economic results:
Energy payments to the grid ($)
Carbon taxes for grid purchases ($)
Fuel cost for on-site generation (include the fuel for sales back to the grid) ($)
Variable cost for on-site generation ($)
Carbon taxes for on-site generation ($)
Capital investment costs (included installation) ($)
62
NG purchases for direct burning (boiler, boiler plus absorption chiller) ($)
Carbon taxes for direct burning of NG ($)
Carbon emissions for meeting total SM demand (tones CO2)
Carbon savings over basic grid plus boiler scenario (%)
Energy sales back to the grid ($)
Net Present Value (NPV) ($)
Money savings over basic grid plus boiler scenario (%)
For the constant electricity price scenario, will be given some of the above due to the
fact that the goal of this scenario is the verification of the model and not the actual
results.
5.3 Overview of spot market prices results scenario
The usage of distributed generation technologies (DGT) in a shopping mall and
generally in a microgrid can substantially reduce the annual energy costs. The figure
32 below represents the percentage of annual bill savings of some examined DGT
scenarios over the basic grid + boiler scenario. It becomes clear that all the DGT
cases (some are independent to the grid and some not) have considerable savings with
the final case to have 51% savings over grid + boiler, the case without CCHP 48%,
the case without CHP/CCHP 44%, the case at least 7 PV 38%, and finally the PV+
Grid + Boiler 16% (all these cases/sensitivities will examined analytically at the sub-
section each case detailed results). The reduction of the final cost/NPV is greater in
the final case, where all the technologies are accessible, and is available not only the
CHP but also the CCHP (invest on CCHP and take the advantage of absorption
cooling). The second cheapest solution (without CCHP) takes the advantage of using
CHP (to meet the SM heating demand for ‘free’) but has a bit worse results than the
final case due to the absence of the absorption cooling (lose the ability compared to
the previous scenario to use some ‘spare/waste’ heat to drive the absorption cooling
and meet some cooling demand for free). The third case in the row analogically can
63
take the advantage of all the DGT technologies except the ones with CHP or CCHP.
Also here existed very promising result, and the deference with the previous cases is
that here we don’t utilize at all the waste heat. The next case allow to the model to
use only PV, grid and boiler (PV + Grid + boiler). Also here the model invests on
PV’s and gives considerable savings (16%). The final case was the one in which the
model had the constraint that must invest at least 7 PV, and find the optimum
combination with this constraint (note that in the model you can put any constraint
and start the optimization from this point). In all the above cases, with exception the
PV + Grid + Boiler, the power purchases from the grid were negligible and almost all
the demand were met on-site by a DGT combination. The last shows that the model
find cheaper to buy, install, and run DGT than buy the power from the grid. Note that
the model ensures that every time is chosen the combination of technologies
(including grid) that minimizes the cost to meet the customer demand (internalize
also the carbon emissions externality).
Figure 32 Bill savings over grid + boiler basic scenario
At the figure 33 below we can observe for each of the above cases the carbon results
compared to the basic case. All the cases have considerable carbon savings except the
first case which is without CHP/CCHP. The last result was expected due to the fact
that in this case the DG doesn’t take the advantage of the recovered heat, which is
one substantial difference between DG and the big power stations (as regards the
efficiency of burning the fuel). The big power stations due to the economies of scale
work like in this first case (without utilizing in most cases the waste heat) but with
64
higher efficiencies and for that reason emit less carbon per KWh produced (but big
power station are substantially more expensive due to the need of transmission,
distribution and supply). On the other hand, the other two cases (without CCHP, and
final case) which are using natural gas for power, heat and cooling (CCHP) have less
emissions because they utilize the waste heat and finally reach higher efficiencies of
burning the natural gas. Finally the two last cases have even better carbon results (but
worse financial results see above) because of the utilization of PV which are ‘carbon
free technologies’ (not considered the carbon from manufacturing).
Figure 33, Carbon savings over basis grid + boiler scenario
Finally at the figures 34 to 39, represented the net cost breakdown of the above five
cases. At the figure 34, we can observe that only the cases PV + Grid + Boiler and the
basic case have important grid purchases. The first two cases (without CHP/CCHP)
have extremely small purchases and the final scenario and the remaining at least 7 PV
are completely independent.
-10
0
10
20
30
40
50
60
Pe
rce
nta
ge o
f ca
rbo
n s
avin
gs o
ver
grid
p
lus
bo
iler
bas
ic c
ase
(%
)
case
Percentage of carbon savings over grid +boiler basic scenario (%)
DG without CHP/CCHP
Without CCHP
Final Case
PV+Grid+Boiler
At least 7 PV
65
Figure 34, Energy payments to the grid
The next figure 35 depicts the capital investment costs. The cases without PV’s have
smaller investments (all annualized) compared to the ones with (the basic case have
zero investment). Figure 36 represents the power sales back to the grid, sales which
are small compared with the NPV because of the small prices that a microgrid can
sell back to the grid and the high running costs of the DGT (not PV). Exception is
only the case with at least 7 PV, and this because of the existence of the PV’s (once
installed they use all the spare capacity after meeting the demand to sell back to the
grid no matter what the price is). The same is not happened and in the case of PV+
Grid + Boiler due to the fact that all the PV’s capacity is used for the on-site demand
(less economically to sell from PV’s and buys from the grid).
Figure 35, Capital investment cost (includes installation and fixed costs)
0
200
400
600
800
1000
1200
1400
1600
case
Tho
usa
nd
s $
Energy payments to the Grid (K$)
Without CHP/CCHP
Without CCHP
Final case
PV+Grid+Boiler
At least 7 PV
grid+boiler
0
100
200
300
400
500
600
700
case
Tho
usa
nd
s$
Capital investment cost (include installation and fixed costs) (K$)
Without CHP/CCHP
Without CCHP
Final case
PV+Grid+Boiler
At least 7 PV
Grid + Boiler
66
Figure 36, Energy sales back to the grid
The next three figures 37, 38, 39 show the Net present value, the carbon taxes and the
fuel costs respectively. It is worthwhile to notice in the figure 39 the different cost for
natural gas purchases. For the first three cases these costs are high because the SM
meets its demand by burning NG. For the PV + Grid + boiler and the Grid + Boiler
that purchases are narrowed just to the fuel needed to be burned in a boiler for
meeting the heating demand. Finally for the case at least 7 PV there is a reduction of
fuel usage compared to the three first cases because of the power produced from
PV’s.
Figure 37, Net present value (all included)
0
10
20
30
40
50
60
70
80
case
Tho
usa
nd
s$
Energy sales back to the grid
Without CHP/CCHP
Without CCHP
Final case
PV+Grid+Boiler
At least 7 PV
Grid + Boiler
0
200
400
600
800
1000
1200
1400
1600
1800
case
Tho
usa
nd
s$
Net present Value (NPV)
Without CHP/CCHP
Without CCHP
Final case
PV+Grid+Boiler
At least 7 PV
Grid + Boiler
67
Figure 38, Carbon Taxes (all included)
Figure 39, Natural gas payments (all included)
5.4 Assessment of specific cases
In this section will be examined all the different sensitivities for the spot market
prices scenario in detail. Note that for some cases the graphs will be transferred to the
Appendix.
5.4.1 Case 1: Grid plus boiler
For the grid plus boiler case the model results breakdown are depicted in the Figure
40. This case and the results are simple due to the fact that there is no other
technology and the whole electricity-only and cooling demand met by the grid while
0
20
40
60
80
100
120
140
case
Tho
usa
nd
s$
Carbon taxes
Without CHP/CCHP
Without CCHP
Final case
PV+Grid+Boiler
At least 7 PV
Grid + Boiler
0
100
200
300
400
500
600
700
800
case
Tho
usa
nd
s$
Fuel costs (NG)
Without CHP/CCHP
Without CCHP
Final case
PV+Grid+Boiler
At least 7 PV
Grid + Boiler
68
the heating demand is met by burning natural gas in boilers with efficiency 80%. The
NG purchases during the different months and the different hours of the day are
represented in figure 41 and the total electricity demand in figure 42 (coincide with
the SM demand). Two factors that make this scenario extremely expensive are:
Firstly the high 2008 electricity prices, and secondly the facts that the SM purchases
electricity during periods when the spot market prices are extremely high like 12:00
August/July (0.45$), 18:00 November (0.55$) and 19:00-20:00 October (55$) (of
course and the added costs for transmission and distribution). On the other hand when
DGT existing in SM these peak hours are beneficial to sell back to the grid and make
a profit.
Figure 40, Energy balance and economic result for the grid plus boiler case
Electricity-only demand(KWh) 81470
Cooling demand (KWh) 68432 Heating demand (KWh) 47439 Total demand(KWh) 197340 Electricity met by Grid (KWh) 81470 Cooling demand met by the grid (KWh) 68432 Heating demand met direct NG burning(KWh) 59298 NG purchases (KWh) 74123 Energy payments to the grid ($) 1360706 Carbon taxes for grid purchases ($) 96149 NG purchase for direct fire ($) 74331 carbon taxes for direct burning of NG ($) 17507 Carbon emissions for meeting on-site total demand (tonnes) 2841 NPV 1548694
69
Figure 41, NG purchases for meeting the SM heating load (Grid plus boiler case).
Figure 42, total electricity purchases from grid, for all months and hours (grid plus boiler case)
8.4.2 Case 2: Without CHP/CCHP
At this case, the SM is able to meet its electrical demand (electrical-only, cooling) by
purchasing electricity from the grid, invest in PV’s, invest in power production by
burning NG (no CHP/CCHP) or a combination of the three above. The heating
demand can only be met by burning NG in boiler.
The results of this case are represented on figure 43. For this scenario the technology
adopted was one MW Natural gas engine (electrical). It is obvious from the results all
the electrical-only demand met by self-generation. The cooling demand also met by
70
DG except only 20 KWh that met by the grid during the 15:00 of the August (figure
44) and that due to the fact that the DG capacity wasn’t enough to cover all the SM
cooling needs . The heating demand met all by boilers (not recovered heat existed).
Figure 43, Energy balance and economic results for without CHP/CCHP case
As regards the economic results of this case, the savings over the basic grid plus
boiler are significant with a 44% reduction in the final energy bills of the SM. We can
also identify some sales back to the grid (figure 45). These sales have been made
during the peak hours when the spot market price were too high and could cover the
production cost and the reduced price that SM is able to sell back to the grid (also
there was spare capacity after meeting the on-site demand). Finally, there is an
Energy Balance results Column1
Technology Adopted (name,capacity) NG-1000E
Electricity-only demand(KWh) 81470
Cooling demand (KWh) 68432
Heating demand (KWh) 47439
Total demand(KWh) 197340
Electricity met by Grid (KWh) 0
Electricity met by on-site generation (KWh) 81470
Electricity sales back to the grid (KWh) 1523
Cooling demand met by the grid (KWh) 20
Cooling demand met by on-site power generation (KWh) 68412
Cooling demand met by on-site recovered heat (KWh) 0
Cooling demand met by direct burning of NG(KWh) 0
Heating demand met by recovered heat(KWh) 0
Heating demand met by direct burning of NG(KWh) 47439
NG purchases (KWh) 518074
NG purchases without those for selling to grid(KWh) 513608
Economic Results Column1
Energy payments to the grid ($) 237
Carbon taxes for grid purchases ($) 13
Fuel cost for on-site generation (with selling to the grid)($) 568719
Variable cost for onsite genaration ($) 41475
Carbon taxes for onsite generation ($) 104859
DG annualized capital cost(include installation) 73384
NG purchase for direct fire ($) 74331
carbon taxes for direct burning of NG ($) 17507 Carbon emissions for meeting on-site total demand
(tonnes) 3033
Energy sales back to the grid ($) 9323
NPV 871203
Savings over basic grid plus boiles scenario (%) 44
Carbon savings over basic grid plus boiles scenario (%) -7
71
increase of carbon emissions to the environment in this scenario with a negative
carbon savings over the basic scenario. This result could be predictable if we take
into account the bigger efficiency a power plant can reach due to the bigger capacity
compared with the one MW installed in the SM. The same is not happening and with
the electricity price because of the transmission, distribution and control needed for
the electricity produced in the power station. For this case the overall efficiency of
burning NG to meet the onsite demand is 40%.
Figure 44, Total electricity purchases from the grid (without CHP/CCHP case)
Figure 45, Sales back to the grid (without CHP/CCHP case)
8.4.3 Case 3: Without CCHP
This case is very familiar with the previous one, with the only difference that now the
customer is able to invest also in CHP technologies but still isn’t available the CCHP
options. CHP technologies give the opportunity through the usage of heat exchangers
72
to recover some ‘waste heat’ and utilize it in order to meet some or all the heating
demand of the SM while producing power to meet the electrical-only and cooling
demand of the SM. This feature of this technology is also the main advantage of the
DG over the centralized power plant due to the fact that the power production is near
the heating load and can be utilized, unlikely with a power station which usually is far
from the heating loads (e.g. cities), and as it is widely known the heating is not
economically travelling long distances (electricity does).
In this specific SM’s case the model decide to invest in a one MW CHP Natural gas
engine, and the energy balance results are represented in figure 46. Similarly with the
case without CHP/CCHP almost all the SM’s electrical demand met by on-site
generation (exception was 20KWh cooling load met by the grid), and the schedule of
the self-generation output and the schedules (month, hour) are exactly the same as the
SM’s demand profile (see in customer description section). As regards the heating, on
the contrary with the previous case now the SM meet this entire load by the recovered
heat with a 12% reduction of NG usage in this scenario compared to the previous one.
At the same time this heat utilization improves not only the economics results (figure
47) of this case compared to the grid plus boiler scenario from 44% to 48% but also
invert the negative carbon results from -7% to +9% and makes this case more
sustainable. Finally we can see clearly from the results an overall burning efficiency
of the NG for meeting onsite demand of 45% (much bigger compared to the previous
case)
73
Figure 46, energy balance results for without CCHP case
Figure 47, economic results for without CCHP case
5.4.4 Case 4: Final case
Final case is the most cost effective solution for meeting the SM’s demand by
allowing the model to choose between all the technologies without constraint. Due to
the fact that in this economic model we internalize the carbon externality, by turning
the carbon emissions into cost, we could say that this case is the more sustainable
Energy balance results Column1
Technology Adopted (name, capacity) NG-1000CHP
Electricity-only demand (KWh) 81470
Cooling demand (KWh) 68432
Heating demand (KWh) 47439
Total demand (KWh) 197340
Electricity met by Grid (KWh) 0
Electricity met by on-site generation (KWh) 81470
Electricity sales back to the grid (KWh) 1523
Cooling demand met by the grid (KWh) 20
Cooling demand met by on-site power generation (KWh) 68412
Cooling demand met by on-site recovered heat (KWh) 0
Cooling demand met by direct burning of NG(KWh) 0
Heating demand met by recovered heat (KWh) 47439
Heating demand met direct NG burning(KWh) 0
NG purchases (KWh) 443951
NG purchases without those for selling to grid(KWh) 439485
Economic results Column1
Energy payments to the grid ($) 237
Carbon taxes for grid purchases ($) 13
Fuel cost for on-site generation (with selling to the
grid)($) 568719
Variable cost for onsite generation ($) 41475
Carbon taxes for onsite generation ($) 104859
DG annualized capital cost(include installation) 96316
NG purchase for direct fire ($) 0
carbon taxes for direct burning of NG ($) 0
Carbon emissions for meeting on-site total demand
(tonnes) 2595
Energy sales back to the grid ($) 9323
NPV 802297
Savings over basic grid plus boiler scenario (%) 48
Carbon savings over basic grid plus boiler scenario (%) 9
74
ones. This case also don’t differ a lot from the previous two ones, because the
technology choice remain the one MW natural gas engine with the difference that
hear the model decide to invest in CCHP (combined cooling heating and power).
Obviously the model find that the utilization of the recovered heat for not only
heating (part or all of them) but also cooling (part or all of them) loads overcome all
the other costs (capital, variable/fixed costs, carbon taxes etc) and makes this case the
most attractive of all. Indeed the economic results (figure 49) of this case are stunning
with an annual energy bill saving reaching 51% (compared to the basic grid plus
boiler scenario) while at the same time the carbon savings to be near 20%. The
energy sales back to the grid increased almost 300% (compared the two previous
cases 2, 3) (figure 52) and simultaneously the microgrid is completely autonomous
from the grid.
As regards the self generation output and schedule for the electricity-only end use
(figure 50) and the recovered heat which is going to meet the heating demand (figure
53) are coincide with the corresponded electricity-only SM demand and the SM
heating demand (actually figure 53 give the recovered heat need to cover the SM
heating demand, recovered is the 120% of the heating demand due to the fact that his
heat passes from heat exchangers that have efficiency 80%).
The interesting part in this final case is how the CCHP is going to meet SM’s cooling
demand by utilizing the whole spare recovered heat (figure 52) (after meeting the
heating demand) to drive absorption coolers and then meet the rest of the cooling
demand driving electrical chillers (usual air-conditioning systems) and using by self-
generated power (figure 51). Actually, if we notice in more detail the figures 52 and
53, becomes clear that these stunning results came naturally because of the different
hours the heating demand and the cooling demand peaks and thus there is no
antagonism of the two different demands (cooling, heating) for the recovered heat.
For the numbers, the cooling that has been met from the recovered heat is 21% (out
of the total cooling demand, due to low conversion of recovered heat by absorption
cooling), and the overall efficiency for burning on-site NG reached the 50% (much
higher from the usual conventional centralized power stations). Finally this case
meets the same demand with 20 % and 10 % less NG compared to the two previous
scenarios respectively.
75
Figure 48, energy balance results (final case)
Figure 49, economic results (final case)
Energy balance results Column1
Technology Adopted (name,capacity) NG-1000CCHP
Electricity-only demand(KWh) 81470
Cooling demand (KWh) 68432
Heating demand (KWh) 47439
Total demand(KWh) 197340
Electricity met by Grid (KWh) 0
Electricity met by on-site generation (KWh) 81470
Electricity sales back to the grid (KWh) 4733
Cooling demand met by the grid (KWh) 0
Cooling demand met by on-site power generation (KWh) 53982
Cooling demand met by on-site recovered heat (KWh) 14449
Cooling demand met by direct burning of NG(KWh) 0
Heating demand met by recovered heat (KWh) 47439
Heating demand met direct NG burning(KWh) 0
NG purchases (KWh) 411055
NG purchases without those for selling to grid(KWh) 397176
Economic results Column1
Energy payments to the grid ($) 0
Carbon taxes for grid purchases ($) 0
Fuel cost for on-site generation (with selling to the
grid)($) 525969
Variable cost for onsite genaration ($) 38402
Carbon taxes for onsite generation ($) 97089
DG annualized capital cost(include installation) 120825
NG purchase for direct fire ($) 0
carbon taxes for direct burning of NG ($) 0
Carbon emissions for meeting on-site total demand
(tonnes) 2345
Energy sales back to the grid ($) 24297
NPV 757988
Savings over basic grid plus boiles scenario (%) 51
Carbon savings over basic grid plus boiles scenario (%) 17
76
Figure 50, NG-1000CCHP power generation for electrical-only end use loads (final case)
Figure 51, NG-1000CCHP power generation for cooling end use loads (final case)
(α) * EfficiencyheatExchanger) in CHP unit than meet the correspondent electricity and
heating demand separately by using grid and boiler. At the same time the capacity
which invest (100KW) is small (not follow all the heating demand), due to the fact
that in order CHP to be more economical compared to the grid plus boiler
combination must have high load factor (work almost 100% throughout the year in
order to cover the investment and fixed annual costs).
The above become much clearer if we look the CHP electrical and heating production
in figures 101 and 102 respectively. Finally the energy balance and economic results
are represented in figure 103 (appendix).
92
Figure 101, NG-100CHP total electrical production (Electricity price from 0.09 to 0.12 $/KWh case)
Figure 102, Heating demand met by NG-100CHP (Electricity price from 0.09 to 0.12 $/KWh case)
5.5.3 Electricity price 0.13 $/KWh
For 13 p/KWh electricity price the model decides to further invest in one 60 KW
CHP natural gas engine. The same things existing as discussed before with the
difference that now the model almost covers all SM heating demand by CHP, and this
can be clearly seen in figure 106 were the represented the NG purchases for direct
burn in boiler. The electrical and heating outputs of the NG-60 CHP are depicted in
93
figures 104 and 105 respectively (appendix). We can understand from that case that
the one p/KWh difference in electricity price from the grid can compensate for the
lower load factor of the small natural gas engine, which also follows strictly the
heating loads. Finally the energy balance and economic results depicted in figure 103
(appendix).
Figure 106, Purchased NG to meet heating demand (Electricity price 0.13 $/KWh)
5.5.4 Electricity price 0.14$/KWh
In this case the model decides that the more sustainable combination is a CCHP NG-
300 engine combined with the grid. CCHP will cover the whole heating demand
(figure 110 appendix) and the base electrical load (electrical-only and cooling) while
grid will meet the rest electrical demand (figure 107, appendix) which usually is peak
load demand. Figure 108 show the electrical output of the CCHP, while figure 109
depict the cooling load me by the recovered heat remained after meeting the heating
demand. We can notice that this system works with a very high load factor (CCHP
works 100% most of the time) and utilize all the ‘’waste heat’’ and thus we can say
that this system has very high efficiency. All the energy balance and economic
results are represented in figure 111 (appendix).
94
Figure 108, NG-300CCHP total electricity production (Electricity price 0.14$/KWh case)
Figure 109, NG-300CCHP cooling production from recovered heat (Electricity price 0.14$/KWh case)
5.5.5 Electricity price from 0.15 to 0.49 $/KWh
This case is the same with the final case of the spot market electricity price scenario.
Here the model decides that the sustainable technology solution is to invest on a NG-
1000CCHP engine and meet the whole demand on-site (not use boiler and grid). The
energy balance and the economic results are represented in figure 112 (appendix).
The electrical-only loads are met by on-site power generation while the heating loads
95
are met only by the recovered heat from the power generation. Finally, cooling loads
are going to be met both by self-generated power but also and from recovered heat
which driven through absorption chiller. The graphs representing the electrical-only
outputs and heating output are exactly the same as the final case explained in the
previous sections and thus skipped. The only graphs that are different from the final
case are the ones have to do with the cooling demand. The on-site power production
for meeting cooling loads depicted in figure 114 and the recovered heat which going
to meet cooling loads depicted in figure 115.
Picture 114, NG-1000CCHP power generation for electrical-only end use loads (Electricity price from 0.15 to 0.49 $/KWh)
0
50
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150
200
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350
400
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0 5 10 15 20 25 30
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Hour
DG power generation for cooling end use loads
January
February
March
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96
Picture 115, recovered heat going to meet cooling demand (Electricity price from 0.15 to 0.49 $/KWh)
5.5.6 Electricity price from 0.5 to 0.57 $/KWh
For electricity prices 0.5 $/KWh to 0.57 $/KWh the model finds optimum not only to
use the NG-1000CCHP for meeting the onsite demand but also to use and the spare
capacity (capacity not used for the on-site demand) for selling electricity back to the
grid (figure 118). What we can conclude from these results is that the fuel (plus
variable) cost for producing one KWh e is lower than the price that we can sell back
to the grid and thus is profitable (the investment and fixed costs are already in place).
Here due to the high power generation production for selling back to the grid we have
a great portion of recovered heat which is used to meet almost half of the cooling
demand through the absorption chillers (figure 117, note that this is the recovered
heat which will pass through absorption coolers to produce cooling and not the
displaced cooling demand), unlike with the previous case where the biggest quantity
of the cooling demand was met by on-site power generation. The economic and
energy balance results depicted in figure 116 (appendix).
0
200
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0 5 10 15 20 25 30
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Hour
Recovered heat going to meet cooling demand
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97
Figure 117, recovered heat going to meet cooling demand (Electricity price from 0.5 to 0.57 $/KWh)
Figure 118, energy sales back to the grid (Electricity price from 0.5 to 0.57 $/KWh)
0
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Recovered heat going to meet cooling demand
January
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98
5.5.7 Electricity price from 0.58 $/KWh
From this electricity price and above the SM makes huge profits by turning into a
power station (invest in large number gas turbines). Obviously the cost of just
producing electricity (included the investment, OMVar, OMFic costs etc) without
utilizing almost any of the recovered heat is lower than the selling price back to the
grid and thus is economically profitable to transform the SM into a power station.
The results for this case are depicted in figure 119 (appendix).
Figure 119, energy balance and economic results (Electricity price from 0.58 $/KWh)
Energy balance results Column1
Technology Adopted (name,capacity) 100 GT-40000E
Electricity-only demand(KWh) 81470
Cooling demand (KWh) 68432
Heating demand (KWh) 47439
Total demand(KWh) 197340
Electricity met by Grid (KWh) 0
Electricity met by on-site generation (KWh) 81470
Electricity sales back to the grid (KWh) 959875302
Cooling demand met by the grid (KWh) 0
Cooling demand met by on-site power generation (KWh) 68432
Cooling demand met by on-site recovered heat (KWh) 0
Cooling demand met by direct burning of NG(KWh) 0
Heating demand met by recovered heat (KWh) 0
Heating demand met direct NG burning(KWh) 59298
NG purchases (KWh) 2815076266
NG purchases without those for selling to grid(KWh) 513666
Economic results
Energy payments to the grid ($) 0
Carbon taxes for grid purchases ($) 0
Fuel cost for on-site generation (with selling to the grid)($) 517592
Variable cost for onsite genaration ($) 122727222
Carbon taxes for onsite generation ($) 610979477
DG annualized capital cost(include installation) 241351243
NG purchase for direct fire ($) 74331
carbon taxes for direct burning of NG ($) 17507
Carbon emissions for meeting on-site total demand (tonnes) 15274925
Energy sales back to the grid ($) 0
NPV -39357324
99
6.Conclusions
Buildings are a growing sector of energy consumption and shopping malls are in
particular consuming more energy than the other buildings and appear increasingly
across the world. The potential of DG and Energy efficiencies measures in the
building sector and especially in commercial sector has been discussed as part of the
energy agenda in the last decade.
On-site generation offers to commercial customers the opportunities to increase the
electricity production efficiency decrease the energy bills costs and finally decrease
the carbon emissions compared to energy from the conventional centralized power
stations. These above results achieved through the utilization of the ‘’waste heat’’
from CHP/CCHP schemes and the absence of transmission, distribution and control
of electrical energy. Moreover self-generation gives advantages of autonomy and
reliability in energy purchases.
Many research projects led by academics and engineers have been steered towards
decreasing and meeting the basic demand in buildings in different ways. Most of
these projects are using long analytic tradition methods for getting an efficient
selection and operation of distributed generation technologies in large centralized
power stations or large scale community heating CHP’s. In contrast the methods for
optimizing in commercial level are very rare and usually the ones existed are very
limited.
At this thesis was developed an optimization mathematical model, model which is
able to minimize the annual energy bills (also takes into account the carbon
emissions) of a shopping mall (a general building or Micro-grid) by finding the
optimal combination of distributed generation technologies (included grid), the
capacity of those technologies and finally the operating schedule of them during the
year. The model takes as inputs the customer load (electrical, cooling and heating),
the different technologies (their economics and characteristics), market prices
(electricity and natural gas prices) and gives as outputs the combination of
technologies, their capacities, their operating schedule, their energy balance and
100
economic results. The given name of this mathematical model is Distributed
Generation Technology Selection Model (DGT-SM) and was developed as a GAMS
model.
The model was tested under many different scenarios and sensitivities. The two
different scenarios was diverged in the way customer buy’s electricity from the grid,
and the one incorporates the actual spot market prices for the whole year (hourly and
monthly changes), while the other scenario use fixed electricity prices throughout the
year. As regards the different sensitivities, for the spot market price scenario was
examined thoroughly a variety of different cases in order both to validate the model
but also to scan some real life cases (different places, different needs, and different
constraints).
The results were really encouraging, and actually all the examined cases invested in
distributed generation and gave astonishing annual energy bills and carbon savings.
Firstly was examined the common now case of grid plus boiler in order to use these
results as a guide to the other cases. Then the first case with DG availability was the
one without CHP/CCHP. In this case the model was able to invest in every
technology but without the ability to purchase CHP or CCHP systems (only electrical
production), which means in other words that there is no heat utilization. The results
of this case were the expected ones with annual energy bills saving (44% over grid
plus boiler case) but with worse environmental impact (-9% over grid plus boiler
case). We refer the outcome as expected because as regards the economic results the
absence of the transmission costs makes the on-site power generation cheaper, but
worse environmentally due to the lower efficiency (not utilization of heat) compared
with the big centralized power stations.
When the CHP option unlocked in the model (the model invest in a NG-1000CHP),
case without CCHP, except the economic results (48%) and the environmental results
(always compared to the grid plus boiler case) turned to be positive (9% annual
savings). This outcome is logical due to the utilization of the waste heat, waste heat
which usually dumped in the conventional power stations. Finally, in the final
scenario the adoption of the combined cooling, heating and power (NG-1000CCHP)
gave the opportunity to the model to utilize almost all the waste heat, reach very high
101
efficiencies of burning the fuel on-site (50% burning on-site natural gas) and gave the
astonishing annual economic savings of 51% and carbon savings 17%. Further,
examined the case were the only DGT option except the basic grid and boiler was the
PV’s. Also in this case the model found more economic to invest in 700 KW PV
panels and meet almost half of the SM total electrical demand onsite. Concerning the
results of this case, there was a 16% annual bill savings and 41% carbon savings. The
reason for carbon savings is obvious, but the most interesting is the economic savings
despite the high capital PV costs and these are existed due to the fact that the
productive sunny hours for PV’s coincide with the Peak SM’s and spot market
(during lunch time observed the higher electricity prices) electrical demand which
means that the PV’s displace the higher and most expensive SM’s electrical loads.
Astonishing results accomplished also in the case at least seven PV’s. The constraint
in this case was that the model has to invest at least on seven PV panels. This case
seems somehow unrealistic but it’s not if we consider that many customers are
interested more for their ‘’Green profile’’ no matter what is the cost. The interesting
in this case compared with the previous one is that the model finds optimum to also
invest in a NG-1000CCHP engine in order to meet the electrical demand the hours
that PV’s are not producing (and not purchase from the grid like before) and at the
same time meet almost all the heating demand by recovered heat. This scenario has
38% energy bill savings and the highest carbon savings over all the cases (53%).
Except the very good results of this case this combination gives and a good security
(NG/electricity prices, CCHP out of work for sometime) over the volatile period of
time.
Model, also experience very high NG prices (50% more expensive) and extremely
low grid electricity prices (50% less cost from grid), in two cases in order to see what
will happen if a very sharp change happen to the market. The results didn’t change
from the final scenario and the SM find under these sever circumstances that is more
economic feasible to be independent from the grid and produce all the electricity and
heat on-site by using the NG-1000CCHP engine. Finally, the last interesting case was
the one where the PV panels prices reduced by 20% and the carbon price set to 100$
per tone CO2 emitted. This case is also very likely to happen in the near future due to
the continuing innovation on PV panels, and the increasing environmental problem.
102
At this scenario the model invests not only in a NG-1000CCHP but also and in two
PV-100 KW panels. By this way the model cover the peak hour demand by PV’s and
uses the CCHP in the base load in order to keep utilizing almost the whole waste heat
and reach high burning efficiencies (reduce the CO2 emissions which now are
computable).
In the fixed price scenario (see figure 99) we examined the adequacy of the model for
different electricity prices from the grid and the results indicates that the model
decides logically. Up to 9p/KWh the model decides not to invest in DGT and meet
the demand by using grid and boiler. For electricity price from 9p/KWh and up to
12p/KWh the model except from grid and boiler invest in one 100 KW CHP natural
gas engine. In this case the model find economic feasible to use the NG-100 engine
and follow the SM base load heating demand and cover at the same time some
electric base load. It is clear in this case that in order the CHP to be cheaper from the
grid must work almost 100% all the time. For 13p/KWh the model except from the
previous 100KW invest in 60KW more and now meets the biggest part for heating by
recovered heat (also meets a small portion of the electrical load). After this point and
for price at 14p/KWh the model invest in 300KW CCHP and meet the entire heating
load (recovered heat) but also a great part of the electrical load also. Here the model
stops to invest in CHP and started to invest to CCHP because finds more economic
feasible to pay more for the absorption coolers and use the rest recovered heat (after
meeting heating load) to meet some cooling demand than just buy it from the grid.
The next gap is from 15p/KWh to 49p/KWh. Here the case is the same with the final
case from the spot market price scenario and the model invest in a NG-1000CCHP.
Now the SM is in depended from the grid and meet the whole demand onsite but is
not selling back to the grid. From 50p/KWh to 57p/KWh the model finds
economically feasible to use the spare capacity and sell back to the grid and make
some profit. The final case after this is when the electricity price is so high to be
profitable to turn the SM into a power station.
Besides the fact that the model seems to be able to find accurately results as regards
the selection of technologies, capacities and operating schedule improvements must
be undertaken in the inputs of the model as regards both the validity but also the
detail and the more sophisticated structure of them. Moreover changes needed to the
103
thermodynamic model for more accuracy and finally more options (e.g. technologies)
and a graphic environment is under way for the next version of this model.
104
Bibliography Administration, N. O. (2007, February). Global trends in major greenhouse gasses. Retrieved from Homepage of U.S. federal goverment: http://www.cmdl.noaa.gov/albums/cmdl_overview/Slide11.sized.png Administration, N. O. (2007, February). Global trends in major greenhouse gasses. Retrieved from Homepage of U.S. federal goverment. Anandalingam, G. B. (1996). Sustainable urban energy-environment management with multiple objectives. Oxford: Pergamon Press. Bailey Owen, C. C. (2003). Distributed Energy Resources in Practice:A case study analysis and Validation of LBNL's Customer Adoption Model. Berkeley: Environmental Energy Technologies Division, Ernesto orlando Lawrence Berkeley National Lab. C.Lam, J. (1998). Energy analysis of commercial buildings in subtropical climates. Elsevier, Direct Science . C.Lam, J. (2000). Energy analysis of commercial buildings in subtropical climates. Building and Environment 35 , 19- 26. Consultants, P. (2000). The Eco-indicator 99, A damage oriented method for the life cycle impact assessment. Amersfoort,Netherlands. Deng Shi-Ming, B. J. (2000). A study of energy performance of hotel buildings in Hong Kong. Energy and Buildings , 7-12. F, C. G. (2005). Energy efficiency in buildings. London: CIBSE. F. Javier Rubio, A. S. (2001). CERTS Customer Adoption Model. California: Lawrence Berkeley National Laboratory. Firestone, R. (2004). Distributed Energy Resources Customer Adoption Model Technology Data. California: Ernest Orlando Lawrence Berkeley National Laboratory. Goldstein, L. B. (2003). Gas-Fired Distributed Energy Resource Technology Characterizations. California: National Renewable Energy Laboratory. Gumerman, E. Z. (2003). Evaluation framework and tools for distributed energy resources. CA, USA: LBNL. Hassan, R. (2008). A systematic methodology for optimising the energy performance of buildings in Bahrain. Energy and Buildings . Heijungs R, S. S. (2002). The computational structure of life cycle assessment. Dordrecht, Netherlands: Kluwer Academic Publishers. IEA. (2004). World Energy Outlook. IEA. IEA. (2007). World Energy Outlook:China and India insights. IEA. Joseph C. Lam *, R. Y. (2004). Electricity use characteristics of purpose-built office. Energy Conversion and Management 45 , 829–844. Joseph C.Lam, D. H. (2003). An analysis of electricity end-use in air-conditioned office buildings in Hong Kong. Building and Environment 38 , 493 – 498. Joseph C.Lam, D. H. (2003). Electricity consumption characteristics in shopping malls in subtropical climates. Energy Conversion and Management 44 , 1391–1398. Jun-hyung Ryu*, †. a. (2007). Multiperiod Planning of Enterprise-wide Supply Chains Using an Operation. Ind. Eng. Chem. Res., 46 , 8058-8065. L.T. Biegler, I. G. (1997). Systematic Methods of Chemical Process Design. Englewood Cliffs. Lam JC, C. A. (1995). Energy audits and surveys of air conditioned buildings. Australia: University of Cnbera. Lemar, P. (2001). The potential impact of policies to promote Combined Heat and Power in the S Industry,. Liu Pei, D. I. (2007). Modeling and optimization of polygeneration energy systems. Catalysis Today 127 , 347–359. Lovins, A. B. (2002). Small is profitable: the hidden economic benefits of making. CO, USA: Rocky Mountain Institute, Snowmass. M. Medrano, J. B. (2008). Integration of distributed generation systems into generic types of commercial buildings in California. Energy and Buildings 40 , 537–548. Maribua Karl Magnus, F. R. (2007). Distributed energy resources market diffusion model. Energy Policy 35 , 4471–4484. Marnay Chris, G. V. (2007). Optimal Technology Selection and Operation of Microgrids in Commercial Buildings. Environmental Energy Technologies Division . Marnay Chris, J. S. (2001). Modeling of customer adoption of distributed energy resources. California: Lawrence Berkeley National laboratory.
105
Marnay Chris, R. M. (2007). Microgrids for Commercial Building Combined Heat and Power and Power and Heterogeneous Power Quility and Reliability. Berkeley: Environmental Energy Technologies Division, Ernesto Orlando Lawerence Berkeley National Laboratory. Michael Stadler, R. F. (2006). On-site Generation Simulation with EnergyPlus for Commercial Buildings. Ernesto Orlando Berkeley National Laboratory . Nan Zhou a, *. C. (2006). An analysis of the DER adoption climate in Japan using optimization results for prototype buildings with U.S. comparisons. Energy and Buildings 38 , 1423–1433. Nan Zhou a, ,. C. (2006). An analysis of the DER adoption climate in Japan using. Energy and Buildings , 1423–1433. Pan Yiqun, Y. R. (2007). Energy modeling of two office buildings with data center. Energy and Buildings . Partnership, L. E. (2006). London Carbon Scenarios to 2026. London: London Energy Partnership. Ryan, M. C. (2007). Microgrids: An emerging paradigm for meeting building electricity and heat requirements efficiently and with appropriate energy quality. Environmental Energy Technologies Division. SEA/RENUE. (2006). London Carbon Scenarios to 2026. London: partnership, London Energy. Sezgen Osman, K. J. (2000). Interactions between lighting and space conditioning energy use in US commercial buildings. Energy , 793-805. Tester W. Jefferson, D. M. (2005). Sustainable Energy: Choosing among options. Cambridge: Massachusetts Institute of Technology. Wall, P. Centralized versus Decentralized Information Systems in Organizations. Ireland: Waterford Institute of Technology Department of P&Q. Wang Weimin, Z. R. (2005). Applying multi-objective genetic algorithmsin green building design optimization. Building and Environment , 1512-1525. Williams, H. (1999). Model Building in Mathematical Programming, 4th edition. Chichester: John Wiley and Sons Ltd. Williams, P. a. (2001). Costing and pricing of electricity distribution services. (15(3)). Yang Z. X.H. Li, Y. H. (2006). Study on solar radiation and energy efficiency of building glass system. Applied Thermal Engineering , 956-961. Yiqun Pan, R. Y. (2007). Energy modeling of two office buildings with data center for green building design. Shanghai: Elsevier. Yongwen Yang, W. G. (2006). Optimal Combination of Distributed Energy System in an Eco-Campus of Japan. Environmental Energy Technologies Division.
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Appendix
Figure 72, energy balance and economic results (high carbon price scenario)
Energy balance results Column1
Technology Adopted (name,capacity) NG-1000CCHP
Electricity-only demand(KWh) 81470
Cooling demand (KWh) 68432
Heating demand (KWh) 47439
Total demand(KWh) 197340
Electricity met by Grid (KWh) 0
Electricity met by on-site generation (KWh) 81470
Electricity sales back to the grid (KWh) 1857
Cooling demand met by the grid (KWh) 0
Cooling demand met by on-site power generation (KWh) 54357
Cooling demand met by on-site recovered heat (KWh) 14075
Cooling demand met by direct burning of NG(KWh) 0
Heating demand met by recovered heat (KWh) 47439
Heating demand met direct NG burning(KWh) 0
NG purchases (KWh) 403719
NG purchases without those for selling to grid(KWh) 398273
Economic results Column1
Energy payments to the grid ($) 0
Carbon taxes for grid purchases ($) 0
Fuel cost for on-site generation (with selling to the grid)($) 516695
Variable cost for onsite genaration ($) 37717
Carbon taxes for onsite generation ($) 238391
DG annualized capital cost(include installation) 120825
NG purchase for direct fire ($) 0
carbon taxes for direct burning of NG ($) 0
Carbon emissions for meeting on-site total demand
(tonnes) 5879
Energy sales back to the grid ($) 11371
NPV 902257
107
Figure 73, energy balance results (High carbon price with a 20% PV capital reduction case)
Figure 74, economic results (High carbon price with a 20% PV capital reduction case)