Energy Systems Optimization
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Transcript of Energy Systems Optimization
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Distributed Generation Technology Selection Model
DGT-SM
Aristotelis Giannopoulos
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Urban growthThe 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
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How currently buildings meet their demands?
•Low efficiency•High emissions•Poor reliability and power quality•Expensive trasmission networks
Where is the problem?
•Electricity from grid•Heat by using boilers
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Sustain this Urban Development in an environmental friendly way
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
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Superstructure of DERSources Generation
Technologies
Conversion
Technologies
Demand
GRID Electricity
Natural Gas Biomass
IHT
PV
CHP/CCHP
Boiler
VC air cooled VC water cooled
Absorption Cooling
Lighting Technologies
Electricity
Lighting
Cooling
Heating
Waste Heat
Stirling Engine
Wind
Solar thermal
ElectricityHeatCoolingNatural resureces
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Which technology to be installed? What is the appropriate level of installed
capacity? How should operate the installed capacity?
Identify the problem
+
With Objective:• Minimize the energy cost• Minimize the environmental effect
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Proposal
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For each of the three seasons (summer, winter, mid)
Average and peak day load profileEach profile 24 hourly electricity/heat
loads (kW)
Customer load
TAS/IES
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Market info Natural Gas Price
Biomass Price
Electricity Price
Trasmission/Distribution
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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)
Technology info
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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
Mathematical model
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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…
Mathematical model
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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.
Optimization in GAMS
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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
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�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
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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
Scenarios and Sensitivities
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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
Outputs
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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
Assumptions
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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
Further work