Energy Systems Optimization

Distributed Generation Technology Selection Model

DGT-SM

Aristotelis Giannopoulos

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

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

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

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

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

Proposal

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

Market info Natural Gas Price

Biomass Price

Electricity Price

Trasmission/Distribution

Capicity (KW) Lifetime (years) Capital cost ($/KW) Installation cost ($/KW) Operation & Maintenance variable/fixed cost

($/KWh, $/KWh) Heat rate (Kj fuel/KWh) Heat to power ratio (α)

Technology info

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

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

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

GAMS is especially useful for handling large, complex, one-of-a-kind problems which may require many revisions to establish an accurate model.The user can change the formulation quickly and easily, can change from one solver to another, and can even convert from linear to nonlinear with little trouble.

provides a high-level language for the compact representation of large and complex models

�allows changes to be made in model

specifications simply and safely �allows unambiguous statements of

algebraic relationships and permits model descriptions that are independent of solution algorithms

While there are some other optimization software packages that have these same qualities, GAMS is widely used for energy optimizations

Three scenarios describe the conditions under which the customer purchases electricity

Fixed price Tariff Energy Market + revenue neutrality

Sensitivities:

Base case (real prices, without subsidy) High NG prices, Low NG prices DER subsidy (e.g. PV, HQ CHP) Decrese in DER technology cost

Scenarios and Sensitivities

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

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

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