Samira Fazlollahi1,2, Gwenaelle Becker 2, Michel Guichard 2, Francois Marechal 1

1 Industrial Energy Systems Laboratory, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland (EPFL)

2 Veolia Environnement Recherche et Innovation (VERI), 291 avenue Dreyfous Ducas, 78520 Limay, France

1. Motivation & Objectives

Develop a systematic procedure for design and operation

optimization of district energy systems

Developed model includes:

• Energy integration

• Multi-objectives

• Process design

• Daily thermal storage

• GIS base network design

2. Methodology1

The basic concept of the developed model is the decomposition of

the problem into several parts (Figure 1). Three major steps are;

I. Structuring phase for collecting and manipulating data

II. Multi-objective nonlinear optimization 3 phase for optimizing

the design and operation of an energy system.

III. Post-Processing phase for detail evaluation of the Pareto

frontier.

III- Post-Processing phase

Available

equipment

Energy

demand

Energy

sources

I-Data

Structuring

Figure1 . District energy system design framework implemented in Matlab

Backup

Technology

c) Energy integration

d) Thermal storage integration5

Acknowledgments: The authors would like to acknowledge Veolia Environnement Recherche et Innovation (VERI) for the financial support.

[3] S. Fazlollahi, P. Mandel, G. Becker, F. Marechal, Methods for multi-objective investment and operating optimization of complex energy systems, Energy 45 (2012) 12 – 22

[1] S. Fazlollahi, F. Marechal, Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP), Applied Thermal Engineering (2011) Volume 50 Issue 2, Pages 1504-1513

[2] S. Fazlollahi, S. L. Bungener, G. Becker, F. Marechal, Multi-Objectives, Multi-Period Optimization of district energy systems: I-Selection of typical days, submitted to Computer Aided Chemical Engineering (2012)

Solution B:

80% less Exergy

losses after EI

[4] S. Fazlollahi, G. Becker, F. Marechal, Multi-Objectives, Multi-Period Optimization of district energy systems: III-Networks design, submitted to ESCAPE23, 2013, Lappeenranta Finland

a) Thermo-economic evaluation

b) GIS base network design4

•12% reduction in coal

consumptions,

•15.8% CO2 emissions

reduction

[5] S. Fazlollahi, S. L. Bungener, F. Marechal, Multi-Objective, Multi-Period Optimization of Renewable Technologies and storage system Using Evolutionary Algorithms and Mixed Integer Linear Programming (MILP), PSE, 2012, Singapore

OP : Operating expenses, OPIN: operating cost including incomes, INV: annual investment

cost, Total: Total cost, NetCO2: Net CO2 emissions, GPC : total cost divided by supplied heat

OP

[106 €/an]

OPIN

[106 €/an]

INV

[106 €/an]

Total

[106 €/an]

B 68 33 17 51

J 73 39 5 43 NetCO2

[103 kg/MJ]

CO2

[103 kg/MJ]

GPC

[103 €/MJth]

B −39 32.2 8.9

J −6 65 7.5

B: gas boilers, steam turbines,

gasifier and biogas engines

J: gas boilers, steam turbines

and gas engines

economical targets

environmental impacts

Pareto

optimal

Performances

evaluation Final decision

III-Post-processing phase

Next iteration

II-Master non-linear optimization (EMOO)

Environomic objectives:

OPEX, CAPEX, CO2

emissions

Thermo-economic

states of selected

superstructure Data

processing

II.3-Slave MILP

optimization (EIO)

Next ite

ratio

n

Optimization includes:

- Energy-integration

- mass balance

- networks design

- thermal storage

II.4-EE

Environomic evaluation:

Economical & environmental

Post

evaluation

Thermo-economic

Simulation

Master optimization Evolutionary, multi

objective algorithm

II.1 EMOO

II.2-TES

Master set of

decision variables: Type & maximum size

of equipments,

CO2 taxes

Optimal system

configuration and

operation

Sensitivity

analysis

Data

base

Data

base

Data

base

Data

base

3. Preliminary results

Design a district energy system for a city with 550,000 inhabitants1;

C) List of equipments1

b) A list of available energy

source1

Equipment Reference:

[MWth/el]

Ranges:

[MWth/el]

βs

[€/kW/an]

αs

[k€/an]

O&M

[€/Mw]

Boiler (NG)

Boiler (BM)

Engine (NG)

Engine (SNG)

Gasifier

Gas turbine

Steam turbine

ORC

42th

42th

5el

5el

20bm

20el

30el

2el

[0 210]

[0 210]

[0 100]

[0 50]

[0 200]

[0 200]

[0 200]

[0 20]

14

17

25

25

67

73

32

38.5

84

84

15

15

103

14

272

96

3.5

10.4

10

12

1

50

10

30

I- Data structuring

a) Demand profile: typical days2

Resources ∆ CO2:

[kg/MJ]

Price:

[€/MJ]

Electricity

Natural Gas

Biomass

SNG

0.3071

0.0641

0

0

0.0198

0.0092

0.0036

0.0099

BM: Biomass, NG: Natural gas,

Decision variables in the multi-objective optimization phase

Optimization steps Variables

Master Optimization: (EMOO)

Thermo-economic simulation

models: (ETM)

Slave optimizer: (EIO)

Type of equipments and their maximum available size.

The corresponding thermodynamic states and the investment

turnkey cost of equipment

Utilization rate and the operation strategy of selected equipment

II- Multi-objectives, multi-periods optimization results

Respect to three objectives: i. the annual investment cost

ii. the operating cost

iii. the overall CO2 emissions

Solution B: Network design

1

2 3