Long Term Power System Expansion Planning

28th – 29th November 2017

Long Term Power System Expansion Planning

Amman, Jordan

Increasing shares of Variable Renewable Energy

Long-term planning and flexibility of the electricity system

2

1. Scenario Development, Planning, Forecasting

2. Hybrid Systems

3. Energy Efficiency and Energy Management

4. Techno economic planning, Running interconnected power Systems

3.1 Smart Energy Hub

2.1 Hybrid Configurator

3


2. Hybrid Systems





4

Basic Management Techniques for Energy Strategies

Structured Analysis Procedure

Reference Energy System

Strategy Management Tableau

5

Planning of Realization

Monitoring

Decision

Identification of Problems

to be Solved

Objectives Object and Purpose

Scope and Limits

Frame Data

Ref. Energy System

Scenarios

and Strategies

Selection of

Methodology and Tools

Data Collection

Conduct Analysis

Development of Policies

Structured Analysis Procedure (SAP)

6

Reference Energy System Methodology

Power Plant

Electr.

Energy

Distr.

Heat

Coal

Emission

7

Reference Energy System

Resources Demand,

Services

Energy System Boundary

8

Reference Energy System (RES)

Legend:

IND

US

TR

Y

TR

AN

SP

OR

T

HO

US

EH

OL

DS

Process Flow Input Output

Power

Room

Heat

Process

Heat

Mining & Process.

Mining & Process.

Pipeline

Heat& Power

Wholesale

Gasturbine

Nuclear - Power

Coal - Power

Hydro - Power

Wholesale

Transmiss. Electicity Distrib.

Gas Distrib.

Distr.Heat. Distrib.

Fuel Oil Dirstrib.

Refinery Wholesale

Boiler

Gasoline Distrib.

Railways

Cars

DH-Supply

Gas-Heating

Oil-Heating

Elect.Heatg.

El. Motor

DH-Suppl.

Electric.

Gas

Furnace

Furnace

IMP

OR

TS

Electricity

Room

Heat

Electricity

Person km

Freight km

SYSTEM LIMIT

9

Energy Politics Infrastructure Opt.

BAU Massive DH

Expansion

REN

Sbg.

IV. Quantitative Results

BAU Massive DH

Expansion

REN

Sbg.

III. Strategies as Options

Low Price

Resources / Primary

Energy Prices --

High Price

Resources / Primary

Energy Prices ++

II. Development scenarios of exogenous factors

I. Definition of Parameters

Scenario Management (Technique)

10

"Robust"Strategies

Strategy IV is "robust"

in all "Cases"

FRAME DATA

Scenario A

Strategy

I Strategy

II Strategy

III Strategy

IV

Scenario B

Strategy

I Strategy

II Strategy

III Strategy

IV

Objectives

AI AII AIII

BI BII BIII AIV BIV

ECU

AI AII AIII

BI BII BIII AIV BIV

CO2

11

Thermodynamic Principles

12

1st Principle of Thermodynamics

„what comes in-must go out“

Power Station

P=50 MW

Ŋ=0.4

I(t) O(t)

L(t)

][75

50125

MWh

IOL

)()( tOutputstInputs

][50

][1][50

MWh

hMWO

tPO

][125

4.0

50

)(

)(

MWh

OI

tI

tO

13

2nd Principle of Thermodynamics

T

Tu

S

T

T

T

TT

ST

STT uuuC

1

)(

C

Entropie

Energy Terms along the Value Chain

Coal

Industry

Household

Appliances

Power

Generation

Power

Transmissio

n

Power

Distribution

Final

Energy

Energy End

Users

Energy

Services

Energy Supply Side Energy Demand Side

Primary

Energy

15

Classification of Energy Analysis and Planing Tools

Category Structure Purpose Method I Energy Information

System

Database

+

Analysis Tool

Data Storage, Retrieval,

Presentation,

Processing, Evaluation

Relational Database,

Hierarchical Database,

Network Database

II Energy Demand

Model

Stand Alone Program Analysis of

Determining Factors of

the Energy Demand

and Structure of Energy

Requirement

Econometrics

Process-Engineering

III Energy Supply and

-System Model

Stand Alone or Model

Set

Identification of

Suitable Energy Supply

Structure and Impacts

Simulation Optimization

(LP, Dynamic-

Programming, System

Dynamics) IV Integrated Energy-

Economy Model

Stand Alone Program

with one Set of

Equations

Analysis of the

Interaction of the

Energy Sector and the

Economy

Simulation, Optimization

I/O

Econometrics

V Modular Packages Set of Modules

Integrated through a

Database

Support of the Energy

and Environmental

Planning Process

Combination of

Different Methodologies

CO2-DB

MAED, „EXCEL“

MESSAGE, MARKAL-TIMES, LEAP,ESM,WASP, GTMAX

ETA-MACRO

MESAP, ENPEP

16


2. Hybrid Systems





17

Hybrid Power Systems

Characteristics of hybrid power system:

• combination of two or more power generation technologies to complement each other.

• The combination achieves benefits which the separate technologies could not.

Hybrid Power Systems

Diesel and gas

generator Photovoltaic (PV) Wind power

Electrical storage /

batteries

Existing grid

connections

Concentrated Solar

Thermal - CSP

18

Hybrid Power Systems – PV / Diesel

Text Text

Most common Hybrid Power Systems: combination of Diesel generator and PV

to save fuel.

Due to the strongly decreasing costs of PV and rising fuel costs the return on

investment for such systems is extremely low (today).

19

Hybrid System Technology – PV/Diesel Hybrid Layout

Text Text

Source: Donauer

Typical layout:

• Diesel Generator

• PV Plant

• PV Interface

• Data Interface

• Main Controller

(Master Unit)

Optional:

• battery storage

20

Grid connected PV-Diesel Hybrid System

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Po

we

r [k

W]

48 Hour Power Profile of PV/Diesel Hybrid System (example)

Potential PV Power [kW]

Load [kW]

Real PV Power [kW]

Real Diesel Power 1 [kW]



load

PV-

potential

Power

from PV

Power from

Diesel 1 Power from

Diesel 2

Power from

Diesel 3

Less Power generation from Diesel when Power generated by PV

Avoid operation of Diesel engine and save fuel

21

Provide flattened PV generation profile

Grid code requirements: ramp rates must be fulfilled, even with higher shares of PV generation.

Solution: Battery storage controls the ramp rates and flattens the generation profile.

PV generation

Battery Storage

PV + Storage Output

+

=

PV-Battery Hybrid System

22

Management and shift of PV generation

PV-Battery Hybrid System

PV generation

allowed capacity

max. power infeed

Power infeed up to

the allowed capacity

Provision of power at other

times

Provision of energy at

peak demands

avoided curtailment of

PV power

1.

2.

3.

4.

5.

6.

23

Hybrid IPP – Dairy farm in Saudi Arabia

23

Project Background 75,000 cows at 30-50°C in the Arabian desert

Peak electrical demand between 75 – 100 MWe

Heating and Cooling demand for milk processing

Currently powered by multiple 4-8 MW diesel generators

Challenge Increasing production and energy demand

Fuel price increase

Fuel budget limitation

Disposal of manure

Hybrid Project Solution with storage Fuel saver

Increase maximum power output

Implementation in stages until 2022

Project Background

24 24

Diesel Generator specifications:

8 x 8 MW engines running on HFO

48 MW Back-up generators distributed over Dairy

farms running on LFO

Objective:

Main power supply

Active and reactive power control

Ramp rate + frequency control

Peak power supply

Back-up power

Generation costs:

HFO: 3.1 USct./kWh

LFO: 4.9 USct./kWh

Diesel Generators


25 25

Objective:

Provision of electricity (active + reactive)

Increase maximum power output during day

Save diesel fuel

Specifications:

40 MVA (Stage I) + 50 MVA (Stage II)

DC/AC ratio: 1.1 (cos Phi requirement)

Interfaces:

Switchgear at Main Power Station

Estimated generation costs:

1-axis tracked: 4.65 USct./kWh (2016)

Fixed mounted: 4.85 USct./kWh (2016)

PV-Plant


26 26

Objective:

Increase of maximum power output

Provision of reactive power

Provision of spinning reserve (bridging the time the diesel

generators need to start up)

Firming of short-term fluctuations of PV (e.g. clouds, sand

storms) until ramp rate capabilities of the diesel

generators

Load shifting of PV energy production to increase PV

utilization

Black-start capability

Specifications of Storage:

Lithium-ion technology

50 MW / 25 MWh (Final Stage)

CAPEX assumption: 28 mio. USD (2016)

Source: A123 Systems

Battery Storage


27


2. Hybrid Systems





28

CSP

Wind

Grid

Working principle of Hybrid Configurator

Solar

Plant

CAPEX /

OPEX

System Costs

Energy Flow

Cost Optimization

Wind

Turbines

CAPEX /

OPEX

Fossil

Plant

CAPEX /

OPEX

Storage LoadCAPEX

/OPEX

Hybrid Configurator

PV

Simulations, Modelling

PVSyst

WindPRO

SolPRO

Grid

Simulator

Helios

29

Hybrid Configurator – Input / Output

Solar

Plant

CAPEX /

OPEX

System Costs

Energy Flow

Cost Optimization

Wind

Turbines

CAPEX /

OPEX

Fossil

Plant

CAPEX /

OPEX

Storage LoadCAPEX

/OPEX

Hybrid Configurator Input

Fossil Engines

Fuel cost and development

Consumption at partial load

Prime/max/min power

CAPEX/OPEX

PV Plant

Annual/initial degradation

Own consumption

PVSyst ref. plant output

CAPEX/OPEX

Electrical Storage

Cycle efficiency

Cycle/system life

Depth of Discharge

Discharge rate

Aging

CAPEX/OPEX

Wind turbines

Power curve

Roughness of landscape

Wind profile

CAPEX/OPEX

Additional

Discount rate

Demand development

Cost escalation

Emission certificate cond.

Output (Report)

Optimized Configuration

PV Plant (Modules, Inverters,

Mounting structure orientation)

Wind turbines (type, amount,

distance)

Electrical Storage (Technology,

Capacity, Power)

Type and technology of

reciprocating engines

Technical

Saving in Emissions

System availability

Fossil fuel consumption

Share of renewable energies

Usage of renewable potential

Area required

Financial

Net Present Value

Levelized Costs of Electricity

Internal Rate of Return

CAPEX distribution

OPEX distribution

Revenues

30

Hybrid Configurator – Results

Hybrid Configurator Results

Techno-Economic Optimization

Optimized energy system configurations for NPV, LCOE, MIRR, Payback period, fuel

savings, emission savings, etc.

load

usage of PV

battery – state of charge usage of wind

Example graphs from

project on Maldives

PV potential

all Diesel Gensets

combined

no operation of Diesel Gensets

31

Hybrid Configurator – Results

Power output of

hybrid Plant [MW]

Storage Capacity [MWh]

Levelized Cost of

electricity [$/kWh]

most economic plant

configuration

32

45.0

46.0

47.0

48.0

49.0

50.0

51.0

52.0

0 1 2 3 4 5 6 7 8

Time [s]

Frequency [Hz]; PV Penetration = 63.8 %

Frequency [Hz]; PV Penetration = 80.4 %

Frequency [Hz]; PV Penetration = 120 %

PV

off

supply of optimum hybrid system configuration

Static and dynamic simulations of networks

to verify grid stability parameters of

optimized PV and wind penetration

(DigSilent)

Consideration of normal operation

conditions (e.g. radiation and load change)

and fault operation conditions (e.g. short

circuit events, sudden loss of PV and/or

wind generation or load)

Voltage and frequency check of hybrid

network according to ISO 8528-5

Hybrid

Configurator

Economic optimum

(hybrid system configuration)

Output Grid Simulation (with Power Factory)

Assess power quality and security of supply

PV off

sole operation of Diesel engines

adjust configuration of Hybrid System if necessary

Assessment of power quality and security of

33

Modeling of additional reverse

osmosis (RO) plants

Innovative concept: RO

operates with overload to

maximize renewable utilization

Water storage replaces

expensive electrical storage

Optimization of power plant

sizes

Wind Farm (offsite)PV Plant (offsite)

MV Switchgear

Reciprocating Engine Plant

Project Site Water

Transmission

Reverse Osmosis Plant

Brine

Feedwater

RO

Post-Treatment

Pre-Treatment

Storage

Tank

PV on SWRO Buildings

Electrical storage

Solar

Plant

Wind

Turbines

Diesel

Plant

Electrical

StorageLoad of grid

Water

storage

Water

grid

CAPEX /

OPEX

CAPEX /

OPEX

CAPEX /

OPEX

CAPEX/

OPEX

CAPEX

System Costs

Energy Flow

Cost Optimization

Load of

RO plant

Example - Hybrid power and RO plants

PV wind

Diesel engines

water

supply

34


2. Hybrid Systems





35

Measures classification according to the related costs

Emphasis Examples Measures

• teaching, training….

• switch off when not required

• repair leaks

• reschedule loads/usage

behaviour of people using

existing installed technology at

peak energy efficiency

No cost

• maintenance

• meters

• M&T

• simple controls

• training end users

a combination of investment in

low cost technology and people

involvement

Low cost

• cogeneration

• heat recovery

• insulation

• retrofit with controls

• energy management systems

investment in high cost

technology with some people

involvement

High cost

36

Example areas of energy consumption

Factory services Industrial Heating Process Building services

Air Conditioning and Ventilation

Lighting

Motors and Drives

Compressed Air

Refrigeration

Chilled and Cooling Water

Boilers

Low Temperature Processes

High Temperature Processes

Building shell

37

Energy Monitoring & Targeting (M&T) approach

1. Measuring the energy consumption of an Energy Accountable

Centre (EAC) over a specified period of time

Action Reporting Comparison :

actual - target Set targets

Energy

consumption

standard

Measure energy

consumption

2. Relating energy consumption of each EAC to a measure of output,

e. g. production quantity, to define energy consumption standard

3. Setting targets for reduced energy consumption

4. Regularly comparing energy consumption with the target

consumption

5. Reporting variances in EAC consumption

6. Taking action to correct variances

38

Operation of M&T

Repeat

and

refine

Take

saving

actions

Analyse and

report data

and results

Agree on

consumption

targets

39

Model Architecture as base for ISO 50001 Energy

Management System and Energy Monitoring System

Organisation

Procedures and

Core Processes

Process Components

and Services

Information and Control Flow

Process Control, Metering and IT-Systems

Building Model and Energy ModelEnergy

IT

Processes

Organisation

40


2. Hybrid Systems





41

• Optimization

• Automation

• Cloud-based

• Real time

Operators of Infrastructures

Smart Energy Hub – Field of Application

• Consumption of energy

• Generation of energy

• Procurement of Energy

• Storage capacities

• Flexibilities

• Industries

• Airports

• Large buildings

• Cold stores

• Swimming pools

Energy Market

Price peaks,

low level prices

Technological

progress

+ - + -

Complexity,

options

010010011 100101101

Knowledge,

intelligence

@

Technologies Services

Examples Characteristics

• Reliable, solid

• Cost efficient

• Continuous optimization

Energy-Services

• Already available

• Not used

Data

Smart Energy Hub

Tool for energy management of infrastructures based on sensor-data and forecasts

IT

€

t

€/MWh

t

Chance

& Risik

make accessible, usable make manageable make use of chances, avoid risks

42

Smart Energy Hub – Features

Output

• Internal Data: production planning,

energy consumption, temperature,

passengers, Produktion, …

• External data: forecasts: el. prices,

weather, ...

Data

Energy:

• procurement

• generation

• storage

• utilization

Technical infrastructure

Input

• Visualisation collected data, pattern of system,

optimization results assistance for decision making

• Analysis of data operation pattern, correlations

• Forecasts e.g. load curves, input for optimization

• Optimization across different sectors and media

output: schedule for plant operation and procurement

• Simulation changes within existing technical infrastructure,

e.g. upgrading

43

From Big Data to optimized operation schedules

• ambient temperature

• global radiation

• passengers

Identification of

• interdependencies

• processes

in a system

Operation

• chillers

• boilers

• temperature level

in the terminal

Collection of time series,

e.g. tracking consumption

°C

1011010101001100110010110 11110100010001001101101101100

°C

11011011010101100110010010 Big

Data

Analysis of correlations

(of big data)

Characteristics known e.g. load (heat, cold) depending on weather, number of passengers Smart

Data

Optimization of energy

supply

operation planning: flight schedules, passengers

Correlations Forecast weather

Forecast load (internal)

Forecast electricity price

Optimization plant operation

Plant Data

Operation schedule (per plant)

Optimization,

Control

• ambient temperature

• global radiation

• number of passengers

• temperature level in the terminal

• operation of plants (e.g. boilers)

11100101000100010011010010110

10111010001000100110111011011

11011101010010010100111101101

10101111010001000101100110110

10010010011010001001001101110

Trend towards online optimization

44

Avoidance of natural gas procurement peaks

• Consumption of natural gas is determined by operation of IC-engine and boilers

• Boilers can be fired by natural gas or fuel oil

• Natural gas price is composed by unit rate and capacity price

• Natural gas consumption can be

• postponed, brought forward (heat storage)

• replaced by fuel oil consumption

Principle

• Reduction of costs for natural gas procurement peaks(capacity price)

Target

Example 2

45

Load curve natural gas – Cogeneration Plant North

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

1 501 1001 1501 2001 2501 3001 3501 4001 4501 5001 5501 6001 6501 7001 7501 8001 8501

La

st [

kW

h]

Heizwerk-Nord

Last HW Nord

Mittelwert

• Load curve natural gas cogeneration plant north 20.-31.12.2014

• High natural gas procurement level (nearly 12,000 kW)

Example 2 - Avoidance of natural gas procurement peaks

Cogeneration Plant North

load

mean value

loa

d [kW

]

46

Optimization fuel oil vs. natural gas consumption


Optimization result:

cost optimum of fuel oil and natural

gas consumption at 7,134 kW 0

2000

4000

6000

8000

10000

12000

14000

Arb

eit

[kW

h]

Zusammensetzung der Jahresarbeit aus Gas und Öl

Jahresarbeit Gas Jahresarbeit Öl Leistungslimitnatural gas fuel oil capacity limit

loa

d [kW

]

Composition of optimized fuel consumption

47


Phase 2: fuel switch to fuel oil

Phase 3: decreasing

temperature level in

terminal 1 – depending on

air traffic and weather

Gas procurement below limit

Phase 1: charging heat storage

Early warning,

forecasted

natural gas peak

Phase 1: utilization of heat

capacity of buildings

Optimization based on new criteria and determination of optimized operation mode

48

Avoidance of natural gas procurement peaks

0

2.000

4.000

6.000

8.000

10.000

12.000

00

:00

05

:00

10

:00

15

:00

20

:00

01

:00

06

:00

11:0

01

6:0

02

1:0

00

2:0

00

7:0

01

2:0

01

7:0

02

2:0

00

3:0

00

8:0

01

3:0

01

8:0

02

3:0

00

4:0

00

9:0

01

4:0

01

9:0

00

0:0

00

5:0

01

0:0

01

5:0

02

0:0

00

1:0

00

6:0

011

:00

16

:00

21

:00

02

:00

07

:00

12

:00

17

:00

22

:00

03

:00

08

:00

13

:00

18

:00

23

:00

04

:00

09

:00

14

:00

19

:00

00

:00

05

:00

10

:00

15

:00

20

:00

01

:00

06

:00

11:0

01

6:0

02

1:0

0

20.12.14 21.12.14 22.12.14 23.12.14 24.12.14 25.12.14 26.12.15 27.12.15 28.12.15 29.12.15 30.12.15 31.12.15

La

stg

an

g G

as

[kW

]

Lastgang Gas begrenzt Zu verschiebende Last verschobene LastJahresarbeit Öl Leistungslimit Grenze Öl (Jahresoptimierung)

peak

load

bottleneck is noticed

early by SEH /

operating staff

Charging storage,

utilization of heat

capacity of buildings

decrease temperature

level – where possible Fuel switch to fuel oil


load to be switched

switched load

replacement of gas by oil

limited load of gas

49


2. Hybrid Systems





50

xxx

Csells_Amman_Reuter.pptx

51

Contact

Dr. Albrecht Reuter

Telefon +49 711 8995 - 1964

Mobil +49 177 8997 964

Fax +49 711 8995 - 1450

E-Mail [email protected]

Internet www.fit.fichtner.de

Tobias Rehrl

Telefon +49 711 8995 - 597

Mobil +49 178 8995 597

Fax +49 711 8995 - 459

E-mail [email protected]

Internet www.fichtner.de

Documents

Long Term Power System Expansion Planning