management of power generation presentation

8/12/2019 Management of Power Generation Presentation

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igo de la Parra

Munich

4thJune 2014

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A design and simulation toolbox

Management of PV Power Generation

Less Variability and More Predictability


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Introduction

PV power fluctuation

Accurate Forecast

Storage Systems Requirements

General EMS and toolbox

Future Work: Accurate Forecast + Energy Storage System


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Introduction

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Changes in cloud cover

Significants fluctuations in PV power

output

For t


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Introduction

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New grid codes deal with this topic

PUERTO RICOSOUTHFRICA MEXICO

10%/minFrom 1 to 5%/min


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Introduction

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A lot of enterprises do not know how to face this problem

PV CROPS is trying to solve it!


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Introduction


Accurate Forecast





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Experimental Set-Up

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Characteristics:

Synchronized recording every second of the power generated by 7 PV plantslocated in Spain (up to 20 MWp), separated by distances ranging from 6 km

to 345 km.

PV plant power: ranging from 958 kWp y 9.5 MWp.

Two sections of the same plant (Milagro), 48 kWp y 143 kWp.


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Time series data

1s

t

Power fluctuation:

[%]P

P(t) t)P(t(t)P*t

t t+t


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PV power fluctuation distributions

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48 kWp

9,5 MWp

t =20s

The larger the plant size, the lower the power fluctuations.


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48 kWp

9.5 MWp

Maximum Power Fluctuations

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Relationship between Max(Pt) and the PV plant area:

Max(Pt)= 90 % (1 - e-0,24 t) S c[%]

For t= 1s, cis 0.5, the maximum

fluctuation is inversely

proportional to S

For t= 600s, cis 0.02, the plant

size is not a smoothing factor for

the fluctuations

For small samples of time the smoothing effect is

proportional to the square root of the PV plant surface


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Influence of the number of plants grouped, N

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N= 1

N= 6

The smoothing effect extends as far as the long sampling times (t= 600 s).

For a given N, no plant combination had a greater smoothing effect than

another.


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Influence of the distance between PV plants

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Minimum distance between plants in order to ensure that the plant fluctuations

remain independent. 6 km(Arguedas-Castejn) y (Arguedas- Socullamos), 345 km.

6 km is enough to guarantee smoothing by geographical dispersion


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Size vs. Dispersion

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Max(Pt,N)= 90% (1 - e-0,24 t) S c N a a, c > 0

N P* MW) Max P600,N)1 100 86

10 10 31100 1 11

The smoothing due to dispersion is greater than due to plant size


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Simulation of Fluctuations. Transfer Function

for a PV plant

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What we have learnt is...

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The larger the plant size, the lower the power fluctuations

[%]P

P(t) t)P(t

(t)P *t

For small samples of time the smoothing effect is proportional to thesquare root of the PV plant surface

The smoothing effect extends as far as the long sampling times (t= 600 s).

For a given N, no plant combination had a greater smoothing effect than

another.

6 km is enough to guarantee smoothing by geographical dispersion

The smoothing due to dispersion is greater than due to plant size

PV power fluctuations are really fast


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Solar Forecast

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Is the art of predict solar radiation in a specific place withdifferent time horizons.

Different time horizons: NOWCASTING (0-3 hr)

SHORT-TERM FORECASTING (3-6 hr)

FORECASTING (few hours to several days)

CLIMATE PREDICTION (seasons-years)


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Nowcasting

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PV power fluctuations are so fastNOWCASTING:

Very short-range period with Temporal horizon of 3-4 hours.

High spatial resolutions (specific place).

Usually based on ground based (sky cameras, ceilometers andradiometers) and satellite imageries.

Cloud motion and aerosol load prediction is the one of the main

issues to be solved


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Nowcasting Tools

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Raw data is obtained in several different ways:

Sky cameras:

Very high resolution (over solar plants)

Forecast in quasi-real time ( with a delay of 15-30 minutes).

Cloud detection and classification according to optical features andheight, using a Neural network.

Cloud motion, using different algorithms


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Nowcasting Tools

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Ceilometers:

Clouds height Detect high and thin clouds (sky cameras cant)

Using them in combination with sky cameras the DNI forecast willimprove.

Also to measure aerosol concentration.

Meteorological station:

DNI Pyrheliometer

GHI Pyranometer (photovolatic; thermopile)

DHI Pyranometer with a ring

S i i h N i


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Summarizing the Nowcasting

state of the art

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Summarizing the nowcasting state of the art (AEMET)

A combined approach using in-situ observations, satelliteobservations and RTF models appears to be able to provide themost accurate results for cloud-DNI-GHI nowcasting at CSPs andCPVs.


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What we have found

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258 259

0

100

200

300

400

500

600

700

800

900

1000

Hourly predicted Irradiance vs. Measured Irradiance

Day of the year

m)

GHImed

GHIfore


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What we have found

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Introduction


Accurate Forecast




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How have we done it?

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Experimental Data

One year 5 second power measurements were recorded at the output of550kW inverters at the 38.5 MW Amareleja (Portugal) PV plant.

Combining several inverters, any PV plant power size from 0.5 to 38.5 MWcan be considered


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PV power fluctuation without storage


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276000 6500 7000 7500 8000

0

200

400

600

800

1000

1200

Tiempo (s)

Potencia(kW)

PV power fluctuation with storage

PFVPgrid


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Toolbox Development

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Minimize

storagerequirements

Efficientcontrol

Maximize

economicoutput


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Capacity vs. PV plant size

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Ram-Rate Control

For PREPA Regulation:Times of storage very

low

1 MW o 40 MW

The same storage time


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Introduction


Accurate Forecast





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Running the toolbox

Input Parameters is an ExcelTemplate

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The software calculates the PVpower to install and the batterysize to attenuate up to dailyfluctuations in the powerexchanged with the grid


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Output report

The toolbox calculates the appropriate parameters andsimulates until one year of operation.

The results are shown in an automatic generated report

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Introduction


Accurate Forecast





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Future work

350 50 100 150

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Minutos

Precio(/MWh)

Evolucin precio energia "2010/08/15"

Precio

Descarga

Carga

0 12 24

Hours


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Future work

360 5 10 15 20 250

100

200

300

400

500

600

Horas

C-bat(kWh)

Planificacin bateria "2010/08/15"

C-firmingC-reg.frec

C-firming

SOC-recom

SOC-recom-mod


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Experimental validation

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At AccionasTudela PV PlantLithium-ion Battery

At vorasuniversityVRB


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Thank you for your attention

Q&A

For more info:

[email protected]

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management of power generation presentation

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