predictable performance of utility scale pv in smart ...€¦ · short company profile automotive...

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Leading specialist for advanced PV monitoring data analysis and performance improvement

Rising Alternative Energy Pvt Ltd

Gerhard MütterTechnical Director

Predictable performance of utility scale PV in smart environments

Technical quality of park operations and maintenance as a key factor

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Technical Quality as a Key Factor of Success

Overview of the Introduction

Rising Alternative Solutions Pvt Ltd Overview about services provided

Short Company Profile

Automotive Engineer, Technical Mathematics & Informatics 25 Years Software Development Since 2005 in Renewable Energies, Product Development & Photovoltaics MSc Renewable Energies at Technical University Vienna Technical Director of AES, Vienna O&M Supervision for > 700MWp Technical Director of Rising Alternative Energy Pvt Ltd, New Delhi, India Member of the EU PVSEC Scientific Committee

Gerhard Mütter

Predictable Performance

PV plants in smart grids Life cycle and losses of PV plants Life cycle and losses of PV modules

How to ensureTechnical Quality

AES PIT a toolbox for advanced data analysis The process to ensure technical quality

Some examples Inverter performance Analysis String performance map to localize underperformance The impact of vegetation

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Rising Alternative Energy Pvt Ltd (Joint Venture Company)Rashi Renewable Energy Pvt Ltd and Alternative Energy Solutions GmbH have formed a JV Company for India

Photovoltaic stands for reliable and sustainable generation of clean electricity from solar energy and attracts a wide range of different investors with individual requirements of their PV service providers.

Due to our longstanding experience, Alternative Energy Solutions has the capabilities to offer tailor-made, modular consultation, planning, supply chain and implementation services. We assume various roles as technical consultant, owner‘s engineer, EPC, O&M and asset manager as well as photovoltaic solutions and technology provider and place considerable importance on transparencyand long-term partnerships.

Whether technically or commercially related, pure consultation or turnkey delivery, Alternative Energy Solutions GmbH provides its customers with manufacturer-independent and trade-spanning services, best-in-class products delivering the best price-to-performance ratio, ensuring the technical and commercial success of complex projects in a continually changing future market.

We support developers and investors in making the right decisions…

• Feasibility Studies

• Regulatory Issues

• Finance Structuring and Modelling

Development Services

We provide a customized package tailored to each client’s needs…

• Engineering Services

• Procurement Services

• Construction Management Services

EPCm Services

Once the development is completed, we act on the owner’s behalf…

• Technical Operation Management & Maintenance Services

• Commercial Operation Management

• Asset Optimization Services

Asset Management

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Asset Management Services

Overview

Photovoltaic power plants have a life span of at least 25 years and need professional operation and maintenance to ensureoptimal performance throughout their lifetime.

Our Asset Management Services include:

Technical Operation Management & Maintenance Services

We provide a customized package tailored to each of your needs.• Monitoring – remote plant monitoring and supervision of the local O&M provider; preparation of energy yield and plant performance analysis• Technical Reporting – implementation of reporting and documentation (monthly, quarterly, annually)• Maintenance – site visits; preventive and corrective maintenance; insurance, HSEQ, contract and claim management

Commercial Operation Management

We make sure that you are able to reduce costs and maximize revenues and profits of your PV assets.• Budgeting, liquidity and cash management, payment approval, accounting and cost controlling of the assets under our management• Preparation and/or analysis of P&L, balance sheets, cash flow statements and tax declarations• Commercial and financial reporting – implementation of reporting and documentation (monthly, quarterly, annually)

Asset Optimization Services

We help you to optimize your design and the operation of your PV assets to achieve best in class performance.• Analyze the whole power generation system in detail to identify improvement potential• Yield optimization through corrective maintenance, system upgrades and quick payback solutions to increase the economic asset performance• Demonstrate the potential reduction in levelized cost of electricity (LCOE) and the improvement of the internal rate of return (IRR)

Experience and Track Record:

Our team manages several hundred MW capacity in solar emerging regions

• AES supervises >500 MW PV power plants in Europe• AES provides asset optimization and performance improvement advisory to various customers in Europe and Asia

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Short introduction large scale PV in smart grids

Predictable load profiles Efficient userproducer communication Predictable production on all components

Knowing the plant Precise weather forecast Continuous high quality of the plant

Underperfoming equipment Local shadowing Dust & Dirt Local breakdown Degradation (LID, PID) Wearout


Factors for well working smart grids

Predictable Production of PV plants

Main Issues to take care in PV Plants

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Typical failure distribution during live time of PV plant

Co

sts

of

loss

es o

n f

ailu

re

Time of operation

Early failures Random failures and degradation Wear out failures

Advanced data analysis

AES PITbenefit

Start of wear out can only be detected with permanent

observation of key parameter

Significant shortening of startup phase

Unlocking hidden values by advanced data analysis reduces losses by faults the whole live circle of a Plant

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Relevance if PV Plant supports

smart grid

Faster acceptanceof smart grid by the clients

Longer stable and reliable power support for complete smart grid

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Orignal Source: IEA PVPS 2014

StandardMonitoring

Advanced Data Analysis required

Unlocking hidden values by advanced data analysis reduces losses by fault the whole live circle of a Plant

Typical performance reduction causes during live time of PV panels


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Performance improvement using AES Performance Improvement Technology

AES PIT toolbox for remote analysis of PV plants on top of your monitoring software

Unlocking hidden value through the AES Performance Improvement Tools (AES PIT) using advanced data analysis

Visualization of large number of plant components and measurement data to localize underperformance

Performance analysis, error detection and evaluation of the magnitude of impact on the energy yield

Performance Improvement Analyses and Performance Reports of PV plants (full scope)

Assessment of weather corrected electricity generation (real vs. prediction)

Technical standard checks for the remote early detection of underperforming plant equipment

Individual checks on suspected defects (based on results of performance reports and technical

standard checks)

Weather corrected electricity generation

Underperforming plant equipment and mitigation of defects

O&M quality check and handling of defects

Dust and soiling

Module degradation

Vegetation and shadowing

Design and construction mistakes

Reliability of sensors

Electricity generation forecast for electricity trading


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AES Performance Improvement Technology

AES PIT Analysis Procedure

Input Inverter/String: power, DC/AC, status, installed capacity Weather: irradiation, ambient temperature, wind, rain Additional: module temperature

Pre-processing Checks: validation of sensors, isolation of time frames for

further checks, missing and unreliable data records Data Screening: filtering, compression, normalizing

AES PIT Checks based on advanced data analysis on top of monitoring

Electricity Generation

Underperforming Equipment

Fault Handling

Dust & Soil VegetationModule

Degradation

Design and Construction

MistakesSensors

FINAL REPORT

AES PIT Reports

Post-processing Expert Evaluation: isolating and quantifying of relevant

findings, recommendations, potentials for upcoming faults Report Finalizing: scope, summary, annexes

AES PIT Reports

AES PIT Reports

AES PIT Reports

AES PIT Reports

AES PIT Reports

AES PIT Reports

AES PIT Reports

Handing over to client Send FINAL REPORT Explanation of Results

workshop, telco, meeting

Scri

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10Strictly Confidential

Example of PIT analysis output (screenshots) – Remote detection of defect inverters and O&M mistakes

AES Performance Improvement TechnologyVisualization of large number of plant components and measurement data to localize underperformance

May 2016

May 2015

No

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Inverter shows wrong AC/DC wiring plus wrong calibration (above 100%, which is not possible) in 2015 and 2016

Picture above shows regular behavior of inverter and a number of inverter errors remotely detected by AES PIT


Remote detection of defect inverters not possible to detect through monitoring software


Solution

Automatic recording of local faults and creation of a priority list indicating the e.g. 50 faults with largest production impact.

Result

Fast mitigation of faults and reduction of losses from 4.5% to <0.2%. Development of high sensibility of O&M team for faults with relevant impact.

Situation

Defect electrical components cause local faults with significant impact on production.

Benefits for 20 MW plant (Loss reduction from 4.5% to <0.2% )

1,320 MWh 6.6 mn INR/a*

* assuming 1,500 kWh/kWp and a PPA tariff of 5 INR/kWh

Loss reduction from 4.5% down to <0.2% between 2013-2015 and for all subsequent years


Example of PIT analysis output (screenshots) – Remote detection of defect modules, damaged PV cables


String performance map

String Performance Map / 2016-04-29 whole day

GCB\Station PTR_1 PTR_2 PTR_3 PTR_4 PTR_5 PTR_1 PTR_2 PTR_3 PTR_4 PTR_5 PTR_1 PTR_2 PTR_3 PTR_4 PTR_5 PTR_1 PTR_2 PTR_3 PTR_4 PTR_5

B1.01 0,9 17,1 2,3 0,0 4,9 B2.01 1,8 3,4 0,0 0,0 6,0 B3.01 2,0 2,4 0,0 0,0 4,8 B4.01 1,2 3,0 0,0 0,0 5,5

B1.02 0,5 5,0 1,7 0,0 3,3 B2.02 1,8 3,7 0,6 0,4 7,9 B3.02 1,5 2,4 2,4 0,0 6,8 B4.02 0,8 0,6 0,3 0,5 9,0

B1.03 0,0 0,0 0,0 0,0 0,2 B2.03 0,0 0,0 0,7 0,4 9,2 B3.03 0,0 0,0 0,1 0,0 5,0 B4.03 0,0 0,0 0,5 0,3 8,9

B1.04 3,3 2,1 4,8 0,0 6,2 B2.04 3,1 6,4 3,4 1,7 10,0 B3.04 3,1 6,4 1,7 0,0 6,9 B4.04 5,5 5,0 1,4 2,6 7,7

B1.05 3,5 1,6 3,7 0,0 4,0 B2.05 4,4 3,2 1,6 1,2 8,4 B3.05 1,2 9,8 0,9 0,0 7,1 B4.05 3,8 4,7 0,8 4,0 7,5

B1.06 1,9 0,1 1,7 0,0 6,8 B2.06 2,3 1,4 3,8 3,5 8,0 B3.06 0,0 7,3 2,5 4,3 10,7 B4.06 3,6 2,8 3,3 3,6 7,9

B1.07 4,1 3,7 6,2 0,0 3,1 B2.07 6,5 5,0 0,0 2,5 0,1 B3.07 4,1 7,2 1,8 2,2 0,0 B4.07 7,3 7,4 4,8 3,6 0,0

B1.08 6,3 5,8 5,8 0,0 2,9 B2.08 8,1 5,5 6,8 4,9 1,1 B3.08 5,6 7,3 2,9 0,0 0,4 B4.08 7,6 5,5 5,6 5,6 1,1

B1.09 0,8 0,5 0,0 0,0 0,0 B2.09 0,2 0,0 1,4 0,0 0,2 B3.09 2,0 0,0 3,5 2,2 0,1 B4.09 2,9 1,1 3,0 4,0 0,1

B1.10 6,0 5,8 5,1 0,5 5,1 B2.10 4,4 6,1 2,2 2,5 1,1 B3.10 4,6 7,3 3,8 0,0 10,6 B4.10 7,6 10,0 4,3 5,6 8,3

B1.11 5,7 5,8 4,6 0,5 2,8 B2.11 2,4 4,7 3,1 3,4 0,0 B3.11 3,4 9,6 3,1 0,0 6,8 B4.11 7,3 8,8 3,7 4,8 7,7

B1.12 4,3 3,4 1,7 1,0 1,4 B2.12 2,4 2,2 3,5 4,1 1,7 B3.12 1,4 9,6 4,2 2,8 9,4 B4.12 7,1 10,9 5,3 6,1 6,9

B1.13 6,3 8,0 0,0 0,9 5,1 B2.13 6,5 5,0 4,5 4,3 1,3 B3.13 6,5 9,8 4,7 2,2 1,5 B4.13 9,2 9,4 8,4 6,1 16,0

B1.14 6,3 8,0 0,0 2,9 4,5 B2.14 8,1 6,9 5,8 4,8 2,6 B3.14 6,3 14,5 5,8 5,0 2,6 B4.14 7,6 6,2 8,8 7,7 17,2

B1.15 4,2 7,7 0,0 6,4 2,3 B2.15 4,4 3,0 5,8 6,0 4,6 B3.15 2,9 7,2 5,9 5,0 1,7 B4.15 9,2 5,2 9,0 8,2 25,5

GCB\Station PTR_1 PTR_2 PTR_3 PTR_4 PTR_5 PTR_1 PTR_2 PTR_3 PTR_4 PTR_5 PTR_1 PTR_2 PTR_3 PTR_4 PTR_5 PTR_1 PTR_2 PTR_3 PTR_4 PTR_5

B5.01 2,9 2,0 0,0 0,1 0,0 B6.01 0,0 2,2 0,0 0,0 6,5 B7.01 0,0 2,4 0,7 2,6 0,2 B8.01 0,0 1,3 2,2

B5.02 2,9 4,2 0,6 0,2 0,5 B6.02 1,2 2,0 0,0 0,1 7,2 B7.02 2,3 2,5 1,5 0,8 3,6 B8.02 2,6 1,1 1,4

B5.03 0,0 0,1 0,4 0,0 0,0 B6.03 1,2 0,0 1,1 0,9 5,7 B7.03 2,3 1,4 0,0 0,2 0,0 B8.03 0,0 0,1 0,0

B5.04 5,0 4,3 2,2 3,5 3,7 B6.04 1,2 3,2 2,8 1,5 0,0 B7.04 2,3 2,6 0,6 0,0 1,6 B8.04 2,3 1,4 0,2

B5.05 5,0 2,3 0,9 3,4 2,8 B6.05 1,2 0,3 3,1 0,1 0,0 B7.05 5,8 0,7 0,0 0,0 1,0 B8.05 1,8 0,0 0,4

B5.06 2,2 0,5 4,0 4,6 4,3 B6.06 1,2 0,0 4,2 2,7 1,7 B7.06 2,3 0,8 1,4 0,8 2,9 B8.06 2,9 1,4 3,5

B5.07 7,0 3,9 2,7 9,8 4,9 B6.07 4,7 3,0 6,3 3,2 5,4 B7.07 5,8 2,5 7,1 2,2 5,2 B8.07 3,0 2,5 5,1

B5.08 4,4 5,8 4,7 10,7 6,2 B6.08 4,7 3,0 9,7 4,0 5,4 B7.08 9,3 4,0 8,0 4,9 5,2 B8.08 5,6 3,5 6,0

B5.09 0,0 2,9 3,4 3,2 7,9 B6.09 4,7 0,3 7,2 0,9 3,9 B7.09 5,8 2,1 2,9 0,9 0,0 B8.09 0,1 1,0 1,4

B5.10 5,6 2,2 4,9 9,7 7,0 B6.10 4,7 7,4 10,0 5,6 0,9 B7.10 2,3 0,6 1,4 4,2 B8.10 1,9 7,5 5,5

B5.11 0,0 0,4 5,0 11,3 10,2 B6.11 4,7 5,8 10,8 4,6 1,5 B7.11 5,8 0,0 1,3 4,0 B8.11 0,6 5,1 4,6

B5.12 0,0 0,0 18,8 8,0 7,5 B6.12 4,7 4,8 10,3 5,2 3,2 B7.12 2,3 1,3 6,3 3,6 B8.12 1,7 4,8 3,9

B5.13 7,2 8,0 6,8 10,3 13,4 B6.13 8,2 11,3 11,5 6,8 3,2 B7.13 5,8 4,4 3,2 5,2 B8.13 5,0 8,1 4,1

B5.14 5,2 10,9 7,7 12,7 18,0 B6.14 8,2 7,4 13,8 7,1 3,2 B7.14 2,3 3,9 5,7 55,3 B8.14 3,8 8,5 5,0

B5.15 2,4 5,0 8,6 13,3 15,6 B6.15 8,2 5,6 12,1 9,2 3,2 B7.15 0,0 3,8 5,7 4,2 B8.15 4,9 6,0 4,9

Burned connection (junction) box Wrong module type installed

Burned PV cable

Hotspot

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Strictly Confidential

Visualization of negative impact of shadowing, caused by long grass


Result

Clear visibility of regions with loss impact, detected remotely. Claim to O&M contractor and improved cutting schedule to avoid future losses.

Benefits for 20 MW plant (3% losses)

1 Week 16,100 kWh 80,500 INR*

Situation

Grass cutting has not been executed, at least not at scheduled time intervals.

Losses

Loss increase of >3% power generation losseswithin only one week.

Solution

Comparison of local string losses against maximum possible power generation and summary of regional losses.

Zones with losses caused

by growing grass

* assuming 1,500 kWh/kWp and a PPA tariff of 5 INR/kWh

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Statements on REI 2016

Speed Opening session REI 2016, Delhi , Sept. 7th

Scale The answer on: “How to reach renewable targets for India ?”

Skill We take care of all 3 aspects

Session “Looking Beyond Installation” REI 2016, Delhi Sept 7th

3rd party audit for energy enhancement is a essential component of a successful O&M strategy

That´s our core business

Shri UpendraTripathy

MNRE

PremchanKarunakaran

TATA Power Solar

Some quotes of previous speaker on RENEWABLE ENERGY INDIA 2016


Thank you for your attention

Leading specialist for advanced PV monitoring data analysis and performance improvement

Rising Alternative Energy Pvt Ltd

Gerhard MütterTechnical Director

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Visualization of negative impact of shadowing, e.g. caused by long grass or other vegetation Refinement of performance ratio (PR) analysis considering weather, temperature, irradiation and wind variations (weather

corrected yield checks) Analysis of the relevance of detected faults and impact on plant performance in relation to overall energy yield of the plant

(magnitude of the faults)


Performance improvement of your operating PV assets using AES PIT

Overview of unique AES PIT output compared to the limited information provided by standard monitoring software (SCADA) and the findings from visual plant inspections and infrared thermography inspections

Examples for AES PIT output

Remote visualization of: Underperforming or defect modules,

strings, combiner boxes and inverters; not possible to detect through monitoring software

Module level on top of monitoring system (SCADA)

Combiner box level on top of monitoring system (SCADA)

Inverter level on top of monitoring system (SCADA)

Cabling level on top of monitoring system (SCADA)

Verification of early or fast degradation of modules, typical for (some) thin film modules or module production problems

Examples for AES PIT output

Module Level detectable withon site

verification

SCADA PIT visual IR

Hotspot 1)

Delamination 1)

Glass breakage 1)

Soiling

Shading

Snail Trail 1)

Cell cracks 1)

Defective backsheet 1)

Overheating junction box 1)

Linear degradation

PID Potential Induced Degradation

Failure bypass diode and junction box 1)

Corrosion in junction box

EVA discoloration

Theft of module 4)

Broken module 2)

1)

Damage by snow 2)

1)

Inverter Level detectable withon site

verification

SCADA PIT visual IR

Fan failure / overheating 3)

Switch failure / damage 4)

Inverter firmware issue

MPP tracking problem

Efficiency reduction

Switch failure master/ slave logic

Wrong time to switch master / slave

Polluted air filter – derating

Inverter pollution

Data entry broken

Display off/ broken

Burned supply cable

Combiner Box Level detectable withon site

verification

SCADA PIT visual IR

Main switch open, does not reclose 4)

Broken general switch 4)

String fuse burned

Local underperformance of soiling or vegetation

Data missing

Cabling detectable withon site

verification

SCADA PIT visual IR

UV-Aging

Theft of cables 4)

Loose contact 1)

Broken or burned connectors

Data missing

Cable damage, no loss still working

Cable damage, partial loss but working 1)

Cable damage, circuit interrupted

1) if losses have effect at string level

2) if total loss

3) at lower level than SCADA

4) only data provided by SCADA and analyzed by PIT

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AES Performance Improvement Technology

AES PIT Toolbox Overview and Data Requirements

Main topic Description Examples Input Data Granularity

required optimum

Electricity Generation

Assessment of weather corrected electricity generation (yield, real vs. prediction)

daily yield status

monthly yield status

monthly and annual yield

comparison with previous periods

yield of desired part of PV plant

irradiation *1

ambient temperature

day day

Underperforming Equipment

Remote early detection of underperforming equipment (modules, combiner, inverter) and loss mitigation

inverter efficiency curves

localization and quantification of local losses

optimization of switching points for master/slave inverters

verification of temperature coefficient of modules

AC/DC values at inverter *2


irradiation *1

ambient temperature

module temperature *3

hour 1 min

Fault Handling Remote verification and recording of local faults with impact on electricity production

duration and impact of faults at inverter, combiner and string level

reaction time of O&M team

impact of faults outside working hours


irradiation *1

ambient temperature

15 min 1 min

Dust & Soil Performance effects due to dust, soiling or similar environmental influences

evaluation of impact on yield

optimization of cleaning- and/or predictive maintenance schedules


irradiation *1

ambient temperature

day 1 min

Vegetation Dynamic effects of vegetation shadowing and impact of local obstacles

quantification of losses due to dynamical changing shadows of growing vegetation

quantification of losses caused by other obstacles (lightning poles, buildings,..)

indices for recommendation for self-cleaning and anti-reflexive coatings

yield of desired part of PV plant *4

irradiation *1

ambient temperature

15 min 1 min

Module Degradation

Remote localization and verification of accelerated module degradation

accelerated degradation at (some) thin film modules or production errors

potential induced degradation (PID)

light induced degradation (LID)

yield at string level

irradiation *1

ambient temperature

15 min 1 min

Design and Construction

Mistakes

Remoted detection and quantification of losses caused by design and construction mistakes

improvement of wiring in multi-row arrangement of modules

losses and limitation caused by undersized components


irradiation *1

ambient temperature

15 min 1 min

Sensors Remote verification of reliability of sensor values in monitoring system

reliability of pyranometers and module temperature sensors

double check of power sensors at string and combiner level

values of the desired sensors in best possible granularity

15 min 1 min

Scri

pts

1..

o

*1 ... preferred in module plane *2 ... only for inverter efficiency curves *3 … only for temperature coefficients *4 … for tree shadows at string level required

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AES PIT brings large PV power plants to higher annual energy production and the regular use of AES PIT verification checks for all installed equipment components enables the suitable preventive maintenance actions, long before even partial breakdown of thePV plant equipment occurs.

The payback period of the AES PIT analysis usually lies below 1 year.


Performance improvement of your operating PV assets using AES PIT

AES PIT software structure and flow chart of monitoring system data and PV plant information

AES PIT approach

AES PIT conducts advanced data analysis on top of the monitoring system and based on the monitoring system data input (availability if monitoring data as pre-condition for AES PIT).The flow chart on the left describes the AES PIT software structure and the flow of monitoring system data and PV plant information and illustrates the extraction and processing of selected data based on software routines (scripts) for regular or special reports as well as for individual checks on suspected faults.

AES PIT benefit

Monitoring System 1..m

PV Plant Documentation 1..n AES PIT Database

Import Filter 1..m

Scripts 1..o

AES PIT Report 1..p

Import from Monitoring System Every system own import filter

Database

Standardized extract of monitoring data

independence from daily business

Extract of selected data needed for report

significant reduction of data to handle

reliable calculation times

Filled up on demand of Scripts

PV Plant Documentation with Flash lists

Park structure

Capacities

….

Scripts for Reports Extra small local programs for special reports

Level of detail depends on level of report

Prepared for automated post process for huge

assets

predictable performance of utility scale pv in smart ...€¦ · short company profile automotive...

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