Capacity value of intermittent generators Preliminary findings - - PowerPoint PPT Presentation

Capacity value of intermittent generators

Preliminary findings

Market Regulations May 2018

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Outline

• 1. Introduction
• 2. Capacity value assessment methods
• 3. Current method
• 4. Current issues in the SWIS

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1.Introduction

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Background

• Capacity value: the contribution a capacity makes to system adequacy
• Relevant Level Methodology – RLM
• contribution of variable generation to system adequacy in the SWIS
• The ERA is currently required to review the method every three years
• IMO last reviewed the RLM in 2014

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Capacity valu lue outcomes

• Significant change in the RLM
• Previous method: average output of IGs
• Change in method transitioned over 3 years
• Current method: average output during high-risk periods

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0% 10% 20% 30% 40% 50% 60% 100 200 300 400 500 600 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019-20 Capacity credits proportion of installed capacity (%) Capacity (MW) Installed Capacity Certified Capacity Accredited capacity as share of installed capacity

Use of average output Current risk-based method

Basis is of capacity valu luatio ion

• Effective load carry

rying capability (ELCC): the amount of incremental load that a resource can serve without a change in the system reliability

• ELCC considers:
• probabilistic nature of generation output
• random forced outages
• Correlation between system random variables

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Basis is of capacity valu luatio ion

500 1000 1500 2000 2500 3000 3500 100 200 300 400 500 600 700 800 900 1000

Load (MW) Interval

LOLP = 10% Available firm capacity 2,400 MW

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Capacity valu lue of fir irm capacity - example

• Reliability target: LOLP=10%
• Additional generation: 100 MW installed capacity (firm)

500 1000 1500 2000 2500 3000 3500 100 200 300 400 500 600 700 800 900 1000

Load (MW) Interval

Load (MW) Net load

LOLP = 10%

Effective Load Carrying Capability (ELCC) = 100 MW Capacity value = 100% of the installed capacity

2,300 MW

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Addition of random capacity (low penetration)

• Generator: Normally distributed output, 𝑛 = 100 𝑁𝑋, 𝑡 = 50 𝑁𝑋
• Assume: generator output is independent of load distribution

500 1000 1500 2000 2500 3000 3500 100 200 300 400 500 600 700 800 900 1000

Load (MW) Interval

Load (MW) Net load

𝐹𝑀𝐷𝐷≈100 𝑁𝑋 ELCC is close to mean output of the generator

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• 1500
• 1000
• 500

500 1000 1500 2000 2500 3000 3500 100 200 300 400 500 600 700 800 900 1000

Load (MW) Interval

Load (MW) Net load

Addition of random capacity (high penetration)

• Generator: Normally distributed output, 𝑛 = 1000 𝑁𝑋, 𝑡 =

500 𝑁𝑋

• Assume: generator output is independent of load distribution

𝐹𝑀𝐷𝐷 ≈ 470 𝑁𝑋 ELCC is close to 47% of mean

• utput of the generator

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The effect of correlation (ext xtreme example)

• Assume the generator with 100 MW mean output and 50 MW std.
• dev. is not available during extreme demand periods (above 2,200

MW)

500 1000 1500 2000 2500 3000 3500 100 200 300 400 500 600 700 800 900 1000

Load (MW) Interval

Load (MW) Net load

𝐹𝑀𝐷𝐷 ≈ 0 𝑁𝑋

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2017 WEM distribution

1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

Demand (MW) % Time demand exceeded

Load Duration Curve Net load duration curve Net Load

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2017 WEM distribution

2750 3000 3250 3500 3750 0.0% 0.5% 1.0% 1.5% 2.0% 2.5%

Demand (MW) % Time demand exceeded

Load Duration Curve Net load duration curve Net Load

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Win ind capacity valu lue in in other jurisdictions

Source: Milligan et al. 2017, Capacity value assessments of wind power

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• 2. Capacity valu

lue assessment methods

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Assessment methods in in practice

• Two approaches for ELCC calculation:
• Fundamental analysis (reliability model)
• Approximation method: to approximate the outcomes of fundamental

analysis

• Data required for calculation
• Coincident data during high LOLP/peak intervals:
• Output of intermittent generators
• Output of conventional generators
• System load

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Fundamental l analy lysis (ELCC)

Base System Base System

+ additional resource 1000 MW

LOLE=1.5 day in ten years LOLE=0.8 day in ten years Base System Base System Decreased load

• 300 MW

Increased load +100 MW LOLE=1 day in ten years

+ additional resource 1000 MW

ELCC = 400 MW

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ELCC calc lculation challenges

• Historical data is usually not sufficient (for rare events in the system)
• Eg. In the SWIS (between 2006 and 2012) we never experienced a

peak load above the one in ten year peak forecast

• We need a model to forecast how IGs perform during extreme

demand/high-risk periods

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• 3. Current method in the SWIS

IS

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Current method

• Approximation method to estimate (individual) ELCCs
• Mean output during peak LSG (net load) intervals
• Less:
• K factor (define)
• To account for variability of IGs
• Previously was 0.003 (international experience) but in 2014, Sapere estimated it for the SWIS

(set to 0.000)

• U-Factor
• To account for the (negative) correlation of IGs with load during high-risk periods

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Capacity valu luation in in other jurisdictions

• Approximation methods
• average output of IGs
• Time-based approaches: specified (peak/high-risk) intervals
• Risk-based approaches: when the system is under the highest reliability risk
• Fundamental analysis:
• Mid-continent ISO (MISO)
• System-wide ELCC calculation (wind resources)
• Deterministic allocation of ELCC to individual IGs (based on historical performance)

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Jurisdiction Reliability criteria Method PJM 1 in 10 year LOLE Approximate Time-based Mean output during peak periods SWISS Hybrid:

• 1 in 10 year peak demand LOLE
• <0.002% USE

Approximate Risk-based Adjusted mean output during peak net load (LSG) NYISO 1 in 10 year LOLE Approximate Time-based Mean output during peak intervals ISO-NE 1 in 10 year LOLE Approximate Time-based, also allows for intervals with system- wide shortages California ISO 1 in 10 year LOLE Approximate Time-based Mean capacity during peak intervals (70% exceedance factor) MISO 1 in 10 year LOLE Fundamental analysis Calculation of system-wide ELCC Allocation of ELCC to individual wind farms based

• n historical data

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For 2018-19, MISO uses the average

• f 11 points

ELCC=15.2% 5% 45% Wind penetration (% of peak load) ELCC (%) 2006 2008 2017 20XX 2018 penetration of wind (~14%)

Does fundamental l analy lysis provide an exact capacity valu lue number?

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Fundamental l vs approximation methods

• Fundamental analysis entails building a reliability model:
• Can a Plexos model be ready in time?
• Constraints on the use of data collected (eg SRMC data) to use for purposes
• ther than 2.16
• Do the results of such analysis provide a significantly different

estimate of ELCC (than approx. methods)?

• Fundamental analysis is more complex and less transparent
• Approximation methods:
• Relatively simple
• More transparent
• However, underlying assumptions may no longer be valid

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• 4. Current issues in the SWIS

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Valuation of capacity in a security constrained network

• The PUO’s consultation paper:
• the valuation of capacity in a security constrained network design
• Resources to receive capacity credits subject to network constraints
• Current RLM does not consider capacity constraints
• Timing of PUO’s review:
• Capacity valuation method review after outcomes of network access review
• PUO is exploring design of different mechanisms to provide for

system adequacy and security

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Collgar’s rule change proposal

• Collgar: use of mean output at peak LSG periods is discriminatory
• Does not reflect the contribution of IGs to peak demand periods
• AEMO argued that contribution towards high-risk periods is more

relevant (noting the increased penetration of IGs)

• Some (including the PUO) supported Collgar’s argument
• Others noted the upcoming review of the capacity valuation method

by the ERA

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Collgar’s rule change proposal…

• Hybrid reliability criteria defined in the market rules
• With increased penetration of IGs the likelihood of energy shortfall

during not highest peak periods increases

• If most of energy shortfall events happen during highest peak

periods:

• Use of peak LSG and peak demand interval would provide similar results (in

theory)

• If energy shortfall events and highest peak do not coincide:
• Peak LSG (net-load) can be relevant for the calculation of ELCC

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Technology differences

• Emergence of behind the meter technologies.
• Differences in operational characteristics (solar, wind)
• Battery storage installed with intermittent generators
• Battery combined with intermittent capacity : firm capacity
• How to value such capacity?
• MISO uses a system wide ELCC and allocates that to individual IGs based on

historical performance

• In the SWIS, ELCC is calculated individually (with a common adj. factors)

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SWIS characteristics

• What has happened

since last review

• facilities retired or slated

for retirement,

• addition of wind/solar/

emerging technologies

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Tim iming and market reforms

• ERA’s draft report published end Oct 2018 plus 6 weeks consultation
• Final report and recommendations due 1 April 2019
• Any associated rule change proposal, is unlikely to be progressed before next
• ne or two capacity cycles (beginning Oct 2019 or Oct 2020), so will need to

calculate K&U values in the interim.

• Market reform activity:
• Mid-2018 – compensation for unconstrained generators - partial or fully

constrained network access, plus ancillary service review findings

• Sep 2018 – recommendations on capacity pricing

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