All-island grid studyIreland

Jenni Lairila, Konsta Turunen, Sami Sihvonen

Background

• What is The All Island Grid Study?• A comprehensive assessment of the ability of the electrical power system on the island of Ireland to absorb large

amounts of energy produced from renewable energy sources• The objective of the study is to assess the technical feasibility and the relative costs and benefits associated with

various scenarios for increased share of electricity sourced from renewable energy

• Work is divided into five work streams concentrating on different aspects of an integration study• Portfolio generation• Resource assessment• Network assessment• Dispatch simulations• Analysis on cost and benefits, summary

Summary of the study

• Six electricity generation portfolios comprising a range of different renewable and conventional technologies in varying compositions were created

PortfolioRenewable energy share of demand, %

Renewable energy share of installed capacity, %

Installed wind power capacity, MW

Wind energy penetration level, % of demand

1 16 23 200011

2 27 364000 21

3 27 364000 21

4 27 364000 21

5 42 476000 30

6 - 598000 36

Key assumptions

• A strictly cost based approach was applied throughout the study• No specific market design, market power or other elements associated with real-world markets were incorporated• No specific regulatory framework was considered

• Analyses the impacts of the various generation portfolios for one particular year in the future (2020)• Assumptions of which existing conventional generators would remain in operation in the year 2020 and which will

have ceased operation to that date• An independent network development scenario• Time series for system load in the year 2020 was based on projections of the system operators

• Total electricity demand: 54 TWh• Minimum load: 3500 MW• Maximum load 9600 MW

Key assumptions

• 1000 MW interconnection to the GB power system and simple assumptions of the future generation structure of GB

• Interest rates• Weighted average cost of capital of 8% was assumed• Discount rates defined by regulators were applied for calculations of annual cost of network

assets

• Cost assumptions based on cost data for the year 2006

Input data and optimization for

portfolios

Optimization – creating

the portfolios

Structure of least-cost generation portfolio Optimization

• Uncertain factors:• Gas price (2€/GJ…12€/GJ)• Carbon price

(0€/tonCO2…1000€/tonCO2)• Wind turbine costs• Weighted average cost of capital

(6%, 8%, 10%)• Benefits for renewables (0, 5, 10

€/MWh)

Wind generation

• Cost of turbines are uncertainTwo scenarios:• High cost, where turbine costs are higher cost of electricity is 59 €/MWh• Low cost, where turbine costs are lower cost of electricity is 48 €/MWh

• Capacity up to 9500 MW was considered• Curtailment was considered so that maximum of 2/3 of the load can

be produced by wind• Based on single year of wind output data

Wind generation costs with effect of capacity factor, network costs and managment costs assumptions

Thermal generation

Base load renewables

Vairable renewables

Treated as base renewables overestimation of this class

Other assumptions

• Average annual growth of 3 % in load• Load was divided in 18 parts to deduct appropriate mix of base-load,

mid-merit and peaking type plants• 1000 MW of interconnection, which can import energy with price of

4% greater than new CCGT• Fuel prices • coal=1,26€/GJ • peat=3,57€/GJ • lignite=0.77€/GJ • distillate=7,99€/GJ

Other limitations

• Focuses on generating portfoilos for a single year• No hourly dispatch model, but generation capacity based on system’s

load duration characteristics• Average unit availability, not simultaneous plant outages• Approximations of reserve, start-up and ramping costs was used

(more detailed analysis later)

Portfolios

Wind 11% 21% 21% 21% 30% 36% penetration(from energy demand)

Resource analysis

Resource analysis

• Objective is to cover existing and future resources• Future projects are compared with levelised cost method• Discount rate = 8 %, project life = 20 years

Wind resources

• Including 2MW, 3MW, 4.5MW also 7MW turbines are availaibe• Spacing of 5 times rotor diameter for onshore and 3 times for

offshore• 1 km² grid areas were investigated and gross annual energy

production calculated• Losses from efficiency, array, icing etc. was estimated to be 16 %

• Wind speeds were modelled for hourly for 366 days for 15 years• Total wind generation of 1520 MW was assumed

Distribution of wind farms (portfolio 2, 3 and 4 (4000MW))

Ocean energy resources• Wave:• Reference machine is 7MW floating machine• Hourly wave data predictions of 3.5 years• Located 120-200 m depth, with asusmption of connection to the nearest

onshore link Levelised cost = 0,104-0,112 €/kWh

• Tidal:• 1.2MW converter as reference, but assumed that second generation

converters are available from 2015 onwards• Resources are within 22km from coast• Mean annual accessible resource is 914 GWh• Great amount of uncertainty two stages of costing process (portfolios 1-4

and 5-6) Levelised cost = 0,22-0,25 €/kWh and 0,1 €/kWh

Other resources

• New hydro and solar power projects were left out of the study

Resource type Capacity (MW) Levelised cost (€/kWh)MSW incineration 152 0,073-0,155Landfill gas 46,9 0,4-0,15Bilogical municipal waste 41,6 0,25-0,46Sewage gas 4,5 -Wet agricultural waste 91 0,1-0,46Dry agricultural waste 24 0,07Wood waste 12,7 0,07-0,09Wood Co-combustion 103 0,08-0,09

Simulations

• Limitations and assumptions• Simulation methdos• Capacity value of wind power• Reserve requirements• Production cost simulation and

flexibility assessment• Transmission grid simulations

Assumptions

• interconnection to Great Britain grid 1000MW • spinning reserve interconnection up to 100MW• costs of carbon dioxide 30 euros / tonne CO2 emitted and• gas costs 22 euros / MWh of thermal production• no iteration between the work streams -> no network restrictions in

the dispatch

Limitations

• dynamic study was not carried out-> frequency stability and transient stability were not taken into account-> underestimation of dispatch restrictions, operational costs, required wind curtailment and carbon dioxide emissions

• the model allowed one unexpected loss of a transmission line at once but did not take into account the maintenance of the lines, when a line cannot be used

-> underestimation of the required instances of generation constraint

Simulation methods

• Wilmar Planning Tool• Scenario Tree Tool• Scheduling Model

• Scenario Tree Tool• Forecast errors in wind and load forecasts• Reduces the number of scenarios• Forced outages in the scenarios• Demand for replacement reserves-> input data for Scheduling Model

• Scheduling Model• Minimizes operation costs for all the portfolios

• Transmission grid simulations method not explained

Capacity value of wind power

• Input data• wind speed and/or wind power production data• historical electricity demand data• assumptions about wind production forecast accuracies and load forecast

accuracies for different forecast horizons• data on the reliability of conventional power plants

• Forced outages simulation results describe the availability of a unit classifying the availability in different statuses• Probabilites of the forced outages

Reserve requirements

• Simulations divide reserve into 4 categories• Three categories for spinning reserves with activation times less than 5 min• One category for reserves with activation time of 5 min or more

• Need for reserves vary during the time-> Certain percentage of forecast errors allowed not to be covered

• Dynamic reserve requirements neglected in the study

Production cost simulation and flexibility assessment

• Wind and load forecast error simulates an hourly prediction in a day-ahead basis for the next 36 hours• Number of scenarios reduced to a number that can be handled in

Scheduling Model• Input data for Scheduling Model:

• Demand for replacement reserves• Wind power production forecasts• Load forecasts

• Scheduling Model is a mixed integer variable model• Interconnections to neighbouring power system, Great Britain, taken into

account

Transmission grid simulations

• Overloaded transmission lines• Summer minimum load• Summer maximum load• Winter peak load

• N-1 contingency security• Summer maximum load• Winter peak load

• Randomisation study• Scenarios between maximum and minimum loads

• Network reinforcements• Voltage-reactive power control studies• Network reinforcement costs• No dynamic studies

Analyzing the results

Presenting the results

• Generation mixes are split into conventional and renewable generation and then further into different energy generation types

• The portfolios are compared to each other in many different aspects

• There is no ”base” case for comparison, where no renewable energy is added

• Only relative comparisons can be drawn between the studied portfolios

Presenting the results

• Conventional generation• Characteristics of different generation types• Annual investment costs and operational costs• Capacity factors• Operational modes within one year time period• Revenue distribution (generation/replacement/spinning)• Additional charges due to network reinforcements• Long term security of supply

• Annual fuel consumptions of imported fuels (gasoil, gas and coal)

• The interconnector to GB is taken into account• Operational costs with two combinations of fuel price + co2

price• Finally the conventional generation mixes of portfolios 2-4 are

ranked (3 being the most preferable)

Presenting the results

• Renewable energy

• Characteristics of different renewable energy types

• Total investment costs of renewable energy

• Operational costs close to zero, except for biomass

• Wind curtailment necessity discussion• Due to the limitations of the study’s methodology,

the need for wind curtailment may be underestimated as the penetration levels of wind power in this study exceed what has been demonstrated to date as being technically feasible

Electricity market

• The objective of the dispatch model was to minimize expected cost with no specific market mechanism or behavior of market actors being defines

• For pricing electricity and reserves, the principle of system marginal cost pricing was used• Uses hourly system marginal cost calculations interpreted as the system price• The maximum price when load is not met is assumed to be 4000 €/MWh

• The study assumes that generators can earn additional revenue on separate markets for replacement and spinning reserve• The value of operational restriction interpreted as the marginal cost of providing the reserve• The pricing of spinning and replacement reserve in real world markets may deviate significantly from

the marginal cost based principles applied

• Price volatility is assessed by calculating the standard deviation of marginal system costs across the portfolios• The analysis may not be applicable for real world markets since the dispatch model represents a

continuous re-dispatch every three hours, as opposed to day-ahead power auctions of real world markets

Reliability

• The Loss Of Load Expectation method was used• Portfolios were compared in regard to the hours in a year during

which the generator plant will be inadequate to meet the instantaneous demand

• Previous work streams required that the LOLE is 8 hours annually at most

• This is the recommended practice in wind integration studies

• Problems with ranking the reliability level• Load and wind data only one year• Wind penetration levels as % of instantaneous load can be

problematically high; beyond what has been demonstrated to be manageable for a secure system

Costs associated with the transmission network• Capital investments in the transmission network

• Predefined renewable energy target!

• Annualized network reinforcement costs• Asset lifetime of 50 years and interest rates used by the respective regulators are used• It was assumed that all EirGrid and NIE approved reinforcements are done• Construction of the additional 500 MW interconnection to GB

• Annual maintenance costs based on historic data

• Connection network investments for renewables depicted separately

• Distribution of windfarm connections on voltage levels (km)• Connection estimates are based on the assumption of a separate connection to an existing 110 kV

node for each renewable project

Support requirements and interconnector value• Support requirements for different renewable energy

generators are calculated in respect to production for each portfolio• Lack of market model limits the accuracy of the calculations

• Imports and exports via the interconnector were calculated

• The value of the interconnector is dependent on the price differences on the respective markets• When calculating the annual value of the interconnector, it

was assumed that prices in both systems do not exceed 120 €/MWh

Environmental impacts

• Relative decrease in CO2-emissions in the portfolios

• Impacts on the emissions on the GB power system are taken into account and presented

• New network connections and their impacts on the environment are discussed briefly

Additional costs to society• = The integration cost of the generation mix

• No separate integration cost was presented strictly for wind integration, all renewable energy is pictured as one big generator

• Wind energy does have a major role in the renewable energy mix

• Aggregates all operational costs of the generation mix• The operational costs of conventional generation,

consisting of the fuel costs and the cost of CO2• Charges of the net imports over the interconnector• The total annual investment costs for all renewable

generation• Investment in new conventional generation• Annual investment in network reinforcements

• Impacts of the portfolios on the prices charged to end customers could not be accurately determined due to limitations of the study’s methodology• Components that make up the final price can be identified however

Main results

• Comparisons between the generation portfolios studied

• During the analysis of the dispatch and network implications, portfolio 6 exceeded the limitations of the methodologies applied

• No no-wind case is presented to compare to

-> only comparisons between the portfolios can be made

• Total cost to end users varies by at most 7%

• Significant reductions to carbon emissions compared to portfolio 1

• Lack of iterations between the portfolios and lack of market modeling major limitations