All-island grid study Ireland

1. All-islandgridstudyIreland Jenni Lairila, Konsta Turunen, Sami Sihvonen

2. 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 • Dispatchsimulations • Analysis on cost and benefits, summary

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

4. Key assumptions • A strictly cost based approach was applied throughout the study • No specific market design, market powerorotherelementsassociatedwithreal-worldmarketswereincorporated • No specific regulatoryframeworkwasconsidered • 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

5. Key assumptions • 1000 MW interconnection to the GB powersystem and simpleassumptions of thefuturegenerationstructure 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

6. Input data and optimization for portfolios

7. Optimization – creatingtheportfolios

8. Structure of least-costgeneration portfolio Optimization • Uncertainfactors: • Gasprice (2€/GJ…12€/GJ) • Carbonprice (0€/tonCO2…1000€/tonCO2) • Windturbinecosts • Weightedaveragecost of capital (6%, 8%, 10%) • Benefits for renewables (0, 5, 10 €/MWh)

9. Windgeneration • Cost of turbinesareuncertain Twoscenarios: • Highcost, whereturbinecostsarehigher cost of electricity is 59 €/MWh • Lowcost, whereturbinecostsarelower  cost of electricity is 48 €/MWh • Capacityup to 9500 MW wasconsidered • Curtailmentwasconsideredsothatmaximum of 2/3 of theloadcanbeproducedbywind • Based on single year of wind output data

10. Windgenerationcostswitheffect of capacityfactor,networkcosts and managmentcostsassumptions

11. Thermalgeneration

12. Baseloadrenewables

13. Vairablerenewables Treated as baserenewables  overestimation of thisclass

14. Otherassumptions • Averageannualgrowth of 3 % in load • Loadwasdivided in 18 parts to deductappropriate mix of base-load, mid-merit and peakingtypeplants • 1000 MW of interconnection, whichcan import energywithprice of 4% greaterthannew CCGT • Fuelprices • coal=1,26€/GJ • peat=3,57€/GJ • lignite=0.77€/GJ • distillate=7,99€/GJ

15. Otherlimitations • Focuses on generatingportfoilos for a single year • No hourlydispatchmodel, butgenerationcapacitybased on system’sloaddurationcharacteristics • Averageunitavailability, notsimultaneousplantoutages • Approximations of reserve, start-up and rampingcostswasused(moredetailedanalysislater)

16. Portfolios Wind 11% 21% 21% 21% 30% 36% penetration (fromenergydemand)

17. Resource analysis

18. Resource analysis • Objective is to cover existing and future resources • Futureprojectsarecomparedwithlevelisedcostmethod • Discountrate= 8 %, project life = 20 years

19. Windresources • Including 2MW, 3MW, 4.5MW also 7MW turbinesareavailaibe • Spacing of 5 timesrotordiameter for onshore and 3 times for offshore • 1 km² gridareaswereinvestigated and grossannualenergyproductioncalculated • Lossesfromefficiency, array, icing etc. wasestimated to be 16 % • Windspeedsweremodelled for hourly for 366 days for 15 years • Total windgeneration of 1520 MW wasassumed

20. Distribution of windfarms (portfolio 2, 3 and 4 (4000MW))

21. Oceanenergyresources • Wave: • Referencemachine is 7MW floatingmachine • Hourlywave data predictions of 3.5 years • Located 120-200 m depth, withasusmption of connection to thenearestonshorelink  Levelisedcost = 0,104-0,112 €/kWh • Tidal: • 1.2MW converter as reference, butassumedthatsecondgenerationconvertersareavailablefrom 2015 onwards • Resources arewithin 22km fromcoast • Meanannualaccessibleresource is 914 GWh • Great amount of uncertainty twostages of costingprocess (portfolios 1-4 and 5-6)  Levelisedcost = 0,22-0,25 €/kWh and 0,1 €/kWh

22. Otherresources • New hydro and solarpowerprojectswereleft out of thestudy

23. Simulations • Limitations and assumptions • Simulationmethdos • Capacityvalue of windpower • Reserverequirements • Productioncostsimulation and flexibilityassessment • Transmission gridsimulations

24. 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

25. 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

26. Simulation methods • Wilmar Planning Tool • ScenarioTreeTool • SchedulingModel • ScenarioTreeTool • 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 • SchedulingModel • Minimizesoperationcosts for alltheportfolios • Transmission gridsimulationsmethodnotexplained

27. Capacityvalue of windpower • 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

28. Reserve requirements • Simulationsdividereserve into 4 categories • Three categories for spinning reserveswithactivationtimeslessthan 5 min • One category for reserveswithactivationtime of 5 min ormore • Need for reservesvaryduringthetime -> Certainpercentage of forecasterrorsallowednot to becovered • Dynamicreserverequirementsneglected in thestudy

29. Production cost simulation and flexibility assessment • Wind and loadforecasterrorsimulatesan 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

30. Transmission grid simulations • Overloaded transmission lines • Summer minimumload • Summer maximumload • Winter peakload • N-1 contingencysecurity • Summer maximumload • Winter peakload • Randomisationstudy • Scenariosbetweenmaximum and minimumloads • Network reinforcements • Voltage-reactivepowercontrolstudies • Network reinforcementcosts • No dynamicstudies

31. Analyzingtheresults

32. 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

33. 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) • Theinterconnector 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)

34. Presentingtheresults • Renewable energy • Characteristics of different renewable energy types • Total investment costs of renewable energy • Operationalcostsclose 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

35. Electricity market • The objective of the dispatch model was to minimize expected cost with no specific market mechanismorbehavior 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

36. 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

37. 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

38. 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

39. 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

40. Additionalcosts 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

41. Main results • Comparisonsbetweenthegenerationportfoliosstudied • Duringtheanalysis of thedispatch and networkimplications, portfolio 6 exceededthelimitations of themethodologiesapplied • No no-wind case is presented to compare to -> onlycomparisonsbetweentheportfolioscanbe made • Total cost to endusersvariesby at most 7% • Significantreductions to carbonemissionscompared to portfolio 1 • Lack of iterationsbetweentheportfoliosand lack of market modelingmajorlimitations