Towards the optimum mix between wind and PV capacity in the Greek power system

1. Towards the optimum mix between wind and PV capacity in the Greek power system G.Caralis, S.Delikaraoglou, A.Zervos Presentation: George Caralis

2. Structure of the presentation • Description of the problem - Objectives(National Action Plan, Technological issues) • Definitions (capacity credit, wind power curtailment) • Methodology • Data (wind data, Greek power system, assumptions, load demand) • Application –Results • Conclusions - Discussion

3. Description of the problem National Action Plan • National Renewable Energy Action Plans • 40%, contribution of RES in electricity (today ~10%) • target to reach 13300MW RESinstallations (from ~4000MW toady): • wind 7500 MW and • PV 2200MW plus PV in domestic roofs (systems up to 10kW) • Today: 1300MW wind, 180MW PV, 3059MW Hydro

4. Description of the problem Technological issues • Wind power (variability, stochastic source, geographical dispersion) • Photovoltaics (more predictable, less production, occurred in very specific hours) • Greek power system is a relativelyunstable and weak power system due to the limited existing interconnections with neighboring countries • Target: the maximum effective RES integrationto meet the national targets Source: HTSO

5. Definitions RES energy curtailment and RES Capacity Credit • Dealing with wind power curtailment • technical constraints (units’ commitment, power dispatch) • maximize RES energy absorption and ensure the safe operation of the system • Wind & PV capacity credit • long term national energy planning • expresses the equivalent conventional capacity which is avoided due to wind & PV installed capacity • Two methodologies are adapted: • Steady state analysis, based on time series analysis, for the simulation of the system • Probabilistic model for the reliability of the system and the effect of Wind & PV integration (capacity credit)

6. Methodology Simulation of the Greek power system – Basic principles • steady state analysis, HTSO data • wind power curtailment – PV power curtailment • technical minimums of conventional power units (70% for lignite power plants - 30% for flexible peak supply units) • Wind and load forecast models are systematically used, then both wind & PV availability and variability are considered predictable • Units commitment: • the expected low demand of next 15 days -> base load units • the expected variability of demand and RES power generation for the hours ahead -> peak demand units • dynamic limit of instantaneous wind penetration (60% of the load) • Hydro supply during hours of peak load demand • Wind data are provided by a systematic application of the weather forecast model COAMPS.

7. Methodology Reliability – Probabilistic model • Probability distribution and annual duration curve of the three main independent variables: • Power demand (M situations) • Available conventional power (L situations) • Wind power production (N situations) • PV production is dependent on the hour and period of the year and then it is subtracted from the power demand • For the evaluation of system’s reliability, convolution of the three distributions results in a 3-D matrix MLN. • Each element corresponds to a possible operational mode and the probability of occurrence. The Power sufficiency is the main issue in this analysis.

8. Methodology Methodology – Capacity credit 1st step: The Loss of Load Probability of a System LOLPSwithout wind & PV installations is first calculated. 2nd step: Next, the Loss of Load Probability of a System LOLPW&PVwith wind & PV installations is calculated. Obviously, LOLPW&PV<LOLPS 3rd step: The Effective Load Carrying Capability ELCCof Wind & PV installations is defined as: “Which can be the increase in power demand, so as the System’s reliability is kept at the same level as before Wind & PV installations”. Definition of the Capacity Credit coefficient of Wind & PV installations in the System: CC=ELCC/PW&PV_rated,wherePW&PV_ratedisthe rated installedwind & PVcapacity.

9. Wind Data Wind data • Simultaneous information on wind statistics over every potential area for wind farm development: • Measurements (techno–economically very difficult) • Application of a Numerical Weather Prediction (NWP) model • COAMPS (Naval Research Laboratoryof USA) • Appropriateadjustment of the numerical parameters • Systematic application on a yearly (and beyond) basis • Thorough analysis and processing of wind characteristics • Provides simultaneous wind speed time series at the mesoscale over the whole territory of interest

10. Wind Data Wind data – Geographical dispersion • Aeolian maps (CRES) • Land Planning issues for RES (Areas of Aeolian Priority)

11. Wind Data The effect of geographical dispersion

12. Wind Data Correlated areas in the Greek territory

13. Wind Data Geographical distribution • Application of Generalized Evolutionaly Algorithms, with target: • the wind power absorption • the reliability of the system (capacity credit) Source: G.Caralis, S.Delikaraoglou, K.Rados, A.Zervos, “Towards optimum macro-sitting of wind farms in the Greek power supply system using Generalized Evolutionary Algorithms”, EWEC 2010, Warsaw, Poland, 20-23 April 2010 Areas with maximum wind capacity

14. Data Electricity demand

15. Wind production Data PV production

16. Data The mainland’s power system of Greece • Total installed conventional and large hydro capacity: 11234.3MW • Conventional units: 8175.8MW (7587,9MW net production) • 22 lignite (5288MW) • 4 diesel (750MW) • 4 combined cycle (1630MW) • 3 natural gas (507,8MW) • Mean availability: 89% • Hydro electric power plants: 3058,5MW(5TWh contribution, hydro supply during the peak load hours)

17. Results RES contribution – wind curtailment

18. Results Conventional units: contribution – Load factor

19. Results Required conventional units capacity

20. Results Capacity credit of wind & PV installations

21. Results Daily profile of the demand b)2nd January (low PV generation) a) 15th August (peak demand of the year) c) 23rd April (Low demand during the day) d)2nd June (high PV generation)

22. Conclusions Conclusions - Discussion • wind and PV integration are not independent, and should be together examined in the framework of an integrated energy system analysis • Simultaneous integration of wind energy and PV could have positive effects both on the reliability and on the renewable power absorption. • The optimum mix requires a larger wind than PV integration, which in case of the Greek case study is accounted for at least 2:1 ratio, ignoring financial aspects. • Although a rational PV development has a positive contribution to the operation of the power system and almost facilitates wind power absorption, incontrollable PV integration leads to further wind power curtailment and sets troubles for the system operator. • In the Greek system, the increase of the peak supply power plants is required as they offer flexibility and are able to undertake the increased variations of demand and renewable power output. One of the main challenges is to prove the feasibility of such plants in the framework of a liberalized market.

23. Thank you for your attention! e-mail: gcaralis@mail.ntua.gr

24. Questions If financial aspects are considered WIND PV Tariffs: 100€/MWh 400€/MWh Energy output: 2300 eq.hours (CFW=26%) 1300eq.hours Cost of investment: 1500-2000€/kW 3000-3500€/kW The question is “How much additional cost we are ready to pay for RES in our bill (€/MWh)?” Analysis up to 2015 with parameters: - 20€/tn CO2, 1000tn CO2 /GWh, - Marginal cost of the system: 60-85€/MWh Two scenarios are evaluated for 2015: 1st : 4,65 GW wind – 0,67GW PV: 8.8 €/MWh 2nd : 2,8 GW wind – 2,52GW PV: 20.9 €/MWh