Jean-Michel Glachant (and Vincent Rious) Loyola de Palacio Chair & Florence School of Regulation

1. Robert Schuman Centre for Advanced Studies Florence School of Regulation & Loyola de Palacio Chair Coordination of the location and time of investment of generation and transmission in a liberalised power system Jean-Michel Glachant (and Vincent Rious) Loyola de Palacio Chair & Florence School of Regulation European University Institute in Florence jean-michel.glachant@eui.eu 30th July 2010 11th ACCC Regulatory Conference: Surfers Paradise

2. OutlineNeed for coordination between G & T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? • Conclusions

3. Coordinated gen° and trans° investments by an integrated utility Min: Gen Invst Cost + Fuel Cost + Trans Invst Cost • Integrated Utility • Easy to know the cheapest investment strategy • Location + time of investment G_Inv._CostW < G_Inv._CostE

4. Unbundling and the need for coordination Min: Gen Investment Cost + Fuel Cost + Tariff Liberalised power system Suboptimal location ! Locational signals  Inefficient coordination G_Inv._CostW < G_Inv._CostE G_Inv._CostW+ Trans cost G_Inv._CostE >

5. Unbundling and the need for coordination 5 Liberalised power system >> Prompts investors to choose generation technologies with short construction lead time Congestion while the network is not upgraded Right of way of powerlines facing increasing oppositions  ~ 7 years to build a powerline from study to construction itself because of administrative agreement >> 5 years!

6. The TSOs need to know generation location to develop the network The generators may be constrained because of network congestion Transmission planning and generation location in a liberalised power systemA story of chicken and egg 6

7. Outline • Need for coordination between G and T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? Model and results • Conclusions

8. Coordination with short term locat. signals Min Gen Investment Cost + Fuel Cost + Tariff Network constraints Nodal pricing PNW < PNE GICW < GICE

9. Coordination with short term locat. signals Min Gen Investment Cost + Fuel Cost + Tariff Network constraints Pb: lumpy transmission investment  marginal signals not enough to achieve coordination Lumpiness Nodal pricing PNW < PNE GICW < GICE

10. Coordination with long term locat. signals Min Gen Investment Cost + Fuel Cost + Tariff + Tariff Network tariffs + Tariff TariffW > = 0 TariffE GICW < GICE

11. Outline • Need for coordination between G and T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? Model and results • Conclusions

12. Problem • Coordination of location of generation investments with lumpy transmission investments • Efficiency of network tariffs • ‘Average participation’ tariff = each Gen. pays for her ‘traced’ share of used lines • Thought as a good tariff in economic literature • Game theory (Shapley value: fair & symmetric) • Not been evaluated yet in interaction with investments (Ignacio!) • Jointly implemented with nodal prices

13. One generator (!) Competitive setting -;) Min Gen° investt cost + fuel cost+ tariff One TSO (at least!) Benevolent -;) Min Trans° investt cost + cong° cost  Influenced by market design Nodal pricing or Redispatch No tariff or ‘Average participation’ tariff Structure of the model Network costs  Determined by the generator’s choice

14. Characteristics of the model Good news for simplicity of the model -;)  Optimistic frame -;) No uncertainty TSO perfectly informed of generator’s decision set Generator perfectly informed of TSO’s decision set Bad news for simplicity of the model -;(  Difficulty to solve -;( Double optimization No sequential optimization The 2 functions optimized at the same time Non linear problem Lumpiness of transmission investment ‘Average participation’ tariff

15. 200 MW 200 MW 200 MW 400 MW 400 MW 200 MW 200 MW 200 MW Optimization algorithm for a realistic model! • Problem hard to solve 2%/y

16. 200 MW 200 MW 200 MW 400 MW 400 MW 200 MW 200 MW 200 MW Optimization algorithm for a realistic model! • Problem hard to solve • Solved using an heuristic  Genetic algorithm • System investment strategies as ’’individuals’’ • With reproduction and random mutation process to better off from one period to another 2%/y

17. Optimization algorithm • Use of a Pareto frontier to find the investment strategies equilibria of our double optimization problem Investment strategies = set of generation + transmission investments Value of the investment strategy for the TSO Investment strategies generated “at random” by the genetic algorithm Measure of the coordination reached by the points on the Pareto frontier  evaluation in terms of social cost Value of the investment strategy for the Generator Pareto Frontier Simulated population

18. Nodal pricing Weak effect on coordination Even in favorable cases: No economies of scale Perfect knowledge of economic signals Results • Average participation tariff • Interaction between • network investments  lumpy • locational signals •  Locational signals with ‘bounded’ efficiency

19. Nodal pricing Weak effect on coordination Even in advantagous cases No economies of scale Perfect anticipation of economic signals Results • Average participation tariff • Coordination always improved • But suboptimal coordination • Not enough locational incentives • Or too much in some cases ! • Interaction between • locational signals and • network investments  lumpy •  Locational signals with limited efficiency

20. Results • Interaction between • locational signals and • network investments  lumpy •  Locational signals with limited efficiency Network tariff more important than nodal pricing for efficient location of G investment

21. Outline • Need for coordination between G and T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? Model and results Conclusion #1

22. Coordination with locational signals? • No optimal coordination with locational signals • Nodal pricing + average participation tariff • Because of lumpiness of transmission investment • Even if improved coordination (so locational signals are needed) • And ‘average participation’ tariff more efficient than nodal pricing for efficient location of investment

23. Coordination with locational signals? • These signals = transmit information But limited to current grid and its current use • And (+) many other locational constraints existing for generators: access to water, to fuel, to land, to social acceptance (NIMBY)  lasting congestions  need to develop the network

24. Outline • Need for coordination between G and T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? Model and results • Conclusion

25. Two alternative behaviors for the TSO Reactive behavior Waits for generation connection request to study the need of transmission investments Proactive behavior Anticipates generation connection request in areas with exploitable energy sources Gas Wind Starts first-step investment process as to get administrative green lights already agreed when generators will request for connection 25

26. Pros & cons of the two alternative behaviors 26 • Reactive behavior * No trsm investment cost put at risk **BUT  excessive congestion if CCGTs or wind farms connect while network is still to be upgraded • Proactive behavior * No excessive congestion put at risk **BUT  proactive behavior is itself costly because (if generation finally doesn’t come) TSO did already: • the study to upgrade the network • And went trough all procedures to obtain the administrative green lights to build the line

27. Unbundling and the need for coordination 27 Liberalised power system >> Prompts investors to choose generation technologies with short construction lead time Need for coordination between generation and transmission investments Congestion while the network is not upgraded Right of way of powerlines facing increasing oppositions  ~ 7 years to build a powerline from study to construction itself because of administrative agreement >> 5 years!

28. Outline • Need for coordination between G and T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? Model and results • Conclusion

29. Problem 29 • Efficiency of anticipating generation connection for the TSO in terms of minimization of her total network costs? • If no anticipation  congestion for quite long period while network must be upgraded • If TSO anticipes the ‘’first-stage costs’’  costly if anticipated generators do not connect  One must arbitrate between these two costs • weighed by a probability for the connection of generation (been evaluated by an expert panel « à dire d’expert »)

30. Investment sequencing with reactive TSO 30 0 Year Construction construction Study, admin. proced. d Generation Investment Transmission Investment CW(d) CU(d) Excessive congestion Optimal value for network capacity

31. Investment sequencing with proactive TSO 31 0 Year Construction Study, admin. proced. construction Generation and Network Investments CU(0)

32. “Probability limit” and condition for proactive TSO with known first-stage costs 32 First-stage costs a = 10% of transmission investment costs

33. “Probability limit” and condition for a proactive TSO with unknown first-stage costs With first-stage costs a = 50% of transmission investment costs  Still efficient for the TSO to be proactive when 40% probability for the connection of a new plant

34. Outline • Need for coordination between G and T • Tool #1 = locational signals • Tool #2 = anticipation Scope for maximisation of social welfare? Model and results • Conclusions

35. Conclusion (1/4) A model to evaluate the efficiency of anticipating plants investment to minimize the total network costs Illustration on simple realistic examples (like CCGT or wind farms) Efficient to anticipate the connection of power plants for the TSO Planning in advance network reinforcement Reduce congestion costs 35

36. Conclusion (2/4) Efficient for TSO to anticipate the connection of new generator All the more that (a/) Proactive behavior favours dialog between TSO and market participants about planning assumptions Facilitates coordination through sharing information Facilitates dialog and acceptability from local populations 36

37. Conclusion (3/4) All the more that (b/) Possibility to send potential locational signals related to the anticipated network Volume signals: new generation capacity that will not constrain the grid Tariff and price signals: potential levels of locational network access fees and energy prices for different network and generation scenarios 37

38. Conclusion (4/4) All the more that (c/) Possible to incentivise TSOs to be proactive Make them bear a part of congestion costs Example in France: the TSO compensates the wind farms when they have a ‘curtailment obligation’ for more than 3 years 38

39. Future research on network anticipation Feedback effects with locational signals Effects of (milestones payment) in connection tariffs to create increasing locational commitment from generator 39

40. Conclusion of conclusions • Tool 1- Coordination of generation and transmission investment with signals because of intrinsic lumpy network cost structure • information sharing with Generation • Necessary but not sufficient locational signal when generation quicker to develop than the network

41. Conclusion of conclusions Tool 2- Importance of information / consultation platform for network planning Information sharing  building of common knowledge about possible future(s)  more certainty for investors All the more needed to integrate new generation technologies  new location, new network usage

42. That’s it: Even in a Surfers (non)Paradise more coordination between Gen. and Transmission is not necessarily a luxury…

43. Robert Schuman Centre for Advanced Studies Florence School of Regulation & Loyola de Palacio Chair Coordination of the location and time of investment of generation and transmission in a liberalised power system Thank you for your attention Questions ? Comments ? Jean-Michel Glachant Loyola de Palacio Chair & Florence School of Regulation European University Institute in Florence jean-michel.glachant@eui.eu