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Impact of Fixed Costs on Greece's Day-Ahead Scheduling Problem

Explore the influence of fixed costs on Greece's electricity market Day-Ahead Scheduling Problem, analyzing reserve and fixed costs impact. Discuss market strategies, pricing schemes, and system operations.

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Impact of Fixed Costs on Greece's Day-Ahead Scheduling Problem

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  1. Impacts of Reserve and Fixed Costs on Greece’s Day-Ahead Scheduling Problem PanagiotisAndrianesisa, George Liberopoulosa KostisSakellarisb,c, Andreas Vlachos b a Department of Mechanical and Industrial Engineering, University of Thessaly, Volos, Greece b Regulatory Authority for Energy, Athens, Greece cAthens University of Economics and Business PROMITHEAS-2 International Black Sea Energy Policy Conference "Energy Investments and Trade Opportunities" 8,9 October 2008, Athens, Greece

  2. 1. Introduction • European Directive 96/92/EC: • liberalization and integration of the national electricity markets • GREECE: • Regulatory Authority for Energy (RAE) • Hellenic Transmission System Operator (HTSO) • Grid Control and Power Exchange Code for Electricity (2005): • Day-Ahead Market • Real Time Dispatch • Imbalances Settlement • Capacity Assurance Mechanism

  3. 1. Introduction • Day-Ahead Scheduling (DAS) Problem: • basis for the wholesale electricity market • DAS: • aims at minimizing overall cost of serving energy load, under conditions of reliable system operation, ensuring adequate reserves, • i.e., a security-constrained unit commitment program, • co-optimizing energy and reserves

  4. 2. Greece’s Electricity System Generation mix

  5. 2. Greece’s Electricity System Yearly load profile for 2007

  6. 2. Greece’s Electricity System • Frequency-related ancillary services (“reserves”): • Primary reserve requirement : 80 MW • Secondary reserve requirement : 150-300 MW • Tertiary reserve requirement: 300-600 MW

  7. 2. Greece’s Electricity System North: 2/3 of installed capacity Transmission Constraint South: 2/3 of load

  8. 2. Greece’s Electricity System • 2-zone model : North – South • Producers face different Marginal Generating Prices, when the transmission constraint is activated • Suppliers always face a uniform System Marginal Price (SMP) • Incentives: installation of new generation near consumption

  9. 3. Day-Ahead Scheduling Problem • INPUTS: • Energy offers • Reserve offers • Fixed costs (start-up, shut-down, minimum-load) • System load • Reserve requirements • Transmission constraints • Units’ technical characteristics (technical minimum, technical maximum, maximum reserve availability, minimum up/down times, ramp up/down limits) • OUTPUTS: • Unit commitment • Energy and reserve scheduling for each hour of the next day

  10. 3. Day-Ahead Scheduling Problem DAS problem formulation (MILP): overall variable costs overall fixed costs minimize + Variable cost coefficients Fixed cost coefficients Integer variables (status, start-up, shut-down) Continuous variables (energy, reserve)

  11. 3. Day-Ahead Scheduling Problem subject to: • Market clearing constraints: • Individual constraints: • Initial conditions: and

  12. 3. Day-Ahead Scheduling Problem DAS problem formulation : overall energy cost overall reserve cost overall fixed costs + + minimize start- up shut-down minimum-load subject to: energy balance reserve requirements market-clearing constraints technical minimum technical maximum maximum reserve availability minimum up/down times ramp up/down limits individual constraints

  13. 3. Day-Ahead Scheduling Problem DAS problem formulation : overall energy cost overall reserve cost overall fixed costs + + minimize start- up shut-down minimum-load subject to: energy balance reserve requirements market-clearing constraints technical minimum technical maximum maximum reserve availability minimum up/down times ramp up/down limits individual constraints

  14. 3. Day-Ahead Scheduling Problem 3.1 Impact of Reserve Offers • Questions: • Pricing reserve as separate commodity ? • Priced reserve offers ? • Offers included in the objective function ? • Pricing scheme? • Impact on scheduling ? • Rules (price caps…) ?

  15. 3. Day-Ahead Scheduling Problem 3.1 Impact of Reserve Offers • Pricing schemes for reserve: • Scheme based on shadow price: • a. Non-priced bids (sorting rule based on energy bids) • b. Priced bids included in the objective function • 2.Scheme based on highest bid accepted: • a. Bids not included in the objective function (sorting rule based on reserve bids) • b. Bids included in the objective function • Pay-as-bid scheme: • a. Bids not included in the objective function (sorting rule based on reserve bids) • b. Bids included in the objective function

  16. 3. Day-Ahead Scheduling Problem 3.2 Impact of Fixed Costs • Fixed costs introduce non-convexities • Non existence of equilibrium prices in a Walrasian auction • Relevant literature: O’Neill et al. (2002, 2005) • Hogan and Ring (2003) • Bjørndaland Jörnsten (2004) • DAS problem: • - Should fixed costs be included in the objective function or not? • - Should producers be paid for their fixed costs? • - If not paid, they must internalize fixed costs in their energy offers, • distorting the SMP.

  17. 4. Illustrative Example 8-unit example: Energy offers

  18. 4. Illustrative Example 8-unit example: Reserve offers

  19. 4. Illustrative Example 8-unit example: Units’ data

  20. 4. Illustrative Example Adjusted demand (load curve) Reserve requirement: 600 MW

  21. 4. Illustrative Example • DAS problem: • modeled with mathematical programming language AMPL • solved with ILOG CPLEX 9.0 optimization software package

  22. 4. Illustrative Example Energy prices (SMP) and Reserve Prices (RP) for different pricing schemes

  23. 4. Illustrative Example Energy prices (SMP) and Reserve Prices (RP) for different pricing schemes SMPs RPs

  24. 4. Illustrative Example Energy prices (SMP) and Reserve Prices (RP) for different pricing schemes SMPs RPs: shadow price scheme RPs: highest bid accepted scheme

  25. 4. Illustrative Example Units’ net profits in €

  26. 4. Illustrative Example Units’ net profits in €

  27. 4. Illustrative Example Units’ net profits in €/MWh

  28. 4. Illustrative Example Units may incur losses even if they get paid for their fixed costs WHY? Need for a bid/cost recovery mechanism

  29. 4. Illustrative Example Overall Payments Reserve Payments: range from 0.9 – 2.6 % of energy payments Fixed Costs Payments: about 2.2 % of energy payments

  30. 4. Illustrative Example Unit Commitment

  31. 5. Summary and Conclusions • Sketch of Greece’s electricity system • Simple model of the Day-Ahead Scheduling problem • Emphasis on: • frequency-related ancillary services (“reserves”) • fixed costs (start-up, shut-down, minimum-load) • Various reserve pricing schemes: • shadow price • highest bid accepted • pay-as bid • Illustrative 8-unit example

  32. 5. Summary and Conclusions • Units may incur losses through DAS participation • Bid/cost recovery mechanism is needed • Reserve payments contribute to the same direction • DAS: very complicated problem • due to energy – reserve interaction, and • non-convexities introduced by fixed costs • careful and incentive-compatible design is needed

  33. Questions ?

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