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

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

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

2. Greece’s Electricity System TypeNumber of unitsCapacity (MW) Lignite Oil Combined Cycle Natural Gas Small Thermal Hydro Renewables/Cogeneration> Total Capacity: Total Capacity (thermal plants): Generation mix

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

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

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

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

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

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

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

3. Day-Ahead Scheduling Problem DAS problem formulation : overall reserve cost overall fixed costs + minimize overall energy cost + 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

3. Day-Ahead Scheduling Problem DAS problem formulation : overall reserve cost overall fixed costs + minimize overall energy cost + 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

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…) ?

3. Day-Ahead Scheduling Problem 3.1 Impact of Reserve Offers Pricing schemes for reserve: 1.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 3.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

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) Bjørndal and 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.

4. Illustrative Example 8-unit example: TypeUnitCapacity (Technical maximum) Technical minimum Energy bid Ligniteu Oilu Gasu Gasu Gasu Gasu Gasu GTu Energy offers

4. Illustrative Example 8-unit example: TypeUnitReserve availabilityReserve bid Ligniteu Oilu2505 Gasu31504 Gasu Gasu51506 Gasu Gasu71493 GTu81412 Reserve offers

4. Illustrative Example 8-unit example: UnitStart-up/ shut-down cost Minimum-load cost Minimum up/down time Initial condition u ON u OFF u ON u OFF u ON u ON u OFF u OFF Units’ data

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

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

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

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

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

4. Illustrative Example UnitCase 1aCase 1bCase 2aCase 2bCase 3aCase 3b u u u u u u u u Units’ net profits in €

4. Illustrative Example UnitCase 1aCase 1bCase 2aCase 2bCase 3aCase 3b u u u u u u u u Units’ net profits in €

4. Illustrative Example UnitCase 1aCase 1bCase 2aCase 2bCase 3aCase 3b u u u u u u u u8N/A Units’ net profits in €/MWh

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

4. Illustrative Example Case Overall Energy Payments Overall Reserve Payments Overall Fixed Costs Payments 1a b (as 1a) 2a(as 1a) (as 1a) 2b(as 1b) (as 1a) 3a(as 1a)65 032(as 1a) 3b(as 1b)64 722(as 1a) Overall Payments Reserve Payments: range from 0.9 – 2.6 % of energy payments Fixed Costs Payments: about 2.2 % of energy payments

4. Illustrative Example TypeUnitFixed costs included for all cases Fixed costs excluded for all cases except for 3a Ligniteu11-24 Oilu Gasu31-24 Gasu4-- Gasu51-24 Gasu69-24 Gasu GTu81-24 Unit Commitment

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

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

Questions ?