EVALUATION OF THE IMPACT OF IPPs ON THE GREEK WHOLESALE AND RETAIL ELECTRICITY MARKETS Pandelis N. Biskas, Christos K. Simoglou, Christoforos E. Zoumas, Anastasios G. Bakirtzis Power Systems Laboratory, Department of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124, Thessaloniki, GREECE E-mail:
[email protected],
[email protected],
[email protected],
[email protected]
ABSTRACT This paper investigates the impact of the installation and participation of conventional generating units owned by Independent Power Producers (IPPs) on the operation of the Greek competitive wholesale and retail electricity markets. A mid-term scheduling model formulated and solved as a mixed-integer linear program (MILP) is presented. This model is based on the day-ahead market clearing algorithm of the Greek wholesale electricity market. The effect of the participation of IPPs is examined in terms of various market indicators through simulations of the Greek wholesale electricity market during the year 2012 with and without the existence of IPPs. KEY WORDS Day-ahead electricity market, generation scheduling, independent power producers, mid-term scheduling, mixed-integer linear programming (MILP), peak-shaving.
Nomenclature b (B ) i
index (set) of steps of the energy offer function of unit i z index (set) of corridors of system operating c (C ) zone z z exp ( Exp ) index (set) of energy exports from zone z
i (I z )
index (set) of generating units of zone z
imp ( Imp ) index (set) of energy imports in zone z z
m (M )
r (R z ) t (T )
z (Z ) NRG πbit π1it
index (set) of reserves types M = 1, 2, 2,3S ,3NS , where m=1: primary, m=2+: secondary-up, m=2-: secondary-down, m=3: tertiary (spinning 3S and non-spinning - 3NS) index (set) of RES in zone z index (set) of hours of the scheduling horizon index (set) of system operating zones price of block b of the energy offer function of unit i, during hour t, in €/MWh price of the primary reserve offer of unit i during hour t, in €/MW
πit2R CLct
cf ct Dzt DTi
Etexp GLFit
I timp
Pitfix Pi max Pi max, AGC Pi min
Pimin, AGC
pit pitnet Qbit
qbit
Rim Ri2R
ritm
price of the secondary range offer of unit i during hour t, in €/MW corridor limit of corridor c during hour t, in MW corridor flow in corridor c during hour t, in MWh load of system operating zone z during hour t, in MWh minimum down time of unit i, in h energy withdrawal of export exp during hour t, in MWh generation loss factor of unit i during hour t energy injection of import imp during hour t, in MWh mandatory injection of unit i during hour t, in MWh maximum power output of unit i, in MW maximum power output of unit i while operating under AGC, in MW minimum power output of unit i, in MW minimum power output of unit i while operating under AGC, in MW energy injection of unit i during hour t, in MWh net energy injection (at the market point) of unit i during hour t, in MWh quantity of block b of the energy offer function of unit i, during hour t, in MWh portion of step b of the i-th unit’s energy offer function loaded in hour t, in MWh maximum contribution of unit i in reserve type m, in MW maximum secondary range capability of unit i, in MW contribution of unit i in reserve type m during hour t, in MW
RRtm RESrt
RDi RU i SDCi SUCi UTi uit
uitAGC
uit3NS
yit
zit
system requirement in reserve type m during hour t, in MW energy injection of Renewable Energy Source (RES) r during hour t, in MWh ramp-down rate of unit i, in MW/min ramp-up rate of unit i, in MW/min shut-down cost of unit i, in € start-up cost of unit i, in € minimum up time of unit i, in h binary variable which is equal to 1 if unit i is committed (on-line) during hour t binary variable which is equal to 1 if unit i operates under AGC and provides secondary reserve during hour t binary variable which is equal to 1 if unit i provides tertiary non-spinning reserve during hour t binary variable which is equal to 1 if unit i is started-up during hour t binary variable which is equal to 1 if unit i is shut-down during hour t
1. Introduction In the past twenty years, the electric power industry all over the world has experienced major regulatory and operational changes towards the introduction of competition in both the generation and supply level [1]. In this context, wholesale electricity markets have initially emerged in several countries to facilitate the competitive operation of the electricity sector, which has been further improved by the operation of respective retail markets [2]. The Greek deregulated wholesale electricity market started its operation in 2000, when the Regulatory Authority for Energy (RAE) and the Hellenic Transmission System Operator (HTSO) were founded. The market design consists of a mandatory pool operated by the HTSO and a day-ahead market that solves a shortterm unit commitment problem performing cooptimization of energy, primary and secondary reserve. However, the first steps towards the actual deregulation took place in 2006, when the first privatelyowned power plant became commercially available. The need to gradually replace the old, polluting lignite units has motivated private investors to construct and operate more gas-fired power units. Five privately-owned combined cycle gas-fired units (CCGTs) have been constructed and operated in Greece during the last five years and one more is expected to become commercially available within 2012. In this paper, the impact that the installation and participation of conventional generating units owned by Independent Power Producers (IPPs) has on the Greek deregulated wholesale and retail electricity markets is studied in terms of various market indicators (e.g. market
clearing price, CO2 emissions, energy imports/exports balance, etc.) through simulations of the Greek wholesale electricity market during the year 2012 with and without the existence of the IPPs. For this purpose, a Mid-Term Scheduling (MTS) model, based on the day-ahead market clearing algorithm (Day-Ahead Scheduling - DAS) of the Greek wholesale electricity market, has been developed. For the formulation and solution of the large-scale MTS model, an integrated software, called Long-Term Scheduling (LTS) program, has been used. In fact, the LTS program simulates the wholesale joint energy and ancillary services market for each day of a whole year and provides results concerning market clearing prices as well as quantities of all commodities (energy, primary reserve, secondary range, tertiary reserve) on an hourly basis. The optimization horizon of the LTS program may vary from one month to 25 years and in this paper is set equal to one year.
2. Mid-Term Scheduling Problem Formulation The MTS is a unit commitment (UC) problem that can be formulated and solved either as a Linear Programming (LP) problem or a Mixed-Integer Linear Programming (MILP) problem [3]. The LP model is useful when fast program executions are needed, but it lacks accuracy in the results when the market design involves solution of a UC problem. The MILP model is able to incorporate unit commitment, respect the units’ technical minimum and facilitate the provision of secondary and non-spinning tertiary reserve as well as the inclusion of the units' intertemporal constraints. The MILP model provides maximum accuracy in the unit commitment problem, but its execution is very time-consuming. Before the formulation and solution of the MTS problem, the well-known “Peak-Shaving” (PS) problem [4] is formulated and solved by the LTS program as an MILP model, in order to compute the hourly mandatory hydro injections that are subsequently used as input in the MTS. The PS problem is briefly presented in Section 2.2. 2.1 Input Data The input data that are taken into account in the PS and MTS problem formulations are the following: a) the system and zonal load, b) the import and export schedules on the interconnections, c) the injection of Renewable Energy Sources (RES), d) the pumping schedule of the hydro pumped-storage units, e) the corridor (inter-zonal) constraints, f) the schedule for the construction of new units and the withdrawal of old units, g) the techno-economic data of the generating units (including the emissions cost of thermal units)
h) the forced outage rates (EFORd) and the maintenance periods of the units. The technical maximum, the EFORd and the maintenance periods of the units define the final maximum hourly unit availabilities that will be considered in the MTS problem, further explained in the following. 2.2 Peak- Shaving (PS) Problem The aim of the PS problem is the computation of the mandatory hydro injections that are used as input in the MTS problem. The PS problem is solved: a) per year, if the optimization horizon is greater than one year, or b) for the entire scheduling horizon, if the optimization horizon is less than or equal to one year. In the first case, the Peak Shaving is performed with consecutive yearly runs, which are hereinafter called “internal Peak-Shaving runs”. In the solution of the PS problem, the thermal unit commitment status (on/off) is not modelled (no commitment binary variables are used). Consequently, the inter-temporal constraints that involve the minimumup/down time are disregarded. On the other hand, the hydro units are modelled with binary variables for on-line status, start-up and shut-down. This is necessary, in order to impose maximum number of start-ups per day. The priced energy offers and reserve offers of the hydro units are not considered in the PS problem. The total hydro injection throughout the year is given as input and is included in an equality constraint. This problem actually simulates the twelve-month usage of hydro resources that is performed by the hydro producers annually. 2.3 Mid-Term Scheduling Problem In this paper the MTS problem is formulated and solved as an MILP problem. The mandatory hydro injections exported from the PS solution (which correspond to the minimum hourly energy injection of the hydro units) are inserted in the MTS problem as non-priced energy offers. All units’ commitment constraints are active in this problem, which is solved successively on a daily basis for the entire year; the solution of the UC problem (DAS) for day N-1 provides the initial state of the UC problem for day N. The basic assumptions and the mathematical formulation of the MTS problem are given in the following subsections. 2.3.1 Modeling of start-up and shut-down phases In the MTS problem, the unit operating phase modeling follows, in general, the one presented in [5]. The only difference is that the synchronization and soak phases (which both comprise the unit start-up phase) as well as the desynchronization phase of the generating units are ignored in the current LTS formulation. The duration of
both phases is considered to be one hour. This is an acceptable model simplification for long-term simulation. 2.3.2 Energy and reserve offers of units The priced energy offer consists of a monotonically increasing step-wise function of up to ten (10) pricequantity steps. However, in the Greek wholesale electricity market, the Public Power Corporation (PPC), which is the exmonopolist dominant power company, owns the majority of the conventional thermal generating units and the entire hydroelectric production. Moreover, it controls almost the entire retail sector. Overall, it is a net buyer in the wholesale market in order to fulfill its retail supply obligations. Therefore, it has an incentive to keep the wholesale market clearing prices as low as possible by offering its generating capacity in the wholesale market at marginal cost or even at lower prices. To avoid strategic behavior that would result in barriers to entry, the energy regulator imposes a floor to the units’ energy offer prices [6]. This floor price is the Minimum Average Variable Cost (MAVC) of each unit and is crucial in the offering strategy of the dominant power company. As a result, all private producers are “forced” to follow a similar offering strategy to secure their dispatch in the day-ahead market and consequently in real-time. Therefore, in this study, all generating units are considered to bid their marginal cost under specific market rules, taking also into account the CO2 emissions cost. Regarding the reserve offers, there are one-step priced reserve offers for primary reserve and secondary range, in cohesion with the provisions of the Greek Grid and Exchange Code (GGEC) [6]. The quantity involved in these offers is the maximum capability of each unit to provide each type of reserve. There are no priced reserve offers for tertiary reserve. Tertiary reserve requirements are defined per dispatch period and are inserted as constraints in the LTS problem. 2.3.3 Monte Carlo Simulation Monte Carlo simulation is widely used for the modeling of stochastic parameters. The only stochastic parameter that is modelled in the MTS is the thermal unit availability. A two-state Markov model is used, taking into account the Equivalent Forced Outage Rate (EFOR) of the units [7]. The MTS problem is solved using external and internal runs. First, the external runs are used for Monte Carlo simulation iterations. A list of values for the hourly thermal unit availabilities is created taking also into account the units' maintenance periods. These values are entered in the LTS solver and the MTS problem is solved in successive internal runs. The Monte Carlo runs are solved successively. The internal runs are used in order to split the optimization period in smaller time-periods (one hour), since the solution time of MILP runs increases exponentially with the number of binary variables.
2.3.4 Objective function The MTS goal is the maximization of the total social welfare (or equivalently the minimization of the total production cost minus the load utility) within a year. The load utility refers to the priced-load declarations of the demand-side. In this paper, the load utility is considered equal to zero, since the system load demand is considered inelastic. The objective function of the MTS problem is described as follows: NRG Min GLFit πbit qbit +π1it rit1 bB i tT iI πit2 R rit2 rit2 SUCi yit SDCi zit
(1)
2.3.5 System Constraints pitnet
iI z
rR z
Dzt
RESrt cfct
cC z From
cf ct
Etexp
expExp z
z Z, t T
(2)
cC zTo
rit1 RRt1
t T
iI
(3)
RRt2
t T
(4)
rit2 RRt2
t T
(5)
t T
(6)
c C, t T
(7)
iI
rit2
Itimp
impImp z
iI
rit3S rit3NS iI
iI
RRt3
0 cf ct CLct
pit Pitfix
Constraints (2) enforce the power balance equation in each zone z for each dispatch period t. Constraints (3)-(6) enforce the system requirements for all types of reserves. Constraints (7) ensure that the corridor flow on each corridor c for each hour t should be less than or equal to the respective corridor limit at that hour.
qbit
i I , t T
(11)
i I , b Bi , t T
(12)
i I, t T
(13)
bB i
0 qbit Qbit pitnet
The total system cost in (1) includes the units' generation cost, the units' reserve provision cost and the units' start-up and shut-down cost. In this model, the startup cost is considered equal to zero and, therefore, omitted from the objective function, in cohesion with the provisions of the Greek Grid and Exchange Code (GGEC) [6]. The problem constraints for each hour of the dispatch day can be classified in the following groups [8]:
Constraints (8) model the logic of the start-up and shutdown status change of unit i, while constraints (9)-(10) enforce the minimum up and down time constraints, respectively, i.e. unit i must remain committed (decommitted) at hour t if its start-up (shut-down) started during the previous UTi -1 ( DTi -1) hours [5].
pit GLFit
Equation (11) enforces that, for each generating unit i, the power injection can be divided into two components: a) The first term represents the non-priced component of the energy offer function of the entity, including the mandatory hydro energy injection and the energy production of units in commissioning tests. This component may follow a constant and pre-specified schedule. b) The second term represents the priced component of the energy offer function of the unit. This component is equal to the sum of the cleared blocks of energy. Constraints (12) denote that the portion of step b of the i-th generating unit energy offer function that is cleared in hour t cannot exceed the size of the corresponding step. Constraints (13) define that the net energy injection at the market point of each unit i for each hour t is equal to the energy injection at the unit metering point multiplied by the respective generation loss factor, GLFit .
uitAGC uit
i I, t T
(14)
rit2 rit2 Ri2R uitAGC
i I, t T
(15)
0 rit1 Ri1 uit 0 rit3S Ri3S uit 0 rit3NS Ri3NS uit3NS rit3NS Pimin uit3NS uit3NS 1 uit
i I, t T
(16)
i I, t T
(17)
i I, t T
(18)
i I, t T
(19)
i I, t T
(20)
pit rit2 Pi min uit uitAGC Pi min, AGC uitAGC
i I, t T
pit rit2 Pi max uit uitAGC Pi max, AGC uitAGC i I, t T
(21)
(22)
pit rit1 rit2 rit3S Pimax uit
i I, t T
(23)
pit pit 1 RU i 60
i I , t T
(24)
pi (t 1) pit RDi 60
i I , t T
(25)
2.3.6 Unit Operating Constraints
yit zit uit uit 1 t
t UTi 1 t
t DTi 1
i I, t T
(8)
yi uit
i I, t T
(9)
zi 1 uit
i I, t T
(10)
Constraints (14) state that unit i may provide AGC if and only if it is committed (on-line). Constraints (15) enforce that the sum of the secondary-up and down reserve provided by unit i, provided that it is under AGC,
3. Test Results 3.1 Simulation Data The MTS problem is solved for the Greek Power System, which currently comprises of 33 thermal units and 13 hydroplants, with a total installed thermal and hydro capacity of 9,417 MW and 3,050 MW, respectively. The IPPs currently own six CCGT units with a total installed capacity of 2,384 MW. The remaining thermal and hydro capacity belongs to the ex-monopolist dominant power company (PPC). An overview of the current Greek Power System (excluding Renewable Energy Sources - RES) is shown in Table 1. Table 1 Greek Power System Overview Unit Type PPC Steam Lignite PPC CCGTs IPPs CCGTs PPC Steam Gas & Oil PPC Hydro Total
Number of Units 17 3 6 7 13 46
Installed Capacity [MW] 4,418 1,405 2,384 1,210 3,050 12,467
MAVC Range [€/MWh] 33.5 - 41.6 84.5 - 93.0 86.0 - 89.6 98.6 - 132.4 -
In this study, the DAS market is considered only and a yearly simulation is performed on a daily basis (MTS daily intervals) under two different scenarios, as follows: Scenario 1 (S.1): All IPP generating units are available. Scenario 2 (S.2): No IPP generating unit is available. The aim of this study is: a) the investigation of the effect that the hourly commitment and operation of the IPP units has on the System Marginal Price (SMP) and the energy production of the PPC plants. b) the comparison of the credits/debits of all wholesale market participants in both cases, and c) the analysis of the effect that the existence of IPPs has on the Greek balance of trade, the financial results of PPC and the activities of the independent electricity retailers. It should be noted that the presented comparison makes the simplifying assumption that no new units would be constructed by PPC during the past 5 years (the optimal expansion plan of the monopolist PPC is not taken into account). 3.2 Simulation Results Figure 1 shows the SMP duration curve for both scenarios. In S.1 (with all IPP units available) the average SMP is equal to 68.43 €/MWh, while in S.2 (with no IPP units available) it is equal to 103.25 €/MWh. Therefore, the mean decrease in SMP caused by the presence of IPPs in the Greek power system is equal to 34.82 €/MWh. In addition, Fig. 2 shows the monthly average SMP for both scenarios. 160 140
SMP [€/MWh]
should be less than or equal to the maximum secondary range capability of the unit. Constraints (16)-(18) set the upper limits of primary, tertiary spinning and tertiary nonspinning reserves, respectively. As shown by constraints (14)-(17), unit i may contribute in synchronized reserves if and only if it is committed. Constraints (19) enforce the tertiary non-spinning reserve contribution to be greater than the minimum power output of unit i. Constraints (20) state that unit i may provide tertiary non-spinning reserve if and only if it is off-line. Constraints (21)-(23) define the limits of the power output of unit i, while constraints (24)-(25) introduce the effect of ramp rate limits on the power output.
120 100 80
60 40 20 0 0
2000
3000
4000
5000
6000
7000
8000
Hours S.1 (with IPPs)
S.2 (without IPPs)
Fig. 1 SMP duration curve 140 120
SMP [€/MWh]
According to the Greek Grid and Exchange Code [6], there are two separate markets operating in the Greek electricity market: a) the Day-Ahead Scheduling (DAS) Market, in which all generating units are remunerated for the energy that they actually inject to the system, following the Uniform Marginal Pricing (UMP) scheme [1]. b) the Capacity Assurance Market (through the Capacity Availability Tickets - CAT) in which energy producers are remunerated for their units availability; this remuneration aims to cover the fixed annual costs (loans, depreciation, etc.) of the operating units. These two markets, even though operating independently one from another, are related in the sense that the profits of the producers from these two markets should cover their total annual costs (fixed and variable) in order to ensure their viability in the long-run.
1000
100 80 60
40 20
0 Jan Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
Month S.1 (with IPPs)
Fig. 2 Monthly average SMP
S.2 (without IPPs)
Table 2 Economic analysis of the Greek Power System for 2012 (Scenario 1: With IPPs) Ancillary Cost Recovery Energy DAS Energy Services Mechanism Injection Revenue/Costs Provision Revenue/Cost [TWh] [M€] Revenue/Cost [M€] [M€] PPC Steam Lignite PPC CCGTs IPPs CCGTs PPC Steam Gas & Oil PPC Hydro Total Units Production RES Imports Total Energy Injection Exports Pumping Total Consumers Load
28.981 3.199 11.979 0.000 3.145 47.305 2.416 4.491 54.212 -2.931 -0.378 50.903
1,951.570 226.078 832.733 0.000 246.502 3,256.884 160.295 322.181 -183.293 -14.956 -3,541.110
0.328 173.321 273.113 0.000 0.000 446.763
DAS Total Revenue [M€]
0.010 2.050 10.435 0.000 11.917 24.412
-446.763
DAS Production Cost [M€]
1,951.909 401.449 1,116.281 0.000 258.419 3,728.058 160.295 322.181
1,080.285 365.820 1,034.190 0.000 7.194 2,487.489
DAS Profit [M€] 871.624 35.629 82.091 0.000 251.226 1,240.570
-183.293 -14.956 -4,012.285
-24.412
Table 3 Economic analysis of the Greek Power System for 2012 (Scenario 2: Without IPPs) Ancillary Cost Recovery Energy DAS Energy Services Mechanism Injection Revenue/Costs Provision Revenue/Cost [TWh] [M€] Revenue/Cost [M€] [M€] 28.993 8.556 0.000 1.161 3.510 42.220 2.416 7.941 52.577 -1.644 -0.019 50.915
2,917.985 896.707 0.000 149.632 412.798 4,377.122 241.962 845.910 -149.959 -0.764 -5,314.272
1.721 18.586 0.000 0.926 18.427 39.660
0.000 81.470 0.000 38.865 0.000 120.336
2,919.706 996.763 0.000 189.423 431.226 4,537.118 241.962 845.910
DAS Production Cost [M€] 1,080.625 839.421 0.000 187.996 7.194 2,115.236
DAS Profit [M€] 1,839.081 157.342 0.000 1.427 424.032 2,421.883
-149.959 -0.764 -5,474.268
-39.660
-120.336
8
35
Energy Injectoin/Withdrawal [TWh]
Yearly Energy Production [TWh]
PPC Steam Lignite PPC CCGTs IPPs CCGTs PPC Steam Gas & Oil PPC Hydro Total Units Production RES Imports Total Energy Injection Exports Pumping Total Consumers Load
DAS Total Revenue [M€]
30 25
20 15 10 5 0 PPC Lignite PPC CCGTs
S.1 (with IPPs)
IPPs CCGTs PPC Steam Gas &Oil
PPC Hydro
S.2 (without IPPs)
Fig. 3 Yearly energy production per unit type Tables 2&3 present a brief economic analysis of the Greek Power System for the year 2012 for both scenarios described, regarding the total energy production per unit type, the energy withdrawal, the energy imports/exports balance, the associated revenues and costs, the wholesale market participants gross profits from the DAS market, etc.
7 6 5
4 3 2 1 0 Imports
Exports
S.1 (with IPPs)
RES
Pumping
S.2 (without IPPs)
Fig. 4 Yearly energy injection/withdrawal The Cost Recovery Mechanism is enacted in the Greek wholesale electricity market and assures that all generating units will cover their variable costs plus an additional remuneration of 10% on their variable costs, in case that they operate in the DAS market under economic loss. Both the cost recovery mechanism revenue and the ancillary services provision revenue (columns 4 and 5,
respectively) are finally paid to the producers by the endconsumers through their retailers as additional charges on their electricity tariffs. Figure 3 and 4 show the yearly energy production per unit type and the yearly energy injection/withdrawal in the Greek Power System, respectively. It is concluded that the lack of production of IPPs in S.2 is mainly balanced by the increased production of the CCGTs, steam gas and oil units of PPC and imports as well as by the significant reduction in energy exports and pumping. The RES injection is considered to remain unchanged in both scenarios considered.
S.2
Difference
Imports [ΤWh]
4.491
7.940
3.449
Exports [ΤWh]
2.931
1.643
-1.288
Imports/Exports Balance [ΤWh]
-1.560
-6.297
-4.737
Payments to Importers [M€]
322.181
845.910
523.729
Revenue from Exporters [M€]
183.293
149.959
-33.334
Payments balance [M€]
-138.888
-695.951
S.1
S.2
Difference
End-Consumers Supply Cost [M€]
3,364.054
5,048.559
1,684.505
Pumping Cost [M€]
14.956
0.764
-14.192
3,379.010
5,049.322
1,670.312
23.191
37.677
14.486
424.425
114.319
-310.106
3,826.626
5,201.318
1,374.692
Total Energy Cost [M€] Ancillary Services Provision Cost [M€] Cost Recovery Mechanism Cost [M€] Total Supply Cost [M€]
Table 4 Energy Imports/Exports Balance S.1
Table 5 PPC Total Supply Cost
Table 6 PPC DAS Income S.1
S.2
Difference
DAS Revenue [M€]
2,611.777
4,537.118
1,925.341
DAS Production Cost [M€]
1,453.298
2,115.235
661.937
Total Emissions [MT]
40.751
43.988
3.237
CO2 Emissions Cost [M€]
346.384
373.897
27.513
Total Gross Profit [M€]
812.095
2,047.986
1,235.891
-557.063
Table 4 shows that in S.2 the yearly energy imports increase by 3.5 TWh due to the significant increase of the SMP, while the corresponding reduction in exports is approximately 1.3 TWh. Overall, the imports/exports balance worsens by 4.74 TWh or 557 M€ without the existence of IPPs. This aggravation may have multifaceted negative effects on the national economic policy, the geopolitical balances and the national security, since it results in the strong dependence of Greece on the electricity imports from neighboring countries. The effect of the existence of the IPP generating units on the financial results of PPC can be examined in terms of (a) the total procurement cost for the delivery of electric energy to the end-consumers, and (b) the total revenue from the electric energy production. Currently, PPC's share in the retail market is approximately 95% and, therefore, it incurs the respective energy procurement cost as well as the additional ancillary services provision cost and cost recovery mechanism cost. As a result, PPC incurs the 95% of the above costs, already shown in Tables 2&3. It should be noted that the pumping cost is totally covered by PPC being the owner of all pumped-storage units in Greece. Table 5 presents the PPC total supply cost in both scenarios. It is clear that, without the existence of IPP generating units, PPC incurs an additional cost of 1,374.7 M€. Table 6 presents the effect of the IPPs existence on the total gross profit of PPC. The PPC DAS revenue and the DAS production cost are computed from the respective results given in Tables 2&3.
The energy production of the PPC thermal generating units (i.e. lignite-fired, CCGTs, steam gas and oil-fired) increases in S.2 as compared to S.1 by 8.88 TWh (41.06 TWh versus 32.18 TWh). This results in a significant increase of the respective CO2 emissions (approx. 3.237 MT). According to forecasts based on the current values taken from the European Energy Exchange [9], the CO2 emissions cost for this study has been set equal to 8.5 €/T, resulting in an additional emissions cost of 27.513 M€ for PPC in S.2. Therefore, the total gross profit of PPC in S.2 increases by 1,235.9 M€ (mainly due to the significant increase of the average SMP). Comparing the results given in Tables 5&6, it is concluded that without the existence of the IPP generating units, PPC would incur an additional cost of 1,374.71,235.9 = 138.8 M€. Finally, regarding the impact of the existence of IPPs on the operation of the Greek retail electricity market, it is evident that the remarkable increase of the SMP in S.2 due to the absence of IPPs (approx. 34.82 €/MWh) is significantly higher than their current profit margin (the profit margin of the independent retailers is defined as the difference between the electricity tariffs provided to endconsumers by PPC and the average electricity supply cost incurred by the independent retailers). On the contrary, the absence of IPPs results in a significant decrease of the cost that accounts for the cost recovery mechanism and the provision of ancillary services by the generating units (approx. 6.413 €/MWh) and which is incurred by the retailers. However, the increase of the average cost for the electric energy procurement from the wholesale electricity market between the two scenarios examined outbalances this benefit and, therefore, constitutes an insurmountable
barrier for the activation of independent retailers in the Greek retail electricity market. All simulation runs of both the PS and MTS models were performed under the LTS program platform on a 3.2 GHz Intel i7 processor with 24 GB of RAM, running 64bit Windows and using the CPLEX 11.0 solver under GAMS 23.0 [10].
4. Conclusion In this paper, a study for the investigation of the impact of the installation and participation of conventional generating units owned by Independent Power Producers (IPPs) on the Greek competitive wholesale and retail electricity markets has been presented. A mid-term scheduling model formulated and solved as a mixedinteger linear program has been used for the simulation of the Greek electricity market for the year 2012. Two yearly simulations (scenarios) on a daily basis were performed, namely with and without the existence of the IPP generating units. The simulation results proved that the operation of the Greek electricity market under the presence of IPPs mitigate the dependence of Greece on electric energy imports, thus stimulating the Greek balance of trade and the national economy. Moreover, the presence of IPPs alleviates the economic burden that PPC would incur otherwise. Finally, the competitiveness in the Greek retail electricity market is also enforced, thereby encouraging the entry of new independent retailers.
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