From a bundled energy-capacity pricing model to an energy–capacity–ancillary services pricing model

ARTICLE IN PRESS Energy Policy 36 (2008) 2878– 2886

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From a bundled energy-capacity pricing model to an energy– capacity– ancillary services pricing model Ricardo Raineri a,, Rau´l Arce a, Sebastia´n Rı´os b, Carlos Salamanca a a b

´lica de Chile, Casilla 306, Correo 22, Santiago, Chile Departamento de Ingenierı´a Industrial y de Sistemas, Pontificia Universidad Cato ´lica de Chile, Casilla 306, Correo 22, Santiago, Chile Departamento de Ingenierı´a Ele´ctrica, Pontificia Universidad Cato

a r t i c l e in f o

a b s t r a c t

Article history: Received 7 January 2008 Accepted 9 April 2008 Available online 23 May 2008

In this paper, we extend the Chilean power generation pricing mechanism, with capacity and energy payments, to one where ancillary services (AS), as frequency regulation and voltage control, are explicitly recognized. Adequacy and security attributes of the electric system and the public good characteristics of AS are set within the payment structure to distribute the financing of AS among those who benefit from their provision. The contribution to finance the provision of AS is determined assessing the value assigned to the system security by each agent, following what’s an efficient pricing mechanism in the presence of public goods. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Ancilary services market, generation

PricingPower

1. Introduction This paper extends the Chilean power generation pricing mechanism with energy and capacity payments to one with energy, capacity and ancillary services (AS) payments, accounting for the public good character of some AS and the value that end users assign to the security of supply. The mechanism is applicable to markets that share the characteristics of the Chilean power supply industry, an integrated system with a system operator (SO) with a vast authority to define the dispatch of the system.1 Reliability of electricity supply depends, among other, on two key characteristics, adequacy and security. Adequacy refers to the medium- and long-term availability of enough resources to satisfy the demand; and security refers to the short-term resilience of the system to respond to standard short run disturbances. The need to satisfy this last characteristic has led many countries to define AS markets as a complement to the energy market, AS, which include services such as frequency regulation, voltage control, and power supply recovery. The adequacy and security attributes are related; however, they do not substitute one for the other. When power supply is competitive, following capacity price signals, private investments solve adequacy requirements. However, security has public good

 Corresponding author. Tel.: +56 2 354 4272; fax: +56 2 556 1608.

E-mail address: [email protected] (R. Raineri). In 2004, the Law of the Chilean Electric Supply Industry (ESI) introduced a requirement to have, as a complement to the energy and capacity markets, an AS market. For a description of the Chilean ESI and its’ electric systems see Raineri (2006a, b). 1

0301-4215/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2008.04.006

characteristics, and this is because at a particular time its provision benefits all the agents that take energy out from the system. This public good character of security must be accounted in the design of a pricing mechanism for AS provision, because it is through AS that the system assure a more secure operation. The proposed pricing mechanism for the Chilean ESI explicitly account for AS transfers among power generators. On one side, and with the generators’ energy injections and available capacity, we consider the generators’ AS injections according to the SO efficient dispatch; and on the other, the generators’ energy, capacity and AS retirements required to fulfill their contracts with end users. In these, the public good character of some of the AS is considered where the distribution of the financing associated to the provision of AS is carried out based on an estimate of the valuation of the system’s security embedded on the end-users contracts. In general, we assume the existence of an independent SO who clears the mandated centrally dispatched market, by dispatching power plants to minimize the overall system variable cost subject to technical constraints, for that it follows the power plants merit order with respect to their variable cost, where the less expensive power plants are dispatched first. If the SO would not be independent, there is the chance that its’ strategic behavior may influence and deviate the dispatch of the system from an optimal dispatch. Section 2 introduces a general description of AS and their technical requirements. Section 3 establishes why the operation’s security can be seen as a public good. Section 4 discusses how to estimate the value that the agents assign to the system’s security. Section 5 describes the pricing mechanism. Section 6 sets an experiment to calculate the payments that results from the proposed mechanism following a simulation on one of the Chilean

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Public goods have particular characteristics that differentiate them from private goods, like their lack of rivalry or exclusiveness in their use or consumption and the difficulty to exclude other

agents from its benefits when consumption is compulsory. In this respect, for the agents that make energy withdrawals in a certain moment, it is technically difficult to disregard the benefits already embedded in the provision of AS, and this must suggest that the participation in the financing of the provision must be considered compulsory. For the electric industry, any generator that sells energy during a defined period of time is benefited by the system’s security. Within the provision of AS, sometimes exist a local component that allows differentiating between the agents that benefit and those who do not benefit from its provision. This leads to distinguish between global and local public goods. If the provision of AS have a global effect on the system’ service quality, everybody that withdraws energy should contribute to finance them; however, if the provision of AS has a local effect, only those who benefit should contribute to finance them. As it happens with any service or good with public good characteristics, a mechanism of voluntary contribution cannot assure an efficient resource allocation (Samuelson, 1954), because the agents have incentives to misrepresent the value that they assign to its provision when financing is made compulsory according to the agents willingness to pay. Furthermore, this situation becomes more critical as the number of agents increases (Olson, 1965). This problem known as the ‘free rider’ problem may imply that in a voluntary contribution mechanism the resources obtained to finance the provision of the public goods will be insufficient from a social point of view. However, if we unveil the agents’ willingness to pay, it’s possible to define a pricing mechanism that gathers this information to obtain enough resources to finance an efficient provision of the public service. Mechanisms like this are inspired in the Lindahl equilibrium concept, which uses customized prices based on the agents’ valuation to define their participation in the financing.3 An efficient mechanism in presence of public goods should prevent dependence between the agents’ payments and the valuation they report (see Vickrey, 1961; Clarke, 1971; Groves, 1973). For this, Groves and Ledyard (1977) propose a mechanism to obtain an efficient allocation including the participation of an organizing entity, idea that was later exploited by Hurwicz (1979) and Walter (1981), who also implemented Lindahl equilibriums with an organizing entity to obtain a Pareto optimal allocation. The existence of a centralized entity is explained because within a voluntary contribution mechanism the agents’ incentives are not to reveal their willingness to pay, so some sort of central coordination is required. In electric systems, this centralized role is assumed by the SO. This paper extends the theory of public goods and proposes a pricing mechanism for Chilean AS markets following what was already highlighted in Raineri et al. (2006), where on a cross country analysis it is possible to identify different mechanisms according to the level of competitiveness that exists in the industry. Worldwide and in general we can distinguish two designs with respect to energy and AS markets (see Wilson, 2002). The first one consists on the Integrated Systems design where the SO has vast authority to manage and operate the system and to define transfer prices for the different products (energy, capacity and now AS). In this type of setting, generally the SO minimizes the operating costs subject to technical and security constraints. The second design consists on Unbundled Systems where the resource allocation is solved by the interaction between agents using

2 The power generation units can operate in ranges where the generation or absorption of reactive power does not affect its active power, operating conditions defined within the P–Q diagram. For operation ranges outside that diagram, the unit must adjust its active energy generation in order to perform voltage control (see Bhattacharya and Zhong, 2001).

3 In a Lindahl Equilibrium the provision of public goods reaches an equilibrium when everyone agrees on the level of goods to be provided given the personalized prices, where the set of personalized prices (Lindahl prices) compromises individual shares of the collective tax burden of an economy. The sum of Lindahl prices is equal to the cost of supplying public goods. See Lindahl (1919).

electric systems (Sistema Interconectado del Norte Grande-SING). Finally, Section 7 presents the main conclusions.

2. AS requirements The requirements of AS depends on the particular electric system. In general, AS can be defined as a set of resources, which are present in generation (mainly), transmission, distribution and large end-users’ facilities, whose objective is to allow the system to respond to disturbances to ensure the quality of electricity supply. In this research, we consider those AS that are supplied by dispatched generators during the operation, such as the case of the primary frequency regulation (PFR), secondary frequency regulation (SFR) and voltage control (VC). We do not consider AS associated with system recovery. Nevertheless the proposed pricing mechanism can be extended to other AS. The function of PFR is made through the automatic speed regulators of those power generation units, which are selected and prepared to carry out such function, balancing generation and demand with an increase or decrease in active power approximately during the first 20 s after a disturbance occurs. This balance is made continuously to keep the system’s frequency near a predefined value. SFR is in charge of returning the frequency to rated frequency, but with a slower response (10–15 min). SFR also allows the units that carry out the PFR to recover their original state. VC tries to keep the voltage in the electric system’s busbars within a normal range and it can be achieved by the generation units or other elements such as capacitor banks or reactive power compensators, which are strategically distributed in the electric system. The VC achieved through generators is made through the reactive power production or absorption, and the capacities of the unit will depend on their technical characteristics.2 For the case of the VC provided by other elements, it will be considered that it follows local requirements that can be covered by bilateral contracts between the suppliers and the beneficiaries of the provision of the ancillary service. Those AS provision that does not result from local requirements will be understood as resources that have an effect on the global stability of the system. For simplicity we assume that in each busbar of the network quality or security is the same. Thus, in this work we assume that all the generators as energy sellers use the same amount of AS for each unit of energy that they take out and sell at a particular moment of time. For simplicity we would not consider the impact of congestion the transmission sector. However, if one also considers the impact of congestion in electricity transmission the optimal dispatch might be affected but not the methodology used here for AS costs allocation. However, it is worth recognizing that the existence of electricity transmission constraints may set higher requirements for the provision of AS, in which case the higher cost must be distributed among those who benefit from the provision of AS.

3. Security as a public good

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tenders with voluntary participation, where becomes fundamental for the SO to consider the possible strategic behaviors of the agents (see Oren, 2001; Papalexopoulos and Singh, 2001). One of the difficulties in designing AS markets is in their interdependence with the energy market, besides the inter-dependence that exists between the different AS (see Hirst and Kirby, 1998). The adequacy and security attributes are also related, because the higher the installed capacity, the larger the spare capacity that’s available to respond to short-term disturbances. The Chilean electric sector only considers payments for energy and capacity transfers and a very simplified form of AS, so there are no explicit price signals that consider the reliability of power supply.

4. Value of the system’s security operation In the proposed mechanisms, we consider customized prices (personalized price, PP) for the value of the systems’ provision of security, expressed in mills/MWh. These prices should be understood in terms of each unit of energy withdrawn at a particular moment of time, because withdrawals are different depending on the supply contracts of each agent. To estimate the value assigned by each agent to the system’s security it is possible to distinguish at least two scenarios. First, in a scenario where there is a homogeneous value for security for each unit of energy withdrawn, happens that customized AS prices are the same. And here each agent’s participation in the financing of the systems’ security is conditioned to the energy that he withdraws from the system. This scenario is mathematically equivalent to consider that the distribution of financing is made in a prorated basis from the withdrawals. A second scenario is one with heterogeneous agents where the value for security is different among the agents and hence the customized prices would also be different. Thus, not necessarily everybody share the same value for the systems’ security embedded in the AS provided within each unit of energy withdrawn. In this second case, the agents participation in the payments is conditioned both, to the variability in the customized prices and the differences in the energy withdrawals. To estimate the value assigned to the systems’ security we use a proxy of the agents’ willingness to pay for it, in other words their willingness to pay for the provision of AS, recognizing that those who benefit are the ones that make energy withdrawals during the period.4 A distribution of the financing of the AS on a prorated basis from the energy withdrawals will be efficient only if the value assigned to the systems’ security is the same for each unit of energy withdrawn. However, if that’s not the case, this is a simplification that could lead to a solution where agents with a lower valuation to security are subsidizing the ones with a higher one. A common element among end users in Chile is that in general they should have long-term contracts with generators, contracts that traditionally have set capacity and energy prices but not AS prices. This happened because AS have been provided without explicit recognition in the pricing system. Because this, and without more unbundled information, we use the contracts energy price for end users to obtain a proxy for the willingness to pay for the systems’ security. For that we assume that current energy prices pay a markup over the generators exchange prices (the spot energy price or system marginal cost). Therefore, we use the difference between the energy prices embedded in the 4

In the case that we have a disturbance in the system which leads to a fault that can’t be isolated and controlled by AS, and the system suffers a blackout, the security of supply can be valued at the price of the unsupplied energy.

contracts and the spot energy price paid by generator to fulfill their contracts as a proxy of the value assigned to the security of energy supply and to determine from there the personalized prices for AS.

5. Proposed pricing mechanism for AS The proposed mechanism considers that the products are energy, capacity and AS, where the SO act as the organizing entity that defines the delivery and marginal cost pricing for each product in the generators market. The generators’ payments to cover the costs associated to AS will be distributed among them through an estimation of the value assigned to the system security embedded in each generator’s contracts with his end users. The AS considered are the PFR, SFR and the VC provided by the power generation units. Other AS with public characteristics has not been considered, like black start. Nevertheless, the pricing mechanism can be equally extended to these cases. The system dispatch is made according to the declared generation variable costs, where the SO objective is to fulfill the demand requirements at a minimum cost. The delivery of AS is defined in the same manner, where the agents declare the additional variable costs associated to each service and the task of the SO is to determine the most economic units to satisfy the AS system’s requirements.5 For the experiments we consider the operation of a system with the characteristics of the Chilean SING system,6 where we assume that during all the hours of the month exist a normal operation except for 1 h where in the first half hour exist a normal operation and in the second half a contingency occurs where one power plant has an abrupt exit. On the demand side, the proposed mechanism to distribute the costs associated with the provision of AS is intended to approach what should be the optimal prices when a market mechanism in a Lindahl sense is in place, market mechanism with personalized prices where the agents have incentives to reveal truthfully their willingness to pay; and on the supply side it is intended to imitate a competitive well-defined auction mechanism, which induce the agents to give evidence of the costs actually incurred, both in the provision of energy and AS. The choice of a central dispatch system on declared and audit marginal costs or an auction mechanism for the allocation of resources will depend on issues like market structure, the level of concentration and the agents’ market power. AS are global public goods under the assumption that there are no characteristics identified that would allow discerning among the benefited agents within those who withdraw energy at a particular moment. Also, transactions associated to AS provision only take place when the suppliers incurs in additional dispatch costs or, when their normal active power delivery is affected, making the supplier to incur in opportunity costs. If the provision of AS convey capital costs, they should be distributed considering the value assigned to security by those who make energy withdrawals. In the particular case of the SING, there are no capital costs for AS because the system as a whole has enough resources. Therefore, in the experiment we only need to account for operating and opportunity costs. SING Statistics show that the system does not require additional investments for the delivery of AS. However, is worth to mention that if the provision of AS imports capital costs (as a response to the additional investments required), those costs should be paid by the ones who benefit and 5 Previously is necessary for the units that provide AS to certify their technical capabilities. 6 See Raineri (2006a, b) for details. Also, for a graphical representation of the SING system see Centro de Despacho Econo´mico de Carga del SING (2007).

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withdraw energy at the particular time. The criterion circumscribed in the proposed mechanism is one where the variable costs resulting from the provision of AS are collected among all players that withdraws energy, and the same can be done for the capital costs incurred in the provision of AS. To estimate the impact of the value of the system’s security on energy, capacity and AS payments, we consider three scenarios. First, a case where prices are inflexible in the short term and the energy demand is inelastic to price fluctuations (the present condition of the Chilean SING system). Second, a case where exist short-term price flexibility, which follows the fluctuations of the spot price and an inelastic demand. And third, a case with shortterm price flexibility and a decrease in energy consumption, as a response to the increase in the spot price at the moment of the failure. The three scenarios put in practice the proposed pricing mechanism considering: (a) the short-term price flexibility to end users and (b) the elasticity of the demand. Both, as a way of screening the ability of the mechanism to deliver allocations that fit in a coherent way with changes in the system operation. With the proposed mechanism we obtain, for each of the three scenarios, the PP to be paid by each end user for each unit of energy he takes out of the system at a particular moment, what represent the value of the security used to define the transaction of AS. Thus, with the proposed mechanism we promote more efficient investments and more efficient energy consumption decisions. In the experiments, there are both, regulated and unregulated end users. Regulated end users have regulated electricity prices, while unregulated freely negotiate electricity prices with generators. For simplicity we assume that the regulated end users are homogeneous with respect to the value assigned to the systems’ security, so their customized prices applied to energy withdrawals are the same. For the unregulated customers we assume that they are heterogeneous, so their customized prices applied to energy withdrawals are different.

6. Experiments and results For the experiments with the SING system we assume that there are no transmission congestions losses and that it’s possible to measure the requirements of AS and identify the suppliers.7 In the SING, the rated frequency is 50 Hz, and regulations require the actual frequency to be between 49.8 and 50.2 Hz during at least 97% of the time, while VC cannot vary more than 5% from the respective rated value at each of the system busbars. Since currently there is no information about the costs of AS provision, we use data reported in the literature (see Hirst and Kirby, 1998). We assume that the costs associated to the provision of AS is approximately 10% of the energy cost, of which about one half is attributable to frequency regulation and voltage control, costs that do not consider the generators opportunity costs. For simplicity, the excess costs for AS will be expressed as a percentage of the declared variable cost to generate energy, estimated as 3% for PFR, 1% for SFR and 1% for VC. The amounts to be financed are obtained from the operation defined by the SO, and they are equal to the amounts of energy associated to the provision of the services valued at the marginal cost of the service, which in Chile represents the additional cost of an extra unit of the service. These induce the agents to reveal the real cost incurred because, in a competitive environment, an artificial 7 Failure statistics are only used to determine the load disconnections, and the additional effects on electricity transmission elements are not considered.

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increase in the revealed cost could let them out of the market. The participation in the financing of the AS provision is obtained from the difference between the end-users energy prices and the monthly average spot price of the simulated dispatch, which amounts to 28.964 mills/kWh. The customized prices have been calculated for the withdrawals for both, unregulated and regulated customers. It should be noted that only one generator withdraw energy for regulated customers (through distribution companies), so it must fully cover the AS costs associated to those withdrawals. During a normal operation, the system faces a demand of 1500 MWh. In the contingency period the outage of a unit with 190 MW of generation is simulated. To estimate the impact of this outage on the system operation we used a 12 months statistical analysis determining that on average a failure of this magnitude cause a drop in frequency to 48.8 Hz and a load disconnection of 119 MW. Thus, failures as this reduce the demand in the malfunction period to 1381 MW (1500–119 ¼ 1381). It is assumed that the failure happens 12 h after the period has started, and then the hourly average generation is 1440.5 MWh (1500 MW  0.5 h+ 1381 MW  0.5 h). Once the generation outage occurs, the SO redefine the dispatch to hold the system security as shown in Table 1. According to SING statistics the quantity of reserve for PFR is approximately 30 MW, where the quantity of reserves required to provide PFR under normal operating conditions is estimated in 15 MW. Also, according to the scheduled PFR reserves and declared costs, the units assigned to provide PFR in normal operating conditions are units U16 and CC1. It is assumed that the proportion with which they respond to the disturbance is 75% and 25%, respectively (see Table 2). To simulate the response of the units during the contingency period, SING SO statistics are used for failures with generation disconnection higher than 150 MW (between December 2002 and 2004). It is estimated that the maximum contribution of PFR from the units, since the moment the failure starts until the minimum frequency is reached, takes on average 10 s. For simplification, it is also assumed that for the rest of the hour PFR requirements corresponds to a normal operation condition and for that purpose are used the same units that were used before the failure. The estimated responses of the power units are shown in Table 2. The second column shows the amount of MW associated with the provision of PFR (real, and measure after the dispatch) for the whole hour in a condition of

Table 1 Units’ dispatch Generation companies

EDELNOR EDELNOR ELECTROANDINA ELECTROANDINA ELECTROANDINA GASATACAMA GASATACAMA AES GENER NORGENER ELECTROANDINA ELECTROANDINA EDELNOR EDELNOR EDELNOR EDELNOR

Unit

Hourly average gross generation (MWh) Normal operation

Operation with contingency

CTM1 CTM3 U14 U15 U16 CC1 CC2 CC SALTA NTO2 TG1 TG2 GMAN GMAR M1AR SUIQ

151.80 190.00 117.39 119.22 232.00 177.88 173.02 235.05 103.64 – – – – – –

151.80 95.00 117.39 119.22 242.00 177.88 173.02 245.05 94.14 5.00 5.00 8.40 4.20 1.50 0.90

TOTAL

1500.00

1440.50

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Table 2 Response of the units to frequency regulation PFR Unit

Normal operation (MWh)

Initial 10 s until the minimum frequency have been reached (MW)

Operation with contingency (MWh)

CTM1 U14 U15 U16 CC1 NTO2

– – – 11.25 3.75 –

3.24 11.01 10.14 25.38 9.10 9.53

0.01 0.03 0.03 11.29 3.76 0.03

TOTAL

15.00

68.40

15.15

Unit

Normal operation (MWh)

Up to rated frequency (6 min.) (MW)

Operation with contingency (MWh)

U16 CC SALTA NTO2 GMAN GMAR

– – 15.00 – –

20.00 20.00 5.80 16.80 8.40

2.00 2.00 14.08 1.68 0.84

TOTAL

15.00

71.00

20.60

SFR

normal operation. The third column measures PFR since the moment when the failure occurs until the minimum value of frequency is reached, that is the point when the units that provided PFR prevent the system from an imbalance that can end in a blackout. The fourth column accounts for the total provision of PFR during the hour with the contingency, that includes: 30 min of normal provision of PFR until the failure occurs (0.5 h), 10 s of PFR according to the third column (up to the point when the imbalance is stabilized, 0.00278 h), and 29 min and 50 s of a normal provision of PFR (the rest of the hour after a normal operational condition is restored, 0.49722 h). A similar criterion applies for SFR in Table 2. According to the SING statistics the quantity of reserve for SFR is approximately 30 MW, which is an amount equivalent to the one calculated for PFR. For the simulated dispatch, it will be assumed that the actual amount associated to SFR is 15 MW. The mechanism will consider that the system’s marginal unit provides the SFR, where also this is the way in which the SO currently defines the unit, which is in charge of SFR provision according to the operating costs minimization criteria. In the efficient SO dispatch the marginal unit is NTO2, so this is in charge of providing SFR during normal operation. In the period with contingency the outage of a power plant decreases supply in 190 MW and demand decreases in 119 MW, so the system should increase its dispatch in 71 MW (190–119 ¼ 71), assuming a linear no dissipative power system, in a statistically obtained period of time of 6 min. As before, it is assumed that during the rest of the hour the SFR faces amounts according to the ones of a normal operating condition and that SFR is provided by the marginal unit. Estimated responses for SFR are in Table 2. The dynamic VC provided by generation units depends on their reactive power production and absorption capacity (primary dynamic reactive reserve), whose maximum values are defined by the unit’s technical characteristics. Thus, to provide VC is possible that one unit will produce or absorb reactive power without incurring in additional excessive costs or without stopping the generation of active power. From a historical analysis of the hourly operation of SING, between October and December 2005, it’s possible to identify that the units in general can provide VC without affecting their active power dispatch. Both opportu-

nity costs and capital costs associated to voltage control can be distributed following the same approach. Thus, if the generator incurs in opportunity cost because of the provision of VC, then the agents’ participation in the financing should be made in the same manner as is done with PFR and SFR. This is also true for the assessment of the opportunity cost—if any—in the case that the generation uses reactive power, but also under the possibility of installation of capacitor banks or other equipment able to perform the voltage control. Therefore, for the experiments it is assumed that the units do not face additional costs associated to VC, and therefore there are no transactions between agents. Nevertheless, if a unit has an opportunity cost, the participation of the agents in the financing should be made in the same manner as for the other two services. In the three experiments considered, the first is the one that best fits the current Chilean conditions because it considers that the prices to end users are fixed in a medium to longterm horizon. The other cases consider prices that fluctuate according to the energy spot price and in the third case also the demand respond in the very short term as a response to price fluctuations. In Case I the prices remain the same under normal and contingency operation. The energy prices to unregulated customers are estimated considering the Market Mean Price (PMM), which also is an average price for a period of 4 months used by the authority as a reference for the regulated end-users price (this is used to calculate both the energy and the power prices). The Market Mean Price is used as a reference because of the lack of information about the prices being paid by free customers. If this information is available it would not be necessary to make this estimate. It should be noted that there is a chance that the Market Mean Price or estimated contract price is under the spot price and, what happened here, is that there is an incentive for the generators to pay the end users to reduce energy consumption, and with that also decrease the AS requirements. The relative relationship between the contracts price and the spot price is decisive, as the spot price serve as a benchmark parameter for the contracts prices. For regulated customers energy price we use the regulated price set in the Authority SING node prices report of April 2005 (35.098 mills/kWh).

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In Case II the prices for unregulated and regulated customers depend on the spot prices fluctuations. The participation in the financing changes respect to Case I and the personalized prices associated to the AS are also different. Case III uses the same prices as Case II, so the participation in the financing is the same. However, here is a reduction in consumption due to the elasticity of both unregulated and regulated customers. For unregulated customers we use an elasticity up to 0.25, and for regulated customers we use an elasticity of 0.05. The reduction in consumption makes personalized prices in the contingency period to be higher, because the financing of the fixed amount of AS must be distributed among a few number of units of energy withdrawn. Table 3 summarizes the results for the three cases analyzed. The column ‘Participation in financing’ shows the participation in the financing respect to the costs incurred with the provision of AS. For unregulated customers, it is calculated as the deviations between the average prices for unregulated customers and the spot price for each agent, standardizing them in a base of 100. For the regulated customers since only one generator sells them, it must cover all the costs of AS for those energy retirements. The separation between unregulated and regulated customers could have also been done between the different unregulated customers of each utility. Nevertheless, as separated information for each unregulated customer was not available, an average price was used in the simulation as representative for all the unregulated customers of each utility. The column Energy Sale Price is the price of the energy to end users (for Case I is the same price in both operating conditions, and in Case II and III it changes because the prices follows the spot price). The columns PP PFR and PP SFR give the AS price that must be paid by each end user according to its energy withdrawal (contingency AS prices are larger because their provision

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increases). For that, in Table 3, the personalized price the agent ‘i’ must pay for the ancillary service ‘AS’ in the hour ‘H’ (PPASH,i), in mills/MWh, is given by the equation CSP H;i  PH Spot   N H;i  PH Spot i¼1 CSP  PN  H H;i i¼1 MC AS Q AS  ,  P  M H;i i¼1 Q W

H;i PPH;i TC H AS ¼ P AS ¼ a

where (aH,i) is the participation in the financing, which is obtained from the difference between the energy contract sales price of that agent (CSPH,i) and the spot price in that hour (PSpotH) normalized to a base of 100. The total unitary cost of the AS in the hour H (TCASH, in mills/MWh) is obtained as the sum of the total payments received by the ‘N’ suppliers of the service, which is a function of the marginal cost of that service (MCASH) and the amount of energy associated to it (QASH,i), and the total energy withdrawals of the ‘M’ agents during the hour (QWH,i). In the simulation only unit U5 presents opportunity costs due to the provision of PFR services, since it reduces its hourly generation in 5 MW. These are distributed among the agents that make withdrawals in the same manner as was done for AS operating costs and, as before, we obtain the customized prices associated to opportunity costs present in Table 4. The opportunity cost is calculated as (spot price–variable cost of the unit)  (rated dispatch power–the actual dispatched power for the period because the unit was involved in the provision of PFR). Contrarily to previous cases, the difference between the withdrawals to unregulated and regulated customers was not made explicit. Energy trades are calculated with the same mechanism used by the SO. For the power trade we redefine the expected power for a generation unit since the current mechanism already considers

Table 3 Participation in the financing, energy sale price and personalized AS price in the spot market Elasticity

Case I Deregulated customers

Regulated customers Case II Deregulated customers

Regulated customers Case III Deregulated customers

Regulated customers PP: personalized price.

0.24 0.11 0.20 0.24 0.25 0.07 0.05

Generation companies

Participation in financing (%)

Normal operation

Operation with contingency

Energy sells price (US$/MWh)

PP PFR (mills/ MWh)

PP SFR (mills/ MWh)

Energy sells price (US$/MWh)

PP PFR (mills/ MWh)

PP SFR (mills/ MWh)

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER GASATACAMA

17.45 10.59 25.93 12.15 23.19 10.69 100.00

34.228 32.158 36.787 32.630 35.961 32.190 35.098

10.953 4.714 10.151 1.277 5.380 3.207 4.245

7.457 3.208 6.910 0.870 3.663 2.182 2.891

34.228 32.158 36.787 32.630 35.961 32.190 35.098

23.527 10.126 21.803 2.743 11.555 6.885 9.117

9.359 4.027 8.673 1.090 4.597 2.740 3.627

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER GASATACAMA

17.30 11.75 24.16 13.02 21.94 11.83 100.00

35.615 33.459 38.276 33.951 37.417 33.493 36.519

10.859 5.232 9.456 1.367 5.090 3.549 4.245

7.393 3.562 6.438 0.930 3.466 2.416 2.891

93.901 88.221 100.920 89.519 98.652 88.307 96.287

23.327 11.237 20.314 2.937 10.934 7.622 9.117

9.279 4.471 8.080 1.168 4.350 3.033 3.627

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER GASATACAMA

17.30 11.75 24.16 13.02 21.94 11.83 100.00

35.615 33.459 38.276 33.951 37.417 33.493 36.519

10.859 5.232 9.456 1.367 5.090 3.549 4.245

7.393 3.562 6.438 0.930 3.466 2.416 2.891

93.901 88.221 100.920 89.519 98.652 88.307 96.287

38.671 13.842 30.157 4.920 18.504 8.593 9.930

15.383 5.506 11.997 1.957 7.361 3.418 3.950

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Table 4 Customized prices associated to opportunity costs Generation companies Case I

Case II

Case III

Normal operation Operation with contingency Normal operation Operation with contingency Normal operation Operation with contingency (mills/MWh) (mills/MWh) (mills/MWh) (mills/MWh) (mills/MWh) (mills/MWh) AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER

142.413 61.287 131.973 16.601 43.887 41.680

617.924 265.919 572.627 72.032 190.421 180.849

141.190 68.020 122.952 17.780 41.524 46.140

characteristics that are associated to the power plant response capacity, such as the start-up time and the load pick-up rate. These characteristics are associated to the security attribute, so was necessary to redefine expected power in a way that only considers the adequacy attribute. Thus, we consider that the power provided by a power plant is calculated as the dispatched average maximum power, adjusted to its probable lack of availability. Power withdrawals are estimated from the year 2005 power balance sheet adjusted to a maximum demand of 1600 MW, obtaining the balances shown in Table 5, where in this and Tables 6–8 negative numbers means that the company has to pay.8 The energy price in the simulated dispatch is 28.898 mills/kWh under a normal operation condition and of 76.192 mills/kWh under the contingency operation condition. The price of power is fixed on a monthly basis and has a value of 7.539 US$/kW-month.9 These prices and the physical balance determine the monetary transactions, presented in Table 6. The AS provision of the companies, the previously defined personalized prices and the estimated withdrawals, determine the AS transaction described by Table 7. With these, total energy, power and AS transactions are described by Table 8. Comparing the companies’ profits under the current pricing mechanism and the one considered in the first scenario (the most similar to the current Chilean situation), is possible to determine the changes in the companies’ profits as in Table 9. With the physical balance of energy and power (where is possible to identify the providers and the sellers) we calculate the firm profits according to the prices in the actual pricing mechanism. Then, we compare it with the proposed mechanism, which consider the same energy transactions, but different power transactions because of the redefinition of the capacity payments. Furthermore, the mechanism considers opportunity cost and AS transactions derived from the personalized prices applied to the energy withdrawals of the agents. Here, the idea of portraying the variations is to illustrate how it would change the distribution of the payments if the proposed mechanism is implemented under the current conditions. The magnitude of the social benefit from its implementation will depend on the characteristics of each electric system and the contingency or conditions it operates. In the other two cases with short-term price flexibility, the existence of a contingency price allows in a better way to follow the value assigned to system security based on the agents’ willingness to pay. The success of a mechanism like these, with short-term price flexibility, depends on the competitive conditions of the sector.

8 9

Source: SING SO Report on transactions valuation, September 2005. The marginal cost used is provided by the node price report, April 2005.

612.619 295.132 533.486 77.149 180.171 200.197

234.068 83.784 182.540 29.781 58.256 52.015

1015.617 363.536 792.030 129.218 252.772 225.693

Table 5 Monthly energy and power physical balances Generation companies

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER

Energy Cases I and II (GWh)

Case III (GWh)

103.37 92.88 140.40 56.20 31.42 63.27

77.19 75.40 98.82 1.90 27.94 70.76

Power I, II and III (MW)

192.31 66.95 123.07 110.06 70.65 67.72

Table 6 Monthly energy and power transactions Generation companies

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER

Energy Cases I and II (US$)

Case III (US$)

2,994,720 2,689,915 4,062,852 1,625,639 909,270 1,832,749

1,479,121 2,689,915 1,861,157 4,647,232 3,171,518 2,500,691

Power I, II and III (US$)

1,449,767 504,738 927,743 829,681 532,578 510,513

7. Conclusions Here we propose a pricing mechanism that considers energy, power and AS transactions explicitly accounting for the public good character of the AS and the value that the customers assigns to the security of supply. The mechanism is applied to the Chilean ESI, an integrated system where the SO has vast authority to dispatch the system and set transfer prices according to declare marginal costs. AS costs are distributed considering their public good character, where we use an assessment of the agents’ value assigned to system security. The value of security was estimated from the difference between the end-users’ energy prices and the spot price or system marginal dispatch cost. A simulated dispatch considering the technical and regulatory characteristics of the Chilean electric systems has been used under two operating conditions and three scenarios regarding the degree of demand response. For the Chilean power industry we propose that capacity payment should only consider the adequacy attribute, where the current capacity payment was redefined, because it unnecessarily considers technical characteristics of the units, which are associated to the system’s security.

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Table 7 Monthly transactions for AS Generation companies

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER

PFR

SFR

Opportunity costs

Case I (US$)

Cases II and III (US$)

Case I (US$)

Cases II and III (US$)

Case I (US$)

Cases II and III (US$)

723 438 1074 2940 262 443

716 487 1000 2904 211 490

491 298 730 342 959 2820

487 331 680 366 924 2787

9423 5718 14,001 47,437 12,522 5773

9342 6346 13,044 46,970 11,848 6390

Table 8 Total monthly transactions for energy, power and AS Generation companies

Case I (US$)

Case II (US$)

Case III (US$)

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER

4,433,851 3,201,107 4,974,790 2,405,285 1,455,592 2,346,658

4,433,943 3,201,817 4,975,871 2,405,811 1,454,831 2,347,355

2,918,343 3,201,817 2,774,175 5,427,405 3,717,078 3,015,297

Table 9 Variation of profits when implementing the proposed mechanism Generation companies

Profits variation (%)

AES GENER CELTA EDELNOR ELECTROANDINA GASATACAMA NORGENER

0.04 2.38 0.24 0.18 0.55 1.99

payments associated to capacity or power without AS parallel markets. The contribution is in defining transfers according to the AS provision that considered the actual costs incurred and the willingness to pay by the agents that withdraw energy at a particular period of time. With the proposed pricing mechanism, adequate signals are delivered to enhance the current pricing system to include AS costs and prices in a more transparent form. Historically, endusers contracts only considered energy and power or capacity as the products, and implicitly AS have been considered a minor component associated to the quality of supply. It is expected that as there is more information available and the ESI becomes more competitive, it will be possible to implement a mechanism as the one proposed here that more closely represents the value assigned to the system’s security. As main lessons of the proposed approach, we have that:

 In general, increased efficiency can be achieved with an

 When we compare the current profits of the companies with the ones they would receive with the proposed mechanism, we obtain changes, which are mostly explained by the redefinition of the capacity payment, where in the particular experiment the payments associated to the provision of AS and opportunity costs are of a lower magnitude. With respect to AS payments, the changes negatively affect the companies that own slower and usually older generation units, and favor the companies that own more diversified and newer technologies. However, when we look up at the total change in the profits received under the proposed mechanism, given by AS payments and the redefined power payment, the company that benefits the most is Celta, which has no ‘new’ units (in fact, the opposite). With the proposed mechanism Celta improves because it is not ‘punished’ with lower power payments. Since the proposal excludes from power payments the attributes associated with security, Celta receives a higher payment for its adequacy. The fact that in the particular case analyzed the proposed mechanism does not ‘reward’ in the total payment, the owners of more efficient or fast units, is only because AS transfers are smaller compared with the redistribution of the proposed power payments. AS payments are small because for the particular failure analyzed where there is enough capacity to provide AS without imposing opportunity costs on the power plants, where we must expect that for a different or larger failure (which sets stricter AS demands on the system) AS transfers must be large. At the end, the proposed mechanism leads to a reallocation of the transactions associated with the system’s security, transactions that before were considered within the









unbundled pricing mechanism, which explicitly accounts for the role of AS. The proposed mechanism separates the incentives for the provision of resource adequacy, energy and AS in a more complete market structure. The public good character of some AS puts a challenge to define personalized prices. Here and following a Lindahl equilibrium concept, we propose an AS pricing mechanism where those who contribute to finance AS are the ones who benefit from the system security at a particular moment. One of the key challenges for the proposed mechanism is in obtaining an estimate of the value assigned by the agents to the security of the system. Here this was made using the difference between selling prices to customers and the spot price as an indicator of the willingness to pay for energy implicit in the contracts. Based on the previous differences, we define personalized prices for the customers who withdrawal energy at a particular moment, what leads to financial transfers to cover the costs associated to the provision of AS. This pull out another challenge, which is the support and rejection that the introduction of a mechanism as the one proposed presents among the different interest groups. Within the context of untying the payments associated with the adequacy and security of the system, we have redefined the current concept of power, so as not to include in the power price calculation attributes associated to the responsiveness of units respect to their capacity to provide AS. For the simulated SING operation scenario, the estimated transfers within the generation companies differs, respect to the current mechanism where are not explicitly accounted. These differences can be explained largely by redistributing transfers associated with system security, which were previously considered within the payments associated with the power, because there was no parallel market for AS.

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