Iberian wholesale power market price modelling

Transcription

1 Iberian wholesale power market price modelling Modelado del riesgo de precio en el mercado ibérico de electricidad Pedro Anaya García Proyecto fin de carrera Escuela Técnica Superior de Ingeniería (ICAI) Universidad Pontificia Comillas Madrid Madrid, June 2005

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3 1. Introduction Project motivation Project organization Power markets The traditional electric sector The liberalized electric sector The Iberian power market Congestion management Regulatory problems: CTCs and CMECs Electricity market risk analysis and management Models for electricity market risk analysis Time series models Game theory models The MARAPE approach for power price risk analysis Production cost models The MARAPE model Adapting the model for the new power market The generation side Configuration of the Portuguese hydroelectric system Albufeiras Lima river basin Cavado river basin Duero river basin Guadiana river basin Mondego river basin Tajo river basin Fíos de Agua Duero river basin Tajo river basin Modelling of the Portuguese hydroelectric system UGH grouping criteria

4 6.2 Time series quantitative analysis Available data Parameters that characterize the UGH EDP in the MARAPE model UGH modelling in MARAPE Available data Model parameters Time series analysis of the production of energy Missing data Correlation with rainfall data Principal Components Analysis The multivariate hydraulic scenario generator model Deterministic component analysis Stochastic component analysis: vector autoregressive process Hydro production scenario generation Conclusions General conclusions The Iberian power market and the adaptation of the MARAPE model References

5 1. Introduction 1.1 Project motivation Nowadays there are many electric systems whose regulation is or has been greatly modified in order to liberalize the sector, and thus incentivize the generating firms to take more efficient decisions. Several countries have recently decided to liberalize their electricity markets and to organize competition in electricity generation, assuming that economies of scale and entry barriers in the generation sector were sufficiently small to make competition viable. As these deregulation processes have caused many changes in normative and proceedings regarding the system exploitation and the determination of investments, they have also been the reason for the appearance of new tools aimed to support the decision taking task in the electricity supplying industry. These changes have also reached the generating firms, who have been modifying their operation proceedings and their mentalities. Some examples of the impact of the sector liberalization are the restructuring of the organization of the generating firms, or the operative improvements in such crucial aspects as fuel purchasing or risk management. It will be essential for these generating firms to have new models that are able to represent the new environment and the market behaviour and future evolution. In particular, the focus of this project is on the methodology and tools that are required to analyze the market and simulate the expected market behaviour for a mid-term scope. The project is mainly focused on the fundamental risk analysis models that model explicitly the economical and technical system characteristics. The first fundamental analysis models that were used to study the different electricity markets considered the market as a perfect competition market. The outcome of the analysis undertaken under this assumption was an algorithm that was identical to the cost minimization engineering models that were used to study the traditional systems. Nevertheless, the perfect competition assumption cannot be 5

6 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas held in most of the real electricity markets due to the presence of market power. Generators often succeed in driving up prices significantly above competitive levels, so that prices above marginal production costs have been shown to exist in several markets. Therefore, the effects of a few dominant agents with the capability of influencing the prices have to be explicitly considered and modelled. The traditional fundamental analysis economic models ([Tirole 90]) were designed to enable the calculation of a solution of this type of non-perfect competition markets. However, they were excessively limited to represent an electric system due to the simplifications they required, such as considering no technical restrictions or considering the representation of the agents costs by a continuous curve. During the last years, a new type of models have been developed that can be used to analyze an oligopolistic market, and that they use relatively new and sophisticated mathematical techniques that enable them to take into consideration the main operative restrictions (technical minimums, ramps, etc.) and the economic characteristics (start-up costs, coupling costs, etc.) of the generating groups owned by an electric firm, as well as its strategies. However, as these models are relatively new they are far from being perfect, and a great number of studies are being undertaken to help their improvement and development. Moreover, the constant evolution of the electricity markets and the regulatory changes require that these tools are flexible and that they are able to reflect and integrate the changes in the market environment. Furthermore, there is a clear drive towards liberalization of energy markets in the European Union that forms part of a global process of deregulation. The objective in the EU is to establish an internal energy market, which should cover the electricity industry, as well as the natural gas industry. Risk analysis tools and models have to be prepared to enable the study of these wider and unified markets. As electricity markets are evolving into unified markets, it can be assumed that most of the market variables evolution is correlated to some others, and that past realizations of them may gather relevant information about their future evolution. The key of analyzing risks is to seize as much significant information as possible, which leads 6

7 to the convenience of designing multivariate models that are able to make use of all the information available in order to reach more accurate and trustworthy predictions through this analysis. This drive towards liberalization and unification of electricity markets also affects Spain, whose electricity market was recently liberalized and who is immersed in the unification process of its electricity market with the Portuguese one, so that the MIBEL (Electric Iberian Market, Mercado Ibérico de Electricidad) is created. It will be essential to develop and use these multivariate models in order to be able to achieve an accurate analysis of the new market environment. The goal of this project is to develop a multivariate model that enables a more precise analysis of a multivariate system, so that future trajectories may be predicted for the different variables. It will be studied and developed at the same time as the Portuguese hydroelectric system is modelled and integrated in a model that enables the fundamental analysis of an electricity market where the potential oligopolistic behaviour of the market agents is explicitly considered. This model is the MARAPE model, which has been developed by the IIT and which is described in chapter 4. In order to achieve this goal, the following tasks have to be performed: Study of the configuration of the Portuguese hydroelectric system: main plants, river basin where they are located, technical characteristics, etc. Study of the best way to model and represent the Portuguese hydroelectric system, studying the optimal number of composite representations named UGH (Hydraulic Generation Unit, Unidad de Generación Hidráulica) that should be used in order to achieve accuracy as well as simplicity. Once the optimal number of UGHs is established, the parameters that characterize them in the MARAPE model have to be determined so that they can be integrated in the model that will now be able to undertake analysis on the MIBEL. 7

8 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Finally, an analysis of the time series of the hydro energy production of the Iberian Market, and the development of a multivariate hydraulic scenario generator model. 1.2 Project organization This document is organized in the following way: In chapter 2 there is a brief description of the traditional electric systems, the problems they presented and the main reasons and motivations for the liberalization of the electricity market. The Spanish electricity market is also briefly described in this chapter, as well as the MIBEL. An introduction to risk analysis and management can be found in chapter 3, together with a description of the main models that can be used for electricity market risk analysis and the peculiarities of this type of market. In chapter 4 the basic features of the MARAPE model are described, so that the tasks that are required in order to integrate the Portuguese hydroelectric system in the model can be detected. In chapters 5, 6 and 7 the Portuguese hydroelectric system is studied and modelled so that it can be integrated in the MARAPE model. The system configuration is analyzed in chapter 5, detecting the main hydro plants and their characteristics. Then, after the collection of historical data, the optimal representation of the system is studied in chapter 6. Finally, in chapter 7 the parameters that characterize the optimal representation Portuguese hydroelectric system in the MARAPE model are determined. The Iberian Market s hydro energy production time series is built and analyzed in chapter 8, where the theoretical base for the development of a multivariate hydraulic scenario generator model can also be found. Finally, the project conclusions can be found in chapter 9. 8

9 2. Power markets 2.1 The traditional electric sector The main objective that should be considered in an electric energy supplying system is to supply energy to the consumers in the most efficient way, which it is at the lowest possible cost. In order to achieve this goal, all the different elements involved should be taken into account: generating plants, geographically dispersed consumers and distribution lines. However, if the energy demand is relatively big, it will be technically impossible for a single generating plant to be able to satisfy this demand. Therefore, all the technical constraints should be taken into consideration, including the generators ones (maximum capacity, technical minimums, etc.) and those related to the distribution network. As electric systems started to develop, when they were inevitable of small size, there were two factors (which still affect the emerging electric systems, such as the ones that are being developed in most African countries) that conditioned the mechanisms that were designed to achieve the aforementioned efficiency goal: The presence of economies of scale. In a system of small size, due to the fixed costs of the generation technologies used, the electricity supply total cost will be lower if a unique generator of, for example, 100 MW is used instead of two generators of 50 MW. The existence of economies of coordination. In isolated regions, each area s generating plants will be managed in the best possible way. However, when there are interconnected regions, all the generators have to be considered in order to achieve the most efficient total system operation. Generating plants that were not being fully used in their region could be more economic that those that were being used in other regions, so that due to the interconnection the first ones will be used before than the latter ones, achieving a better total system operation. 9

10 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Due to the necessity to capture these economies of coordination, larger systems were gradually established. At the same time, the electric system developed into a natural monopoly because of the presence of economies of scale, as the most efficient sector organization was achieved with a centralized and strongly regulated approach. The solution that was typically adopted was to have one or few agents that take the investments and system operation decisions, under the strict normative of the corresponding regulation, who determine the system development and exploitation general guidelines and who guarantee a certain retribution, so that the service price was not determined by the generating firms. This was the situation of the electricity supplying industry until the early eighties: a few large national or private firms, strongly regulated, were in charge of the system planning and development. The operation decisions were taken in a centralized way to minimize the total system cost and the investment decisions were taken regarding the total system benefits. However, there are certain inefficiencies in the system: The entity responsible for taking the decisions should have access to perfect information. However, there is an obvious information asymmetry: the system operator cannot know all the relevant data whereas the generating firms are the ones that have the best available information about their own plants. The system s responsible entity s willingness and ability to search for the greatest possible efficiency is also a very important issue. Errors can be made in the decisions taken by the centralized entity, due to the lack of interest or risk management inability, and the final consumers have to pay the extra costs derived from these errors. There are times when the government s roles as regulator and as proprietary interfere with each other. This situation could lead to the introduction of several regulation criteria that modify the sector functioning so that inefficiencies and absurd situations might appear in the mid-term system operation. 10

11 2.2 The liberalized electric sector Until the early eighties, a market didn t exist in the electricity supplying industry. The business was organized as regulated, vertically integrated utilities whose cost was determined by the regulator. The development of technology together with new economic conditionings launched the introduction of competition in the generation industry. New market ideas were gradually established in the electricity sector of several countries, such as Chile, England, the Scandinavian counties, Spain, Australia... As it was previously seen, one of the main reasons to explain the existence of monopoly companies is the presence of economies of scale along the entire range of the demand. In such a circumstance, it is optimal to allow the production of a good to be met by only one firm while it could, on the other hand, exclude from the marketplace some alternative production technologies. As electric systems were larger, the effect of the economies of scale was diminished. As the system s transmission capacity augmented, there were more markets that the generating firms could access. These demand increments allowed the firms to install bigger generating plants, until the economic optimal size was reached and the economies of scale disappeared. Moreover, when we consider a supra-national system, such as a single electricity European market, this argument loses even more credibility as in that situation the demand curve that each national utility will face is not the local demand but the aggregation of the demands from the different countries. As a result, national companies that used to operate under a monopolistic scheme as the cheapest solution can be considered to be in a competitive environment in the unified market, since the total demand size clearly overtakes the supply capacity of any producer. Furthermore, the technological development has also contributed to diminish the effect of the economies of scale, as it has provided opportunities for the new entrants on the electricity market generation. New technologies, like gas turbines, have provided the opportunity to decentralise the power production without any significant losses compared to the centralised system and to invest in less capital 11

12 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas intensive technologies, allowing the appearance of smaller companies that can now manage to survive when they face the larger generating firms. Therefore, the natural monopoly characteristics in the electricity supplying industry have been eliminated and the classical microeconomic theory can now be applied. The basic concept behind the marginalist theory is that in a perfect market (a market made up of many small companies) the result should be the same that in a centralized system with perfect information, so that all the firms behaviour could be represented with just a single optimization problem (an Optimal Power Flow). The marginalist theory was the main impulse behind the liberalization of the electricity supplying industry. The aforementioned problems related to a centralized regulator and that prevented the system to achieve the desired efficiency were thus eliminated. In a liberalized system there are more incentives to achieve economic efficiency, as the firms benefits directly depend on their own decisions. Moreover, if an error is made the consumer will no longer suffer the consequences, as the market will choose the most efficient firms and thus the market agent that committed the error will be the only one affected by its wrong decision. The disappearance of economies of scale has permitted the liberalization of the electricity supplying sector, though not all of the sector activities are included under this statement. Neither the transmission nor the distribution activities can be operated in a competition environment, as it does not make sense from an economic point of view due to the high additional cost that will involve enabling the competition. The optimal size for a generating firm is much smaller than the total generation required, so that many generating firms can coexist and compete with each other in order to supply the total demand. However, the optimal size of a transmission line is very similar to the required transmission capacity, so that generally the coexistence of several lines for a same area is not profitable and there are economies of scale in the transmission and distribution sectors, which thus acquire natural monopoly characteristics. 12

13 The liberalization of the electricity supplying industry has been possible due to the separation of the sector activities. As it has been seen, the transmission and distribution activities inevitably had to remain being strongly regulated so that the advantages of competition can be enjoyed while the technical sector particularities remain being respected. The sector consists of a set of generating firms that compete with each other, backed by a strongly regulated transmission firm as well as several distribution companies that are also strongly regulated, without being any of them able to participate in the market. Therefore, the electricity systems regulation has been modified in a very significant way with the purpose of increasing the sector efficiency through the introduction of competence in the generation and commercialization activities. The change of mentality regarding these two electric business areas has had two immediate and important consequences that condition the behaviour of the agents participating in the electricity market: The decisions are no longer taken in a centralized way, as each firm is now responsible for its own decisions, which have to be taken without knowing exactly its competitors hypothesis and market valuations. The increase of risks, as the relative security that generating companies had traditionally enjoyed regarding their costs recovery is being gradually reduced. Traditionally, generation planning has had as main objective to elaborate a new installations investment agenda. This planning duty used to be carried out by the central Administration, which could make wrong hypothesis and be subject to pressure applying groups that could influence the decisions. Since the market has been liberalized, the planning and management philosophy have experienced a change as the former central cost minimization objective function has been replaced by a profit maximization objective function per each generating firm that participates in the market. Each generating firm has to take its own planning decisions, making its own predictions and hypothesis, with no guarantee of cost recovery and getting used to consider new factors such as: the possible actions the 13

14 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas competitors might take, the uncertainty in electricity or fuel prices, the possibility to win or lose clients, etc. Not only do the generating firms have to operate their plants the best way to accomplish their own profit objectives but they also have to make their bids on the market and estimate the system agents expected production costs and resources capabilities together with their most likely strategies. In the electricity market generating firms can predict their own costs better than anyone else and moreover, they are the only ones that actually know their strategies. When bidding into a dayahead market on an hour-by-hour basis, suppliers do not know the market clearing price in advance. When submitting their initial bid, they do not know if their bid will be accepted. After a first round, the market operator determines and announces the initial dispatch for the following day. During a short period of time suppliers can adjust their bids. After that, the market operator makes the final dispatch determination. Except by bidding a very low price, a supplier cannot be assured that a unit will be dispatched. But a very low bidder might wind up selling power at less than variable cost. A supplier whose unit is not dispatched has lower than optimal capacity utilization and thus higher average costs. However, most electricity markets face one important problem: production capabilities are not identically distributed between all the generating firms and the larger firms can enjoy a certain market power. Size is a very important issue, as firms with significantly larger production capabilities have an additional advantage: they know their own strategies, as it happens with the rest of the firms, but their strategies have a significant impact on prices. A market dominant generating firm could follow a certain bidding strategy in order to maximize its profits that could greatly influence the prices. The two main reasons for market power in the electricity sector are the nonstorability of electrical energy and the low demand elasticity. Both market characteristics make that unilateral withholding of production output can be highly profitable for firms. Especially in periods of peak demand, generators are often producing close to the technical maximum output of their generation plants, which inevitably leads to steep supply functions. The residual demand functions faced by 14

15 the generators are therefore often steep as well. Generation market power becomes even more pronounced because technical constraints in the transmission network do not allow generators to effectively compete with each other. Furthermore, generation plants are capital intensive, and investments require a long lead time. The aim of the market liberalization was to increase the sector efficiency so that this improvement would be reflected in the tariffs paid by the consumers. In a perfect market environment, this efficiency is inevitably achieved as each market agent tries to maximize its profits. However, most electricity markets are affected by market power, which could prevent them from reaching the desired efficiency. One of the possible solutions for this problem is to reduce the concentration in the market by means of the integration of markets and the addition of new medium-sized agents. 2.3 The Iberian power market The MIBEL integrates the Spanish and Portuguese electricity markets in a unified system and represents a very important step towards the economic integration of both countries, in a sector traditionally characterized by both political and economic barriers. The MIBEL represents more than fifty million consumers, who have the opportunity to freely choose their electricity provider, following the Scandinavian countries as a European precedent. The Spanish and Portuguese governments firmly believe that this unified market will allow electricity tariffs to be lowered and will imply the improvement of the overall electric service. Spain is the only country with whom Portugal is exchanging electricity. The net balance shows that Portugal is importing more than exporting and that the electricity imports/consumption relation is also increasing. This fact can be related with the overcapacity installed in Spain and the prices that were during the last years more competitive in Spain than in Portugal. The Pan-European electricity sector is not a reality for Portugal neither for Spain. These two countries are too influenced by the technological constrains and the Iberian market is like an island in the European geography. This means that the competitive landscape for the Spanish and Portuguese players is restricted to the Iberian context. 15

16 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas In spite of the privatization of EDP, few changes have been noticed in the Portuguese electricity market structure, with the electricity system being almost totally controlled by EDP Group. In the present Portuguese electricity market we can see two different systems, the Public Electricity System (SEP) and the Independent Electricity System (SEI). EDP Group, with its different business units, dominates the SEP and has also activities under the SEI. There is no Portuguese pool system. The power generation revenues of EDP are secured by PPAs (power purchase agreements) with the grid for all its generation capacity, which covers the useful life of these plants. In Spain, with the electricity reform the electricity chain was disintegrated with the separation of the different activities (generation, transmission, distribution and retailing), competition was introduced in generation and progressively in supply, and the transmission and distribution businesses stayed as regulated activities. The generation business was liberalized, by which was introduced the liberalization of power capacity construction and the type of fuel used. With this new system the generators started to sell electricity to a pool and the prices started to be set in a competitive bidding process, the Pool. Daily, intra-daily and ancillary services markets were created, jointly with the physical services market. The pool is the spot market for electricity, where generators sell their production and other agents (i.e. distributors, suppliers, other generators and eligible clients) buy electricity. The different market agents can also buy electricity using the bilateral contracts. Generators bid each day the amount and price which they are willing to produce electricity for each hour of the following day (the daily market). The merit order establishes production of electricity for each hour of the day based on price. The pool price for each hour of the day equals the bid price of the last generator scheduled to fulfil demand at that hour (i.e. marginal price). The marginal price is therefore designed to compensate generators for variable cost of production. An intra-daily market will adjust between forecast and actual demand during the day. On the demand side of the pool, all eligible consumers (including distribution, supply and generation companies) will be able to purchase electricity from the pool. 16

17 Under this context the development of the new capacity is liberalized and is not subject to government planning, requiring only authorizations similar to those of any other industrial facility. Also, the type of fuel used is now liberalized compared to the preview situation in which the system operator decided which companies were dispatched and what fuel was burned. However, an incentive to burn certain amounts of domestic coal will be maintained during the Transition Period. A new independent Market Operator (OMEL) was created in Its main function is to manage the liberalized market, establish a spot market, operate the futures market, and assure the timely settlement of payments. The market operator will receive bids from generators and from the demand side, and will establish the merit order for generation, subject to technical restrictions. It will also set the pool price for each hour of the day, based on the prices bid by generators. Finally it will manage the settlement of payments between buyers and sellers. The wholesale electricity market framework can be seen in the following figure. Fig.1. Wholesale electricity market framework Two market operators have to be initially created so that the MIBEL works correctly: the OMIE (Operador del Mercado Ibérico de España, Iberian Market Operator in Spain), which is responsible for the daily transactions or short-term trading and it s based in Madrid, and the OMIP (Operador del Mercado Ibérico de Portugal, Iberian Market Operator in Portugal), which centralizes the long-term electricity purchases and it s based in Lisbon. Two years after the opening of this market, these two operators are 17

18 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas expected to merge into just one market operator, the OMI (Operador del Mercado Ibérico Único, Iberian Market Operator). This event was scheduled to happen in 2006, but changes of government both in Spain and Portugal might postpone it. Both Spain and Portugal have some practical motivations for the creation of the MIBEL, besides their beliefs that their firms will be able to succeed in the new market: For Spain it represents the opportunity to reduce the concentration in the market by adding a new medium-sized player (EDP). Portugal can manage to liberalize the market without having to split EDP, entering a wider relevant market that can provide the reference market price required to implement the CMECs. However, there are several problems related to the transmission and several regulatory issues that have to be settled so that the MIBEL can finally start up Congestion management It has to be considered that in a market where agents have market power and follow a strategical behaviour, the existence of a transmission network could influence their behaviour, as it could represent a new opportunity to take advantage of their dominant position. However, if the network is sufficiently meshed (as it happens in the Spanish case), its influence in the generating firms behaviour could be disregarded, so that the standard microeconomic theory could be applied in that market. Nevertheless, it is not always possible to consider that the network is sufficiently meshed. As it has been previously commented, nowadays the authorities are aiming at the integration and interconnection of electricity markets as a way of increasing the market competition and thus, the efficiency. In these cases the interconnections are usually made up of only a few transmission lines, whose capacity is generally lower than the one desired, so that the transmission capacity through them is very limited, presenting congestion problems on a standard basis. 18

19 Although this problem does not affect the market directly (as the transmission network is a regulated monopoly, apart from the market), it can influence the agents behaviour. For instance, if the interconnection could be easily saturated an area would be isolated from the rest of the system and the generating firms operating in that area would have greater market power. From an economic point of view, the transmission network can be understood as an agent that buys energy in a particular place and sells it in a different one. Then, as the owner is a market agent trying to maximize its profits, the energy will be bought in the places (network buses) where it is cheaper and it will be sold wherever it is more expensive. Therefore, the system operator is an agent that maximizes its profits by buying energy in the cheapest buses and then selling it in the most expensive ones, limited by the network constraints. The system operator will then determine the energy flows that maximize the total profit. Traditionally, in centralized electricity systems, the problem consisted only in solving an optimal power flow so that the costs were minimized. However, in nonperfect competition environments it is very important to keep the transparency conditions that are usual in other markets that enable the appearance of futures and other financial derivative products that confer dynamism and liquidity to the market. Several alternatives to the optimal power flow have thus been designed in order to guarantee the respect of the network constraints and the transparency of the market clearing algorithms. The choice of a market clearing algorithm is determined by the transparency and efficiency conditions, and it depends greatly on the characteristics of the electricity system. Thus, there is not a standard solution which could be universally adopted. Some systems, such as Argentina, perform market coupling by means of an algorithm that incorporates a complete representation of the transmission network. The offers made by the agents feed the optimal power flow so that the result automatically respects the network constraints, manages the resources efficiently and ensures the optimal system operation. However, the drawback for such an algorithm is that it is not transparent. 19

20 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Sweden and Norway represent very clear examples of congestion management using very transparent procedures. In both countries there is a single-bus energy market, and after the dispatching obtained, the transmission constraints are verified to be respected. When a problem is detected, the congestion management mechanisms which are different for each country are applied. In Sweden, the system operator has to solve the congestion by buying energy from the importing area and selling it to the exporting area, so that a counterflow is created that compensates the initial excess of power through the line. This method is called redispatching and its costs are recovered through the transmission tariff. An extension of this internal redispatching method to several transmission systems is the countertrading method. In Norway, the procedure applied is called market splitting, and the system is divided into two areas: an up-stream and a down-stream zone from the congestion, performing the clearing separately for each zone, so that the flow for the line equals the maximum capacity, originating different prices for each area. However, the advantages of these methods are based on the radial characteristics of the network in which they have been implemented. In a more meshed network, the problem is that it is not very easy to determine which nodes are up-stream or down-stream from congestion. Countertrading may be compared to market splitting insofar as both solutions try to overcome physical boundaries to trade. They both take advantage of downstream generation, and reach the willingness to pay of market participants. The principles of these methods are similar but market splitting requires that the European network is covered by bid areas, while countertrading is based on a co-ordination of neighbouring transmission systems. So far, several solutions have been commented in which only one market was taken into account. However, several methods have been designed based on biddings on the transmission capacity. A transmission market is opened so that the agents make offers over the amount of money they are willing to pay for the right to use the lines. The drawback for these methods is that large generating firms, located at one end of a saturated line, could increase their market power. They could acquire 20

21 transmission rights in the saturated line, but without using them, so that the energy imported in the node would be smaller, and thus the price will be higher. When the MIBEL case is taken into account, the same problem appears because of a low capacity interconnection between the Spanish and the Portuguese electricity systems, which were separately sufficiently meshed. In this case, the transmission regulation is essential as a compromise must be taken between the transparency and the efficiency in the market clearing. Nowadays, operating costs are cheaper in Spain and probably the price of a joint single-bus market will be closer to the Spanish price. Therefore, a countertrading mechanism would be good for Portuguese consumers, who would buy at cheaper prices, but it would be bad for Spanish consumers, who would pay higher congestion charges. Obviously, Portugal supports countertrading while Spain supports market splitting, and no solution has been reached yet Regulatory problems: CTCs and CMECs In Spain the CTCs (Costes de Transición a la Competencia) interfere with the functioning of the wholesale market as two generating companies that sell the same amount of energy simultaneously in the same market receive different retribution. The CTCs are stranded costs that were introduced to minimize the risk of lowering their income that generating companies could suffer once the market was liberalized. As the introduction of competition in electricity should lead to lower electricity prices, these lower prices could lead to serious financial difficulties for producers and suppliers who are at present faced with costs based on the situation before competition was introduced. Under the new legislation they will have to grant access to their wires or compete with new entrants to the market that are not faced with these extra so-called stranded costs. They are called stranded costs, because there is no way that an electricity company will be able to recuperate them under a competitive market regime. These costs can take several forms, for instance: 21

22 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Obligations imposed on electricity companies in the past, for instance for social or environmental reasons, leading to extra investments that can not be recovered. Fuel and power purchase agreements with duration beyond 1999, which have been concluded on the basis of the expected price level before liberalization and would be too expensive under competitive circumstances. In Portugal the generators were subject to long-term contracts (PPA), named CAEs. The power generation revenues of EDP were secured by these PPAs (power purchase agreements) with the grid for all its generation capacity, which covers the useful life of these plants. The proposed solution for this problem is to apply the CMECs, the basic idea being that extra payments will be calculated as the difference between the market income and the PPA income. A market simulation model is used to determine the hourly optimal production for each unit and the extra payment will be associated to this optimal production. There are some concerns about the model from the Spanish point of view, as it requires estimations on the prices of Spain and other variable and because it has been designed by EDP. Both of these problems will have to be solved in order to avoid distortions in the market and a regulatory mechanism will have to be designed so that it is consistent with both cases. As the solution for this problem is of a regulatory nature, it is obviously out of the scope of this project. 22

23 3. Electricity market risk analysis and management Risk management explores the limits of the outcomes of the business activity. The only way to yield profits is to optimize risks: it s not a matter of avoiding risks because no risks taken implies no profits obtained, as the risk & return function crosses the origin. There are two ways of considering risk: as a major pitfall for an economic activity or as an added opportunity of business. By management it is understood the process of taking the proper decisions to mitigate the possible injuries or losses we are exposed to and to optimize the return that can be obtained assuming a certain exposure. Risk-management strategies generally use some type of financial or contractual methods to reduce the variability of future costs. Without any risk management efforts, all parties are subjected to cost variations inherent in the marketplace. Risk management strategies include participating in forward markets, vertical integration, horizontal integration, long-term contracting, commodities hedging on the natural gas and electricity markets and, of course, diversification of fuel supplies, suppliers and technologies. By not implementing any risk mitigation options, a party simply accepts the price variance associated with fluctuations in fuel prices and the availability of supply technologies. Each risk mitigation strategy has a cost associated with its implementation. Each strategy can be differentiated with respect to its cost and its efficacy in mitigating risk under a restructured electricity marketplace, and its applicability to user versus supplier risks. The benefit of risk mitigation is, in essence, the avoided cost of exposure to variable electricity costs. These avoided costs are related to the time value of money, the cost of alternatives sources of capital to the party in question (user or supplier), and the value of avoiding certain types of risk. This last component is the most difficult to measure because it varies greatly by individual and circumstances. Active risk management strategies include participating in forward markets, vertical integration, horizontal integration, long-term contracting, commodities hedging on 23

24 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas the natural gas and electricity markets and, of course, diversification of fuel supplies, suppliers and technologies. Suppliers can also use fuel contracts to lock in supplies and prices. The most interesting strategies for generating firms are: Long-Term Contracting. One way of avoiding risk is to structure a long-term contract in such a way as to cause another party to bear the risk. Thus, consumers might enter into a long-term contract with suppliers to provide electricity at a fixed or at least a capped price. Similarly, suppliers who are exposed to the volatile natural gas market can enter into long-term contracts with their gas suppliers. Generally, the party laying off the risk has to pay the party assuming the risk a higher than normal price. Historically, policy makers have been opposed to long term contracting in electricity markets. They feared that long term contracts between incumbent generators and retailers might slow down entry in the generation market. They also assumed that long term contracting would decrease the transparency and the liquidity of the spot markets. There is no theoretical evidence that forbidding long-term contracting will increase liquidity on spot markets. With liquid long term contracts, information becomes public sooner than with spot markets. This might increase liquidity. Long term contracts make spot markets also more competitive; prices will therefore be closer to competitive levels, and less prone to manipulation. Illiquid spot markets would lead to inefficient real time production decisions, and would also make entry more difficult. A small entrant will have to rely on the spot market to balance the difference between the energy sold and the energy produced. Currently, policy makers are changing their mind, and are becoming more favourable towards long term contracts. They hope that long term contracts will ease entry in the generation market by reducing the risk for entrants and will reduce market power in the spot market. Long term contracts will also help retailers who sell electricity at fixed regulated prices to hedge their price risks. Futures Markets. Organized markets exist for both electricity and natural gas. Both futures and futures options are traded for both of these commodities. Futures contracts are offers to buy or sell standardized quantities of the 24

25 commodity at a specified time and place. Options contracts are offers of the option (but not the obligation) to buy or sell futures contracts at a specified price within a specified period of time. Trading strategies which can be used to reduce price instabilities can be developed using these financial instruments. In practice, only a small fraction of futures trades results in physical delivery. In concept, this large volume of trades relative to the physical commodity can be used to cause the underlying commodity price to converge and stabilize by creating a liquid market. This environment change has forced the generating firms to change their mentality and attitude, and to develop and use new management and planning tools. The generating firm of the future has to aim beyond technological excellence, not only has it got to produce more efficiently than its competitors but it also has to be able to sell this production at a good price. The key to success is to be able to manage risk correctly: the firm has to actively control its risk exposure and risks have to be regarded as business opportunities. Price modelling in electricity markets is a very troublesome task because of the complexity of the production process of the underlying asset. The electricity price formation is affected by many complex factors, due to the singular nature of the underlying asset and the particular structure of the recently created electricity market. These peculiarities make electricity an asset hardly modulable and electricity markets hardly comparable with any other market already known. Unlike the traditional assets traded in financial markets around the world, electricity is an asset hardly modulable because its nature does not always remain the same due to the following reasons: The non-storability of the asset, as there is no way to physically store electricity, which therefore has to be consumed at the very same time when it is produced. Some electricity stocks could be thought of in an indirect way, as the fuels from which the electricity can be produced (oil, gas, coal, water, etc.). However, we must be careful with this consideration because, while it is true that they can be 25

26 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas interpreted as energy stocks, there are several constraints and restrictions that enable the production of the desired amount at once. The strong dependence on the season, that takes two forms, one highly uncertain, the meteorology, and another one fully deterministic, the sun cycle. - The influence of the meteorology. On the supply side, in systems with hydro plants, the rain has an evident and direct influence on the production costs. On the demand side, temperature has also an obvious impact, the more extreme the temperature turns (high or low), the higher the electricity demand will be (air conditioning or heating, respectively). An additional and key effect introduced by meteorology is consideration of extraordinary events, such as draughts, flooding, etc. - The sun cycle. The number of hours of light in a day has a direct impact on the electricity consumption, and follows a year cycle as the Earth goes around the Sun. The supply curve, characterized by steep upward slope increments that are due to the diverse generating technologies available, which have their own different costs structures and production processes. The high inelasticity of demand, which combined with the supply curve shape aggravates the appearance of price spikes. The demand curve has been presenting a permanent growth trend, associated to the economic development, and it also introduces a cyclical trend in the price evolution due to the human routine, as it has a changing intra-day behaviour (demand is higher during the day than during the night) and it is also affected by a smaller activity in nonworking days. The many technical restrictions that characterize generating plants, which link costs, and therefore prices, in consecutive hours. This link also depends on the agents different strategies to internalize these costs in the hourly bids. These electricity peculiarities cannot be avoided, but some improvements could be expected in the current and rather inefficient electricity markets in order to allow a 26

27 better financing management. Some of these inefficiencies that will be commented now are supposed to be reduced just with the passing of time, while the evolution of others is much more uncertain due to their dependence on factors such as political decisions. The immaturity of the electricity markets. There is very little historical information and the constant evolution of the markets make these data of little use and the future market structure and limits unpredictable. Most of the current electricity markets are very un-perfect. Market power is a major concern and a big burden that holds back the advance of market efficiency, as the dominant agents have the ability to influence the market price. There are many markets around the world but it is hard to find two of them hardly comparable, so any risk management policy will have to be developed from scrap, with the corresponding slow down of the development of new risk methods and techniques of general use. There are many regulatory mechanisms that interfere in the market development that are often far from well designed and that induce market agents to take advantage of them in a non desirable way (from the total society benefit achievement point of view). There is also a lot of controversy about them, being subject to a high uncertainty as changes are rather common and as they ultimately depend on political decisions. The very first step of risk management consists in the identification of all the risk sources and, once detected and classified, to decide which of them should or want to be considered. Risk is very dependent on the asset or market that is subject to analysis. In our case, in the electricity market, we are going to focus on the market risk, which is the risk associated with market price and its estimated volatility. Its analysis may be focused on the factors that affect the value of the being asset, such as commodity prices, weather conditions... Thus, the first and essential task is to perform a deep and thorough risk analysis, which could be: 27

28 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Quantitative: uses mathematical models and statistical methodologies to study market behaviour. This approach would be suitable for an agent that takes part in the market without any influence in the price formation and with no wish of understanding the ultimate causes of the price drivers. The basic models that undertake a quantitative analysis are the Time series models. Fundamental: tries to understand and describe the behaviour of the market through the identification, measure and analysis of the price drivers. This was the approach chosen for the MARAPE model (which will be explained in the following chapter), so that a detailed study of the price drivers was required. The most common models that perform a fundamental analysis are the Game theory models. The different electricity price drivers, named risk factors, are classified from the point of view of an electricity supplier acting in the market and thus, they are gathered around what it should be the most suitable way of representing them in a fundamental model. Decision variables: those over which the agent has the ability to influence on (e.g. the bidding strategy, financial contracts, investment decisions, etc.). These variables have to be parameterized as they are governed by the ultimate firm s strategic objective, represented for example by its desired risk exposure. Exogenous variables, which can also be divided in two groups: - The measurable exogenous variables take values that follow no correlation with the rest of the variables that characterize the electricity market. Their uncertainty is inherent to the nature of the problem and they should be derived from historic data and supported with heuristic algorithms. This category includes variables such as demand, fuel prices, units outages, interest rates, etc. The knowledge the agent has over the nature of these variables and their impact on prices is a key issue. 28

29 - The non- measurable exogenous variables are those for which it does not make sense to associate a stochastic function, e.g. regulatory changes, market agents mergers, etc. Once all the information is gathered and the risk sources have been detected, classified and analyzed, it will be the time to design the right model. 3.1 Models for electricity market risk analysis As the electricity markets were gradually changing, both economic and engineering science put their eyes in the study of the new electric environment, and both are trying to take advantage of their former experience. The challenge is to adapt the models designed until now for other markets, in the case of economists, and for a regulated environment, in the case of engineers, so that the peculiar electricity markets may be analyzed with them. Ideally, the model would have to be able to derive the price evolution from all its possible risk factors, and it should be able to represent these last ones in detail. It may be assumed that the price is made up of two basic components: A deterministic short-term component where uncertainty is studied by means of econometric and statistic tools, without minding about the causes that are behind the price evolution. A long-term component that reflects the strategic interaction between the different agents, which is essential in a non-perfect competition environment and requires studying the agents behaviour. As a detailed representation of all the risk factors would require an enormous effort, the existing models have to simplify some aspects and are therefore classified and described attending to the way they handle the analysis, from the more general but less detailed approach, the Time series models, to the more particularized but more detailed one, as Game theory. The MARAPE model faces the matter of analyzing the electricity market taking Production cost models as a starting point, as it will be explained in the following chapter. 29

30 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Time series models Time series models were the first models considered, and they faced price evolution analysis without minding about the causes behind it, applying mathematical statistics and tools of statistical inference to the empirical measurement of the evolution of prices. This quantitative analysis is the only available approach for many market agents who are not able to gather enough information. This approach constitutes a rather simple characterization of the stochastic properties of prices, as no additional conjectures about market relationships are made, and therefore the consequent modelling risk is avoided. In a fundamental model, specific assumptions about the market relationships have to be made, which requires a deep knowledge of the market, introducing a significant modelling risk, as the price projections generated by this approach will be very sensitive to violations of these assumptions. Time series models are appropriate tools to compare small and short-termed transactions that require fast evaluations. However, when an electricity generating firm faces a market risk analysis, just the characterization of market prices is not enough, as it is also interested in studying its portfolio future performance. Moreover, due to the immaturity of the electricity market, it seems that the past history of electricity markets gathers just a little proportion of the future phenomena that are likely to take place. There is very little historical information available, and as markets are still under constant evolution and market agents have not enough knowledge of the future market evolution and its limits and dependence on several uncertain factors such as political and national strategic interests, these data may be of little use. These models are not enough for electricity price analysis, as there are some aspects that the quantitative approach is not able to cover. Nevertheless, they will be helpful as a reference to other approaches, as they can offer the limit assuming that historical data summarize what can come about in the future and that the market is liquid and without market power. In order to cope with the aspects that a quantitative approach is not able to cover, rather than undertaking a stochastic analysis of electricity price directly, the spot price is expressed as a function of two drivers, load and supply, under the assumption that all the relevant factors can be related in some way to them. This 30

31 step to fundamental analysis allows the modeller to consider possible cases that not necessarily could be found in past history prices. Introducing the idea of price sensitivity to drivers allows the model to consider situations that may not have happened in the past, as these drivers could adopt extreme values at the same time. This kind of models are very convenient to analyze immature markets as it is the electricity market as it is possible to gather good information about the price driver, even if the context could have changed Game theory models Game theory models are the most commonly used models to undertake a fundamental risk analysis. In a perfect competitive market there is no need to care about the interaction between agents, as no agents strategy has a significant effect on price. However, in markets where there are few agents involved, such as the electricity market, the returns obtained will depend on the agents actions. In an oligopolistic market, the agent is no longer in a passive environment, so if the price wants to be derived from its drivers, a representation of the players strategies is required. Game theory studies the behaviour of agents facing decision problems in economic environments. The oligopolistic behaviour is modelled as a non-cooperative game in which each agent acts moved only by its own interest. The Nash equilibrium is the basic concept in game theory. A set of actions is considered a Nash equilibrium if, once the rivals actions are known, an agent cannot increase its own benefit by choosing a different action from the equilibrium one. The Nash equilibrium is generalized for dynamic situations (where the decisions taken in a particular period affect the objective function and the possible choices of the following period) and for problem of imperfect information. One of the areas in game theory analyzes strategic market equilibrium, meaning the set of strategies such that no player can improve its position if the other players hold their strategies on (Nash equilibrium). Strategic Market equilibrium models try to find the agents strategic equilibrium outcomes. When a market cannot be considered fully competitive or a perfect market due to the fact that one or more 31

32 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas agents have in some way market power, prices corresponding to this equilibrium do not reflect marginal costs since the dominant agents take advantage of their position. However, this approach also has its drawbacks. These models provide a more qualitative analysis, as they give good hints about whether prices might be above marginal costs and how this might affect agents outcomes. They are very useful to improve the agent understanding of the market peculiarities but they don t provide a valid solution if a more quantitative analysis wants to be undertaken. There are some other drawbacks related to the search of the consistent equilibrium and the lack of calculation speed. Equilibrium and risk do not appear to be very harmonized, as the first one leads to a single and stationary point while the latter one tries to cope with all the possible points, the most common and the least ones. In order to develop a complete risk analysis based in this equilibrium methodology a lot of work is being done to manage to introduce stochasticity in these models, so that the risk strategy would be determined under the assumption of not only one single context. This is a troublesome task, as it requires tough model designing processes as well as the development of complicated decomposition techniques to make the optimization time affordable. Risk analysis requires market models to be able to provide prices in very little time, as there are a huge number of cases that need to be considered. 32

33 4. The MARAPE approach for power price risk analysis 4.1 Production cost models As it was mentioned in the previous chapter, the MARAPE model faces the matter of analyzing the electricity market taking Production cost models as a starting point. Production cost models (PCM) have existed for more than fifty years and have been intensely used in the electricity field ever since. These models were primarily designed to calculate generation production costs, fuel consumption and energy exchange requirements. Probabilistic models were developed and integrated in order to take into account the uncertainty of future demand levels and the failures of generating units. They were used initially for system planning but the scope was widened later for analyzing the effects of load management, fuel shortages and reliability. PCM take into account the generating units efficiency characteristics, including fuel costs per unit of energy supplied as well as a representation of economic scheduling and dispatching of the units in the system. Future energy costs are computed through expected load modelling and simulation of the operation of the generation. The results obtained are the system price-duration curve as well as the income and expected costs of the units in the system. These models provide an excellent balance between real electricity system representation, flexibility and calculation speed, what makes them a meaningful approach for risk analysis. PCM were used as tools for electricity systems analysis. Under the word system, an idea of a centralized market was hidden, so that decisions were assumed to be taken in a unified way. However, as it was previously mentioned, the centralized electricity market has been recently replaced by a liberalized market where decisions are no longer unified, so that additional complications have to be handled in order to be able to model the new electricity market framework. In oligopolistic markets as it is the case of most of the present electricity markets big-sized firms enjoy market power as they are able to withhold part of their generation to raise prices at a higher level than the corresponding to marginal costs. As a realistic 33

34 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas model cannot be just cost-based anymore, a way of modelling and parameterizing this oligopolistic effect has to be found. With this aim, an original evolution of the Production cost models was developed: the MARAPE model is a Strategic production cost model, that represents the commitment of the electricity production plants based in the strategic bids of the market agents in an oligopolistic market, and which will be explained in the following chapter. 4.2 The MARAPE model Decision-making and risk analysis in this new competitive environment require powerful simulation models to assess alternatives and evaluate risks. These models have to be able to represent adequately all the peculiarities that distinguish a real electric market from other commodity markets. With this aim, the MARAPE model has been developed to analyze electricity market risks and their impact from the point of view of a generating company. It is a tool that can be used to study the new market environment, taking into account not only the traditional sources of risk (demand growth, fuel prices, hydrology, etc.) but also the different strategies that the agents could follow and their impact on the evolution of prices and profits. The MARAPE model is a strategic competition electricity market model where uncertainty is simulated via scenario generation. The future electricity price distribution is obtained by feeding the MARAPE model with scenarios representing the possible realizations of these relevant risk factors, considered to be independent of each electricity agent s behaviour. Basically, the design of a tool to analyze prices in any market is characterized by three main features: the supply model, the demand model and the representation of the way both sides interact in the market. The modelling of the latter two, demand and agents strategies, can be derived almost univocally from the representation of the supply side. So far, the MARAPE model has been used as a tool to support the longer-than-short term decision-making task, analyzing the Spanish electricity market risks and their impact from the point of view of a generating company. However, as the MIBEL (Mercado Ibérico de Electricidad, Iberian Power Market) has been recently created, the 34

35 Gas Oil 0.2%S CIF Eu rope North We ster n E urop e (GO-CE- NW E) His tori cal dat a Sa mp le d pa th 25/I/95 29 /I II/ /II I/ model needs to be updated with the Portuguese electricity system data so that a complete analysis can be achieved. The model general framework, illustrated in Fig.1, consists in a set of independent and interrelated scenario generators, allowing the development (e.g. new data updating) of each of them according to the market evolution. GEDA z e l m SCENARIO GENERATORS GECA w z yt, y GEHA z h h m Market information Agents strategies MARKET MODEL Strategic PCM SCENARIO ANALYSIS Fig.2. Model general framework The tools developed are the load scenario generator, named GEDA (Generador de Escenarios de Demanda Aleatorios), the fuel prices random scenario generator, GECA (Generador de Escenarios de precios de Combustibles Aleatorios) and the hydro scenario generator, GEHA (Generador de Escenarios Hidráulicos Aleatorios). The aim of the model is to derive the electricity market price from the evolution of its drivers, which are modelled quantitatively through time series processes. Thus, it allows to analyze combinations that may not have occurred so far and to integrate possible structural changes in the electricity market (e.g. MIBEL, MDE, CTCs, etc.). Each generating market analyzed with this model has to be modelled explicitly as well as its production process, taking advantage of all the agent s available data (costs, regulatory mechanisms, agents strategies), which requires a detailed process of gathering and elaborating data so that a further understanding and awareness of the market are achieved. 35

36 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas The most relevant risk factors in this model (load, fuel prices and hydro energy) are characterized by a high degree of complexity, so that each of them has to be represented through a multidimensional stochastic variable. To model these variables, complete paths based on their historic evolution are generated decomposing the generation process in two stages: First, time series theory is used to model an aggregated representation of the variables as a function of time. The time series characterized in each scenario generator are: - In the GEDA, the monthly load in the whole market. - In the GECA, the weekly prices series of all the indexes considered as base fuels. - In the GEHA, the series of hydro energy produced monthly in the whole system The second step deals with getting the final multidimensional matrices of the risk factors that will feed the market model. In this final stage some heuristics based on the actual peculiarities of each risk factor have to be designed, taking advantage of the know-how of the firm and making the best possible usage of the few data available. 4.3 Adapting the model for the new power market The MARAPE model is going to be used to analyze the MIBEL, which it will imply that the Portuguese electricity system has to be modelled and integrated with the former model so that a proper and complete analysis can be carried out, studying the impact of the MIBEL creation and comparing the MIBEL electricity price to the former electricity prices in Spain and Portugal. In order to include the Portuguese electricity system in the model, there are three tasks that have to be performed: the modelling of the Portuguese generating system, the demand evolution and the interconnection capacity impact. The aim of this project is to focus on the first aspect. 36

37 4.3.1 The generation side The MARAPE model classifies the generating plants into three groups with a different modelling approach for each one: thermal, hydro and base load plants. Thermal plants. While the term thermal is related directly to the type of fuel from which electricity is obtained, it will be understood that a generating plant is a thermal plant as opposed to a limited energy plant (LEP), which is the term typically used for hydro plants. These thermal plants are characterized by just two constant parameters, so it only requires a data gathering process: - The capacity constraint of the thermal plant - The variable cost of the thermal plant Hydro plants. Modelling hydro plants is an extremely challenging task as the input-output characteristic curve is very complex just for one single plant, without even taking into account the fact that storage plants are usually interconnected up and down-stream (in both series and parallel), introducing spatial and time-linking constraints. A simplified representation of the hydroelectric system is required in order to reduce the complexity of this analysis. Therefore, groups of hydro plants are resumed through composite representations named UGH (Unidad de Generación Hidráulica, Hydraulic Generation Unit), according to several criteria which will be discussed later. Base load plants. These plants represent the fact that part of the generation park is modelled as a base generation, which is not offered in the normal pool market. Under this idea of base generation the following concepts can be included: long-term contracts, the contract with France, the renewable electricity production, etc. The study of the Portuguese hydroelectric system will take up the first part of the project, obtaining the optimum number of UGH needed and the parameters by 37

38 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas which they are characterized. The REN (Rede Electrica Nacional) and the Portuguese Water Institute web pages have been used in order to obtain the required data for this analysis. Another aim of this project is to develop a multivariate hydraulic scenario generator model, using the historic production data of all the UGH included in the MARAPE model, so that a more exact hydro energy production prediction can be achieved for each UGH. 38

39 5. Configuration of the Portuguese hydroelectric system The main difference between a hydro plant and a thermal one is that it is subject to an energy constraint, as it is not possible to produce at maximum power any time desired. On one hand, the ability to obtain water provisions is limited and often unforeseen. Water cannot be bought to meet one s needs, as there is no market to buy water unlimitedly. The solution is therefore to manage wisely the water inflows that are provided naturally. On the other hand, this managing capacity is also subject to many constraints. Ideally, the entries to study the hydro system in an electricity market model should be the water inflows expressed in volume units. The model should then be able to calculate for each period the equivalence between these inflows and the available energy, and it should also be able to represent the reservoir management capabilities. In order to proceed this way, an explicit representation of the capacity of the reservoirs would be required, as well as a stochastic programming algorithm. However, while in a thermal plant a coefficient relating the quantity of fuel burned and the electricity obtained from it can be easily measured, the input-output characteristic curve, which expresses the energy that can be generated from a water inflow of one volume unit, for just a single plant is very complex and non-linear as it depends on the vessel of the reservoir and even on the quantity of water released. Reservoir management has not been taken into account since it exceeds the scope of this model, which has been developed to make analysis, not management. By analysis we mean the study of the consequences of the previously detected as possible injuries or losses assuming that no future action is taken in order to mitigate their undesirable effects while by management it is understood the process of taking the proper actions to do it. Therefore, the hydro model entries considered will be the hydro plants energy production in each period instead of the corresponding water inflows. There are some reasons to justify this decision: - First of all, there are some practical reasons as the design of each reservoir would be of such complexity, that it would be hardly possible to carry out an analysis of real size electricity markets. Risk analysis requires running many 39

40 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas scenarios, so some simplifications have to be made to manage some calculation speed. Also, the aim of the model is to face a medium-term analysis (e. g. one or two years) divided in periods covering several weeks of a system where the hydro system is mainly configured by hydro plants without multiannual regulation capacity (the Spanish electricity system). In this context, we expect most of the hydro reservoir managing policies to be rather constrained by the annual reservoir level goal, what implies that an annual or biannual scenario of inflows could not be managed in very different ways. - There is also another practical reason. Building hydro production scenarios is possible since often it is easier to have historical data at one s disposal (resulting from the market schedules), which is not the case with data of inflows. Due to the aforementioned complexity, it is required to consider simplified representations of the hydroelectric system in order to carry out medium and longterm analysis, as it is the aim of this model. Hydro plants are usually interconnected with other plants up & downstream (in both series and parallel) which introduces spatial and time-linking constraints. Therefore, a group of hydro plants set in a river basin and operated by the same firm is modelled through a composite representation, named as hydro units (UGH). In fact, this aggregation is formally established in electricity markets, where groups of storage plants bid as a single agent. It has been proved that, when dealing with composite representations of the hydroelectric system, the calculation of a proper expression of the input-output characteristic curve and of the reservoir levelmaximum capacity ratio required to deal with water inflows as model entries, is very complex and it requires performing many arguable simplifications. These simplifications are often based on low quality and raw data that finally leads to mistrust in the model results, so the entries for the hydro model will be the historical hydro production data for each composite representation or UGH of the hydroelectric system. 40

41 The first step that has to be taken is to study the configuration of the Portuguese hydroelectric system in order to be able to obtain the UGHs that will have to be introduced in the MARAPE model. The Portuguese hydroelectric plants are going to be studied according to the classification proposed by R.E.N. (Rede Electrica Nacional): Albufeiras Fíos de Agua The different plants will be grouped according to the river basin they belong to, with additional information about the following concepts: River where de plant is located. District to which the plant belongs. Hydro plants up-stream. Hydro plants down-stream. Electric power installed. Annual energy production in an average raining year. 41

42 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas 5.1 Albufeiras Lima river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Alto Lindoso Lima Viana do Castelo N/A Touvedo 2 X 317 0,97 Touvedo Lima Viana do Castelo Alto Lindoso N/A Fig.3. Lima river basin ALTO LINDOSO TOUVEDO Fig.4. Lima river basin structure 42

43 5.1.2 Cavado river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Alto Rabagao Rabagao Vila Real N/A Venda Nova Paradela Cavado Vila Real Alto Cavado Salamonde 426 m 253 Vila/Venda Nova Rabagao Vila Real Alto Rabagao Salamonde Salamonde Cavado Braga Venda Nova/ Paradela Canicada Vilarinho F. Homem Braga N/A Ruaes Canicada Cavado Braga Salamonde Ruaes 60(2) 346 Fig.5. Cavado river basin 43

44 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas ALTO RABAGAO ALTO CAVADO VENDA NOVA PARADELA SALAMONDE VILARINHO CANICADA RUAES Fig.6. Cavado river basin structure Duero river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Tabuaco Távora Viseu Bezelga Regua 2 X The Duero river basin figure can be seen later in Fig.12 44

45 5.1.4 Guadiana river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Alqueva Guadiana Beja Otros Pedrogao ND ND Fig.7. Guadiana river basin 45

46 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Mondego river basin Name River District Up-stream Down-stream Generator Power [MW] Annual Energy [GWh] Caldeirao Caldeirao Guarda N/A N/A 36 ND Aguieira Mondego Coimbra Otros Raiva 270(3) 237 Raiva Mondego Coimbra Aguieira Ponte Coimbra 20(2) 49 Fig.8. Mondego river basin AGUIEIRA CALDEIRAO RAIVA PONTE COIMBRA Fig.9. Mondego river basin structure 46

47 5.1.6 Tajo river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Cabril Zézere Castelo Branco Otros Bouca 97(2) 330 Bouca Zézere Leiria Cabril Castelo Bode 50(2) 165 Castelo Bode Zézere Santarem Bouca N/A 139(3) 429 Pracana Ocreza Castelo Branco Otros Fratel Fig.10. Tajo river basin CABRIL PRACANA BOUCA FRATEL CASTELO BODE Fig.11. Tajo river basin structure 47

48 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas 5.2 Fíos de Agua Duero river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Miranda Duero Braganza N/A Picote 174(3) 880 Picote Duero Braganza Miranda Bemposta 180(3) 1038 Bemposta Duero Braganza Picote Pocinho 210(3) 1086 Pocinho Duero Guarda Bemposta Valeira 186(3) 534 Valeira Duero Braganza Pocinho Regua 216(3) 801 Regua Duero Vila Real Valeira/ Tabuaco Carrapatelo Carrapatelo Duero Viseu Regua Crestuma Crestuma Duero Oporto Carrapatelo/ Torrao N/a 105(3) 399 Torrao Tamega Oporto Otros Crestuma 146(2) 228 Fig.12. Duero river basin 48

49 MIRANDA PICOTE BEMPOSTA TABUACO TORRAO POCINHO REGUA VALEIRA Fig.13.Duero river basin structure CARRAPATELO CRESTUMA Tajo river basin Name River District Up-stream Downstream Generator Power [MW] Annual Energy [GWh] Fratel Tajo Portalegre Otros Belver 130(3) 379 The Tajo river basin figure can be seen in Fig

50 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas 6. Modelling of the Portuguese hydroelectric system 6.1 UGH grouping criteria Once we have studied the characteristics of the different hydro plants in Portugal, it is necessary to group them in UGH (Unidades de Generación Hidráulica) in order to simplify the analysis and to be able to study them in a more comfortable way that enables us to introduce this information in the MARAPE model. The first obvious criterion for grouping the plants is the geographic proximity (as rainfalls will be quite similar) and, especially, the river basin the hydro plants belong to. According to this criterion and as we have previously seen in the configuration of the Portuguese hydroelectric system, there are five possible UGH: Lima, Cavado, Mondego, Duero and Tajo. By means of a comparative study of each UGH s weekly energy production it can be noticed in the following graph that the UGH Duero s energy production accounts for most of the total energy production. Thus, there is a dominant UGH, which could be a very important factor in this analysis as it will be seen later Duero Lima Cavado Mondego Tajo GWh Weeks Fig.14. Weekly energy production for the different river basins Following up with this geographic criterion, we study the possible correlation between the historic weekly energy production series of UGH that are geographically close to one another: Lima Cavado 50

51 We obtain a correlation coefficient of 89.5%, so it makes sense to group them together under one UGH as they have behaved historically in a very similar way, as it can be seen in the following graph Lima Cavado 70 GWh Weeks Fig.15.Weekly energy production of the UGH Lima and Cavado Lima Cavado Mondego Although according to the geographical proximity criterion it would make more sense to try to include the UGH Duero together with Lima and Cavado, as the time series correlation studies are performed it s discovered that the UGH Mondego has a very similar historic behaviour to those of the UGH Lima and Cavado, with correlation coefficients of 90.14% between Lima and Mondego, and of 79.16% between Cavado and Mondego. Thus, these three UGH are grouped together as a single UGH that it will be named UGH Norte. The correlated behaviour of the three UGH can be seen in the following graph Lima Cavado Mondego GWh Weeks Fig.16. Weekly energy production of the UGHs Lima, Cavado and Mondego. 51

52 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas Duero Tajo A time series correlation study shows that these two UGH have a very similar historic behaviour, as we obtained a weekly energy production correlation coefficient of 88%. Therefore, it makes sense to group them together under just one UGH that will be named UGH Sur. The correlated behaviour can be seen in the following graph Duero Tajo GWh Weeks Fig.17 Weekly energy production of the UGH Duero and Tajo By means of the geographic proximity criterion we have managed to group all the Portuguese hydro plants under just two UGH: Norte, where all the hydro plants belonging to the basins of the rivers Lima, Cavado and Mondego are included, and Sur, under which the hydro plants located across the Duero and Tajo river basins are grouped. However, there is a very important characteristic of the Portuguese hydroelectric system that is essential when it comes to determine the final number of UGH that will be included in the model: the fact that there is just a single owner of all the hydro plants in Portugal, which is EDP. This is very important from a strategic point of view, as all the hydro plants will be managed according to the same company strategy and objectives. Moreover, it would make sense to group all the hydro plants under a single UGH provided that they were sufficiently correlated. Studying the correlation in the historic time series between the previously obtained UGH Norte and Sur, we obtain a correlation coefficient of 85.73% for the weekly energy production and one of 76.96% for the weekly maximum power. 52

53 Sur Norte GWh Weeks Fig.18.Weekly energy production of the UGHs Norte and Sur Sur Norte MW Weeks Fig.19. Weekly maximum power of the UGH Norte and Sur. Taking the different criteria into account, we can conclude that the Portuguese hydroelectric system can be perfectly represented by just a single UGH, which will be denoted as UGH EDP. A high correlation in the historic production profiles supports the single ownership fact, so that grouping all the hydro plants under the UGH EDP makes perfect sense from a strategic point of view, as well as it simplifies the data gathering process. 53

54 Instituto de Investigación Tecnológica (IIT) - Universidad Pontificia Comillas 6.2 Time series quantitative analysis Available data A data gathering process was needed in order to study the configuration of the Portuguese hydroelectric system and to analyze how to group the hydro plants in UGHs. Through the official website of R.E.N. (Rede Electrica Nacional) we can obtain data for the daily activity of all the hydro plants mentioned before. Daily maximum power and daily energy production data are obtained for each hydro plant from the REN_HID_DIARIO files, using this data to obtain the weekly data for each hydro plant for the years available at the R.E.N. website. Fig.20. Files used to obtain each hydro plant s daily data Afterwards all these hydro plant s time series were grouped in different UGH following the criteria that were previously mentioned. The UGH correlation studies were made with these time series, enabling us to reach the conclusion that the Portuguese hydroelectric system can be introduced in the MARAPE model as a single UGH, which will be called UGH EDP. For the elaboration of the UGH EDP time series we used other available data, as it will be explained in the following point. 54