MULTICRITERIA DECISION ANALYSIS FOR ENERGY INFRASTRUCTURE PLANNING: THE STUDY-CASE OF NATURAL GAS IN ITALY

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1 Politecnico di Torino Laurea Specialistica in Ingegneria Energetica e Nucleare A.A. 2012/2013 MULTICRITERIA DECISION ANALYSIS FOR ENERGY INFRASTRUCTURE PLANNING: THE STUDY-CASE OF NATURAL GAS IN ITALY Student: Giulia Forno Professors: Andrea Carpignano Raffaella Gerboni Internship supervisors: Rocco De Miglio Maurizio Gargiulo July 2013

2 Introduction: Natural Gas in Italy In this chapter a brief overview of the Italian natural gas system is given. In 2011 in Italy 184,2 Mtep of primary energy were consumed, of which the 35% came from natural gas. primary electricity 6% renewables 13% Primary energy matrix, 2011 solid 9% oil 37% natural gas 35% Figure 1: Primary energy matrix. (Ministero dello Sviluppo Economico, 2012) Even though in the last years natural gas consumption in Italy decreased, due to the economic crisis, it still remains the primary source for electricity generation, accounting for more than a half of national electricity production, and its consumption has consistently increased over the last two decades Internal production The indigenous production of natural gas in Italy is led by Eni S.p.A, which is the principal owner of the deposit (more than 90% of the total fields). Due to the scarcity of resources the internal production represents actually just a 12% (2012) of the total supply. In the decade a decrease of 53% in production was observed, however this tendency is currently changing and the last two years showed an increase in extraction activities. 13

3 Figure 2: Natural gas production (Ministero per lo Sviluppo Economico, 2012) The wells are small, very fragmented and often located at great depths or offshore, and this makes it more difficult both their search and their exploitation. The most important fields are located in Sicily and offshore of the island, others are sited in Basilicata and Emilia Romagna (the Orsini field, near Ravenna). Proven reserves account for Mm3 and the estimated recoverable ones slightly more than Mm Natural gas market Italy imports almost 90% of the natural gas it consumes, and most imports come from non-eu countries. Natural Gas per origin, % Algeria 2% 0% 9% 11% 29% Libya Russia Quatar 2% 6% 29% 12% Norway Netherlands Others Egypth Nigeria Internal prod Figure 3: Natural gas per origin. (Autorità per l'energia Elettrica e il Gas, 2011) 14

4 From 2003, following the liberalization in the natural gas market, the final user can choose its supplier. The freedom to choose the supplier (obviously linked to the fact that there are more subjects to the sale operating in the area of reference) is guaranteed by the formal separation between different actors in the gas market: Carrier: owns and manages the infrastructure for the transportation of high pressure gas from the extraction place to the entry point into the low pressure network. Wholesaler: is the owner of the gas inside the pipelines operated by the carrier. Distributor: is the owner or more often the handler of low and medium pressure distribution networks (urban networks). Sales companies: own the gas networks in low-pressure and sell this gas to end customers. In summary, therefore, there is a clear separation between the transport operators of the infrastructure and those responsible for the gas sale. The gas contracts are designed to safeguard the parties through a system of guarantees and constraints: clause Take or Pay (ToP) that engages the importer to the supplier to ensure a revenue stream, regardless of the volumes taken, for an amount generally ranges between 70% and 95% of the total value of the contract; duration of between 20 and 25 years; base price of bargaining, depending on the characteristics of the importer; indexation mechanisms and renegotiation linked to the price of a basket of oil products Infrastructures The main import infrastructures will be discussed in Chapter 2.1 Problem structuring, as they constitute the framework from which the decision problem is constructed. Internal gas network is shown in the figure below. 15

5 Figure 4: Italian natural gas network. (Snam) Snam Rete Gas is the main operator for transport and dispatching of natural gas. It owns great part of the national gas network. The principal direction of the gas flow is from South to North, because more than 30% of the supply comes from the South, while consumption is basically more intensive in the Center and North. There is just a 20% of the transport gas flow coming from North to South Natural gas companies Companies can have production concessions, can own and manage the national or regional network, distribute the gas or have concessions for storage. Production companies: the main ones are Edison and Eni. Transmission companies: Snam Rete Gas (owning the majority of both national and regional network), Società Gasdotti Italia, Edison. Distribution companies: the ownership of natural gas distribution facilities remains fragmented, with about 250 companies operating in the sector (this compares, however, 16

6 with over 430 in 2005). The principal operator is still ENI, which controls 22.9% of the market, in terms of distributed volumes Future projects The gas market in Europe has changed considerably over the past few years, due to the demand decrease following the economic crisis, the emergence of shale gas and rise of new energyconsuming countries, particularly China. Even more, there is uncertainty about Russian supplies, which now have the EU countries as the only buyers, but could move to Asia, thus decreasing the quantities exported to Europe. In this context, Italy could be a crossroads for natural gas that passes through the Mediterranean. In spite of the uncertainty currently in Europe there are several pipelines projects under study, some of them involving Italy as well. Figure 5: New gas line projects. (Rotondi, 2012) The destiny of most of them is still undecided: the Nabucco project (South Stream) will probably be abandoned, while it is not clear if ITGI and TAP will be realized or not. Also not all the regasification plants present in the picture will likely be constructed. The National Energy Strategy (Strategia Energetica Nazionale), proposed in 2012, establishes seven objectives, to be reached in 2020: two of them regard the natural gas system directly. In particular, one of the priorities is to promote a more competitive natural gas market, becoming the main South-European hub for gas. This means that imports should be enhanced, to allow natural gas export toward Northern Europe through a flux inversion in the Transitgas pipeline: at the moment the pipeline is not used at its full capacity. The objective is to increase its utilization factor by 17

7 exporting gas to the Central and Northern Europe markets. This would also require to increase imports and to empower the national network in the South-North direction. A second objective of the National Energy Strategy is to promote a sustainable development of national hydrocarbons production, in order to reduce the dependency from foreign countries. The production objectives at 2020 are shown in the figure below. Figure 6: enhancing national hydrocarbons production. (Ministero dello Sviluppo Economico, 2012) 18

8 1. Multicriteria methods Management science s mandate is to improve and support decisions of all kinds by using rational, systematic, and science-based techniques. Several methodologies, which differ for number of decision-makers (from one to N), number of objectives (from one to N) or criteria, and completeness of information (a perfect information leads to deterministic approach, while an imperfect information implies stochastic approach) have been developed and employed to handle complex decision making problems. According to this, one possible classification of these techniques can be presented as follows: Linear programming one decision-maker, one objective, perfect information; this is the case of the cost optimization performed by Pérez and from which this work starts; Stochastic mathematical programming one decision-maker, one objective, imperfect information; Game theory several decision-makers, one to N objectives, perfect information; the aim is to analyze conflicting situations where each of the stakeholders is carrier of a point of view, potentially contrasting with the others, and to find solutions to conflicts through mathematical models; Stochastic game theory - several decision-makers, one to N objectives, imperfect information; Multiple criteria decision analysis (or MCDA) one to N decision-makers, N objectives, perfect information. We focus on this last category. MCDA methodology can be again classified in: 1. Multiobjective is a continuous analysis, where the solution is not given but has to be found among infinite possibilities. It generally includes several decision variables (objectives), to which we give weights, and there might be constraints to respect. The aim is to find the optimum point amongst infinite possible solutions. Multiattribute is a discrete analysis, where solution is chosen among a set of possible alternatives. The attributes (called criteria) and alternatives (called actions), are linked together to find the best outcome, which will be a compromise between different preferences. We use this second approach. It is important to notice that all complex problems are multicriteria in nature, since every decision we take requires a balancing of multiple factors, and a single-criteria problem is just a multicriteria one where one objective (for example cost) has been identified as prevailing and 100% weight is attributed to it How does the method work? MCDA process starts with the definition of the problem: in fact in reality it is really unlikely that the analyst finds itself already provided with a well-defined set of alternatives and criteria. More likely, the MCDA process is embedded in a larger process of problem structuring and resolution. The messy figure below shows the basic stages of multicriteria analysis, underlying the fact that the practice is to move back and forth frequently to modify or revise hypothesis and choices made earlier. 19

9 Figure 7: the MCDA process. (Belton & Stewart, 2002) Once the problem has been defined, in short, the model construction consists in selecting a range of alternatives and a set of criteria, and evaluating the performances of the alternatives over the whole set of criteria. These data is collected into the so called Evaluation Matrix. Then to each criteria is attributed a weight, proportional to its relative importance. Weights have to be normalized to 100%. Finally, to operate on the evaluations matrix, which contains nonhomogeneous data, some of which are qualitative and other quantitative, expressed with different units of measure we need some mathematical functions which make the comparison possible. This is done by the Preference Functions. There are several techniques to assess Preference Functions and calculate the final ranking of alternatives, the most common of which are: MAVT (Multiattribute Value Theory) the intention is to construct a way to associate a real number to each alternative, in order to produce a preference order of alternatives. In other words, we want to associate a value V(a) to each alternative a, in such a way that a is judged to be preferred to b taking all criteria into account. This is true if and only if ( ) ( ) MAUT (Multiattribute Utility Theory) can be seen as an extension of value measurement, including also the use of probabilities and expectations to deal with uncertainties. Outranking once partial preference functions z i (a) have been defined for all criteria, the method, consisting in a pairwise comparison of alternatives, is applied. The method is based on the dominance concept: we say that alternative a outranks b if there is sufficient evidence to justify the conclusion that a is at least as good as b, taking all criteria into 20

10 account. With formulas, a dominates b if z i (a) z i (b) for all criteria, and there is at least on criterion for which z i (a) > z i (b) (strict inequality). We can immediately conclude that a should be preferred to b. This last methodology is the one applied in this study Selection of the tool To support the developing of the study, proper tools to perform the multicriteria analysis are required. Several software were considered and compared: the features of the analyzed tools are reported in the following table: Software 1000Minds Suppor ted MCDA Metho d PAPRIK A Pairwi se Compa rison Tim e Anal ysis Sensit ivity Analy sis Grou p Evalu ation Risk Manag ement Yes No Yes Yes No Web Base d Yes (Only Web Base d) Qualita tive And Quanti tative No User - Frien dly Inter face Analytica No Yes Yes No Yes Yes? Yes Criterium Decisionplus AHP, SMART Yes No Yes No Yes No?? Decideit MAUT Yes No Yes Yes Yes Yes Yes Yes Decision Lens Visual Promethee D-Sight Expert Choice AHP, A NP PROME THEE, GAIA PROME THEE, UTILITY Yes Yes? Yes Yes Yes Yes? Yes Yes No Yes Yes Yes Not Avail able Yet Yes Yes Yes No Yes Yes Yes Yes Yes Yes AHP Yes No Yes Yes Yes Yes? Yes Hiview3 No No Yes Yes No No? Yes Logical Decisions AHP, MAUT Licence Academic Business Professional Education /Student Version Business Academic Business Demo Business Launch Edition Business Academic Demo Online Academic Online Desktop Online Limited Academic Version Desktop Student Standard Yes No Yes Yes Yes No?? Table 1: Software comparision. (Belton & Stewart, 2002) For several of the listed tools free trials were available, so we had the opportunity to test the software before making the choice. The most important features considered are the possibility to 21