How simulators can be useful in scenario analyses assessing the impact of network constraints on market prices M. Gallanti, G. Migliavacca CESI RICERCA CESI RICERCA Electricity market performance under physical constraints - September, 25th 2007 Index • Introduction of the Italian market (IPEX): the PUN and its effects • Impact of congestion on market efficiency and market power • Effects of increasing transmission capacity between zones • Aims and methodologies of market simulation • SREMS: a tool to investigate strategic competition CESI RICERCA The Italian day-ahead electricity market • The Italian Power EXchange (IPEX) is operative since April 1st, 2004 and features three physical markets (day-ahead, adjustment, ancillary services). • The day-ahead is zonal, structured in 22 zones (7 geographical, 6 limited production poles and 9 border virtual zones). Market splitting is used for congestion management. • It is a non-compulsory (mixed) market: both spot market and bilateral physical contracts are allowed. CESI RICERCA The Italian day-ahead electricity market • Zonal sell prices in the IPEX in the last year Source: GME monthly report - July 2007 CESI RICERCA The Uniform Purchase Price (PUN) • Unique feature: generated power paid at the zonal price, while purchase price is uniform on the national territory (PUN = Prezzo Unico Nazionale). The PUN is obtained as an average of all zonal sale prices weighed with the purchase volumes. Q p PUN Q Cz z z Cz z Inter-zonal transmission constraints respected? Calculation of energy exchanges between zones Sale and purchase bids CESI RICERCA Yes National price No Separation of the market in zones Injection and withdrawal programs compatible with the constraints • zonal sale prices • uniform purchase price Some important side-effects of the PUN • The PUN destroys any locational signals given to the load (while they remain on generation). • With the PUN the concept of “congestion rent” becomes opaque and difficult to understand by market operators. • The grid structure and power plants distribution among generating companies in the different areas may determine market concentration in some areas. – Local Market concentration may turn out in local market power – In Italy the incumbent generation share is very significant in the South • The PUN introduces further incentives to exercise local market power: – Suppose a zonal system with one exporting zone (A) and one importing zone (B). Demand elasticity and competition would act discouraging GenCos from raising prices in B, even if pivotal: a GenCo that is not monopolist in B by raising prices would give up part of its production in favor of its competitors, with a possible loss of profits. – The PUN weakens this effect: loads in B pay the PUN, that is lower than the relevant zonal price. – The greater the demand in A wrt B, the weaker the influence of zonal price in B onto the PUN. CESI RICERCA Effects of network congestion on prices • Since the opening of the Italian market, CESI RICERCA has studied the effects of network congestion on the electricity prices • The economic impact of the unavailability of the Matera – S. Sofia line was assessed through a market simulator (fall 2004) . Price duration curve (simulated) in the South zone without the Matera - S. Sofia line CESI RICERCA Effects of network congestion on prices Price reduction duration curve (simulated) in the South zone due to the presence of the Matera - S. Sofia line. – The availability of the Matera – S. Sofia line made it possible to increase the the transits capacity among the zones The return for the consumers would have been an annual cost reduction of 40 M€. The total cost of the Matera - S. Sofia line was estimated around 100M€. CESI RICERCA Congestion and social welfare in zonal markets NC NG i 1 j 1 SW QCi PoffCi QGj PoffGj Load offer prices are indicative of the value of the goods produced Generators bid prices should be indicative of the costs for producing energy • The merchandise surplus corresponds to the congestion rent extracted by the TSO. • The dead-weight loss is an indicator of efficiency loss in the market due to congestion. Congestion means lower market efficiency CESI RICERCA Effects of an increase of interzonal capacity • Increasing the capacity of congested tie-lines decreases the dead-weight loss and increases the social welfare. The market solution is more efficient. • However, the increase of p p B A social welfare (A>B) is not necessarily matched with a reduction of loads payments. In this example: p3 p2 p1 Export zone CESI RICERCA q Import zone q – if pure zonal market: loads in the export zone pay price p2 >p1, those in import zone pay the same; – if PUN is introduced: all the loads pay more (the weighed average grows). Congestions and market power • Market power can be exercised both at global (market) level and locally. • Textbook definition of local market power consists in a producer actuating bidding behaviors such as to artificially cause congestion between market zones. In this way, he prevents the import of cheaper energy from other zones and creates pivotality conditions for its own local generators in the importer zone. The action of creating congestion may be not profitable in itself, but the sum with the exercise of market power by local generators turns up to be very profitable. • This kind of local market power may only be exercised in meshed networks, not in tree-like ones (as the Italian market) A 1. Create a saturation between A and B B CESI RICERCA A C 2. C can’t import any longer (parallel flows); local generators are pivotal B C By saturating A-B, B receives more power. Either it uses it locally or exports it to C. In both cases, C imports the same power or more and this reduces (or doesn’t increase) the pivotality of local generators. Congestions and market power • However, this does not mean that a producer with market power on the whole national territory cannot find it profitable to congest one or several connections. By comparing the results of perfect and strategic competition in simulations carried out with SREMS, we see there are several hours in which the leader could strategically congests the tie line between Sicily and continent. One example: Perfect competition: P = 48.61 €/MWh DL=-186 MW DF=+229 MW +43 Strategic competition: P = 50.34 €/MWh P(Sicily)=59.31 €/MWh The leader increases his surplus by: 60.8 k€ DQ=+43 DL=0 DF=0 DL=0 DF=0 DL=-43 DF=0 CESI RICERCA +43 (saturated) What are market simulators • Electricity market simulators are used to forecast market results under given scenario hypotheses, like: – fuel prices – characteristics of generators, distribution on the territory and among the generation firms – characteristics of the transmission network (transit limits) – legislation (e.g. emissions of pollutants) • Simulators are classified by their reference time horizon: 1 hour 1 week Short term • Detailed description • Hourly bidding optimization CESI RICERCA 1 month Miedium term • Average detail level • Scenario analyses 20 years 1 year Long term • Low detail level • Analysis of evolution of generation set Who uses market simulators Analysis of bid strategy Producers 1 hour Short term CESI RICERCA 1 week Scenario analysis Regulatory impact analysis Research centers and stakeholders Regulators 1 month Medium term 20 years 1 year Long term Market analysis tools Data analysis to examine past market behavior by means of indices and different data aggregations Price forecasting tools extrapolating market results to the future (ARMAX, etc) Optimization tools least cost generation decisions, once provided supply and demand schedules, keeping into account operational constraints Suppliers’ strategy models simulation using games theory (Cournot, Bertrand, Stackelberg, Supply Function Equilibrium, conjectural variations) CESI RICERCA Main features of SREMS SREMS is a short-medium run electricity market simulator based on game theory. SREMS calculates price makers’ hourly strategic bids, supposing they actuate both a bid-up and a capacity withholding strategy. Additionally, it allows to define a certain number of price takers. Demand is inelastic, defined hour by hour and zone by zone. The network is supposed non-meshed (tree-like). Nodes represent market zones separated by interconnectors with min/max transit limits. The electricity market is modeled with hourly detail, but the scheduling of reservoir hydro and pumping power plants is carried out monthly. Characteristics of thermal power plants are taken into account with a high level of realism (quadratic cost curves, maintenance periods, accidental outages). Each producer can appoint a percentage of his power to physical bilateral contracts, function of total load, market share and attitude towards the risk. CESI RICERCA Input XLS file: • load, limits of transit • generators characteristics • calendar of maintenances • monthly production and pumping of hydro power plants • overall bilaterals percentage Input reading and internal database build-up M=1 Unavaliability and maintenance Preferential transactions (CIP6, import) and hydro plants Unit commitment (twice a week) Physical bilateral contracts COMPETITIVE LOAD H=1 Perfect competition Strategic competition H+ M+ CESI RICERCA no Month end yes Output XLS file: • thermoelectric generation • transits between zones • zonal prices • producer surplus Preferential transactions and hydro plants Leq L( t ) PR ( t ) PCIP 6 ( t ) PImp ( t ) PRe s ( t ) PP ( t ) L PSmax Run-of-the-river ESmax Power constraint active CIP6 Import Power constraint not active Power constraint not active Power constraint active PTma x ET Ppmax Epmax L( h* ) h(t) CESI RICERCA Unit Commitment of thermal power plants • Setting up a UC strategy for a producer is very complex. Many aspects have to be considered: – If the sum of minima of all committed generators is higher than the load, price is zero (even variable costs are not recovered) and the generators must bid in the adjustment market. – In general, too much committed power generates low prices. However, generators are also interested to have many plants committed to maximize produced power. – Load changes along the day: during the night few plants should be on (sum of technical minima must stay below the load), while during the day power should be available to catch peaking prices. However, flexibility to switch on and off is limited and depends from the plant technology. CESI RICERCA Strategic competition: the algorithm Vampiro Let’s suppose a leader knows the bids of all his competitors and, on the basis of them he wants to calculate the most favorable market solution (highest producer surplus), in terms of produced quantity and zonal prices. Vampiro solves this problem resorting to the following logical steps: – – – – write market clearing problem, translate it into a set of equilibrium conditions, write producer’s problem, solve the latter adding the equilibrium conditions of the market clearing problem as a set of additional constraints. The resulting optimization problem is not convex: local minima do exist! CESI RICERCA Strategic competition: calculating bids • Then, it is necessary to translate the information on leader’s optimal quantities into an optimal bidding strategy (price-quantity) capable to induce the optimal market clearing. P P Pz Pz 1 Bids Ci Ci Bids 0 q Omothetic shift CESI RICERCA q Aggressiveness tuning Typical trend of one week of zonal hourly prices Perfect competition Strategic competition Market outcome 400 350 300 250 200 150 100 50 0 1 CESI RICERCA 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 A simulation of the Italian market • Aim of the simulation is to assess the economical benefits that an enlargement by 300MW of the maximum allowed transit on the connector between Sicily and continent. • Benefits are measured in terms of total saving of the customers against value of the investment in new infrastructures (1.8 M€/km x 40 km = 72 M€ + additional expenditures = 80 M€). • The simulations have been built upon a scenario 2005 of the Italian market: – – – – – – 19 GenCos, 170 thermal units, 53 hydro units; 4 macro-zones (Nord, Centro-Sud, Sicilia, Sardegna); fuel prices: 26.14 €/Gcal (oil), 31.55 €/Gcal (methane); real hourly load assigned to each macrozone; max bid price tuned on real market price peaks; monthly CIP6 and import provided as input data. • Preliminary simulation results show that investment costs could be recovered in less than two years. However, the non-convexity of the problem solved by Vampiro invites to some precaution in reading the results of comparative simulations (like this one). CESI RICERCA Conclusions • SREMS is a reliable tool allowing to perform scenario analyses on real electricity markets over a short-medium run time horizon and to assess the capability of the market leaders to exercise market power, both market-wide and at local level. The true challenge would be to incorporate real strategies instead of sheer profit maximization, to make the market equilibrium model acquire a true predictive role. No literature paper has tackled the problem yet. • On the basis of simulations, building inter-zonal tie lines to decongest the most critical connectors would probably create an amount of benefit sufficient to recover investment costs in few years. Increasing TTC between zones would also make the market more efficient and the exercise of market power more difficult. CESI RICERCA Thank you for your attention... Massimo Gallanti Gianluigi Migliavacca CESI RICERCA via Rubattino,54 20134 Milano (Italy) E-mail: [email protected] [email protected] CESI RICERCA