Kontakt DW Unser Zeichen Ihr Zeichen Datum Alexandra Gruber 211 AG/Ha 15/

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1 ENTSO-E Task Force Avenue de Cortenbergh Brussels Belgium Kontakt DW Unser Zeichen Ihr Zeichen Datum Alexandra Gruber 211 AG/Ha 15/ ENTSO-E BZ Stakeholders Advisory group: Survey on market efficiency with regard to bidding zone configuration, June 2016 Comments by Oesterreichs Energie (Register ID number: ) Dear Madam, Dear Sir, Oesterreichs Energie, the Association of Austrian Electricity Companies, welcomes the opportunity to comment on the Survey on market efficiency with regard to bidding zone configuration discussed in the Stakeholders Advisory Group meeting on Oesterreichs Energie represents more than 140 energy companies active in generation, trading, transmission, distribution and sales which in total cover more than 90 per cent of the Austrian electricity generation and the entire distribution. Below, we highlight following the survey s structure the indicators, critical thresholds, data required and the assessing methodology we recommend for each criterion reconsidered. Enclosed please find our response to the consultation questions. Accuracy and robustness of simulated prices An important aspect in any bidding zone review will be a forecast of how system cost (including the cost of dispatching and redispatching power stations and the cost of electrical losses on the system) change with alternative bidding zone configurations. In this, to avoid ill-informed decision in the bidding zone review, it is important to consider the impact of potential inaccuracies and lack of robustness of simulated forecast cost and prices for the bidding zone review Wien Fax Oesterreichs Energie 1/11

2 Testing for accuracy of simulated prices and impact in the bidding zone review The accuracy of simulation market results should be benchmarked against historic market outcomes (the so-called back testing 1 ). Exhibit 1 provides an overview of relevant indicators and potential data sources. Exhibit 1. Indicator Price level (base, peak) Indicators for the accuracy of simulated market outcomes and potential data sources (to be checked through back-testing) Price volatility (e.g. standard deviation) Generation by fuel/plant type Utilisation (full load hours) by fuel/plant type Cross-border exchanges by border Data sources Power exchange Power exchange Eurostat, ENTSO-E data portal, national statistical offices, publication by industry associations Eurostat, ENTSO-E data portal, national statistical offices, publication by industry associations ENTSO-E data portal Testing for robustness of simulated prices and impact in the bidding zone review Robustness in this instance should be defined as the invariance of a recommendation of a certain bidding zone design to variations in the assumptions that enter the modelling. In the context of the bidding zone review, we would consider assessing the robustness of results in relation to: Alternative scenarios ENTSOE is running the simulation for the market and network model using two TYNDP scenarios for demand, generation and network expansion which should reflect the possible states of the world in the relevant reference years (2020/2025): Robust: the recommendation for the optimal bidding zone configuration are the same for the two TYNDP scenarios; Non-robust: the recommendation from the results for the reconfigured bidding varies substantially between the two TYNDP scenarios. Sensitivities further sensitivity analysis should be undertaken within each scenario. These sensitivities should include variations to: Interconnection capacity at bidding zone borders; Residual load, which will significantly be driven by renewable capacities and generation; Fuel and CO 2 prices. 1 In this sense, back-testing is a precursory step to the forecasting required for the actual bidding zone review. Back-testing involves comparing modelled results for a historic period (say modelling of the year 2014 based on information of key input parameters known with hindsight) against the actual market outcomes in the respective year (e.g. 2014) Wien Fax Oesterreichs Energie 2/11

3 Market liquidity Market liquidity is a key indicator for a well-functioning electricity market. The change in market liquidity as a result of a proposed redesign of bidding needs to be considered in any bidding zone review. We propose a stepwise approach: Exhibit 2. Including market liquidity in bidding zone review Metrics for Comparison Defining status quo of status quo Defining indicators for Data and vs. critical market collection reconfigured reconfigured thresholds liquidity bidding bidding Step 1 Defining suitable indicators We propose to define multiple indicators in order to capture different aspects of market liquidity for different segments of the market. We propose to use: Exhibit 3. Indicators for market liquidity Intraday Day ahead Forward (1-year ahead) Forward (beyond 1- year ahead) Metric 1: Bid/ask spread X X X X Metric 2: Weighted average/ maximum trading horizon Metric 3: Median number of deals per day Metric 4: Churn rate/trading volume X X X X X X X X Step 2 Defining critical thresholds We propose to consider the following approaches in defining critical thresholds: Absolute benchmark (binary qualification, i.e. liquid or illiquid) in case of an absolute benchmark a market segment is liquid if it meets the critical value and illiquid if it does not. Intermediate qualifications would also be possible. We see two sources to qualify the thresholds: Market survey For example, in the context of defining critical thresholds for the gas target model II a market survey was undertaken to identify what stakeholders require as critical thresholds for market liquidity. A similar approach should be used in the bidding zone review. Established indicators used in related sectors It would also be useful to consider any of the critical values for liquidity indicators that have already been established for the gas market in the course of a review of regulators and also by commercial information providers (e.g. ICIS Heren) and define analogous indicators for electricity; and/or 1040 Wien Fax Oesterreichs Energie 3/11

4 Best practice as benchmark Based on market indicators for existing bidding the liquidity from the best-performing bidding can be used as relative benchmark. The approach adopted by ACER 2 in the gas market offer as useful guidance. Step 3 Collecting data The data required to calculate the liquidity indicators for the status quo should be collected from OTC trading platforms; and electricity exchanges. The data shall also be collect from bidding which are not considered for a bidding zone review as they might provide some empirical information on the relationship between structural market parameters and the liquidity metrics. Step 4 Reporting metrics for existing bidding (status quo) and prospective reconfigured The metrics of the existing bidding (status quo) and the proposed reconfigured bidding zone shall be reported and compared to the defined critical thresholds and the results summarised in a traffic light system for the existing and the proposed bidding zone configuration: Green: high liquidity; Orange: moderate liquidity; and Red: low liquidity. Information on the likely performance of proposed reconfigured bidding can be gleaned from the historic performance of another bidding zone that has (had) similar structural characteristics. For example, assume that bidding zone A was to be split into A1 and A2. Then it should be checked whether there are exiting bidding B or C in the EU which have similar characteristics to those that A1 and A2 would have. Then the experience from B and C can help forecast the liquidity performance in A1 and A2. Step 5 Comparison of status quo vs. reconfigured bidding The final step compares the indicator values for the status quo and the reconfigured bidding to assess the relative change of the market liquidity indicators resulting from the reconfiguration. Any change in traffic light will indicate that as a result of the bidding zone reconfiguration liquidity increases (e.g. when a traffic light moves from red to green) or falls (e.g. when a traffic light changes from green to red): 2 Acer, European Gas Target Model review and update, Annex 5 Market Participants Needs metrics: Results for Selected European Gas Markets, January Wien Fax Oesterreichs Energie 4/11

5 Exhibit 4. Intraday Day-ahead Forward (one year) Forward (beyond one year) Comparison of status quo vs. reconfigured bidding Illustrative example for splitting A Status quo A Reconfigured bidding A1 A2 The illustrative hypothetical example in Exhibit 4 shows that the split of the existing A into A1 and A2 has an adverse effect on the market liquidity in the new configured A2 (while A1 being unaffected) and especially on forward liquidity. Market concentration and competition effects Market splitting may scale down the geographic market into smaller regions. This may result in higher market concentration in the new bidding, raising the possibility that major players could profitably exercise market power (i.e. prices above competitive levels). The merger of bidding could conversely help reduce market concentration and thereby spurn competition in the merged. In order to assess the effect of bidding zone configuration on market concentration and competition, we propose a stepwise approach: Exhibit 5. Including market concentration in bidding zone review Metrics for Comparison Defining Identifing status quo of status quo Defining indicators for necessary and vs. critical market data and data reconfigured reconfigured thresholds concentration collection bidding bidding Step 1 Defining suitable indicators for market concentration and competition We propose to use as suitable and formally established indicators of market concentration: HHI (Herfindahl-Hirschman-Index) - in order to consider information on the market shares we propose to use the HHI (the sum of squares of market shares of individual companies in the relevant market). Market shares and HHI are based on generation shares and they consider actual competition and thereby players actually in the market. When applying the HHI it is important to undertake the calculation for the relevant geographic market which typically includes the relevant bidding zone, but which may be wider in case the bidding zone in question is strongly interconnected with a neighbouring bidding zone Wien Fax Oesterreichs Energie 5/11

6 Residual supply index (RSI) - in order to obtain information on the pivotality of individual players in the market we propose to use the RSI. The RSI considers whether a large company under consideration is an essential or pivotal player to meet overall demand in the market. 3 The RSI can be calculated for the individual (8760) hours in a year. If a player were pivotal in a certain proportion of hours, then a market is highly concentrated and competition concerns arise. In contrast to the HHI, the RSI considers: The level of demand if (maximum) demand was low compared to capacity of competing players, then the sector tends to be competitive; Potential supply RSI not only considers capacity that actually supply the market, but also those that could supply the market, e.g. if an incumbent were to behave noncompetitively; Interconnector capacities the potential supplies considered for the RSI also include interconnector capacities. The more a certain bidding zone is interconnected with its neighbours the more likely concentration is low and competition intense. Step 2 Defining critical thresholds When defining critical threshold we propose to refer to threshold commonly used in EU and national competition law: Thresholds for HHI - based on the European Commission: 4 Not concentrated: <1.000; Moderately concentrated: (a priori no concerns if HHI does not increase by more than 250 following a change in the market 5 ); and Concentrated: >2.000 (a priori no concerns if HHI does not increase by more than 150 following a change in the market 6 ). Thresholds for RSI - based on the assumption from the German Cartel Office and the EC: No significant market power: if the RSI is above 110% in more than 95% of hours; Significant market power: if the RSI is below 110% in more than 5% of hours; and Market dominance (strongest form of market power): if the RSI is below 100% in more than 5% of hours. 3 The RSI is calculated as the generation capacity of all suppliers without the player under consideration divided by aggregate demand in the market. An index value below 100% indicates that the investigated player is pivotal as all other suppliers together would be unable to serve maximum demand. 4 See EC, Horizontal Merger Guidelines 2004/C 31/03. 5 The respective change referred to in EU competition law is that of the merger between two companies active within the same relevant market. A liberal extension of this logic would be to consider the change in HHI following the merger of bidding (rather than the merger of companies). 6 See FN Wien Fax Oesterreichs Energie 6/11

7 Step 3 Necessary data Exhibit 6 illustrates the data requirements to calculate the HHI and RSI and some of the challenges involved in the calculation. Exhibit 6. Data requirement for HHI and RSI Data HHI RSI Demand side not considered explicitly Residual load (indigenous load minus RES generation) Supply side Generation by firm Available (non-renewable) capacity by firm Treatment of export/imports (implicitly included in market result) Available interconnector (IC) capacity Granularity Annual Hourly (focus on critical hours), results can be aggregated to show annual metrics (e.g. % of hours in which RSI > X) Fundamental power market model required to forecast? Model independent challenges Yes (generation based on optimised dispatch) Analysis has to consider individual plants No (focus on capacity and not generation) Ownership allocation for multiple shareholders of a single plant and long-term supply contract Assessment of available IC capacity in critical hours Power plant availability in critical hours Treatment of non-renewable embedded generation Model specific challenges Inherent challenges of any market modelling exercise Forecast of maximum demand has significant impact on results Much of the required data will be available from the model simulations for ENTSOE. In addition this information will need to be matched with information on plant or asset ownership. A first cut of such data is available from commercial information providers (e.g. Platts). From experience, such data will need to be reconciled with the help of national industry associations and ultimately the individual generators. Some clarifications on data definitions will be required especially in relation to the treatment of jointly owned plant and contracted out plant Wien Fax Oesterreichs Energie 7/11

8 Step 4 Calculating metrics for existing bidding (Status quo) and reconfigured bidding In this step the market concentration indicators (HHI and RSI) are calculated for the relevant reference years of the model simulation (e.g. 2020/2025) for the existing bidding zone configuration; prospective alternative bidding zone configuration(s). The results are summarised in a traffic light system for both bidding zone configurations: Green - no market concentration/power; Orange - moderate market concentration/power; Red - significant market concentration/power. Step 5 Comparison of Status quo vs. Reconfigured bidding The final step compares changes to market concentration between the Status quo and the reconfigured bidding to assess the relative change of the market concentration indicators resulting from the reconfiguration for the relevant reference years of the model simulation (e.g. 2020/2025). In case market concentration increases (indicated by a change in traffic light to orange/red) the analysis could proceed to assess the possibility of incentives for anti-competitive behaviour as a consequence of the bidding zone reconfiguration. This assessment may be supplemented by an additional analysis considering the modelling of likely behaviour of key market players or drawing on more generic analysis regarding the link between market concentration (and RSI) and the potential for price mark-ups. Exhibit 7. HHI RSI Incentive to abuse market dominance Comparison of Status quo vs. Reconfigured bidding Illustrative example for splitting A Status quo A Reconfigured bidding zone A1 A2 The illustrative hypothetical example in Exhibit 7 shows that the split of the existing A has an adverse effect on market concentration in the newly configured A2 (while market concentration in A1 is only marginally affected) Wien Fax Oesterreichs Energie 8/11

9 Retail competition and renewable integration The impact of a bidding zone redesign on competition in the retail market, should be included in the assessment of market power and concentration. A key concern here is whether the reconfiguration of bidding creates risks and transaction costs that preclude potential competitors from entering the geographic market or that could even induce existing players to exit the market. Such analysis will entail Market liquidity (as discussed earlier) - The analysis of market liquidity will be a key ingredient to the analysis of retail competition. The less liquid the market and the higher the transaction cost are (as measured e.g. by the bid/ask spreads in the short-term and year-ahead market) the less competitive the retail market tends to be. Overall size of the retail market retail players require a critical mass of retail customers to become efficient. While there is no hard and fast rule for the size of an efficient retail business, the indication is that geographic markets which are smaller in size than several million retail customers may not exhibit ideal for a fully competitive retail market. As regards the effect of bidding zone configuration on renewable integration, ENTSOE s 7 costbenefit-analysis for Grid Development Projects serves as a best-practice example. The approach considers the role of Renewables, Energy Efficiency and CO2 emissions in the assessment. We propose to follow a similar approach to include the impact on renewables in the analysis and to extend the assessment including energy efficiency and CO 2 emissions: RES integration facilitated by connection of RES generation to the electricity system and the reduction of renewable generation curtailment; Energy efficiency measured by variation in losses; Variation in CO 2 emissions. Transition and transaction costs Given the complexity of defining transition and transaction costs we recommend that ENTSOE puts a similar effort into estimating these costs as into estimating monetary benefits from the reconfiguration of bidding using market model simulations. For estimating transition/transaction costs we propose the following steps: Exhibit 8. Including transition/transaction costs in bidding zone review Definition of cost guideline Including questionnaire Collecting cost data Including plausibility Finalising cost estimates Ranges for different scenarios 7 ENTSOE, ENTSO-E Guideline for Cost Benefit Analysis of Grid Development Projects, page 40-43, Final approved by the European Commission, 5 February Wien Fax Oesterreichs Energie 9/11

10 Step 1 Definition of cost guideline ENTSOE should draw on the answers received in response to the questions on transition and transaction costs in the Survey on market efficiency with regard to bidding zone configuration when preparing a cost guideline. This guideline and analysis has to include all relevant dimensions on the part of stakeholders. At least the following aspects should be covered by the cost guideline: Exhibit 9. Adjustments in Government institutions Energy companies Trading companies Transmission operators Transition-/transaction cost-matrix Legal design X Organisational Operational IT Contracts X X X X X X X X X X X OTC platforms X X X Electricity exchange Clearing houses X X X X X X Customers X X X X In order to allow a reasonable cost estimates, the cost guideline has to be specific on: Fast/slow transition period the cost crucially depends on the length of the transition period: by increasing the lead time until a new bidding zone configuration takes effect, the extent to which existing contracts need to be changed and to which (additional) external IT system providers need to be brought in to cover peaks when changing internal processes can reduced, also lowering the costs of advisory services. Hence, the cost guideline should be explicit on this, e.g. by either fixing a transition period or defining a range for the transition period. Reconfiguration along/within balancing the necessary adjustment in energy companies (IT) systems crucially depend on whether the reconfiguration is along or within existing balancing. Hence, the cost guideline has to be specific as to which reconfigured bidding the stakeholders shall make their cost estimates. The cost guideline should be supplemented by a user-friendly questionnaire for collecting costs Wien Fax Oesterreichs Energie 10/11

11 Step 2 Collecting cost data and plausibility check In this step ENTSO collects and aggregates the information on costs and other related topics with regard to the transition/transaction costs from the stakeholders. This step includes a plausibility check of the received costs data in order to avoid that outliers distort the results. This may also include going back to individual stakeholders for further clarifications. Step 3 Finalising estimates for transition/transaction costs This step finalises the estimates for the transition/transaction costs for all existing bidding currently under review. These cost estimates are separated for different reconfiguration scenarios initially defined in the cost guidelines. Thank you for taking our comments into consideration. Please do not hesitate to contact us for further information or clarification. Yours sincerely, DI Wolfgang Anzengruber President Dr. Barbara Schmidt Secretary General Enclosure: Response by Oesterreichs Energie to the Consultation Questions 1040 Wien Fax Oesterreichs Energie 11/11