CREATING A COMMUNITY OF EDUCATED SOFTWARE BUYERS IN SEE

CREATING A COMMUNITY OF EDUCATED SOFTWARE BUYERS IN SEE

CREATING A COMMUNITY OF EDUCATED SOFTWARE BUYERS IN SEE

Last year at E-World in Essen, Germany, one of the take home messages for us was “Money is made at the frontiers”. Luckily for us, Southeast Europe is one such frontier for the development of energy markets and it comes with a set of specific challenges that are waiting to be tackled. Among them are unstable regulatory framework, concentrated production assets, general backwardness with respect to liberalization, intercompany debt, and EU regulatory frameworks also hitting the local players. In this setting, the questions about reducing complexities of trading and improving operational efficiencies have fallen slightly behind in the past years. We think that the topic about business software in the niche needs to be urged back and put towards the top of the agenda.

Energy and commodity trading typically require a suite of complex solutions supporting the operational and market activities of sector companies. From wholesale market operations where the main topics are load forecasting, load management, load decomposition, and optimization of deliveries to retail operations where the purchased energy has to be delivered to multiple customers which require a high level of operational efficiency in order to ensure lower cost of customer support, invoicing, reconciliation against network companies, etc., a company which can start with a small investment and a laptop has to invest in itself to keep a good competitive edge.

ROITI has set a target for itself: to create a community of educated buyers and sellers in the region where it is headquartered: Southeast Europe. For this reason, we teamed up with the editors of the Utilities magazine from publics.bg and initiated the First Energy Software Day SEE in Sofia, Bulgaria. Our goal is to stir up the conversation between potential customers and vendors and help identify the key problems specific to the region, the quick wins in terms of maximum business improvement at a minimum investment, and the potential solutions that will make the life of market players simpler and longer. We will be happy to welcome you there and share our and leading market participants and organizations’ views on the status quo and the ways to improve business practices, cut costs, and increase the value proposition of your company.

ETRM: WHERE’S THE FUN IN NATURAL GAS?

ETRM: WHERE’S THE FUN IN NATURAL GAS?

ETRM: WHERE’S THE FUN IN NATURAL GAS?

Following on the more general post on flow and bulk commodities, we wanted to go a little bit into the individual commodities and try to describe where the fun in each of them is. Natural gas won the first place, as it is a personal favorite, and it is probably the commodity which we have implemented the highest number of times.

Market Specifics

Several topics belong here – the differentiation between TSO and market area, the valuation within and across markets, and the units of trading. More details on each below.

TSO vs Market Area

Typically, in the natural gas market the area of a TSO and the market area coincide. A prominent exception is Germany and I will take it as an example.
In the past 5-6 years, the market areas there have been reduced to 2 from 16 (to boost market liquidity) – but the number of TSOs is still 16. This means that if you want to model the delivery structure you will need to balance between capturing the market area (in which the traders are interested) and the TSO area (which is key for the scheduling teams). Additionally, you will have to deal with points which are on the border between market areas and TSO areas at the same time. If the system is primarily used for trading, then you can get away with modelling the market areas only – the problem with such an approach is that you would have to enrich the data before putting it into a scheduling system.

Valuation

A second thing that requires thinking when configuring is the valuation of different types at the same location. For example, a forward contract is valued at the market price of the market area but an entry or exit capacity contract in the same market area on its own is valued at zero (we need to keep in mind the difference between pricing and valuation). The value of capacity can be calculated only when you have both parts – exit from one market area and entry into a neighboring one, which are still obtained at two separate biddings from different TSOs in many cases, despite the recent introduction of bundle contracts, which should make things much more straightforward.

The implication of the above for a system, means that a categorization of locations is required, which should be tied to the valuation you apply at the specific location.

Units of Trading

Units of trading – and I would add settlement – are a typically supposed to be a straightforward topic, which should be easy to grasp. SUPPOSED to be – in my experience, this is one of the things, that projects tend to underestimate until it causes a lot of defects during UAT.

For gas, the main issue is switching between granularities (daily vs hourly units – eg Therms/day vs MW) and between volumetric and energy units – cubic meters (cm) and MWh. The first one is a problem mostly because of daylight saving days, where a straightforward division by 24 does not work. The second one is solved differently in different countries:

  • The Netherlands use a fixed conversion rate between cm and MWh, which make trading and settlement easier
  • Belgium has two – one for high calorific gas, a different one for low calorific gas
  • In Germany, TSOs publish the calorific value after the deliveries for the month have been made

A variable conversion rate by location and time is something which makes a system implementation overly complicated. Often, a better choice might be to accept a certain minimal level of error and live with it, rather than bear the costs of a system implementation (or, to use some words that should probably be forbidden: move to Excel).

Types of Contracts

The different types of contracts bring different types of complexities. Below an overview of some of the interesting ones. I am skipping over futures, as they are standardized contracts and the functionality there is not necessarily gas specific (and – as in other cases – we are planning to dedicate to them a separate post).

Forwards

The forwards are typically straightforward to model. Some of the more complex ones are floating forwards, where formula functionality will be needed to capture price dependencies on different indexes, and weighted average publications (eg Heren’s weighted average month ahead publication). The latter brings in complexity, as the publisher takes into account the prices and volumes of all month ahead transactions within the current month when calculating the publication for today. This has two implications:

  • The price will settle on the last day of the current month against the last published price
  • Despite this, there needs to be a delta decay on each day within the month (as the exposures decreases in reality)
  • Depending on how you model underlying prices and historical prices in the system, you might need to calculate your current price estimation as an average between the last known price for the publication and the balance of month price

Storage

In my book, gas storage is one of the most complex to implement types of contracts. I will look into gas storage in more detail in a following post, but below in short are some challenges that an implementation needs to face when gas storage is concerned:

  • Capturing the different terms of working gas payment, injection and withdrawal payments (which can be based on prices of different commodities – power, gas, LNG, etc)
  • Capturing initial fill level and reflecting it correctly in the balance and the value of the storage
  • In storage transfers and their reflection in balance and value
  • Valuation – gas storage is typically valued as a time spread between value at time of withdrawal and at time of injection

Capacity

There are several scenarios regarding gas capacity that need to be thought through. Capturing entry and exit capacity is the first one – including the typically lump sum payments for them. The second is bundle capacity which is a multi-legged deal – having legs in 2 different areas. Finally, rarely in my experience, there are still some point to point capacity contracts, which are technically similar to the bundle ones (in that they are multi-legged) but different in terms of points of origin and destination – they are not two sides of a border.

Along with the modeling of these contracts the valuation needs to be considered – practically an option on the min (exit, entry) capacity at a certain border.

Options

Options are a fairly big topic, so I will rather deal with them in detail separately. The main things to mention at a high level are the need for different exercise granularity options – eg daily, monthly, quarterly (meaning the decision to exercise or expire would be taken each day, month, or quarter), and the need to determine a business and a system process (e.g. who and when exercises, what happens upon exercise – is there a separate contract or not, how the value of the underlying is calculated once exercised, which pricing model will be used, how the volatility and correlation data will be calculated and imported/used in the system).


These are on a very high level, some of the interesting challenges in a gas implementation. Following up on this overview, there will be deep dives into the specific topics regarding individual contract types in terms of capturing and valuation considerations.

INTERRELATIONSHIP BETWEEN CONCESSION AGREEMENTS AND COMMERCIAL CONTRACTS IN OFFSHORE WIND PROJECTS

INTERRELATIONSHIP BETWEEN CONCESSION AGREEMENTS AND COMMERCIAL CONTRACTS IN OFFSHORE WIND PROJECTS

INTERRELATIONSHIP BETWEEN CONCESSION AGREEMENTS AND COMMERCIAL CONTRACTS IN OFFSHORE WIND PROJECTS

Since I joined the wind power industry the offshore installations in Europe grew 8 times and are now exceed 8000 MW. The future will bring even more: EWEA has identified 22 GW of consented offshore wind farms and future investment plans for more than 133 GW.

There are several developments accompanying this growth such as technology R&D, construction innovation, as well as financing schemes, etc.

In this blog post I will focus on some of the challenges related to the commercial contracts management (more particularly on the Turbine Supply & Installation Agreements and Service Agreements) and their interrelationship with the so called Concession Agreements (CA).

Let us take the example of offshore wind in Denmark. In order to be eligible to build and operate an offshore project the investor has to participate in a tender organized by the state authorities. The successful tenderer is awarded on the criteria of lowest bidding price and is eligible to sign the CA, and receive the related licenses and authorization.

Here are some of the main characteristics of the Concession Agreement:

  1. Revenue model
  2. The investor participates in the concession tender with a binding price quoted in Danish “øre” per kWh. This price is fixed and covers only a predefined production volume (e.g. 20 TWh for a 400 MW farm). It is the owner’s responsibility to sell the production directly on the power market. The actual revenues are the sum of two components: (i) the spot power price, which is calculated as the mean hourly spot price on Nordpool for the relevant market zone and (ii) a price supplement calculated per hour as a difference between the quoted price and the spot price.

    The supplement is not paid during the hours when the spot price is negative. The price supplement is a negative value during hours when the spot price is higher than the quoted price. Balancing costs for the electricity from the wind turbines are not compensated.

  3. Penalty of defective performance
  4. By entering into the CA, the investor undertakes to build the wind farm. If a power plant is not build (for whatever reason) OR the construction work is not commenced by a defined date, a fixed penalty becomes immediately payable to the authorities. The amount of this penalty can reach tens of millions of Euro. A guarantee from top rated financial institution shall cover this obligation.

  5. Delay penalties
  6. If less than 95 % of the capacity of the farm is connected after a pre-defined target date, the production eligible for price supplement is being reduced by certain portion (e.g. 0,2 TWh for each 6 months). When calculating the percentage of connected capacity, the capacity of one turbine is included starting from the time the first KWh to the grid has been delivered. This shall apply even if subsequent technical problems should temporarily render the turbine out of service.

The impact on commercial contracts

The above framework sets challenging conditions that need to be respected during the entire contract management lifecycle (negotiation, implementation and day-to-day management). Let us see how they impact some specific areas in the commercial contracts with the wind turbine supplier.

  • The revenue model has a significant impact on the Long Term Service Agreements. Contrary to the situation where the power is purchased at a fixed price (Feed-in-Tariff) here we have an offtake price depending on the time of production. This means that the asset owner should have the right to shift the scheduled maintenance planning. Additionally, the varying power price changes the way we look at the Availability Warranty. They owner and the contractor have an incentive to look a bit further beyond the standard time- or energy-based availability definitions and discuss a value-based availability concept.
  • The penalty for defective performance under the concession agreement sets quite a big challenge during the contracting process. The owner would like to pass a bigger portion of this risk to his contractors.

    One specific topic is agreeing on the amount of Liquidated Damages for Delay (Delay LDs). Under this framework the owner should ensure not only that the power plant is completed on time, but also that the installation works have started before the pre-defined deadline. Hence, the parties will potentially need to discuss how the set the LD amount with respect not only to the cause and the time of eventual delays, but also to the consequence of such delays.

    A separate (but even more significant) issue is the total limitation of liability (caps) under the turbine supply and installation contract. Firstly, the total cap normally never exceeds 100% of the contract value (remember, in case of delays, the owner might lose much more than lost production). Secondly, the turbine supplier usually sets sub-caps for Delay LDs that are a fraction of the total cap.

  • The financial sanctions for delayed connection to the grid imapct several areas in the turbine supply and installation agreement (TSA). Here again the owner may lose much more than the opportunity to produce power.

    Let us not forget that “grid connection” under the CA is defined per wind turbine as the moment when it produces the first kWh. This is not the only precondition for Taking Over of the turbines by the owner under the TSA (e.g. the taking over usually happens after the tests on completion that include much more than just 1 kWh). Simply said: the turbine can be “connected” as per the CA, but not “taken-over” as per the TSA.

    On top of that there is a challenge related to the fact that under the CA the owner has limited excuses for delays. In different situation the parties will be able to agree relatively easy on the cases where Extension of Time may be granted to the contractor (e.g. changes in law, permits, adverse weather, etc.). As there is almost no relief for the owner, he would seek to pass this to the contractor too.

Trading and asset operations teams need to work closer

The CA framework is relevant not only for the contracting, but also for the execution and operation phase. The direct marketing of power and revenue model alone mean that contract managers, asset operation teams and power trading units will have to work in coordination.

Zero payments during hours with negative spot prices means that the wind operator is paid nothing and the farm should be paused. In order to enable trading optimization, the farm should be equipped with proper production forecasting system, etc. I will focus on that in one of the following posts.

As the industry grows the commercial contract management is becoming more complex task than ever.

WHAT DOES YOUR ETRM SYSTEM ALLOW YOU TO HEDGE WITH?

WHAT DOES YOUR ETRM SYSTEM ALLOW YOU TO HEDGE WITH?

WHAT DOES YOUR ETRM SYSTEM ALLOW YOU TO HEDGE WITH?

Hedging. When I started out as an ETRM consultant it took me a while to get the difference between trading and hedging – or at least get a feeling that I understand. For the sake of an ETRM implementation the exact differentiation does not matter that much (as long as everyone agrees hedging is about removing risk from portfolios). What matters is to allow traders and risk managers to report exposure against the right products (and what is “right” is a whole other different topic).

Business scenario

You are a trader and have an open position against a less liquid product (eg 10 MW power delivery in Czech Republic several months from now – let’s assume Sep 2015). There is no liquid market for Czech power derivatives and there is no way you can hedge that position with a perfect hedge.

What do you do?
You hedge with a liquid product that is highly correlated with the underlying you need to hedge – and you would eventually re-hedge if things in Czech Republic become liquid closer to delivery or leave things as they are if you are happy.
What could such a product be?

  • It could be the same product in a neighboring market – for example, you have found out a high correlation between Czech and German prices – so you can hedge with a German futures if they are liquid enough
  • It could be a different product in the same market – for example if Q3-15 is liquid you can buy roughly 3.4 MW of Q3-15 and then re-hedge when the individual months become liquid. And if you are wondering why a 10MW Sep contract will be hedged with 3.4 MW Q3-15 contract, you should read this
  • It could be a combination of the two (ie a Q3-15 German contract, which will then have to be re-hedged, but if it was easy it wouldn’t be fun) or any other thing that you have found to be correlated enough and liquid enough (if Czech gas turns out to be very liquid and very correlated, there you go)

Implications for reporting

The implications of this scenario (and it is a very viable one across all energy markets – examples further down) are the following:

  • The system needs to be able to “translate” one exposure into another – ie report the German power equivalent of a Czech contract – so it can tell you exactly how much of German power you should buy
  • The system should be able to work well with different contract lengths for flow commodities – ie figure out that 10 MW Sep-15 and 3.4 MW of Q3-15 are the same thing – as you need to know how much of Q3-15 you should buy to hedge your position
  • The system should be able to handle either / or reporting – ie be able to report original position and hedges as German power (or as Q3-15) OR both as Czech power equivalent – and be able to keep the respective positions against the respective markets

Now imagine this in a scenario where you have a gas TPP in Germany, selling its output in Czech Republic, working on low calorific gas in the NCG market area, and your gas is supplied via a still unchanged old contract and is priced based on the German HEL publications (leichtes heizoel = gasoil in German). If such a scenario actually exists it is very helpful to my point for the following reasons:

  • You might need to hedge your Czech power sales to eliminate the power market risk – and you are likely to do it with German products
  • Low calorific gas is not a liquid product – there are some pipelines in Germany in both gas market areas that transport it, but the liquid products are the high calorific gas derivatives in NCG and Gaspool. Hence, if you want to hedge the gas price exposure, you might figure out that the low calorific pipelines are linked to the Dutch gas pipeline network, and in general NCG L gas is very highly correlated to the TTF price in the Netherlands – and TTF trading is liquid
  • Finally, the HEL prices are published long after the actual delivery – and it is also not a liquid product. Fortunately, HEL actually comes either via transport or via pipelines from the Netherlands, and is traditionally hedged using Rotterdam FOB barges delivery Gasoil 0.1% sulfur. The GO FOB Rotterdam 0.1% swaps are traded liquidly and might be able to provide a good hedge. However, if your risk managers do not like it you might want to consider hedging using the ICE ULS (ultralow sulfur) Gasoil futures

All of the above means:

  • You have to translate Czech power into German one (and vice versa)
  • You have to translate German L Gas into TTF (and again vice versa)
  • You will need to calculate the GO 0.1% FOB Barges Rotterdam equivalent of your HEL exposure and then turn this into ULS Gasoil futures (by roughly splitting a month into 40% of this months’ futures contract and 60% of next months’ futures contract – but this is a separate topic, that goes in between through the Gasoil 1st line swap on ICE, so I will keep things simple for now)

Implications for the ETRM solution

For the ETRM solution the above might not mean anything – it will not be an exception if the main solution traders capture deals in offers reporting positions only against the traded positions or the direct underlying asset prices. This would translate the example above in the best case in a position in Czech power, a position in NCG L Gas (or NCG only if it cannot or is not configured to differentiate between L Gas and H Gas grid within a market area) and HEL.

However, if you are a bit more aspiring about functionality, then the example about would mean several interesting things:

  • Capturing correlations between asset prices (eg German and Czech power, NCG L Gas and TTF, HEL and GO FOB Barges Rotterdam 0.1%)
  • If we take capturing correlations one step further – driving curves from each other within the system (a standard case is a curve being modelled as a spread off a highly correlated liquid market)
  • Capturing differences in products on the same underlying: specifically in the oil case above – if you want to be flexible in hedging the HEL exposure with different gasoil products, the system should be model relationship between several curves – the Gasoil futures, the Gasoil 1st Line Swap (which is a monthly swap settled against futures settlement prices for each working day of the month and therefore has exposure against 2 futures contracts – as the futures contract settles in the middle of the month), and the Gasoil FOB Barges Rotterdam swap(which is typically modelled as a spread off the Gasoil 1st Line Swap)
  • The last point makes for 3 products on the same underlying (FOB barges delivery of gasoil in the Rotterdam area), BUT with different price settlement (expiration date for the futures, monthly average of first nearby futures daily closing prices for the 1st line swap, and monthly average of PLATTS published prices for the OTC market). In the oil market this relationship expands across multiple products via crack spreads and diffs and you end up with a curve tree possibly starting with Brent or Gasoil futures (although you might want to model the Gasoil futures using a crack spread from Brent) and then adding up cracks and diffs to arrive at a large number of fuel oil, gasoil, and diesel product variations. If such a tree is to be modelled in the ETRM system, the latter needs to allow for multiple steps in the curve modelling including unit conversions and spreads at each step. The good news is that if mostly financial oil is traded, remembering 3 to 5 conversion factors and what unit the price of a crack spread is in and what unit the notional is in takes you a long way in designing a well working solution

The bottom line

Traders will most of the time not have a perfect hedge available in the market. In such cases they would go to the next best thing that covers their exposure and re-hedge later if market conditions change. This means a solution covering risk reporting (whether in the direction of Middle Office or Front Office) should be very flexible in reporting exposure against the right curve in the right units. The “right” curve might mean either the curve representing the traded underlying or the liquid “best hedge” one. Finally, the relationship between a traded underlying and a best hedge for it might be fairly direct and straightforward or go through several steps of adding spreads, unit conversions, or multiplying with specific correlations that a regression model shows.

GAME THEORY IN WIND POWER CONSTRUCTION CONTRACTS

GAME THEORY IN WIND POWER CONSTRUCTION CONTRACTS

GAME THEORY IN WIND POWER CONSTRUCTION CONTRACTS

Building an onshore or offshore wind farm is a highly complex process which is reflected in the project contracts. Whatever the contract structure is (Turnkey/EPC or Multi contract/split delivery), risk factors remain core issue.
The industry practice is to use international contract templates such as FIDIC. They are a great tool as they are based on accumulated project experience, the structures are well known to the stakeholders (investors, contractors and financiers), and they usually provide a balanced risk allocation.

Why we fight then?

Having said the above, I have experienced an interesting contradiction.

In many cases, after countless hours of fierce contract negotiations I have asked myself: What is the ultimate purpose of the contract? Is it to achieve the highest possible compensation in case of defaults and shift the risk to the other party? Or the goal is to have an operational power plant build on time and within budget?

For sure many of you who have been involved in contract negotiation and execution can relate to this situation.

The reason behind this is that most of the contracting templates are focused on detail risk allocation and managing the consequences of risk, rather than on risk avoidance and issue resolution. The other shortcoming is that traditional contracts treat change as anomaly (just think of Variation Order procedures under FIDIC). Such contracts try to predict and specify every possible case. That would have been sufficient if the real world was not so dynamic and the projects where executed on the same table where the contract was signed (and not in the field or at the sea).

Real world is dynamic. When changes occur the traditional contracts do not focus enough on cooperation which is necessary to deal with the changes and solve problems before they get out of control. Successful contract execution does not depend so much on what the parties have drafted in the contract but rather on the way they handle difficulties during the course of the project.

Contracting is not a game, but game theory applies

Before we can look into the different ways of solving this phenomena, we need to understand some basic principles first. Let’s analyze the contracting process nature using game theory definitions:

A) Construction contracts are not a zero-sum game. In this type of projects we rarely face a situation where one party’s gain is equal to the other’s party loss. Think of turbine Defects Liability for example. The gain that the Employer (Owner) may receive as compensation may be higher than the actual loss of the Contractor (and vice-versa).
B) It is not a competitive but a cooperative game. The main goal of the contract is to set the terms for building a power plant. The parties need to cooperate in order to achieve that. In this sense they are a “coalition” with common interest.
C) There is no antagonism. Antagonism is present when one of the parties aims not only in maximization of its own payoff function, but also in minimization of the other’s party payoff. Image a situation where the Employer’s negotiation strategy is to achieve not only the most profitable project, but also the lowest margin for the Contractor. One can guess the consequences of that.

With the above in mind we can say that the wind construction contract negotiation and contract execution represent a non-zero-sum, cooperative process where both parties have a common goal: successful project completion.

How to achieve that goal?

Cooperative games emphasize participation and problem solving. In contracting terms this can be enforced by gainshare/painshare structures. Here are two of the possible ways to materialize that.

  1. Adjust the FIDIC terms to include real incentives
  2. >> One example of this is the so called “early completion bonus” in FIDIC templates. If set properly it can be a real “gainshare” factor. The important thing is to agree on a sufficiently high amount that can be actually gained by the Contractor on early completion. If the bonus seems too vague or practically impossible to get, then the incentive is not real. (Example: The Employer promises a share of early generation revenues while it is known upfront that “early generation” is not feasible under the PPA conditions).

    >>Play “open-book” on certain items. This is valid approach especially in cases where for some reason the main Contractor is forced to include certain additional scope that is not core part of its business. (Example: if you include foundations in the scope of a wind turbine manufacturer it may be wiser to split this portion on actual cost + profit, rather than agreeing on a lump sum containing too high contingencies.)

  3. Use Relational Contracting (Project Alliancing)

Relational contract theory looks at contracts as relations rather than as discrete transactions. A practical example is the so called “Project Alliancing”. It handles risk completely different from standard contract. Risk is shared and not allocated between the parties. The focus is on cooperation vs. blaming the other, open communication, teamwork and incentives to innovate which in turn stimulate decision making that is best for the project (vs. best for the single party).

The Project Alliancing concept was developed by BP during the 90s for projects in the offshore oil exploration field. Here are its main characteristics:
>> Collaborative target cost development. After careful contractor selection procedure, all parties sit together and develop the so called optimized “target cost”. This is different from the standard approach where bidders are asked to offer the most competitive price based on pre-defined terms & conditions. The rationale behind that is the following: Employers often use the contract as “legal protection” in attempt to shift most of the risks to the contractor using harsh contract clauses. Larger contractors in turn are trying to pass the risk to smaller subcontractor who may not have the financial backing to cover it. Further, harsh contracts discourage responsible bidders and attract bidders that are willing to take any kind of risk. Therefore a competitive bidding process does not necessarily lead to the best possible outcome. The cost level achieved in this process is also far from certain.
>> Uninsurable risk is shared between the parties (and not allocated/split between them).
>> Participant are paid based on an open-book model consisting of guaranteed fees, corporate overhead and profit (the maximum amount the participant can lose for target cost overruns) and a predetermined gainshare/painshare portion depending on the final cost vs target costs.
>> Project is governed by an Alliance Board that takes unanimous decisions.
>> Project management team that handles daily issues is formed by participants from all parties.
>> Disputes are handled internally with litigation left as last resort.

Is it really working?

The above may sound too good. Indeed, the Project Alliancing approach has proven as successful in the oil industry as well as in many Public Private Partnerships.

However, when we talk about wind power projects we need to consider the fact that most of them are financed on a non-recourse project finance scheme (compared to balance sheet financing). Lenders, among other things, require CAPEX certainty.

Therefore one of the main question is: Can you convince them that a cooperation and incentive based contract will give a lower, far more certain cost level than the traditional approach? And is it better to have a fixed values in your spreadsheet or a de-risked contract that results in higher chance for timely project completion?

CHALLENGES IN CROSS-COMMODITY SYSTEM IMPLEMENTATIONS

CHALLENGES IN CROSS-COMMODITY SYSTEM IMPLEMENTATIONS

CHALLENGES IN CROSS-COMMODITY SYSTEM IMPLEMENTATIONS

In complex implementations for bigger energy traders, there is often a need to implement trading of different commodities. When these are close in terms of trading, quality measures, and delivery chain (eg gas oil, fuel oil, crude), solutions are fairly straightforward – you apply a certain logic across the board. When, however, the requirement is to implement flow and bulk commodities at the same time, things get interesting, as these are based on a different production logic which dictates a different market model. This post will cover basics like trading units, settlement units and reporting. In the next posts, we will cover some considerations on risk measurement, valuation, currencies, and other factors that challenge system designers, implementers, and users of ETRM solutions. It will also show why knowing the difference between MW and MWh is a significant step towards the top quartile of ETRM experts:).

Flow commodities

Flow commodities are power and natural gas (in most cases) – where the unit of trading and the price and delivery unit are different. To start with the basics, power is traded in MW (megawatt) – but the price per unit is eg EUR/MWh (megawatt hour) and the settlement is based on the amount of MWh received. MWh is a flow of one MW for one hour. This means:

  • If you buy 10 MW with delivery of 30 minutes, you get 5 MWh
  • If you buy 10 MW for the month of January, you get 7440 MWh (January has 744 hours)

Same logic applies to gas, but with a few twists:

  • In Germany, wholesale trading unit is MW, which brings about a similar case to power (at least before things get to settlement and imbalance calculations, which is done in cubic meters – and the GCV is published by TSOs month by month)
  • In the Netherlands, trading is mostly in MW, but there are also cubic meter (m3) deals (fortunately, the TSO applies a fixed conversion rate from m3 to MWh, so recalculation is easier)
  • In the UK, unit of trading is Therms/Day, while price and settlement unit is Therm – a similar logic applies there – but the flow is daily and not hourly
  • In France, PEG-N trades in MWh/day – again daily flow, but this time measured in MW

Bottom line: unit of trading is linked to the settlement period of the system – be it hourly or daily (or something else) – and the settlement amount is calculated by multiplying by the number of settlement periods within the deal delivery period.

For a system implementation this means:

  • The system has to handle MW as a trading unit and MWh as a settlement and pricing unit
  • The system needs to be able to handle dynamically calculated flow positions. This means for a certain position a good reporting solution should be able to show eg MW on a daily granularity, on a weekly granularity, on a monthly granularity, and on any other required granularity which supports the business model

The latter point is quite complex to achieve for at least two reasons:

  1. The need to combine daily and hourly based flow units and daily and hourly flow reporting:

    Imagine the following situation: there is a gas desk which trades spreads between UK and Germany. The UK positions are in Therms/Day and the German ones are in MW. Obviously, the respective country positions have to be reported in the trading unit of the market. However, in order to make sure the positions are of the same magnitude, you will need to report them both in the same unit – ie both UK and Germany in MW or UK and Germany in Therms/Day or both.

    In most cases, this is straightforward – but for daylight saving days, you will get a difference – as the MW position will produce one hour less or one hour more of MWh depending on whether it is March or October – and the Therms/Day position will remain the same – hence, the MW position for these days will be either larger or smaller than the rest.

  2. Even if you don’t need the scenario above in one report, the system needs to be smart enough to handle both situations separately – ie a way to understand what flow granularity the market trades in, but also allow for differences (an obvious example is the Belgian gas market with its hubs – Zeebrugge Beach is a physical hub which trades in Therms/Day and ZTP is a virtual hub which trades in MW).

Bulk commodities

Bulk commodities like oil, refined products, and coal have different trading units and unit logic behind them. They trade in certain quantities and the delivery is linked not to a specific hour or day, but to period. E.g. you can trade 1000 MT (metric tons) of coal monthly for the first quarter of 2015 which would mean you expect a 1000 MT some time in Jan, another 1000 at some point in Feb and a third 1000 in March. This makes a solution simpler, as trading quantity per period = settlement quantity per period = quantity that needs to be reported per period.

Where the complexity with bulk commodity really comes into play is the logistics. If you have only financial trading things get simpler – the complexity moves to modelling diffs, cracks, and outrights and all the conversion factors between volume and mass units in crude oil and refined products, as well as the NCV conversions between different qualities of coal. We will cover these topics in more detail in following posts.

Flow and bulk combined

Cross commodity trading very often comes into play when the requirement to an ETRM solution is to model power plants – virtual or physical. There, a spread with a specific heat rate is required between the power produced and the potential inputs – be they natural gas, crude, refined products, coal, and the certificates that need to be bought.

All of these in one place mean several interesting requirements:

  • Calculating MW and MWh equivalent of bulk commodities that might fire the power plant – using a plant or model specific heat rate
  • Calculating MW or MWh equivalent of natural gas using a heat rate
  • Calculating emissions exposure based on power produced – again based on plant or model specifics

The above, however, are fairly straightforward if the general functionality covers the specifics of the flow and bulk commodities individually.