Download Modeling the emergence of functioning natural gas wholesale markets

Modelling the Emergence of Functioning Natural
Gas Wholesale Markets
G. Bas - [email protected]
Abstract
Tue European Commission considers liberalized and liquid natural gas
wholesale markets as a priority. Agent-based models are considered suited
to provide insight in what market conditions contribute to the emergence
of functioning natural gas wholesale markets. We present an abstraction of the natural gas market to explore the emergence of functioning
wholesale markets. For this abstraction we apply graph theory, supplier
selection theory, auction theory, and insights from finance to the natural
gas market. The abstraction is developed into a simulation model, of
which different aspects have been validated with economic logic. In this
research we demonstrate that the applied theories and methods can form a
feedback loop that allows us to explore the evolution of marketplaces; and
that the abstraction is valid in the context of natural gas markets. Minor
adjustments to the abstraction allow it to be used to explore other markets
(e.g. biogas). The abstraction can be extended by considering more than
1 wholesale market, including more aspects of the physical infrastructure,
and extent the behaviour of market participants by considering strategic
behaviour.
Keywords: agent-based modelling · bilateral negotiation · contract selection ·
natural gas · socio-technical system · wholesale market
1
Introduction
The European Commission considers centralized trading of natural gas at wholesale markets important for realizing a real competitive natural gas market
(Spanjer, 2008). Traditionally natural gas is traded via long-term gas supply
contracts, of which the terms are negotiated bilaterally between a producer and
a customer (Stern, 2007). In contrast to trading at wholesale markets, trading
via long-term gas supply contracts is decentralized and therefore is considered
to limit competition.
Multiple wholesale markets have been established in Europe, but apart from
the NBP in Britain and the TTF in the Netherlands1 , the liquidity of none of
these hubs is considered high enough to provide a reliable price signal (Heather,
2012). The liquidity of these wholesale markets increases when more market
participants decide to trade natural gas at the wholesale market, rather than
via long-term gas supply contracts. However, regulation of gas networks cannot
oblige market participants to enter a market, it can only create create conditions
1 A minimum churn rate (the measure of liquidity commonly used) is at least 10. The churn
rates of the NBP and TTF are respectively around 21 and 14, and thus meet the minimum
required churn rate. However, in comparison to the Henry Hub in the United States, which
has a churn rate of around 377 (Konoplyanik, 2011), the European natural gas hubs are only
barely liquid.
1
that make it more likely that they are willing to do so (Glachant, 2011).
To explore what market conditions contribute to the emergence of functioning natural gas wholesale markets, we propose the development of a simulation
model. For the development of the simulation model, we consider the natural
gas market from a socio-technical system perspective (cf. Ottens, Franssen,
Kroes, & van de Poel, 2006). The natural gas market consists of a technical
subsystem (of physical artefacts for the production, transportation and consumption of natural gas) and a social subsystem (of market participants and
their relationships for the change in ownership and the transportation of natural gas). Nikolic and Kasmire (2012) argue that “through modelling these
[socio-technical] systems in light of the principles of complex adaptive systems,
we can better understand the specific systems and how to interact with them
in order to achieve goals.” Of the existing modelling paradigms, agent-based
modelling is considered the best suited to represent these socio-technical system (Nikolic & Kasmire, 2012). Agent-based models (ABMs) are models that
consist of a number of agents which interact both with each other and with
their environment, and can make decisions and change their actions as a result
of this interaction (Ferber, 1999). This individual decision making and interaction among agents makes that this modelling paradigm is particularly suited to
represent the trading of natural gas.
In this article we present an agent-based model of a single natural gas market. In this market the market participants have to decide whether to trade on
a wholesale market or via a long-term contract, and they negotiate with each
other to determine the quantity to trade and the price to trade that quantity
for. In their decisions they take into account the perceived historical risk and
price of both ways of trading natural gas. Aggregate behaviour, such as the
liquidity of the wholesale market, emerges through these decisions and interactions. This allows us to research how the market conditions affect the behaviour
of market participants and, through their individual behaviour, the condition
of the wholesale market.
First, we present a conceptual model of a natural gas system, which later
is developed into a simulation model. In section 2 we present an overview
of all components (market participants, physical infrastructure, and contracts)
considered in our system. In this section also the activities undertaken by market
participants are introduced. The behaviour of the market participants is more
extensively discussed in section 3. In section 4 we discuss the implementation
of the conceptual model into a simulation model. In section 5 we discuss the
validation of the simulation model with economic logic. After this, we present
the conclusions from this paper.
2
System overview
The system presented in this section is the basis of the agent-based model. It
consists of different types of components, that all have individual characteristics, and are related to each other. The components that represent agents also
have behavioural rules. In this section we present an overview of the components considered in our system, and also will we briefly introduce the activities
undertaken by the agents.
2
The system considered consists of market participants (producers, customers, and traders) of which the first two are physically connected to an entry-exit
area2 via pipelines. The capacity of these pipelines determines the quantity
of gas that the market participants can either put into or take out the entryexit area. Transmission capacity within the entry-exit area is assumed to be
unlimited, so that natural gas can flow unhindered from an entry point to an
exit point. This allows us to exclude the transmission of natural gas from the
system. The gas exchange, which functions as an abstraction of the wholesale
market, is located at a virtual point in the entry exit area. Since the transmission of natural gas in the entry-exit area is unhindered, the virtual point is
considered to be the entry-exit area, and therefore the gas exchange is located
at the intersection of all pipelines.
All 3 market participants have an interest in a certain quantity of natural;
producers have a stable production capacity (MWh/mo) that they want to sell
to generate the highest profit possible; customers have a monthly fluctuating
demand (MWh/mo) for their business activities that they want to procure for
the lowest price possible.; and traders have a certain capital that they want
to invest to generate the highest profit possible. The behaviour of the market
participants is aimed at securing these interests.
Natural gas is traded by means of contracts, which specify who will deliver
what quantity of gas to whom, at what price and at what time this will occur.
In this system we consider only two types of contracts, which represent the
“traditional” bilateral way of trading and the “new” centralized way of trading.
• The first type is the bilateral contract, which is comparable to the longterm gas export contract (discussed by Konoplyanik (2010)). This contract is signed to ensure a flow of natural gas for a period of time (between
the start and end date of the contract). The quantity of the contract is
delineated by a take-or-pay clause3 with a possible deviation of 25%. The
price of 1 MWh delivered under the bilateral contract is negotiated during a bilateral negotiation, and the destination clause ensures that the
delivered gas cannot be resold.
• The second type of contract is the structured contract. This is a standardized contract, which is traded at the exchange. The quantity is fixed
at 1 MWh, and that quantity will be delivered in a single month, which
is indicated by the expiration date of the contract. The destination of a
structured contract is always the gas exchange, and the origin is a market
participant. Since the destination of every structured contract is the gas
exchange these contracts can easily be re-traded, which entails that gas
can be traded multiple times before it is actually delivered.
The (high-level) activities undertaken by the market participants to trade
natural gas, and thereby secure their interests, are presented in figure 1. These
activities can be divided into two phases; selection and execution4 .
In the selection phase the producers and customers determine how they want
to respectively sell or procure natural gas. These activities are only performed
with a certain interval, which differs per market participant.
2
3
4
For a discussion of entry-exit areas, see Kema Nederland (2011).
For a discussion of the take-or-pay clause, see Konoplyanik (2010)
Sections ?? and ?? extensively discuss the activities that make up these phases.
3
In the execution phase, the market participants try to execute the intends
determined in the selection phase. The first activity in this phase is that the
producers and customers, that want to sell or buy natural gas via bilateral
contracts, perform bilateral negotiations to determine the terms of the bilateral
contracts. Once the bilateral negotiations are finished, all market participants
bid for or offer the structured contracts they want to buy or sell. Every bid or
offer that is submitted to the exchange is directly processed by the gas exchange,
resulting in a signed structured contract or adding the order to the exchange’s
limit order book.
3
3.1
Agent behaviour
Selection phase
The selection phase consists of two activities; select quantity to procure through
each type of contract conducted by customers, and select quantity to sell through
each type of contract conducted by producers.
3.1.1
Quantity to buy
To determine the quantity to procure through each type of contract, the customers determine the quantity to procure through structured contracts, by balancing risk and return of buying natural gas through structured contracts. The
remaining demand, then, is procured through bilateral contracts. To balance
risk and return, the customers use the constant absolute risk aversion (CARA)
utility. The risk involved with buying natural gas via structured contracts is
that the customer is not able to procure the natural gas desired, and the return
is the difference in price of bilateral and structured contracts5 .
Equation 1 show how the quantity to procure through bilateral contracts
(qb ) is determined. In this equation qavg is the average monthly demand in the
current planning period. The CARAexchange is calculated in Equation 2. The
numerator indicates how much more expensive natural gas procured through
bilateral contracts is, by comparing the costs of procuring natural via bilateral contracts (cb ) with the costs of procuring natural gas via structured contracts (cs ). The denominator indicates the risk involved with procuring natural
gas through structured contracts. For this the customer considers the chance
that an order submitted to the exchange results in a signed structured contract
(successexchange ) and its individual risk aversion (λ). successexchange is based
on considering a subset (due to bounded rationality) of the orders submitted
to the exchange, and determining the percentage that has resulted in a signed
structured contract. It thereby represents the perceived chance that a submitted
order results in a signed structured contract.
qb = (1 − CARAexchange )qavg
(1)
5 The customer’s return is positive when the price of bilateral contracts is higher than that
of structured contracts. This implies that it is possible that the return is negative.
4
Figure 1 – High level overview of activities
CARAexchange


0
= 1


(cb/cs )−1
λ/success
3.1.2
exchange
if CARAexchange ≤ 0;
if CARAexchange ≥ 1;
else.
(2)
Quantity to sell
Just as customers consider the security of supply when they determine how to
procure natural gas, the producers consider the security of demand when they
determine how to sell natural gas (van der Linde & Stern, 2004). In order to
balance the risk and return involved with selling natural gas through structured
contracts, the producers also calculate the CARA utility. The risk in this case
is that the customer is not able to sell the desired quantity of natural gas.
For producers the return is the difference in price of bilateral and structured
contracts, but differs from the customers in that the producer’s return is positive
when the price of structured contracts is higher than that of bilateral contracts.
Equation 3 indicates how the producers determine what quantity to sell
through bilateral contracts (qb ). In this equation capprod represents the maximum monthly production capacity of the producer, being the total quantity of
natural gas that is available for sale. Equation 4 shows how the CARA utility is
calculated. For producers the numerator of the calculation indicates how much
higher the profit from selling natural gas through structured contracts (πs ) is
than the profit from selling natural gas through bilateral contracts (πb ). The
denominator indicates how the producer perceives the risk of selling natural
gas through structured contracts, which is calculated in the same way as the
customer calculates its perceived risk.
qb = (1 − CARAexchange )capprod
5
(3)
CARAexchange


0
= 1


(πs/πb )−1
λ/success
3.2
exchange
if CARAexchange ≤ 0;
if CARAexchange ≥ 1;
else.
(4)
Execution phase
In the execution phase the market participants undertake activities to execute
the intends determined in the selection phase. The three activities making up
this phase are bilteral negotiation, submit desired orders to the exchange, and
process orders.
3.2.1
Bilateral negotiation
Bilateral negotiations are the negotiations between a single producer and customer on the terms of bilateral contracts. The bilateral negotiations are abstracted as ascending clock auctions undertaken by producers. The ascending clock
auctions6 are linked to each other through the customers considering which
auction allows them to procure natural gas for the lowest price. The steps
undertaken for a bilateral negotiation are:
1. All producers that have available capacity to sell through bilateral contracts communicate a range of prices7 for which they want to know the
quantity that the customers are willing to procure. The shaded area in
figure 2 indicates the range of prices communicated by a producer.
2. The customers determine the quantity they are willing to procure for the
prices in the range, and communicate this (bid) to the producer of the
specific range. The customer uses its reservation price to determine what
quantity it wants to procure. For a price lower than its reservation price it
wants to procure its demand; for a price higher than its reservation price
it wants to procure nothing. In figure 2 this bid is represented by the bold
part of the demand curve in the upper 3 graphs. For example, customer
2 has a stable demand for all prices in the range, while at a certain price
(its reservation price) in the range the demand of customer 1 drops to 0.
3. The producers aggregate all bids they receive and thereby determine the
quantity of natural gas they can sell for all prices in the range they specified
in step 1. The aggregated demand is presented by the bottom graph in
figure 2. This graph clearly indicates that when the price increases, the
demand for natural gas decreases.
4. The producers determine whether there is a price in the range for which
demand equals supply, called a clearing price. The clearing price is where
production capacity (vertical striped line) and demand (bold line) cross.
6
For a discussion on ascending clock auctions, see Cramton (1998).
In the first round of an ascending clock the lower bound of the range is equal to the
marginal costs of the producer, and the upper bound is 10% higher. In the rounds that
follow, the producer considers the quantity demand for in the previous round. If that demand
is higher than supply, the range becomes higher; if that demand is lower than supply, the
range becomes lower. If a range becomes higher, the upper bound of the old range becomes
the lower bound of the new range and the new upper bound is 10% higher than the new lower
bound. If the range becomes lower, the old lower bound becomes the new upper bound and
the new lower bound is 90.9% of the new upper bound.
7
6
(a) If this is not the case, the producers update their range of prices
(higher if demand exceeded supply, and lower if supply exceeded
demand) and start at step 1 again.
(b) If there is a clearing price, the producer sends an offer to the customers that are willing to pay the clearing price or more. The quantity
offered to the customers is represented by the horizontal position of
the customer’s bid. Customer 2 gets offered all the quantity it has
bid for, while customer 1 only gets offered a part of what it has bid
for, and customer 3 does not get offered anything. The price of the
offer is the clearing price; in the case of customer 1 this is the price
it was willing to pay for the natural gas, while for customer 2 this is
less than it was willing to pay.
5. The customers compare the offers they have received and signs contracts
with the producer(s) that is (are) willing to accept the lowest price.
6. Unless all quantity is sold or the producer believes there is no more demand, the producer starts a new round (at step 1).
3.2.2
Submit orders
To submit orders to the exchange, the market participants first have to determine what orders they want to submit. The way in which this occurs differs per
market participant. However, the behaviour of producers and customers (with
regard to submitting orders) is comparable and thus is discussed together. The
behaviour of traders differs significantly and thus is discussed separately.
For determining which orders to submit producers (customers) identify for
every remaining month in their planning period what quantity to sell (procure)
through structured contracts. This quantity is determined through subtracting
the already contracted quantity or the quantity reserved for bilateral contracts
(depending on which one is higher) from the production capacity (demand) in
a particular month. A positive quantity implies that the producer (customer)
wants to sell natural gas, while a negative quantity implies that the producer
(customer) wants to buy natural gas.
The limit price of the order depends on whether it is an offer (to sell) or a bid
(to buy). When an offer is submitted, the limit price of that offer is equal to the
marginal costs of the producer (customer); when a bid is submitted, the limit
price of that bid is equal to the reservation price of the producer (customer).
Before the producer (customer) actually submits its new orders, it removes its
old orders, so that it is not possible that the producer (customer) duplicates
orders it has submitted previous months.
Since traders are not concerned with physical gas, but merely want to benefit
from price fluctuations, they have another way of determining what orders to
submit to the exchange. A trader starts with determining what the profit will
be from either buying or selling a specific natural gas contract. Equation 5 and 6
present how the trader calculates the expected profit from respectively buying
“long” or selling “short” a risky natural gas contract with expiration date i. In
these equations h is the length of the planning period of the trader, pexp,i is the
expected price of a structured contract at its expiration date, and pmkt,i is the
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Figure 2 – Illustration of the processing of bids for the bilateral negotiation
current market price of a structured contract with expiration date i.
Erisk,i =
pexp,i h−i
r
pmkt,i
(5)
pmkt,i h
r
pexp,i
(6)
Erisk,i =
After determining the expected profit for every structured contract with
an expiration date in the planning period of the trader, the trader determines
every couple of months what percentage of its capital to invest in “risky” natural
gas contracts. For this the trader uses the CARA utility function, in which it
compares the maximum return of a structured contract with the risk free rate
of return. Equation 7 indicates how the percentage to invest in natural gas
contracts is calculated. For this, the trader balances the profit above the risk
free rate (r) with the perceived risk of trading at the gas exchange. This risk
is represented by the variation of the market prices, σ 2 . After this the trader
calculates how much structured contracts it can buy (sell) for the capital it is
willing to risk. It does this by dividing the capital it wants to risk by the market
price of the contracts it wants to buy (sell).
Once this is done the trader determines what the limit price of the order will
be, which is done by determining at what market price of the best performing
contract, its return will be equal to the profits of the second best performing
contract. Submitting the orders to the exchange concludes the activities of the
trader with regard to the orders to submit to the exchange.
x=
3.2.3
Erisk − (1 + r)
λσ 2
(7)
Process orders
When an order is submitted to the gas exchange, it directly processes the order
by consulting its limit order book to match supply and demand. The steps it
8
performs for this are:
1. The gas exchange receives a bid (offer) with a certain limit price.
2. The exchange checks its order book for offers (bids) with a limit price that
is lower (higher) than the limit price of submitted bid (offer). These offers
(bids) are called possible matches.
(a) If there are no possible matches, the submitted bid (offer) is added
to the order book.
(b) If there are possible matches, the submitted bid (offer) is matched
with the offer (bid) with the lowest (highest) limit price. In that case
two contracts are signed; one for delivery to the exchange and one
for delivery by the exchange. The price of the contracts is the price
of the offer (bid) that was already in the order book. The quantity is
either the quantity of the submitted bid (offer) or the quantity of the
offer (bid) that was already in the order book; depending on which
of the two orders has the lowest quantity.
i. If the quantity of the submitted bid (offer) is higher than the
quantity of the offer (bid) that was already in the order book,
after signing the contract, the offer (bid) is removed from the
order book and the bid (offer) is matched with the next best
match (see step 2b). This continues until the entire bid (offer)
has resulted in signed structured contracts or there are no more
possible matches.
ii. If the quantity of the submitted bid (offer) is lower than the
quantity of the offer (bid) that was already in the order book,
after signing the contract, the quantity of the offer (bid) is decreased by the quantity of the contract.
4
Software implementation
The conceptual model, of the system discussed in the 3 previous sections, is implemented in software by using the AgentSpring framework. Using this framework implies that all components8 of the model are stored at the vertices of a
graph database, with the relationships among them being abstracted as edges
of the graph. Another implication of using the AgentSpring framework is that
the agents and their behaviour are decoupled. The behaviour, then, is recorded in scripts, which are “acted” by the agents. The scripts roughly match the
activities discussed in section 3.
For more information with regard to the implementation, we refer to (Bas,
2012) or to the software code, available from https://github.com/EMLab/.
5
Model validation
The simulation model is validated through comparing the system’s behaviour
(in differing scenarios) with economic logic. The validation is performed through
4 different cases, which all highlight a different aspect of the model. However,
8 Producers, customers, traders, exchange, pipelines, bilateral contracts, and structured
contracts.
9
in this paper we present a single case, which is concerned with the availability
of physical gas.
Clingendael International Energy Programme (2008) concludes that when
there is a buyer’s market (an oversupply of natural gas) the buyers are able to
dictate the terms of natural gas contracts and that therefore more natural gas
will be traded at wholesale markets. However, when there is a seller’s market
(an undersupply of natural gas) less natural gas will be traded at wholesale
markets.
For this case we consider 3 different scenarios, which differ through the ratio
of physical supply to physical demand. In the oversupply scenario the ratio of
supply to demand is 5, in the neutral scenario the ratio of supply to demand is
1, and in the undersupply scenario the ratio of supply to demand is 0.2.
For every scenario we determine the extent to which a functioning wholesale
market has emerged by considering 2 indicators; 1) the churn rate of the gas
exchange, and 2) the market share of structured contracts. Thus when the ratio
of supply to demand increases, economic logic dictates that the churn rate of
the gas exchange increases and that the market share of structured contracts
increases. If the model is valid, it should follow this logic.
Figure 3 presents how the churn rate develops over time in the 3 scenarios
discussed before. The graph indicates that when the supply to demand ratio
increases the churn rate of the gas exchange increases as well. It also indicates
that the difference between the scenarios is substantial and sustainable over
time.
Figure 3 – The churn rate at the gas exchange for different supply to demand ratios
The second indicator, presented in figure 4, confirms this. When the supply
to demand ratio increases, also the market share of structured contracts increases. As is the case with the churn rate, the difference between the scenarios
is both substantial and sustainable.
These observations lead to the conclusion that when there is a buyer’s market
the wholesale market prospers, and when there is a seller’s market the development of the wholesale market is hampered. This means that with regard to the
availability of physical natural gas the behaviour of the model is in line with
10
Figure 4 – The market share of structured contracts for different supply to demand
ratios
economic logic and is therefore validated.
6
Conclusions
In this paper we present an agent-based model of trade in the natural gas
market to explore the emergence of functioning wholesale markets. In order to
model the actual behaviour of market participants, we applied supplier selection
theory, auction theory, graph theory, and insights from finance to the natural
gas market. In combination with discussions of the natural gas markets (e.g.
Heather, 2012), these theories and methods were developed into an abstraction
of the behaviour of market participants in the natural gas market. The behaviour of market participants is “neo-classical” utility maximizing, which implies
that strategic behaviour was excluded from the abstraction.
In this research we have demonstrated that the applied theories and methods
can be used to create a feedback loop that allows us to explore the evolution of
marketplaces. We have validated the abstraction in the context of the natural
gas market, by comparing the system’s behaviour with economic logic. This
abstraction may also be applied to other markets, where wholesale markets
have not emerged yet (e.g. biogas).
To further research the emergence of functioning natural gas wholesale markets, we recommend to extend the presented abstraction. Potential extensions
for the abstraction are 1) considering more than 1 wholesale markets, and connecting these markets, 2) include the physical infrastructure in the abstraction,
and 3) extend the behaviour of market participants by including strategic behaviour.
11
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