SOLUTION: Campbellsville University Strategies in Negotiation Article Summary and Analysis

energies
Article
Predictive Trading Strategy for Physical
Electricity Futures
Claudio Monteiro 1 , L. Alfredo Fernandez-Jimenez 2, *
1
2
3
*
and Ignacio J. Ramirez-Rosado 3
Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto,
4200-465 Porto, Portugal; cdm@fe.up.pt
Electrical Engineering Department, University of La Rioja, 26004 Logroño, Spain
Electrical Engineering Department, University of Zaragoza, 50018 Zaragoza, Spain; ijramire@unizar.es
Correspondence: luisafredo.fernandez@unirioja.es; Tel.: +34-941-299-473
Received: 5 May 2020; Accepted: 8 July 2020; Published: 10 July 2020

Abstract: This article presents an original predictive strategy, based on a new mid-term forecasting
model, to be used for trading physical electricity futures. The forecasting model is used to predict
the average spot price, which is used to estimate the Risk Premium corresponding to electricity
futures trade operations with a physical delivery. A feed-forward neural network trained with the
extreme learning machine algorithm is used as the initial implementation of the forecasting model.
The predictive strategy and the forecasting model only need information available from electricity
derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy
has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was
applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months
ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and
the performances of the forecasting model and of the predictive strategy were tested with data
corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark
trading strategies are also presented and evaluated using the Risk Premium in the testing period,
for comparative purposes. The results prove the advantages of the predictive strategy, even using the
simpler forecasting model, which showed improvements over the conventional benchmark trading
strategy, evincing an interesting hedging potential for electricity futures trading.
Keywords: electricity markets; mid-term forecasting; energy trading; electricity price forecasting
1. Introduction
1.1. Context of This Research
New trading scenarios for electricity markets have arisen over the last three decades. Electricity
can be traded as any other commodity: it can be bought or sold in deregulated electricity markets.
The main difference with respect to other commodities is the limited storage capability that electricity
has nowadays. Wholesale electricity markets are organized in a set of different niche markets, in which
agents exchange energy and reserves from mid-term to very short-term periods. Mid-term periods,
from days to some years ahead, are covered by forward markets, while short and very short-term
periods are covered by day-ahead, intraday and real-time markets. In these markets, i.e., day-ahead,
intraday and real-time markets, only agents who sell or buy electricity can trade, unlike forward
markets where financial agents can also operate. In order to distinguish the electricity markets,
the day-ahead market, organized with trade auctions, is called the spot market [1]. The spot market
presents higher price fluctuations than the forward markets.
Forward electricity markets provide hedging opportunities against the price uncertainty of the spot
market, and therefore, they contribute to reducing the operational risks. In Europe, the highest volume
Energies 2020, 13, 3555; doi:10.3390/en13143555
www.mdpi.com/journal/energies
Energies 2020, 13, 3555
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of electricity is traded in forwards markets. The products traded in European forward markets include
electricity forwards, electricity futures, electricity swaps, contract for differences (CfDs), electricity
price area differentials (EPADs) and spreads and electricity options [2]. The most traded product,
by volume of energy, is the electricity forward, which corresponds to the bilateral contract, by which
the buyer and seller agree on a price for a volume of electricity and for a specified delivery period in
the future. Bilateral contracts are traded in a nonstandardized market, the “over the counter” (OTC)
market, and can be arranged directly between buyer and seller or by means of a broker. Most of the
contracts are usually offset in a clearing house and settled in cash.
Electricity futures contracts share the main feature of bilateral contracts as an agreement between
buyer and seller, but these contracts guarantee transparency and anonymity to the agents involved.
Electricity futures are contracts with standardized exchange terms and conditions, carried out on
exchange platforms and with lower fees than other traded electricity products. Electricity futures
can be physical contracts, i.e., exchange contracts for the delivery of a quantity of electricity over a
specified period or financial (cash-settled) contracts, which correspond to back-up transactions or
speculation transactions.
1.2. Literature Review
The price at which agents sell electricity, through futures, is called the bid. The price at which
agents buy electricity, through futures, is known as the ask. The difference between the maximum
ask and the minimum bid is called the bid/ask spread. The agents participating in the electricity
futures market indicate, by submitting asks or bids, their willingness to buy or sell electricity and
their prices. They are, therefore, indirectly reflecting their perception about the average value of the
prices that will be cleared in the spot market during the delivery period. In most commodity markets,
it has been observed that when the end of the delivery period for a futures contract is approached,
the futures price converges to the spot price of the underlying commodity [3]. An initial study [4]
using electricity forward contracts price in the two US markets showed that the forward contracts
price was a downwardly biased predictor of the future spot price under a low power demand and
a moderate risk demand. On the other hand, several studies on European markets have shown the
inefficiency of such markets, revealing that the futures prices are not unbiased predictors of the future
spot prices [5–7]. Nevertheless, other studies, such as [8], have successfully used the futures price to
predict the electricity price on the spot market. The differences in the predictive capacity of futures
prices among diverse markets may be due to the different types of electricity supply in each market.
For example, [9] shows that the storability of the fuels used to produce most of the energy in an
electricity market leads the futures price to contain not only information about expected changes in the
spot price but also risk premiums.
The risk premium (RP) can be obtained as the price of a futures contract minus the price of
the underlying commodity [10]. The ex ante RP is calculated by using the expected price (price
estimate) of the underlying commodity at the time of delivery, while the ex post RP is calculated
by using its real price at this time. Electricity futures are slightly different from commodity futures,
i.e., electricity is delivered over a period (day, weekend, week, etc.); it is not delivered at a specific
moment. Since the calculation of the ex ante RP for electricity forward contracts requires a significant
effort in modelling the dynamics of the electricity market in order to estimate (by forecasting) the
average spot price during the delivery period, in economic studies, it is more common to analyse
the ex post RP. Thus, the ex post RP corresponds to the sum of the ex ante RP and the average spot
price forecasting error (i.e., the difference between the forecasted average spot price and the average
real price during the delivery period) [11]. Several studies have analysed factors affecting the RP in
different electricity markets: [12] found significant RPs in the electricity forward prices in an American
market; [13] reported a term structure of the RP in the German electricity market due to the combination
of two factors related to the agents; [14] reported a positive impact of the water reservoir level on
the RP in the Nord Pool market; [15] found that the RP decreased over time as the delivery period
Energies 2020, 13, 3555
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approached in the Nordic and German/Austrian electricity markets; and [16] described that the RP for
peak and off-peak hours in the UK spot market are clearly different, as they are the RP for the different
seasons in the forward markets. Furthermore, [11] showed that the sign of the ex post forward RPs in
Germany, France and Spain had a positive correlation.
The Iberian Electricity Market (MIBEL) was created in 2004 through the integration of the previous
Portuguese and Spanish electricity markets. Any consumer in the Iberian Peninsula can buy electricity
produced by any power producer or sold by any retailer in the same area. The spot market (day-ahead,
intraday and real-time) is managed by the Iberian Energy Market Operator–Spanish Division (OMIE),
while the forward market is managed by the Iberian Energy Market Operator–Portuguese Division
(OMIP). In the territory covered by the MIBEL, there are two areas, the Spanish and Portuguese areas,
with different locational marginal prices. The electricity prices in MIBEL are identified as SPEL (Spanish
area) and PTEL (Portuguese area).
The power derivatives portfolio for the MIBEL includes physical and financial futures contracts.
The physical futures contracts can be base-load or peak-load [17]. A base-load contract involves the
reception or supply of electricity at a constant power of 1 MW throughout all hours of the delivery
period, while a peak-load contract involves the reception or supply of this constant power every day in
the delivery period but between 8:00 a.m. and 8:00 p.m. The traded contracts correspond to delivery
periods of 1 week, month, quarter or 1 year; therefore, they are called weekly, monthly, quarterly
and yearly physical electricity futures. In practice, futures contracts are classified according to their
maturities. The maturity of a futures contract corresponds to the time that must pass until the delivery
period. For example, a monthly base-load futures contract with a maturity of i months means that the
delivery month is the i-th month after the month of the current day, i.e., if the current day (negotiation
day, n) belongs to June and the maturity of the futures contract is 3 months, then the delivery month
corresponds to September.
The relationship between futures and spot prices in MIBEL was studied by [18], concluding that
there is a unidirectional Granger causality from monthly and quarterly futures price with maturity of
1 month to spot prices. A similar conclusion is reported in [19], which shows that futures prices, close
to the delivery period, are predictive of spot prices, and in [20], which checks the positive correlation
between spot and futures markets across all maturities. Therefore, on the basis of these research studies,
such relationships could be used to establish trading strategies to maximize the profits of the agents
who buy/sell in the MIBEL. Such strategies should rely on mid-term forecasts of the prices settled on
the spot market.
In general, very little research has been conducted on mid-term electricity price forecasting [21].
Forecasting of electricity prices in the mid-term is more complex than in the short term, i.e., an important
characteristic as the trend from the immediate past cannot be used for the mid-term forecast [22].
Despite everything, several studies in recent years have dealt with the mid-term forecasting of the
price that will be settled in the spot market. The techniques used are similar to those used for the
short-term prediction, although models with good short-term predictive performance tend to degrade
with longer forecasting horizons [23]. The techniques include support vector machines [22,24], linear
regression [25,26], factor models [27,28], grey models [29], neural network with extreme learning
machine algorithm and evolutionary optimization [30] and deep neural networks [31]. The forecasting
horizon ranges from 1 week [26,30] to 6 months [22,24], although in the studies with forecasting horizon
over 2 months, the authors assume that the future values of the explanatory variables are perfectly
known (they use real future values, rather than forecasts for some explanatory variables that evolve
over time). A complete overview of the state-of-the-art in mid- and long-term electricity forecasting
models can be found in [32].
There are very few applications of machine learning techniques for trading in electricity markets
described in the international literature [33]. An application for the Colombian electricity market is
described in [34], in which a combination of a fuzzy inference system and learning algorithm provides
information about the amount of electricity to buy through bilateral contracts, according to the agent’s
Energies 2020, 13, 3555
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profile, in order to maximize the profit, but less than a month in advance. For the MIBEL market
Pinto et al. [35] present a decision support scheme, based on a spot price forecasting model, to optimize
agents’ profits. In this sense, several electricity market simulators have been developed in order to gain
insight about the optimal trading strategy [36,37]. A recent work [38] has dealt with the direct forecast
of the RP for year-ahead products in the German electricity market, although it leaves the user to choose
the trading strategy to follow. Other approaches, designed for imbalance or intraday markets and
based on statistical forecasting models, have explored risk-constrained strategies to increase profits and
reduce risk by hedging against penalizing imbalance prices [39] or apply a density forecast provided
by a stochastic latent moment model to predict imbalance volumes in order to optimize the positions
on the intraday market [40].
In the different works published in the international literature, we can find diverse definitions
for the term RP. The terms risk premium, forward risk premium, forward premium or market price
of risk have been used interchangeably [14]. In this article, we used an ex post definition for the RP
corresponding to the difference between the log of the average realized price in the delivery period and
the log of the settlement futures price at the time of negotiation. This definition has been used in [19].
A similar definition, although without the log function, is used in [6,11,15,16]. The MIBEL Board of
Regulators also defines the term risk premium on the basis of such difference [41]. On the other hand,
some authors use the ex ante risk premium, which is defined as the difference between the futures
price and the expected spot price in the delivery period [9,10,12,13]. With the definition used in this
article, operations on the spot market correspond to a null value of RP. Additionally, in Section 2.2 of
this article, we have established two different formulas for RP (with difference in sign) for buyers and
sellers. Thus, with our convention for the RP signal used by the original predictive trading strategy
presented in this article, a positive value of the signal represents always profits or savings for the agent,
and a negative value represents losses, always with respect to the operations (purchases/sales) traded
on the spot market.
1.3. Contributions and Structure of This Article
This article proposes an original trading strategy for both buyers and sellers in futures electricity
markets (predictive strategy). The strategy is based on the use of a decision signal obtained with a new
mid-term forecast of the average spot price for the delivery period. Depending on the forecast and on
the value of the last settlement price for the futures, the decision signal indicates whether the agent
should buy/sell the electricity through futures or to wait to buy/sell on the spot market.
In traditional trading strategy approaches, the “best guess” of the agents is the last known futures
settlement price, reflecting the aggregated guess of all agents about an estimated average spot price in
the corresponding delivery period. The predictive trading strategy proposed in this article achieves
an advantage in relation to the traditional trading strategy, anticipating the good/wrong decision
of buying/selling electricity through futures instead of waiting for the spot market. The predictive
strategy succeeds when it is possible to forecast a mid-term average spot price with better accuracy
than the traditional “best guess” of the agents, used as a benchmark in this article.
We also present a new mid-term average spot price forecasting model, implemented by a
single hidden layer feed-forward neural network, trained with the extreme learning machine (ELM)
algorithm, which exclusively uses available information from electricity derivatives and spot markets.
The selection of the explanatory variables of the mid-term monthly average spot price forecasting
model of this article is based on the experiences of market agents, and the set of explanatory variables
is not exhaustive. The article is not focused on the exploration of improving forecasting performance;
there is still room to improve the forecasts, in terms of both techniques and features. This kind of spot
price forecast (several months ahead) is not as common as a short-term spot price forecast (1–7 days),
but it is an issue of increasing interest for trading agents. For comparison purposes, a linear regression
mid-term forecasting model is also tested. This alternative forecasting model is based on the ordinary
least square (OLS) technique. Both the predictive trading strategy and the spot forecasting models
Energies 2020, 13, 3555
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have been successfully tested with historical data of the MIBEL, although they could be applied to
other electricity markets.
Additionally, an original evaluation of the proposed predictive strategy is presented in this article.
This evaluation is based on the comparison of the trading results achieved with the proposed predictive
strategy with those obtained with two other reference strategies, defined as the “best” and the “worst”
ones. These reference strategies assume knowing beforehand the actual values of the average spot
price in the delivery period. They provide the inner limits of the scale where the RP of the predictive
strategy performs.
The article is structured as follows: Section 2 presents the mid-term average spot price forecasting
model and the trading strategy for agents in the futures market; Section 3 presents the results obtained
for both buyers and sellers acting on the MIBEL and finally, Section 4 presents the conclusions.
2. Conditional Predictive Trading of Electricity Physical Futures
In this article, we present a novel approach for predictive trading and hedging of physical futures
on electricity markets. It is applied to physical delivery futures, where the buyer or the seller will
keep the product until the electricity delivery period (constant power, delivered in a predefined
month). This commitment until the delivery is similar to that established in forward contracts, with the
difference that for futures contracts, trading is carried out in a clearing house for derivatives exchange,
without direct contact between buyer and seller. Instead, the forward contract also has a commitment
to keep the product until delivery, but it is a direct contract between buyer and seller stipulated on the
OTC market.
The objective is to decide, on each negotiation day (from a set of days before the delivery period),
if the agent (buyer or seller) should buy/sell th …
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