scholarly journals Granger Causality Network Methods for Analyzing Cross-Border Electricity Trading between Greece, Italy, and Bulgaria

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 900 ◽  
Author(s):  
George P. Papaioannou ◽  
Christos Dikaiakos ◽  
Christos Kaskouras ◽  
George Evangelidis ◽  
Fotios Georgakis
Keyword(s):  
Granger Causality ◽  
Electricity Markets ◽  
Historical Data ◽  
Spot Price ◽  
Spot Prices ◽  
Cross Border ◽  
Electricity Trading ◽  
Network Methods ◽  
Generation Capacity ◽  
Market Variables

Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on cross border trading (CBT) among neighboring interconnected countries. In this work, we empirically analyzed historical data on fundamental market variables (i.e., spot prices, load, RES generation) as well as CBT data (imports, exports, commercial schedules, net transfer capacities, etc.) on the Greek, Italian, and Bulgarian electricity markets by applying the Granger causality connectivity analysis (GCCA) approach. The aim of this analysis was to detect all possible interactions among the abovementioned variables, focusing in particular on the effects of growing shares of RES generation on the commercial electricity trading among the abovementioned countries for the period 2015–2018. The key findings of this paper are summarized as the following: The RES generation in Italy, for the period examined, drives the spot prices in Greece via commercial schedules. In addition, on average, spot price fluctuations do not affect the commercial schedules of energy trading between Greece and Bulgaria.

A Bivariate Model for Deman and Spot Price of Electricity

2012 ◽  
Vol 433-440 ◽  
pp. 3910-3917
Author(s):  
Hilary Green ◽  
Nino Kordzakhia ◽  
Ruben Thoplan
Keyword(s):  
Electricity Markets ◽  
Historical Data ◽  
Extreme Values ◽  
Spot Price ◽  
Bivariate Model ◽  
Spot Prices ◽  
Short Period ◽  
Detailed Statistical Analysis ◽  
Modelling Methodology ◽  
The Mean

In this paper bivariate modelling methodology, solely applied to the spot price of electricity or demand for electricity in earlier studies, is extended to a bivariate process of spot price of electricity and demand for electricity. The suggested model accommodates common idiosyncrasies observed in deregulated electricity markets such as cyclical trends in price and demand for electricity, occurrence of extreme spikes in prices, and mean-reversion effect seen in settling of prices from extreme values to the mean level over a short period of time. The paper presents detailed statistical analysis of historical data of daily averages of electricity spot prices and corresponding demand for electricity. The data is obtained from the NSW section of Australian Energy Markets.

CALIBRATION OF MULTIFACTOR MODELS IN ELECTRICITY MARKETS

2004 ◽  
Vol 07 (02) ◽  
pp. 101-120 ◽  
Author(s):  
MARTIN BARLOW ◽  
YURI GUSEV ◽  
MANPO LAI
Keyword(s):  
Kalman Filter ◽  
Stochastic Processes ◽  
Extended Kalman Filter ◽  
Electricity Markets ◽  
Kalman Filters ◽  
Historical Data ◽  
Spot Price ◽  
Market Data ◽  
Multifactor Models ◽  
Spot Prices

Spot prices of electricity and other energy commodities are often modeled by multifactor stochastic processes. This poses a problem of estimating models' parameters based on historical data, i.e. calibrating them to markets. Here we show how a traditional tool of Kalman Filters can be successfuly applied to do this task. We study two mean-reverting log-spot price models and the Pilipovic model using correspondingly Kalman Filter the extended Kalman Filter. The results of applying this method to market data from several power exchanges are discussed.

A nonparametric model for spot price dynamics and pricing of futures contracts in electricity markets

2014 ◽  
Vol 18 (5) ◽  
Author(s):  
Katja Ignatieva
Keyword(s):  
Electricity Markets ◽  
Continuous Time ◽  
Regime Switching ◽  
Diffusion Processes ◽  
Market Price ◽  
Parametric Models ◽  
Futures Contracts ◽  
Spot Price ◽  
Nonparametric Model ◽  
Spot Prices

AbstractThis paper deals with the estimation of continuous time diffusion processes describing the dynamics of electricity spot prices. Different parametric models have been proposed in the literature, each attempting to capture empirical characteristics and stylized facts of the electricity market like the spiky behavior of the spot prices. Although jump-diffusion and regime-switching models perform reasonably well, there is always a trade-off between model parsimony and adequacy. The results in the literature indicate that none of the models seem to consistently outperform its counterparts. This paper avoids making parametric assumption about the drift and the diffusion coefficient functions of the underlying electricity spot prices, and estimates these functions together with the market price of risk in a nonparametric way. The latter allows us to price futures contracts written on electricity spots. Using electricity spot prices and futures data from the regional electricity markets in Australia, we show that besides offering a convenient way of estimating the continuous-time models for electricity spot prices, our nonparametric estimation procedure performs well in- and out-of-sample when dealing with pricing of future contracts.

scholarly journals Cross-Border Electricity Trading in Southeast Europe Towards an Internal European Market

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6653
Author(s):  
Despoina I. Makrygiorgou ◽  
Nikos Andriopoulos ◽  
Ioannis Georgantas ◽  
Christos Dikaiakos ◽  
George P. Papaioannou
Keyword(s):  
Electricity Markets ◽  
Congestion Management ◽  
Southeast Europe ◽  
Cross Border ◽  
Electricity Trading ◽  
Border Trade ◽  
Coupling Procedure ◽  
Market Areas ◽  
See Region

The European Commission’s Target Model’s main objective is to integrate European electricity markets, leading to a single internal energy market and guaranteeing the instantaneous balance between electricity generation and demand. According to the target model for electricity trading, proposed by the European Network Transmission System Operators for Electricity (ENTSO-E), within each zone, electricity can be traded freely without taking into consideration network limitations. In contrast, for cross-border trading, the exchanges with other market areas are taken into account. Cross-border trade poses a further burden on the interconnection lines, resulting in increasing network congestion, which in turn restricts electricity trading. Thus, calculating the available capacity for trade has a significant ramification on the market. Today, the Available Transfer Capacity (ATC) mechanism dominates cross-border trading, but this methodology may be replaced by the Flow-Based (FB) approach across Europe. This paper investigates both approaches regarding the cross-border congestion management under the market coupling procedure. In our case study, the Southeast Europe (SEE) region is taken into consideration; it consists of both the FB and ATC approach in a five country (Greece, North Macedonia, Bulgaria, Serbia, and Romania) scenario. The purpose of our tests is to perform, compare, and evaluate the effectiveness of each method for the SEE region, while the main findings are the maximization of social welfare, better cross-border trading opportunities, and price convergence via the FB method.

Optimal Cross-Border Electricity Trading

2019 ◽  
Author(s):  
Álvaro Cartea ◽  
Maria Flora ◽  
Tiziano Vargiolu ◽  
Georgi Slavov
Keyword(s):  
Cross Border ◽  
Electricity Trading

Pricing of Futures Contracts

2018 ◽  
Author(s):  
Timothy A. Krause
Keyword(s):  
Fair Value ◽  
Commodity Futures ◽  
Futures Contracts ◽  
Spot Price ◽  
Futures Prices ◽  
No Arbitrage ◽  
Spot Prices ◽  
Cost Of Carry ◽  
Contract Expiration ◽  
The Cost

This chapter examines the relation between futures prices relative to the spot price of the underlying asset. Basic futures pricing is characterized by the convergence of futures and spot prices during the delivery period just before contract expiration. However, “no arbitrage” arguments that dictate the fair value of futures contracts largely determine pricing relations before expiration. Although the cost of carry model in its various forms largely determines futures prices before expiration, the chapter presents alternative explanations. Related commodity futures complexes exhibit mean-reverting behavior, as seen in commodity spread markets and other interrelated commodities. Energy commodity futures prices can be somewhat accurately modeled as a generalized autoregressive conditional heteroskedastic (GARCH) process, although whether these models provide economically significant excess returns is uncertain.

scholarly journals Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3747
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Portfolio Optimization ◽  
Electricity Markets ◽  
Electricity Market ◽  
Retail Market ◽  
Wholesale Market ◽  
Generation Capacity ◽  
Wholesale Electricity Market ◽  
Portfolio Optimization Model ◽  
Photovoltaic Generators

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

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