Electricity Market

Price volatility is a major issue in liberalized electricity markets as far as risk management is concerned. In this paper we evaluate the impact of the Portuguese and Spanish electricity markets integration on the day-ahead market price volatility. For that purpose we develop an adaptive conjectural variations model which is implemented in GAMS language. We estimate the degree of competition in the Spanish pool using an iterative secant method applied to a conjectural variations oligopoly model. Using the estimate obtained, we simulate the Portuguese and Spanish markets in autarky and the integrated market, known as the Iberian Electricity Market (IBELM). We present conclusions on the impact of the IBELM on prices and volatilities from the Portuguese and Spanish markets point of view.

SUMMARY Nowadays, the installed power capacity of Itaipu Power Plant is 14.000 MW, with a energy production around 90.000 GWh per year. This amount of energy makes the plant responsible for the supplying about 23% of Brazil and 90% of Paraguay electricity markets. Another parameter gives an idea of the dimension of the involved systems: only 6% of the energy

Analysis of market power in electricity markets is relevant for understanding the competitive development of the industry's restructuring and liberalization process. Because the existing literature lacks adequate consideration of line transmission congestion, this paper sets forth a new approach to measuring market power in the Italian Power Exchange (IPEX) by explicitly taking into account transmission line congestion. We construct the residual demand curve to disentangle the measure of unilateral market power from congestion rent for the main Italian generators during the period of April 2004 to December 2007. In Italy, this was a period of stable transmission network structure. Following the approach of Wolak (2003, 2009), we measure the unilateral market power with the Lerner Index (LI) computed as the inverse of the residual demand elasticity. In conclusion, the correct modeling of the residual demand curve including transmission congestions allows us to compute the zonal LI and therefore more accurately measure the market power when congestion occurs. Our results show that various generators exercise market power in only some zones, which provides us with a deeper understanding of the IPEX and better support for designing appropriate market surveillance and competition regulation.

A liberalised market is a market where customers can freely choose their supplier. This market model came with the introduction of competition into non-competitive regulated markets. In a non-competitive environment, supply and prices are regulated, while in a liberalized market regulation aims at avoiding the abuse of market power. In the case of electricity, the starting point was the rigorous analysis of the sectors of the industry which were natural monopolies and the identification of the activities where the barriers to entry were such that no competition will develop naturally. It had been assumed in former market organization that the whole of the electricity industry was a natural monopoly which led to the acceptance of vertically integrated monopoly supply utilities. On a closer inspection it was found that only certain activities were natural monopolies, in particular the transmission and distribution networks. This discovery led reformers to search for mechanisms of intr...

Undoubtedly voltage and current measurements under distorted conditions have recently gained more and more importance in the actual electric market; the deregulation acting in this field with the consequential liberalization and the augmented request of monitoring capabilities on the power quality from the power suppliers, is at the origin of an increased demand of measurement instruments of the various electric

This report gives an overview of the implications of electricity market liberalisation, with special reference to the Nordic market and Finland. The principal focus is on the functioning of the wholesale (generation) and retail (endusers) markets. Furthermore, the handling of risks and the interaction with various environmental policies receive special attention. Essentially the Nordic and Finnish electricity markets have been functioning reasonably well. Yet, under the pressure of the tight market in the winter of 2002, various shortcomings became more obvious. The re-attachment of the wholesale and retail market, inter alia through the (re)introduction of DSM, should be a prime concern. Other points of attention are - among others - the surveillance of the monopolistic services in the market and tendencies for vertical integration.

This work describes the structure and regulation of transmission-related activities in Brazil, and assesses their effectiveness/limitations when addressing issues such as network expansion, locational pricing, transmission financial rights, and others. The paper also discusses the cross-border energy trading between Brazil and neighbor countries, and the perspectives of an integrated regional electricity market.

This paper presents an overview of the Greek Electricity Market. It describes and comments on the evolution of the regulatory environment, including the incorporation of the European Directives in the Greek legislation, towards the liberalization of the Greek Electricity Market. The paper comments on the next phases needed for the full liberalization. Then it provides an overview of the energy mix in installed capacity and electro-production in Greece, with an emphasis on the penetration of Renewables and of distributed generation in the last years. The overview provides a clear judgement that the implementation of any emission reduction targets requires significant investments in new technologies. Finally, it provides an overview of the economic and technical characteristics of the distributed generation units, as they are related to their production costs and the pricing of the electricity.

Vesting contracts can manage the transition to full competition in electricity, manage the financial risk of market participants, and help achieve other deregulation objectives. Vesting contracts could have prevented the financial problems in California.

The study on probability density function and distribution function of electricity prices contributes to the power suppliers and purchasers to estimate their own management accurately, and helps the regulator monitor the periods deviating from normal distribution. Based on the assumption of normal distribution load and non-linear characteristic of the aggregate supply curve, this paper has derived the distribution of electricity prices as the function of random variable of load. The conclusion has been validated with the electricity price data of Zhejiang market. The results show that electricity prices obey normal distribution approximately only when supply-demand relationship is loose, whereas the prices deviate from normal distribution and present strong right-skewness characteristic. Finally, the real electricity markets also display the narrow-peak characteristic when undersupply occurs.

This paper argues that a variety of imperfections in wholesale “energy-only” electricity markets lead to generators earning net revenues that are inadequate to support investment in a least cost portfolio of generating capacity and to satisfy consumer preferences for reliability. Theoretical and numerical examples are used to illustrate the sources of this “missing money” problem. Improvements in “energy-only” wholesale electricity markets, especially those that improve pricing when capacity is fully utilized, can reduce the magnitude of the missing money problem. However, these improvements are unlikely to fully ameliorate it. Forward capacity obligations and associated auction mechanisms to determine capacity prices are necessary to restore appropriate wholesale market prices and associated investment incentives to support the optimal portfolio of generating investments. The deficiencies of the original capacity payment mechanisms adopted in the US are discussed and the necessary improvements identified.

This thesis demonstrates the use of deep learning for automating hourly price forecasts in continuous intraday electricity markets, using various types of neural networks on comprehensive sequential market data and cutting-edge image processing networks on spatio-temporal weather forecast maps. Deep learning is a subfield of artificial intelligence that excels on problems such as these with multifarious input data and manifold interacting factors. It has seen tremendous success on a range of problems across industries, and while it is important to have realistic expectations, there is little reason to believe that intraday electricity markets are different. Focusing on Nord Pool’s intraday market Elbas, we predict Nordic buyers’ volume-weighted average price over the last six hours of trading prior to each delivery hour. Aggregating this window gives buyers flexibility from many trades and sufficient time in which to act on the predictions, and solves issues with data sparsity while keeping sufficient resolution for predictions to be informative.

We develop various neural networks via extensive experimentation, with inspiration from other research and problem domains. To make the findings relevant in practice, we impose constraints on the input data based on what would be available to Elbas market participants six trading hours ahead of delivery. The neural networks are benchmarked against a set of simple domain- based heuristics and traditional methods from econometrics and machine learning. We conclude with a holistic evaluation of the efficacy of deep learning on our problem, whether it is economically justifiable in light of its value-add, what the salient hurdles are to implementing it in practice, and what the implications are for broader applications of AI in intraday markets.

The deep learning models are relatively accurate and reliable under normal market conditions. The average price across all delivery hours in the held-out data is 30.95 EUR/MWh, where our best network is on average off by 2.72 EUR/MWh. It beats the best simple heuristic by 21–25%, and the best benchmark model by 12–16%. The network also anticipates major fluctuations in prices relatively consistently, and generally outperforms all alternative methods when prices are especially volatile or trading activity particularly high. In contrast to the benchmarks, there are also ample avenues for improving the network further. Beyond being promising in its own right, we also argue that the network demonstrates the wider potential of deep learning in a range of applications in intraday markets, and that these are worthy of serious consideration — though one should be aware of the practical hurdles to implementing them operationally.

This document will discuss the profitability of mining Bitcoin (BTC) at the consumer level through analysis of electricity prices, forecasted Bitcoin prices, network difficulty, and current hardware. A breakeven and cash-flow analysis determines the viability of mining and explores potential future scenarios. Finally, the risks associated with mining are evaluated with respect to current and future feasibility.