Interaction of European Carbon Trading and Energy Prices

This paper addresses the economic impact of the EU Emission Trading Scheme for carbon on wholesale electricity and gas prices. Specifically, we analyse the mutual relationships between electricity, gas and carbon prices in the daily spot markets in the United Kingdom. Using a structural co-integrated VAR model, we show how the prices of carbon and gas jointly influence the equilibrium price of electricity. Furthermore, we derive the dynamic pass-trough of carbon into electricity price and the response of electricity and carbon prices to shocks in the gas price.Carbon Emission Trading, Energy Markets, Structural VECM

Modeling the strategic trading of electricity assets

We analyze how strategic asset trading can be used to gain competitive advantage. In the case of electricity markets, companies seek to improve the value of their generating portfolios by acquiring, or selling, power plants. Accordingly, we derive the basic determinants of plant value, explaining how a particular productive asset may have different values for different firms. From this, we develop an evolutionary model to understand how market structure interacts with strategic asset trading to increase the competitive advantage of firms, and furthermore, how this depends upon the actual price-setting microstructure in the wholesale market itselfCompetitive advantage, computational learning, auctions, asset trading, simulation, electricity markets

Agent-based simulation: an application to the new electricity trading arrangements of England and Wales.

This paper presents a large-scale application of multiagent evolutionary modeling to the proposed new electricity trading arrangements (NETA) in the U.K. This is a detailed plant-by-plant model with an active specification of the demand side of the market. NETA involves a bilateral forward market followed by a balancing mechanism and then an imbalance settlement process. This agent-based simulation model was able to provide pricing and strategic insights, ahead of NETA's actual introduction

The Forecasting Accuracy of Electricity Price Formation Models.

In this paper we present an extensive comparison of four different classes of models for daily forecasting of spot electricity prices, including ARMAX, constant and time-varying parameter regression models as well as non linear Markov regime-switching regressions. They are selected for particular reasons related to the emerging body of research on the price formation processes observed in electricity markets. The analyses are conducted for representative trading periods of the day in the UK Power Exchange prompt market, with the price series adjusted for their deterministic components and spikes. They show that relative out-of-sample forecasting performances are distinctly different for each trading period, season and across the actual performance metrics. No model consistently outperforms the others, but the ARMAX approach performs well in most cases and the Diebold and Mariano test indicates that, when it is not the best, the ARMAX model is not statistically different from the best. Nevertheless, we suggest that subtle differences in performance between different methods under different conditions are consistent with the apparent variations in the price formation processes by time of day and by season. We conclude with some observations on the disparities between the model specifications appropriate for understanding in-sample price formation and those for accurate out-of-sample predictions

Incentives and coordination in vertically related energy markets

"Es wird ein Agenten-basiertes Modell eines Energiemarktes mit mehreren Ebenen der Wertschöpfungskette vorgestellt, das Gaslieferanten, Stromerzeuger und Händler berücksichtigt. Es kann gezeigt werden, wie ein vertikal integriertes Unternehmen, das auf oligopolistischen Energiemärkten agiert, die Honorierungsbeziehungen zwischen strategischen Geschäftsbereichen nutzen kann, um seine Gewinne zu steigern. Üblicherweise versuchen Firmen, die die gesamte Wertschöpfungskette integriert haben, ihren Vorteil dadurch zu nutzen, dass sie die Kosten der Wettbewerber durch Preisdiskriminierung erhöhen und den Markt gegen sie abschotten. Das ist in Energiemärkten nicht möglich. Im vorgestellten Modell wird ein Mechanismus gewählt, der den Charakteristika von Energiemärkten angepasst ist, um über Anreize denselben Endeffekt zu erzielen. Dieser beruht aber nicht auf der Marktabschottung, sondern auf einem finanziellen Valorisierungseffekt, bei dem Unternehmensbereiche am Beginn der Wertschöpfungskette die Preisspannen für die Unternehmensteile am oberen Ende vorgeben." (Autorenreferat)"We present an agent-based model of a multi-tier energy market including gas shippers, electricity generators and retailers. We show how reward interdependence between strategic business units within a vertically integrated firm can increase its profits in oligopolistic energy markets. The effects are shown to be distinct from those of the raising rivals' costs model. In our case, higher prices relate to the nature of energy markets, which facilitate the emergence of financial netback effects." (author's abstract

Combining day-ahead forecasts for British electricity prices

This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk managemen

Incentives and coordination in vertically related energy markets

"Es wird ein Agenten-basiertes Modell eines Energiemarktes mit mehreren Ebenen der Wertschöpfungskette vorgestellt, das Gaslieferanten, Stromerzeuger und Händler berücksichtigt. Es kann gezeigt werden, wie ein vertikal integriertes Unternehmen, das auf oligopolistischen Energiemärkten agiert, die Honorierungsbeziehungen zwischen strategischen Geschäftsbereichen nutzen kann, um seine Gewinne zu steigern. Üblicherweise versuchen Firmen, die die gesamte Wertschöpfungskette integriert haben, ihren Vorteil dadurch zu nutzen, dass sie die Kosten der Wettbewerber durch Preisdiskriminierung erhöhen und den Markt gegen sie abschotten. Das ist in Energiemärkten nicht möglich. Im vorgestellten Modell wird ein Mechanismus gewählt, der den Charakteristika von Energiemärkten angepasst ist, um über Anreize denselben Endeffekt zu erzielen. Dieser beruht aber nicht auf der Marktabschottung, sondern auf einem finanziellen Valorisierungseffekt, bei dem Unternehmensbereiche am Beginn der Wertschöpfungskette die Preisspannen für die Unternehmensteile am oberen Ende vorgeben." (Autorenreferat)"We present an agent-based model of a multi-tier energy market including gas shippers, electricity generators and retailers. We show how reward interdependence between strategic business units within a vertically integrated firm can increase its profits in oligopolistic energy markets. The effects are shown to be distinct from those of the raising rivals' costs model. In our case, higher prices relate to the nature of energy markets, which facilitate the emergence of financial netback effects." (author's abstract

An optimal real-time pricing strategy for aggregating distributed generation and battery storage systems in energy communities: A stochastic bilevel optimization approach

The expansion of distributed electricity generation and the increasing capacity of installed battery storage systems at the community level have posed challenges to efficient technical and economic operation of the power systems. With advances in smart-grid infrastructure, many innovative demand response business models have sought to tackle these challenges, while creating financial benefits for the participating actors. In this context, we propose an optimal real-time pricing (ORTP) approach for the aggregation of distributed energy resources within energy communities. We formulate the interaction between a community-owned profit-maximizing aggregator and the users (consumers with electricity generation and storage potential, known as “prosumagers”, and electric vehicles) as a stochastic bilevel disjunctive program. To solve the problem efficiently, we offer a novel solution algorithm, which applies a linear quasi-relaxation approach and an innovative dynamic partitioning technique. We introduce benchmark tariffs and solution algorithms and assess the performance of the proposed pricing strategy and solution algorithm in four case studies. Our results show that the ORTP strategy increases community welfare while providing useful grid services. Furthermore, our findings reveal the superior computational efficiency of our proposed solution algorithm in comparison to benchmark algorithms