Competition in Electricity Spot Markets: Economic Theory and International Experience

auctions, electricity markets

Decision modelling tools for utilities in the deregulated energy market

This thesis examines the impact of the deregulation of the energy market on decision making and optimisation in utilities and demonstrates how decision support applications can solve specific encountered tasks in this context. The themes of the thesis are presented in different frameworks in order to clarify the complex decision making and optimisation environment where new sources of uncertainties arise due to the convergence of energy markets, globalisation of energy business and increasing competition. This thesis reflects the changes in the decision making and planning environment of European energy companies during the period from 1995 to 2004. It also follows the development of computational performance and evolution of energy information systems during the same period. Specifically, this thesis consists of studies at several levels of the decision making hierarchy ranging from top-level strategic decision problems to specific optimisation algorithms. On the other hand, the studies also follow the progress of the liberalised energy market from the monopolistic era to the fully competitive market with new trading instruments and issues like emissions trading. This thesis suggests that there is an increasing need for optimisation and multiple criteria decision making methods, and that new approaches based on the use of operations research are welcome as the deregulation proceeds and uncertainties increase. Technically, the optimisation applications presented are based on Lagrangian relaxation techniques and the dedicated Power Simplex algorithm supplemented with stochastic scenario analysis for decision support, a heuristic method to allocate common benefits and potential losses of coalitions of power companies, and an advanced Branch-and-Bound algorithm to solve efficiently non-convex optimisation problems. The optimisation problems are part of the operational and tactical decision making process that has become very complex in the recent years. Similarly, strategic decision support has also faced new challenges. This thesis introduces two applications involving multiple criteria decision making methods. The first application explores the decision making problem caused by the introduction of 'green' electricity that creates additional value for renewable energy. In this problem the stochastic multi-criteria acceptability analysis method (SMAA) is applied. The second strategic multi-criteria decision making study discusses two different energy-related operations research problems: the elements of risk analysis in the energy field and the evaluation of different choices with a decision support tool accommodating incomplete preference information to help energy companies to select a proper risk management system. The application is based on the rank inclusion in criteria hierarchies (RICH) method.reviewe

Modeling electricity prices: international evidence

This paper analyses the evolution of electricity prices in deregulated markets. We present a general model that simultaneously takes into account the possibility of several factors: seasonality, mean reversion, GARCH behaviour and time-dependent jumps. The model is applied to equilibrium spot prices of electricity markets from Argentina, Australia (Victoria), New Zealand (Hayward), NordPool (Scandinavia), Spain and U.S. (PJM) using daily data. Six different nested models were estimated to compare the relative importance of each factor and their interactions. We obtained that electricity prices are mean-reverting with strong volatility (GARCH) and jumps of time-dependent intensity even after adjusting for seasonality. We also provide a detailed unit root analysis of electricity prices against mean reversion, in the presence of jumps and GARCH errors, and propose a new powerful procedure based on bootstrap techniques

A Review of the Monitoring of Market Power The Possible Roles of TSOs in Monitoring for Market Power Issues in Congested Transmission Systems

The paper surveys the literature and publicly available information on market power monitoring in electricity wholesale markets. After briefly reviewing definitions, strategies and methods of mitigating market power we examine the various methods of detecting market power that have been employed by academics and market monitors/regulators. These techniques include structural and behavioural indices and analysis as well as various simulation approaches. The applications of these tools range from spot market mitigation and congestion management through to long-term market design assessment and merger decisions. Various market-power monitoring units already track market behaviour and produce indices. Our survey shows that these units collect a large amount of data from various market participants and we identify the crucial role of the transmission system operators with their access to dispatch and system information. Easily accessible and comprehensive data supports effective market power monitoring and facilitates market design evaluation. The discretion required for effective market monitoring is facilitated by institutional independence.Electricity, liberalisation, market power, regulation

Transmission congestion management by optimal placement of FACTS devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 13/09/2010.This thesis describes the implementation of the Flexible AC Transmission Systems (FACTS) devices to develop a market-based approach to the problem of transmission congestion management in a Balancing Market. The causes, remedies and pricing methods of transmission congestion are briefly reviewed. Balancing Market exists in markets in which most of the trading is done via decentralized bilateral contracts. In these markets only final adjustments necessary to ensure secure system operation is carried out at a centralized Balancing Market. Each market player can participate in the Balancing Market by submitting offers and bids to increase and decrease its initially submitted active generation output. In this research a method is proposed to reduce costs associated with congestion re-dispatch in a Balancing Market by optimal placement of FACTS devices, and in particular Thyristor Controlled Phase Shifter Transformers (TCPST). The proposed technique is applicable to both Mixed Integer Linear Programming (MILP) and Mixed Integer Non-Linear Programming (MINLP). In the MILP a power system network is represented by a simplified DC power flow under a MILP structure and the Market participants' offers and bids are also represented by linear models. Results show that applications of FACTS devices can significantly reduce costs of congestion re-dispatch. The application of the method based on the MINLP creates a nonlinear and non-convex AC OPF problem that might be trapped in local sub-optima solutions. The reliability of the solution that determines the optimal placement of FACTS devices is an important issue and is carried out by investigation of alternative solvers. The behavior of the MINLP solvers is presented and finally the best solvers for this particular optimization problem are introduced. The application of DC OPF is very common in industry. The accuracy of the DC OPF results is investigated and a comparison between the DC and AC OPF is presented

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

A robust multivariate long run analysis of European electricity prices

This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of our robust multivariate long run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of long term dynamics among electricity prices and between electricity prices and gas prices may prove to be important for long term hedging operations to be conducted even in countries where well established and liquid electricity derivatives markets are not present. Since standard unit root and cointegration tests are not robust to the peculiar characteristics of electricity prices time series, we adapt and further develop a battery of robust inference procedures that should assure the reliability of our results.European electricity prices, Cointegration, Interdependencies, Equilibrium Correction model, Oil prices, Robustness

Virtual power producers integration into MASCEM

All over the world Distributed Generation is seen as a valuable help to get cleaner and more efficient electricity. Under this context distributed generators, owned by different decentralized players can provide a significant amount of the electricity generation. To get negotiation power and advantages of scale economy, these players can be aggregated giving place to a new concept: the Virtual Power Producer. Virtual Power Producers are multi-technology and multi-site heterogeneous entities. Virtual Power Producers should adopt organization and management methodologies so that they can make Distributed Generation a really profitable activity, able to participate in the market. In this paper we address the integration of Virtual Power Producers into an electricity market simulator –MASCEM – as a coalition of distributed producers