The Question of Generation Adequacy in Liberalised Electricity Markets

This paper presents an overview of the reasons why unregulated markets for the production of electricity cannot be expected to invest sufficiently in generation capacity on a continuous basis. Although it can be shown that periodic price spikes should provide generation companies with sufficient investment incentives in theory, there are a number of probable causes of market failure. A likely result is the development of investment cycles that may affect the adequacy of capacity. The experience in California shows the great social costs associated with an episode of scarce generation capacity. Another disadvantage is that generation companies can manipulate price spikes. This would result in large transfers of income from consumers to producers and reduce the operational reliability of electricity supply during these price spikes. We end this paper by outlining several methods that have been proposed to stabilise the market, which provide better incentives to generation companies and consumers alike.Generation adequacy, Liberalised electricity market

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

Strategic Behaviour under Regulation Benchmarking

Liberalisation of generation and supply activities in the electricity sectors is often followed by regulatory reform of distribution networks. In order to improve the efficiency of distribution utilities, some regulators have adopted incentive regulation schemes that rely on performance benchmarking. Although regulation benchmarking can influence the �regulation game�, the subject has received limited attention. This paper discusses how strategic behaviour can result in inefficient behaviour by firms. We also present a survey of issues encountered by electricity regulators. We then use the Data Envelopment Analysis (DEA) method with US utility data to examine implications of selected cases of strategic behaviour. The results show that gaming can have significant effects on the measured performance and profitability of firms

Determinants of power spreads in electricity futures markets: A multinational analysis. ESRI WP580, December 2017

The growth in variable renewable energy (vRES) and the need for flexibility in power systems go hand in hand. We study how vRES and other factors, namely the price of substitute fuels, power price volatility, structural breaks, and seasonality impact the hedgeable power spreads (profit margins) of the main dispatchable flexibility providers in the current power systems - gas and coal power plants. We particularly focus on power spreads that are hedgeable in futures markets in three European electricity markets (Germany, UK, Nordic) over the time period 2009-2016. We find that market participants who use power spreads need to pay attention to the fundamental supply and demand changes in the underlying markets (electricity, CO2, and coal/gas). Specifically, we show that the total vRES capacity installed during 2009-2016 is associated with a drop of 3-22% in hedgeable profit margins of coal and especially gas power generators. While this shows that the expansion of vRES has a significant negative effect on the hedgeable profitability of dispatchable, flexible power generators, it also suggests that the overall decline in power spreads is further driven by the price dynamics in the CO2 and fuel markets during the sample period. We also find significant persistence (and asymmetric effects) in the power spreads volatility using a univariate TGARCH model

Modelling electricity prices: from the state of the art to a draft of a new proposal

In the last decades a liberalization of the electric market has started; prices are now determined on the basis of contracts on regular markets and their behaviour is mainly driven by usual supply and demand forces. A large body of literature has been developed in order to analyze and forecast their evolution: it includes works with different aims and methodologies depending on the temporal horizon being studied. In this survey we depict the actual state of the art focusing only on the recent papers oriented to the determination of trends in electricity spot prices and to the forecast of these prices in the short run. Structural methods of analysis, which result appropriate for the determination of forward and future values are left behind. Studies have been divided into three broad classes: Autoregressive models, Regime switching models, Volatility models. Six fundamental points arise: the peculiarities of electricity market, the complex statistical properties of prices, the lack of economic foundations of statistical models used for price analysis, the primacy of uniequational approaches, the crucial role played by demand and supply in prices determination, the lack of clearcut evidence in favour of a specific framework of analysis. To take into account the previous stylized issues, we propose the adoption of a methodological framework not yet used to model and forecast electricity prices: a time varying parameters Dynamic Factor Model (DFM). Such an eclectic approach, introduced in the late ‘70s for macroeconomic analysis, enables the identification of the unobservable dynamics of demand and supply driving electricity prices, the coexistence of short term and long term determinants, the creation of forecasts on future trends. Moreover, we have the possibility of simulating the impact that mismatches between demand and supply have over the price variable. This way it is possible to evaluate whether congestions in the network (eventually leading black out phenomena) trigger price reactions that can be considered as warning mechanisms.

Systematic analysis of the evolution of electricity and carbon markets under deep decarbonization

The decarbonization of electricity generation presents policy makers in many countries with the delicate task of balancing initiatives for technological change with a commitment to market liberalization. Despite the theoretical attractions, it has become doubtful whether carbon markets by themselves can offer the desired solution. We address this question through an integrated modeling framework, stylized for the Great Britain (GB) power market within the EU ETS, which includes three distinct components: (a) long-term least-cost capacity planning, similar in functionality to many used in policy analysis, but innovative in providing the endogenous calculation of carbon prices; (b) short-term price risk analysis producing hourly dispatch and pricing outputs, which are used to test the annual financial performance metrics implied by the longer-term investments; and (c) agent-based computational learning to derive pricing behavior in imperfect markets. The results indicate that the risk/return profile of electricity markets may deteriorate substantially as a result of decarbonization, reducing the propensity of companies to invest in the absence of increased government support and/or more beneficial market circumstances. If allowed, markets may adjust by deferring investment until conditions improve, consolidating to increase market power, or operating in a tighter market with reduced spare capacity. To the extent that each of these “market-led” solutions may be politically unpalatable, policy design will need to sustain a delicate regulatory regime, moderating the possible increased market power of companies while maintaining low-carbon subsidies for longer than expected. This paper considers some of the modeling implications for this compromise