Az európai villamosenergia-árak központi szabályozásának hatása a fogyasztói árakra (The impacts of the price regulation on the level of retail electricity prices in Europe)

Az Európai Unión belül az elmúlt időszakban megerősödött a vita arról, vajon a Közösség versenyképességének javításához milyen módon és mértékben járulhat hozzá az ipari és lakossági fogyasztók számára kedvező áron elérhető villamos energia. Az uniós testületek elsődlegesen a verseny feltételeinek további javításában látják a versenyképesség javításának fő eszközét, ám egyesek az aktívabb központi szabályozás mellett érvelnek. A jelenleg alkalmazott európai szabályozási gyakorlat áttekintése, a szabályozási modellek és a piaci árak alakulásának vizsgálata hozzásegíthet, hogy következtetéseket vonjunk le a tagállami gyakorlatok tekintetében, vajon sikeresebb-e a központi ármegállapításon alapuló szabályozói mechanizmus, mint a liberalizált piacmodell. ______ There is a strengthening debate within the European Union in recent years about the impact of the affordable industrial and household electricity prices on the general competitiveness of European economies. While the European Institutions argues for the further liberalization of the energy retail sector, there are others who believe in centralization and price control to achieve lower energy prices. Current paper reviews the regulatory models of the European countries and examines the connection between the regulatory regime and consumer price trends. The analysis can help to answer, whether the bureaucratic central regulation or the liberalized market model seems more successful in supporting the competitiveness goals. Although the current regulatory practice is heterogeneous within the EU member states, there is a clear trend to decrease the role of regulated tariffs in the end-user prices. Our study did not find a general causal relationship between the regulatory regime and the level of consumer electricity prices in a country concerned. However, the quantitative analysis of the industrial and household energy prices by various segments detected significant differences between the regulated and free-market countries. The first group of member states tends to decrease the prices in the low-consuming household segments through cross-financing technics, including increased network tariffs and/or taxes for the high-consuming segments and for industrial consumers. One of the major challenges of the regulatory authorities is to find the proper way of sharing these burdens proportionally with minimizing the market-distorting effects of the cross-subsidization between the different stakeholder groups.

Occupant comfort, the housing industry and electricity infrastructure : understanding the synergies

Despite increasingly stringent energy performance regulations for new homes, southeast Queensland has a high and growing penetration of, and reliance on, air conditioners to provide thermal comfort to housing inhabitants. This reliance impacts on electricity infrastructure investment which is the key driving force behind rising electricity prices. This paper reports initial findings of a research project that seeks to better understand three key issues: (i) how families manage their thermal comfort in summer and how well their homes limit overheating; (ii) the extent to which the homes have been constructed according to the building approval documentation; and (iii) the impact that these issues have on urban design, especially in relation to electricity infrastructure in urban developments.

Modelling interregional links in electricity price spikes

Abnormally high price spikes in spot electricity markets represent a significant risk to market participants. As such, a literature has developed that focuses on forecasting the probability of such spike events, moving beyond simply forecasting the level of price. Many univariate time series models have been proposed to dealwith spikes within an individual market region. This paper is the first to develop a multivariate self-exciting point process model for dealing with price spikes across connected regions in the Australian National Electricity Market. The importance of the physical infrastructure connecting the regions on the transmission of spikes is examined. It is found that spikes are transmitted between the regions, and the size of spikes is influenced by the available transmission capacity. It is also found that improved risk estimates are obtained when inter-regional linkages are taken into account.

Effects of Carbon Dioxide Capture and Storage in Germany on European Electricity Exchange and Welfare

28 p.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

European Union emissions trading scheme impact on the Spanish electricity price during phase II and phase III implementation

The European Union Emissions Trading Scheme (EU ETS) is a cornerstone of the European Union's policy to combat climate change and its key tool for reducing industrial greenhouse gas emissions cost-effectively. The purpose of the present work is to evaluate the influence of CO2 opportunity cost on the Spanish wholesale electricity price. Our sample includes all Phase II of the EU ETS and the first year of Phase III implementation, from January 2008 to December 2013. A vector error correction model (VECM) is applied to estimate not only long-run equilibrium relations, but also short-run interactions between the electricity price and the fuel (natural gas and coal) and carbon prices. The four commodities prices are modeled as joint endogenous variables with air temperature and renewable energy as exogenous variables. We found a long-run relationship (cointegration) between electricity price, carbon price, and fuel prices. By estimating the dynamic pass-through of carbon price into electricity price for different periods of our sample, it is possible to observe the weakening of the link between carbon and electricity prices as a result from the collapse on CO2 prices, therefore compromising the efficacy of the system to reach proposed environmental goals. This conclusion is in line with the need to shape new policies within the framework of the EU ETS that prevent excessive low prices for carbon over extended periods of time.

Electricity derivatives pricing with forward-looking information

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Electricity without borders: a plan to make the internal market work. Bruegel Blueprint Series 20, 5 September 2013

The most straightforward European single energy market design would entail a European system operator regulated by a single European regulator. This would ensure the predictable development of rules for the entire EU, significantly reducing regulatory uncertainty for electricity sector investments. But such a first-best market design is unlikely to be politically realistic in the European context for three reasons. First, the necessary changes compared to the current situation are substantial and would produce significant redistributive effects. Second, a European solution would deprive member states of the ability to manage their energy systems nationally. And third, a single European solution might fall short of being well-tailored to consumers’ preferences, which differ substantially across the EU. To nevertheless reap significant benefits from an integrated European electricity market, we propose the following blueprint: First, we suggest adding a European system-management layer to complement national operation centres and help them to better exchange information about the status of the system, expected changes and planned modifications. The ultimate aim should be to transfer the day-to-day responsibility for the safe and economic operation of the system to the European control centre. To further increase efficiency, electricity prices should be allowed to differ between all network points between and within countries. This would enable throughput of electricity through national and international lines to be safely increased without any major investments in infrastructure. Second, to ensure the consistency of national network plans and to ensure that they contribute to providing the infrastructure for a functioning single market, the role of the European ten year network development plan (TYNDP) needs to be upgraded by obliging national regulators to only approve projects planned at European level unless they can prove that deviations are beneficial. This boosted role of the TYNDP would need to be underpinned by resolving the issues of conflicting interests and information asymmetry. Therefore, the network planning process should be opened to all affected stakeholders (generators, network owners and operators, consumers, residents and others) and enable the European Agency for the Cooperation of Energy Regulators (ACER) to act as a welfare-maximising referee. An ultimate political decision by the European Parliament on the entire plan will open a negotiation process around selecting alternatives and agreeing compensation. This ensures that all stakeholders have an interest in guaranteeing a certain degree of balance of interest in the earlier stages. In fact, transparent planning, early stakeholder involvement and democratic legitimisation are well suited for minimising as much as possible local opposition to new lines. Third, sharing the cost of network investments in Europe is a critical issue. One reason is that so far even the most sophisticated models have been unable to identify the individual long-term net benefit in an uncertain environment. A workable compromise to finance new network investments would consist of three components: (i) all easily attributable cost should be levied on the responsible party; (ii) all network users that sit at nodes that are expected to receive more imports through a line extension should be obliged to pay a share of the line extension cost through their network charges; (iii) the rest of the cost is socialised to all consumers. Such a cost-distribution scheme will involve some intra-European redistribution from the well-developed countries (infrastructure-wise) to those that are catching up. However, such a scheme would perform this redistribution in a much more efficient way than the Connecting Europe Facility’s ad-hoc disbursements to politically chosen projects, because it would provide the infrastructure that is really needed.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.