effects of renewables on the stylized facts of electricity prices abstract many countries around the world have increased their renewable installed capacity due to a greater awareness of climate concerns under this new framework with renewables being among the main generation sources the literature warns of a dramatic change in price behaviour some of the most commonly claimed effects of having a significant proportion of renewable generating sources in the total electricity production mix include i a systematic decrease in overall wholesale market prices ii a higher occurrence of price jumps and iii a significant increase in price volatility the goal of the present study is to test whether these changes in price behaviour have actually come about to do so we focus on the spanish day ahead electricity market as a paradigmatic example in line with the literature it is found a statistically negative relationship between the renewable generation share and the day ahead market marginal prices as well we have obtained statistical confirmation of the fact that renewables generation share volatility is transferred to price volatility though similarly to other generation technologies finally in contrast to the general belief that the introduction of renewable generation would give rise to extreme positive prices according to our results increases in renewables generation share reduce the probability of upward jumps in prices the results obtained are of interest for portfolio managers practitioners and regulators

forecasting energy markets using support vector machines abstract in this paper we investigate the efficiency of a support vector machine svm based forecasting model for the next day directional change of electricity prices we first adjust the best autoregressive svm model and then we enhance it with various related variables the system is tested on the daily phelix index of the german and austrian control area of the european energy exchange ee wholesale electricity market the forecast accuracy we achieved is 76 12 over a 200 day period

seasonal factors and outlier effects in rate of return on electricity spot prices in australia s national electricity market this article documents seasonal patterns and other characteristics of electricity spot prices in the australian national electricity market nem over a 7 year sample period the goal is to investigate more specifically the influence of seasonalities and outliers noted in the body of literature on electricity prices the results confirm that electricity prices exhibit significant time of day and day of week effects and monthly and yearly effects are significant to a lesser degree extremely high spikes in the price series are an important characteristic of electricity prices and are shown to be a highly significant component of returns behaviour negative prices are unusual in financial time series data but occur in australian electricity prices and are found to be influential on returns the implications of these finding are that seasonal and outlier effects should not be ignored in efforts to model electricity prices

discovering price load relationships in california s electricity market this paper reports on characterizing recent price behavior in the califomia electricity market market participants that is producers consumers and traders are highly motivated by the potential for profits to develop strategies to explore and exploit the limits of system operation these strategies should be reflected in the market as different price to load relationships we show that a number of regimes i e characteristic behaviors exist in the price time series and provide a brief analysis of each regime knowledge of the number of regimes their characteristics and switching dynamics allows insight into the market and power system performance

job matching in connected regional and occupational labour markets job mobility equilibrates disparities in local labour markets and influences the job matching efficiency we specify a matching function with regional occupational and combined regional occupation

when supply meets demand the case of hourly spot electricity prices we use a supply demand framework to model the hourly day ahead price of electricity the spot price based on publicly available information with the model we can forecast the level and probability of a spike in the spot price defined as the spot price above a certain threshold several european countries have recently started publishing day ahead forecasts of the available supply this paper shows this forecasted capacity is quite successful in predicting spot price movements 24 h ahead

modeling and forecasting electricity prices with input output hidden markov models in competitive electricity markets in addition to the uncertainty of exogenous variables such as energy demand water inflows and availability of generation units and fuel costs participants are faced with the uncertainty of their competitors behavior the analysis of electricity price time series reflects a switching nature related to discrete changes in competitors strategies which can be represented by a set of dynamic models sequenced together by a markov chain an input output hidden markov model iohmm is proposed for analyzing and forecasting electricity spot prices the model provides both good predictions in terms of accuracy as well as dynamic information about the market in this way different market states are identified and characterized by their more relevant explanatory variables moreover a conditional probability transition matrix governs the probabilities of remaining in the same state or changing to another whenever a new market session is opened the model has been successfully applied to real clearing prices in the spanish electricity market

mixed models for short run forecasting of electricity prices application for the spanish market short run forecasting of electricity prices has become necessary for power generation unit schedule since it is the basis of every profit maximization strategy in this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed the main idea is to develop an efficient tool for one step ahead forecasting in the future combining several prediction methods for which forecasting performance has been checked and compared for a span of several years also as a novelty the 24 hourly time series has been modelled separately instead of the complete time series of the prices this allows one to take advantage of the homogeneity of these 24 time series the purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting these results have been obtained by means of a computational experiment a mixed model which combines the advantages of the two new models discussed is proposed some numerical results for the spanish market are shown but this new methodology can be applied to other electricity markets as well

construction of an efficient portfolio of power purchase decisions based on risk diversification tradeoff abstract we present a methodology based on the tradeoff between risk and diversification in order to evaluate a purchase portfolio of energy where the assets refer to purchasing strategies of a retailer generator of electricity in three markets spot regulated and non regulated markets we use two measures of diversification i entropy based on factors constructed by principal components analysis and ii entropy based on asset risk in each case weights for each strategy are estimated by using the interior point method for which monthly forecasts of returns are calculated a year ahead for each market spot prices are modeled using an arima model and bilateral contracts are modeled using growth rates we compare risk diversified portfolios with mean variance portfolio although diversification does not necessarily mean a lower risk we show that the mean variance portfolio s risk is not always lower than the risk diversified approaches also we show that diversification converges to one for the highest risk portfolio but this does not happen in the case of entropy based on factors because one asset can participate in more than one principal component clearly the mean variance approach is unable to perform a diversified allocation these results are useful for retailer generators who want combine the criteria of risk and diversification

oil and gas markets in the uk evidence from a cointegrating approach the paper examines the relationship between uk wholesale gas prices and the brent oil price over the period 1996 2003 in order to investigate whether oil and gas prices decoupled during this period as orthodox gas market liberalisation theory suggests tests for unit roots and cointegration are carried out and it is discovered that a long run equilibrium relationship between uk gas and oil prices exists moreover this relationship pre dates the opening of the uk mainland europe interconnector following a recursive methodology hansen h and johansen s 1999 some tests for parameter constancy in cointegrated var models econometrics journal 2 306 333 it was found that the cointegrating relationship is present throughout the sample period however the long run solutions seem to be more volatile evidence is provided that the short run relationship is linear and impulse response functions are used to examine the effects that a shock in oil would have on gas these findings do not support the assumption that gas prices and oil prices decouple

the influence of temperature on spike probability in day ahead power prices it is well known that day ahead prices in power markets exhibit spikes and time varying volatility spikes and extremely high volatility are the results of short term frictions in demand and or supply conditions it is known that information on load or the reserve margin help to forecast spikes however these variables are not timely available for every market participant and this paper suggests to use temperature as a proxy interpreting the results from several switching regimes models the paper shows that the probability of spike occurrence increases when temperature deviates substantially from mean temperature levels

forecasting electricity prices and their volatilities using unobserved components abstract the liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities both in the short and long term thus there is a need of providing methodology that is able to deal with the most important features of electricity price series which are well known for presenting not only structure in conditional mean but also time varying conditional variances in this work we propose a new model which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices these common factors are jointly estimated as well as their relationship with the original vector of series and the dynamics affecting both their conditional mean and variance the estimation of the model is carried out under the state space formulation the new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the spanish zone of the iberian market several simplified alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power

robust estimation and forecasting of the long term seasonal component of electricity spot prices when building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data while the short term seasonal components daily weekly are more regular and less important for valuation of typical power derivatives the l ong term seasonal components ltsc seasonal annual are much more diffi cult to tackle surprisingly in many academic papers dealing with electricity spot price modeling the importance of the seasonal decomposition is neglected and the problem of forecasting it is not considered with this paper we want to fill the gap and present a thorough study on estimation and forecasting of the ltsc of electricity spot prices we consider a battery of models based on fourier or wavelet decomposition combined with linear or exponential decay we find that all considered wavelet base d models are significantly better in terms of forecasting spot prices up to a year ahead than all considered sine based models this result questions the validity and usefulness of stochastic models of spot electricity prices built on sinusoidal long term seasonal components

an adaptive wavelet neural network based energy price forecasting in electricity markets in a competitive electricity market an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions an adaptive wavelet neural network awnn is proposed in this paper for short term price forecasting stpf in the electricity markets a commonly used mexican hat wavelet has been chosen as the activation function for hidden layer neurons of feed forward neural network ffnn to demonstrate the effectiveness of the proposed approach day ahead prediction of market clearing price mcp of spain market which is a duopoly market with a dominant player and locational marginal price lmp forecasting in pjm electricity market are considered the forecasted results clearly show that awnn has good prediction properties compared to other forecasting techniques such as wavelet arima multi layer perceptron mlp and radial basis function rbf neural networks as well as recently proposed fuzzy neural network fnn

electricity price forecasting using sale and purchase curves the x model abstract our paper aims to model and forecast the electricity price by taking a completely new perspective on the data it will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis instead of directly modeling the electricity price as it is usually done in time series or data mining approaches we model and utilize its true source the sale and purchase curves of the electricity exchange we will refer to this new model as x model as almost every deregulated electricity price is simply the result of the intersection of the electricity supply and demand curve at a certain auction therefore we show an approach to deal with a tremendous amount of auction data using a subtle data processing technique as well as dimension reduction and lasso based estimation methods we incorporate not only several known features such as seasonal behavior or the impact of other processes like renewable energy but also completely new elaborated stylized facts of the bidding structure our model is able to capture the non linear behavior of the electricity price which is especially useful for predicting huge price spikes using simulation methods we show how to derive prediction intervals for probabilistic forecasting we describe and show the proposed methods for the day ahead epex spot price of germany and austria

stochastic joint optimization of wind generation and pumped storage units in an electricity market one of the main characteristics of wind power is the inherent variability and unpredictability of the generation source even in the short term to cope with this drawback hydro pumped storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time this paper investigates the combined optimization of a wind farm and a pumped storage facility from the point of view of a generation company in a market environment the optimization model is formulated as a two stage stochastic programming problem with two random parameters market prices and wind generation the optimal bids for the day ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables a joint configuration is modeled and compared with an uncoordinated operation a realistic example case is presented where the developed models are tested with satisfactory results

labour market externalities and regional growth in sweden the importance of labour mobility between skill related industries boschma r eriksson r h and lindgren u labour market externalities and regional growth in sweden the importance of labour mobility between skill related industries regional studies this study investigates the relationship between labour market externalities and regional growth based on real labour flows in particular it tests for the importance of labour mobility across so called skill related industries between 435 four digit industries within 72 swedish functional regions 1998 2002 both the fixed effect models and generalized method of moments gmm estimates demonstrate that a strong intensity of intra regional labour flows between skill related industries impacts positively on regional productivity growth but less so on employment growth labour mobility between unrelated industries tends to dampen regional unemployment growth while a high degree of intra industry labour flows is only found to be associated with rising unemployment

the dynamics of agglomeration externalities along the life cycle of industries neffke f henning m boschma r lundquist k j and olander l o the dynamics of agglomeration externalities along the life cycle of industries regional studies this paper investigates the changing roles of agglomeration externalities along the industry life cycle it is argued that industries have different agglomeration needs in different stages of their life cycles because their mode of competition innovation intensity and learning opportunities change over time for twelve swedish manufacturing industries it is determined for each year between 1974 and 2004 whether the industry is in a young intermediate or mature stage whereas marshall arrow romer mar externalities steadily increase with the maturity of industries the effects of local diversity jacobs externalities are positive for young industries but decline and even become negative for more mature industries neffke f henning m boschma r lundquist k j et olander l o la dynamique des effets externes d agglomeration tout

how do behavioral assumptions affect structural inference evidence from a laboratory experiment we use a laboratory experiment to investigate the effect that assuming rational expectations has on structural inference on a dynamic discrete choice decision problem our experimental design induces preferences up to each subject s subjective rates of time preference leaving unrestricted only this parameter and the decision rule that the subject uses in solving the problem we analyze the data under the assumption that all subjects use the rational expectations decision rule and also under weaker behavioral assumptions that allow for heterogeneity in the way people form decisions we find no evidence that assuming rational expectations distorts inferences about the cross sectional distribution of discount rates

heterogeneous price dynamics in u s regional electricity markets abstract the u s electricity wholesale market is organized in several deregulated regional markets this paper compares price dynamics of electricity in the u s wholesale markets and shows that electricity prices from the west and east coasts have different regime dynamics our methodology suggests that electricity prices are better parameterized by four regimes with different levels of volatility additionally west and east coast markets differ in the time spent in each regime the extremely high volatility regime describes west coast prices during the california electricity crisis but east coast prices are also frequent in that regime we find evidence of synchronization of price dynamics in the mean reverting and highest volatility regimes i e prices from the east and west coasts tend to be in the same regimes at the same time

hourly electricity prices in day ahead markets this paper focuses on the characteristics of hourly electricity prices in day ahead markets in these markets quotes for day ahead delivery of electricity are submitted simultaneously for all hours in the next day the same information set is used for quoting all hours of the day the dynamics of hourly electricity prices does not behave as a time series process instead these prices should be treated as a panel in which the prices of 24 cross sectional hours vary from day to day this paper introduces a panel model for hourly electricity prices in day ahead markets and examines their characteristics the results show that hourly electricity prices exhibit hourly specific mean reversion and that they oscillate around an hourly specific mean price level furthermore a block structured cross sectional correlation pattern between the hours is apparent

the effect of myopia and loss aversion on risk taking an experimental test myopic loss aversion is the combination of a greater sensitivity to losses than to gains and a tendency to evaluate outcomes frequently two implications of myopic loss aversion are tested experimentally 1 investors who display myopic loss aversion will be more willing to accept risks if they evaluate their investments less often 2 if all payoffs are increased enough to eliminate losses investors will accept more risk in a task in which investors learn from experience both predictions are supported the investors who got the most frequent feedback and thus the most information took the least risk and earned the least money language en

day ahead price forecasting of electricity markets by mutual information technique and cascaded neuro evolutionary algorithm in a competitive electricity market price forecasts are important for market participants however electricity price is a complex signal due to its nonlinearity nonstationarity and time variant behavior in spite of much research in this area more accurate and robust price forecast methods are still required in this paper a combination of a feature selection technique and cascaded neuro evolutionary algorithm cnea is proposed for this purpose the feature selection method is an improved version of the mutual information mi technique the cnea is composed of cascaded forecasters where each forecaster consists of a neural network nn and an evolutionary algorithm ea an iterative search procedure is also incorporated in our solution strategy to fine tune the adjustable parameters of both the mi technique and cnea the price forecast accuracy of the proposed method is evaluated by means of real data from the pennsylvania new jersey maryland pjm and spanish electricity markets the method is also compared with some of the most recent price forecast techniques

on the market impact of wind energy forecasts this paper presents an analysis of how day ahead electricity spot prices are affected by day ahead wind power forecasts demonstration of this relationship is given as a test case for the western danish price area of the nord pool s elspot market impact on the average price behaviour is investigated as well as that on the distributional properties of the price by using a non parametric regression model to assess the effects of wind power forecasts on the average behaviour the non linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial furthermore by evaluating the distributional properties of the spot prices under different scenarios the impact of the wind power forecasts on the price distribution is proved to be considerable the conditional price distribution is moreover shown to be non gaussian this implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have gaussian residuals hence the widespread assumption of gaussian residuals from electricity spot price models is shown to be inadequate for these model types the revealed effects are likely to be observable and qualitatively similar in other day ahead electricity markets significantly penetrated by wind power

short term variations and long term dynamics in commodity prices in this article we develop a two factor model of commodity prices that allows meanreversion in short term prices and uncertainty in the equilibrium level to which prices revert although these two factors are not directly observable they may be estimated from spot and futures prices intuitively movements in prices for long maturity futures contracts provide information about the equilibrium price level and differences between the prices for the short and long term contracts provide information about short term variations in prices we show that although this model does not explicitly consider changes in convenience yields over time this short term long term model is equivalent to the stochastic convenience yield model developed in gibson and schwartz 1990 we estimate the parameters of the model using prices for oil futures contracts and apply the model to some hypothetical oil linked assets to demonstrate its use and some of its advantages over the gibson schwartz model

a stochastic fuel switching model for electricity prices abstract this paper develops and applies a novel electricity price model we reproduce the merit order of a thermal dominated electricity system by establishing a non linear dependency of wholesale electricity prices on the prices of fuels coal and natural gas and of co 2 emission allowances the coefficients are estimated using a markov switching regression this approach might prove valuable for cross hedging positions in the fuel electricity and emission spot markets it is also of use for studying the degree to which electricity prices in different countries reflect fuel and emission cost applying the model to the electricity markets of the uk and germany we find that british electricity prices are quite well explained by short run cost factors while the german ones are less well explained

time dependent correlations in electricity markets in the last years many electricity markets were subjected to deregulated operation where prices are set by the action of market participants in this form producers and consumers rely on demand and price forecasts to decide their bidding strategies allocate assets negotiate bilateral contracts hedge risks and plan facility investments a basic feature of efficient market hypothesis is the absence of correlations between price increments over any time scale leading to random walk type behavior of prices so arbitrage is not possible however recent studies have suggested that this is not the case and correlations are present in the behavior of diverse electricity markets in this paper a temporal quantification of electricity market correlations is made by means of detrended fluctuation and allan analyses the approach is applied to two canadian electricity markets ontario and alberta the results show the existence of correlations in both demand and prices exhibiting complex time dependent behavior with lower correlations in winter while higher in summer relatively steady annual cycles in demand but unstable cycles in prices are detected on the other hand the more significant nonlinear effects measured in terms of a multifractality index are found for winter months while the converse behavior is displayed during the summer period in terms of forecasting models our results suggest that nonlinear recursive models e g feedback nns should be used for accurate day ahead price estimation in contrast linear models can suffice for demand forecasting purposes

a framework for electricity price spike analysis with advanced data mining methods there are many techniques for electricity market price forecasting however most of them are designed for expected price analysis rather than price spike forecasting an effective method of predicting the occurrence of spikes has not yet been observed in the literature so far in this paper a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes combined with the spike value prediction techniques developed by the same authors the proposed approach aims at providing a comprehensive tool for price spike forecasting in this paper feature selection techniques are first described to identify the attributes relevant to the occurrence of spikes a simple introduction to the classification techniques is given for completeness two algorithms support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in detail realistic market data are used to test the proposed model with promising results

a structural dynamic analysis of job turnover and the costs associated with moving to another job this paper provides an empirical analysis of the labor market behavior of employed individuals using a structural on the job search model the costs associated with moving to another job are allowed to be nonzero and may depend on the wage level it is shown that under certain conditions the optimal strategy has the reservation wage property the model is estimated using data on job durations and subjective responses concerning the search strategy the results reveal some strong sources of inflexibility of the labor market copyright 1992 by royal economic society

a hybrid model for day ahead price forecasting this paper presents a hybrid time series and adaptive wavelet neural network awnn model for the day ahead electricity market clearing price forecast instead of using price series one period continuously compounded return series is used to achieve more attractive statistical properties the autoregressive moving average with exogenous variables armax model is used to catch the linear relationship between price return series and explanatory variable load series the generalized autoregressive conditional heteroscedastic garch model is used to unveil the heteroscedastic character of residuals and awnn is used to present the nonlinear nonstationary impact of load series on electricity prices the monte carlo method is adopted to generate more evenly distributed random numbers used for time series and awnn models to accelerate the convergence several criteria such as average mean absolute percentage error amape and the variance of forecast errors are used to assess the model and measure the forecasting accuracy illustrative price forecasting examples of the pjm market are presented to show the efficiency of the proposed method

efficient modeling and forecasting of electricity spot prices abstract the increasing importance of renewable energy especially solar and wind power has led to new forces in the formation of electricity prices hence this paper introduces an econometric model for the hourly time series of electricity prices of the european power exchange epex which incorporates specific features like renewable energy the model consists of several sophisticated and established approaches and can be regarded as a periodic var tarch with wind power solar power and load as influences on the time series it is able to map the distinct and well known features of electricity prices in germany an efficient iteratively reweighted lasso approach is used for the estimation moreover it is shown that several existing models are outperformed by the procedure developed in this paper

energy models for demand forecasting a review abstract energy is vital for sustainable development of any nation be it social economic or environment in the past decade energy consumption has increased exponentially globally energy management is crucial for the future economic prosperity and environmental security energy is linked to industrial production agricultural output health access to water population education quality of life etc energy demand management is required for proper allocation of the available resources during the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs in this paper an attempt is made to review the various energy demand forecasting models traditional methods such as time series regression econometric arima as well as soft computing techniques such as fuzzy logic genetic algorithm and neural networks are being extensively used for demand side management support vector regression ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting bottom up models such as markal and leap are also being used at the national and regional level for energy demand management

application of public domain market information to forecast ontario s wholesale electricity prices this paper evaluates the usefulness of publicly available electricity market information in forecasting the hourly ontario energy price hoep in order to do so relevant data from ontario and its neighboring electricity markets namely new york new england and pjm electricity markets are investigated and a final set of explanatory variable candidates that are available before real time are selected multivariate transfer function and dynamic regression models are employed to relate hoep behavior to the selected explanatory variable candidates univariate arima models are also developed for the hoep the hoep models are developed on the basis of two forecasting horizons i e 3 h and 24 h and forecasting performance of the multivariate models is compared with that of the univariate models the outcomes show that the market information publicly available before real time can be used to improve hoep forecast accuracy to some extent however unusually high or low prices remain unpredictable and hence the available data cannot lead to significantly more accurate forecasts nevertheless the generated forecasts in this paper are significantly more accurate than currently available hoep forecasts to analyze the relatively low accuracy of the hoep forecasts comparisons are made with respect to arima models developed for locational marginal prices lmps of ontario s three neighboring markets and price volatility analyses are presented

component estimation for electricity prices procedures and comparisons abstract electricity price time series usually exhibit some form of nonstationarity corresponding to long term behavior one or more periodic components as well as dependence on calendar effects as a result modeling electricity prices requires accounting for both long term and periodic components in the literature several filtering procedures have been proposed but a standard has not yet been found furthermore since different procedures are applied in contexts that are not homogeneous with respect to data periods and final goals a fair comparison is difficult this work considers several methods for component estimation in a homogeneous framework and compares them according to specific criteria the final purpose is to find an estimation procedure that performs well independently of the intended market and that can be proposed as a reference for electricity price time series filtering

forecasting electricity spot prices using lasso on capturing the autoregressive intraday structure in this paper we present a regression based model for day ahead electricity spot prices we estimate the considered linear regression model by the lasso estimation method the lasso approach allows for many possible parameters in the model but also shrinks and sparsifies the parameters automatically to avoid overfitting thus it is able to capture the autoregressive intraday dependency structure of the electricity price well we discuss in detail the estimation results which provide insights to the intraday behavior of electricity prices we perform an out of sample forecasting study for several european electricity markets the results illustrate well that the efficient lasso based estimation technique can exhibit advantages from two popular model approaches

modeling real time balancing power market prices using combined sarima and markov processes this paper describes modeling of real time balancing power market prices by using combined seasonal auto regressive integrated moving average sarima and discrete markov processes the combination of such processes allows generation of price series with periods where no demand for balancing power exists the purpose of the model is simulation of prices to construct scenario trees representing possible realization of the stochastic prices such scenario trees can be used in planning models based on stochastic optimization to generate bid sequences to the balancing market the spread of the prices in the tree and the shape of the scenarios are of central importance model parameter estimation methods reflecting the demands on scenario trees have therefore been used the proposed model is also applied to data from the nordic power market the conclusion of this paper is that the developed model is appropriate for modeling real time balancing power prices

hmm filtering and parameter estimation of an electricity spot price model abstract in this paper we develop a model for electricity spot price dynamics the spot price is assumed to follow an exponential ornstein uhlenbeck ou process with an added compound poisson process in this way the model allows for mean reversion and possible jumps all parameters are modulated by a hidden markov chain in discrete time they are able to switch between different economic regimes representing the interaction of various factors through the application of reference probability technique adaptive filters are derived which in turn provide optimal estimates for the state of the markov chain and related quantities of the observation process the em algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters we implement this self calibrating model on a deseasonalised series of daily spot electricity prices from the nordic exchange nord pool on the basis of one step ahead forecasts we found that the model is able to capture the empirical characteristics of nord pool spot prices

arima models to predict next day electricity prices price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets both for spot markets and long term contracts price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit this paper provides a method to predict next day electricity prices based on the arima methodology arima techniques are used to analyze time series and in the past have been mainly used for load forecasting due to their accuracy and mathematical soundness a detailed explanation of the aforementioned arima models and results from mainland spain and californian markets are presented

reducing electricity price forecasting error using seasonality and higher order crossing information commodity prices are volatile and their volatility changes over time electricity prices are even more volatile in general because customers cannot easily switch to other energy source when the electricity prices fluctuate electricity prices also have strong seasonal behavior because the demand of electricity depends on the season to address these characteristics a time series forecasting model with seasonality and higher order crossing information for electricity prices is proposed the model measures the similarity between two time series by using their higher order crossing information the model can predict not only day ahead prices short term forecasting but also a series of prices for one week ahead or even longer simultaneously medium term price curve forecasting the model is tested on both high and low volatile data sets to evaluate its robustness and generality it is also shown that the model can produce more accurate prediction than some well known models

working through knowledge pools labour market dynamics the transference of knowledge and ideas and industrial clusters this article explores a prominent cluster in the swedish capital stockholm and its surrounding region the ict information and communications technology cluster in particular the article focuses on the issue of the extent to which labour market and labour mobility are the most likely channels for local and extra local sources of knowledge and ideas thus the article positions itself against a growing literature that focuses on rather diffuse and vague notions that knowledge and innovation reside in the air or in the buzz of urban life instead the underlying hypothesis is that in many sectors and industries such things as a cosmopolitan street life or accidental face to face encounters play relatively little part in the flow of experiences knowledge and innovation rather it is in the workplace that these exchanges and flows are located and it is thus through labour mobility that intracluster exchanges occur the article tests such ideas in relation to the ict cluster and the stockholm region using a uniquely detailed time series data set the data set used is based on official taxation and civil registration records and contains complete details on everything from education to career changes to income levels for every individual resident in sweden the detail of the individual records and the complete nature of the data set mean that it offers a unique possibility to examine on a large scale the micro dynamics of individuals in the labour market and in clusters the data are used to examine whether there have existed over time higher levels of labour market mobility in clusters as opposed to the rest of the urban economy the article empirically verifies the idea that labour market mobility is significantly higher in the cluster than in the rest of the urban economy

electricity market price forecasting based on weighted nearest neighbors techniques this paper presents a simple technique to forecast next day electricity market prices based on the weighted nearest neighbors methodology first it is explained how the relevant parameters defining the adopted model are obtained such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction then results corresponding to the spanish electricity market during 2002 are presented and discussed finally the performance of the proposed method is compared with that of recently published techniques

hedging local volume risk using forward markets nordic case abstract with focus on the nordic electricity market this paper develops hedging strategies for an electricity distributor who manages price and volume risk from fixed price agreements on stochastic electricity load whereas the distributor trades in the spot market at area prices the financial contracts used for hedging are settled against the system price area and system prices are correlated with electricity load as are price differences in practice however this is often disregarded here we develop a joint model for the area price the system price and the load accounting for correlations and we suggest various strategies for hedging in the presence of local volume risk we benchmark against a strategy that ignores correlation and hedges at expected load as is common practice in the industry using data from 2013 and 2014 for two danish bidding areas we show that our best hedging strategy reduces gross loss by 5 8 and 13 6 and increases gross profit by 3 8 and 9 5 respectively although this is partly due to the inclusion of correlation we show that performance improvement is mainly driven by the choice of risk measure

economic impact of electricity market price forecasting errors a demand side analysis several techniques have been proposed in the literature to forecast electricity market prices and improve forecast accuracy however no studies have been reported examining the economic impact of price forecast inaccuracies on forecast users therefore in this paper the application of electricity market price forecasts to short term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed using electricity market price forecasts as the expected next day electricity prices optimal operating schedules and the associated costs are determined for each load these costs are compared with those of a perfect price forecast scenario in which actual prices are used to determine the operating schedules numerical results and discussions are provided based on price forecasts with different error characteristics

econometric modeling of regional electricity spot prices in the australian market abstract wholesale electricity markets are increasingly integrated via high voltage interconnectors and inter regional trade in electricity is growing to model this we consider a spatial equilibrium model of price formation where constraints on inter regional flows result in three distinct equilibria in prices we use this to motivate an econometric model for the distribution of observed electricity spot prices that captures many of their unique empirical characteristics the econometric model features supply and inter regional trade cost functions which are estimated using bayesian monotonic regression smoothing methodology a copula multivariate time series model is employed to capture additional dependence both cross sectional and serial in regional prices the marginal distributions are nonparametric with means given by the regression means the model has the advantage of preserving the heavy right hand tail in the predictive densities of price we fit the model to half hourly spot price data in the five interconnected regions of the australian national electricity market the fitted model is then used to measure how both supply and price shocks in one region are transmitted to the distribution of prices in all regions in subsequent periods finally to validate our econometric model we show that prices forecast using the proposed model compares favorably with those from some benchmark alternatives

flexible transmission expansion planning with uncertainties in an electricity market deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult more flexible transmission expansion plans are needed to cope with the increased risks in this paper a novel planning approach is proposed to meet the above challenge in our approach the planning process is modeled as a mixed integer nonlinear programming minlp problem so that conflicting objectives can be optimized simultaneously to minimize planning risks our method identifies several scenarios based on statistics and expert knowledge the most flexible expansion plan is selected as the plan that has least adaptation cost the proposed method is tested with the ieee 14 bus system promising results are obtained to demonstrate the effectiveness of our method

classification of future electricity market prices forecasting short term electricity market prices has been the focus of several studies in recent years although various approaches have been examined achieving sufficiently low forecasting errors has not been always possible however certain applications such as demand side management do not require exact values for future prices but utilize specific price thresholds as the basis for making short term scheduling decisions in this paper classification of future electricity market prices with respect to pre specified price thresholds is introduced two alternative models based on support vector machines are proposed in a multi class multi step ahead price classification context numerical results are provided for classifying prices in ontario s and alberta s markets

which types of relatedness matter in regional growth industry occupation and education abstractwhich types of relatedness matter in regional growth industry occupation and education regional studies this paper provides a conceptual discussion of relatedness which suggests a focus on individuals as a complement to firms and industries the empirical relevance of the main arguments is tested by estimating the effects of related and unrelated variety in education and occupation among employees as well as in industries on regional growth the results show that occupational and educational related variety are positively correlated with productivity growth which supports the conceptual discussion put forth in the paper in addition related variety in industries is found to be negative for productivity growth but positive for employment growth

a novel approach to forecast electricity price for pjm using neural network and similar days method price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk and it also plays a key role in the economic optimization of the electric energy industry this paper explores a technique of artificial neural network ann model based on similar days sd method in order to forecast day ahead electricity price in the pjm market to demonstrate the superiority of the proposed model publicly available data acquired from the pjm interconnection were used for training and testing the ann the factors impacting the electricity price forecasting including time factors load factors and historical price factors are discussed comparison of forecasting performance of the proposed ann model with that of forecasts obtained from similar days method is presented daily and weekly mean absolute percentage error mape of reasonably small value and forecast mean square error fmse of less than 7 mwh were obtained for the pjm data which has correlation coefficient of determination of 0 6744 between load and electricity price simulation results show that the proposed ann model based on similar days method is capable of forecasting locational marginal price lmp in the pjm market efficiently and accurately

electricity price curve modeling and forecasting by manifold learning this paper proposes a novel nonparametric approach for the modeling and analysis of electricity price curves by applying the manifold learning methodology locally linear embedding lle the prediction method based on manifold learning and reconstruction is employed to make short term and medium term price forecasts our method not only performs accurately in forecasting one day ahead prices but also has a great advantage in predicting one week ahead and one month ahead prices over other methods the forecast accuracy is demonstrated by numerical results using historical price data taken from the eastern u s electric power markets

generation scheduling in a deregulated system the norwegian case the paper gives an overview of the deregulated electricity supply system in norway with emphasis on the generation scheduling approach procedures and tools for long medium and short term scheduling are described scheduling is carried out by each individual utility since scheduling is based on profit maximizing the spot market price is a very important factor this price is varying and uncertain however a part of the paper describes how to deal with this by considering the spot market price as an exogenously given stochastic variable it is shown how to extend the common water value computations so as to handle this case implications to the medium and short term scheduling are also discussed

a statistical approach for interval forecasting of the electricity price electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market rather than forecasting the value market participants are sometimes more interested in forecasting the prediction interval of the electricity price forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions in this paper a novel data mining based approach is proposed to achieve two major objectives 1 to accurately forecast the value of the electricity price series which is widely accepted as a nonlinear time series 2 to accurately estimate the prediction interval of the electricity price series in the proposed approach support vector machine svm is employed to forecast the value of the price to forecast the prediction interval we construct a statistical model by introducing a heteroscedastic variance equation for the svm maximum likelihood estimation mle is used to estimate model parameters results from the case studies on real world price data prove that the proposed method is highly effective compared with existing methods such as garch models

forecasting next day electricity prices by time series models in the framework of competitive electricity markets power producers and consumers need accurate price forecasting tools price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities respectively this paper provides two highly accurate yet efficient price forecasting tools based on time series analysis dynamic regression and transfer function models these techniques are explained and checked against each other results and discussions from real world case studies based on the electricity markets of mainland spain and california are presented

regime jumps in electricity prices many countries are liberalizing their energy markets participants in these markets are exposed to market risk due to the characteristics of electricity price dynamics electricity prices are known to be mean reverting very volatile and subject to frequent spikes models that describe the dynamics of electricity prices should incorporate these characteristics in order to capture the price spikes many researchers have introduced stochastic jump processes but we argue and show that this specification might lead to potential problems with specifying the true amount of mean reversion within the process in this paper we propose a regime switching model that models price spikes separated from normal mean reverting prices

energy price forecasting in the ontario competitive power system market this paper introduces a method for forecasting energy prices using artificial intelligence methods such as neural networks and fuzzy logic and a combination of the two the new approach is compared with some of the exiting methods various factors affecting the market clearing price are investigated results for the ontario electricity market are presented

analysis of the probability distribution of lmp by central limit theorem the main goal of this paper is to provide an analytical study on the statistical behaviors of the electricity price at various conditions of the load and the network in order to achieve this goal a novel two step modeling is proposed for the electricity price at the first step it is proved that there is a linear relationship between the locational marginal prices lmp and the strategies of generating companies gencos in this linear relationship the lmp is decomposed to the weighted summation of the strategies of marginal units plus constant value at the second step the lmp is modeled by classic central limit theorem clt using this model an analytical relationship is derived between the price discovery mechanism and characteristics of its probability density function pdf as a result this paper shows analytically that increasing system load causes the price pdf deviates from the normal distribution on the other hand by decreasing system load the electricity price distribution tends toward the normal distribution the statistical studies on the real power markets data confirm these analytical results furthermore the simulation results of the ieee 300 bus test system illustrate the efficiency of the proposed model

electricity price short term forecasting using artificial neural networks this paper presents the system marginal price smp short term forecasting implementation using the artificial neural networks ann computing technique the described approach uses the three layered ann paradigm with backpropagation the retrospective smp real world data acquired from the deregulated victorian power system was used for training and testing the ann the results presented in this paper confirm considerable value of the ann based approach in forecasting the smp

returning to work regional determinants of re employment after major redundancies abstractusing matched employer employee data on roughly 429 000 workers made redundant from large plant closures or major downsizing in sweden between 1990 and 2005 this paper analyses the role of the regional industry mix specialization related and unrelated variety in the likelihood of returning to work the results show that a high presence of same or related industries speeds up the re employment process while high concentrations of unrelated activities do not the role of related activities is particularly evident in the short run and in regions with high unemployment consequently the prospect of successful diversification is enhanced in regions with related industries

energy clearing price prediction and confidence interval estimation with cascaded neural networks the energy market clearing prices mcps in deregulated power markets are volatile good mcp prediction and its confidence interval estimation will help utilities and independent power producers submit effective bids with low risks mcp prediction however is difficult since bidding strategies used by participants are complicated and various uncertainties interact in an intricate way furthermore mcp predictors usually have a cascaded structure as several key input factors need to be predicted first cascaded structures are widely used however they have not been adequately investigated this paper analyzes the uncertainties involved in a cascaded neural network nn structure for mcp prediction and develops the prediction distribution under the bayesian framework a computationally efficient algorithm to evaluate the confidence intervals by using the memoryless quasi newton method is also developed testing results on a classroom problem and on new england mcp prediction show that the method is computationally efficient and provides accurate prediction and confidence coverage the scheme is generic and can be applied to various networks such as multilayer perceptrons and radial basis function networks

filtering and forecasting commodity futures prices under an hmm framework abstract we propose a model for the evolution of arbitrage free futures prices under a regime switching framework the estimation of model parameters is carried out using the hidden markov filtering algorithms comprehensive numerical experiments on real financial market data are provided to illustrate the effectiveness of our algorithm in particular the model is calibrated with data from heating oil futures and its forecasting performance as well as statistical validity is investigated the proposed model is parsimonious self calibrating and can be very useful in predicting futures prices

an agent based approach with collaboration among agents estimation of wholesale electricity price on pjm and artificial data generated by a mean reverting model abstract this study examines the performance of mais multi agent intelligent simulator equipped with various learning capabilities in addition to the learning capabilities the proposed mais incorporates collaboration among agents the proposed mais is applied to estimate a dynamic change of wholesale electricity price in pjm pennsylvania new jersey mainland and an artificial data set generated by a mean reverting model using such different types of data sets the methodological validity of mais is confirmed by comparing it with other well known alternatives in computer science this study finds that the mais needs to incorporate both the mean reverting model and the collaboration behavior among agents in order to enhance its estimation capability the mais discussed in this study will provide research on energy economics with a new numerical capability that can investigate a dynamic change of not only wholesale electricity price but also speculation and learning process of traders

a history of european electricity day ahead prices in this article we examine the development of day ahead prices in five european markets which became more connected over recent years where previous studies examined the convergence of price levels over time we focus on patterns in estimates for the parameters in a switching regimes model this makes it possible to distinguish between prices under normal market conditions and under non normal market conditions those market conditions that can cause extreme price spikes we expect that increased connectivity yields additional supply in the short term and therefore will reduce the impact of price spikes our results indicate that the impact of price spikes and volatility decreased over time that prices behave more random and that the parameter estimates between various connected markets seem to have converged between the belgian dutch french german and nordic day ahead markets over the years 2003 through 2010 these results can be explained by increased connectivity and improved liquidity

day ahead electricity price forecasting in a grid environment accurate electricity price forecasting is critical to market participants in wholesale electricity markets market participants rely on price forecasts to decide their bidding strategies allocate assets negotiate bilateral contracts hedge risks and plan facility investments market operators can also use electricity price forecasts to predict market power indexes for the purpose of monitoring participants behaviors various forecasting techniques are applied to different time horizons for electricity price forecasting in locational marginal pricing lmp spot markets available correlated data also have to be selected to improve the short term forecasting performance in this paper fuzzy inference system fis least squares estimation lse and the combination of fis and lse are proposed based on extensive testing with various techniques lse provides the most accurate results and fis which is also highly accurate provides transparency and interpretability

the labour market impact of immigration in the first part of this paper we present a stylized model of the labour market impact of immigra tion we then discuss mechanisms through which an economy can adjust to immigration changes in factor prices output mix and production technology in the second part we explain the problems of empirically estimating how immigration affects labour market outcomes of the resident population and review some strategies to address these we then summarize some recent empirical studies for the uk and other countries we conclude with an outlook on what we believe are important avenues for future research

does money illusion matter in intertemporal decision making abstract to examine the degree to which price fluctuations affect how individuals approach an intertemporal decision making problem we conduct a laboratory experiment in which subjects spend their savings to purchase only one commodity over 20 periods in the control treatment the commodity price is constant across all periods in the small large price fluctuation treatment the price rate of change is always 1 20 regardless of the treatment the commodity price and subjects savings change at the same rate over time therefore the optimal amount of consumption is the same in all three treatments our main findings are twofold first the magnitude of misconsumption i e the deviation from optimal consumption is significantly high with the large price fluctuation treatment being the highest followed by the small price fluctuation treatment and then the control treatment second regardless of the presence of price fluctuations subjects exhibit underconsumption oversaving behavior and price fluctuations strengthen this tendency

spatial externalities relatedness and sector employment growth in great britain bishop p and gripaios p spatial externalities relatedness and sector employment growth in great britain regional studies this paper examines the impact of externalities on employment growth in sub regions of great britain by estimating ordinary least squares ols and maximum likelihood spatial models at the two digit level for 23 sectors issues arising from relatedness sector differences competition cross boundary spillovers and spatial autocorrelation are explicitly addressed results indicate that specialization has a generally negative impact on growth whilst the impact of diversity is heterogeneous across sectors and strong local competition has a typically positive impact the results question the merits of policies primarily aimed at promoting regional specialization and suggest that diversity local competition and sector heterogeneity are important policy issues bishop p et gripaios p les effets externes geographiques la connectivite et la croissance de l emploi sectoriel en grand

day ahead electricity price forecasting using the wavelet transform and arima models this paper proposes a novel technique to forecast day ahead electricity prices based on the wavelet transform and arima models the historical and usually ill behaved price series is decomposed using the wavelet transform in a set of better behaved constitutive series then the future values of these constitutive series are forecast using properly fitted arima models in turn the arima forecasts allow through the inverse wavelet transform reconstructing the future behavior of the price series and therefore to forecast prices results from the electricity market of mainland spain in year 2002 are reported