a spatial mean variance mip model for energy market risk analysis the paper presents a short term market risk model based on the markowitz mean variance method for spatial electricity markets the spatial nature is captured using the correlation of geographically separated markets and the consideration of wheeling administration the model also includes transaction costs and other practical constraints resulting in a mixed integer programming mip model the incorporation of those practical constraints makes the model more attractive than the traditional markowitz portfolio model with continuity a case study is used to illustrate the practical application of the model the results show that the mip portfolio efficient frontier is neither smooth nor concave the paper also considers the possible extension of the model to other energy markets including natural gas and oil markets

inefficient credit booms this paper studies the welfare properties of competitive equilibria in an economy with financial frictions hit by aggregate shocks in particular it shows that competitive financial contracts can result in excessive borrowing ex ante and excessive volatility ex post even though from a first best perspective the equilibrium always displays under borrowing from a second best point of view excessive borrowing can arise the inefficiency is due to the combination of limited commitment in financial contracts and the fact that asset prices are determined in a spot market this generates a pecuniary externality that is not internalized in private contracts the model provides a framework to evaluate preventive policies which can be used during a credit boom to reduce the expected costs of a financial crisis copyright 2008 wiley blackwell

medium term power planning with bilateral contracts this paper addresses the optimal management of hydropower resources on medium term the objective is to maximize the expected revenue of a producer and the decision variables are generation and forward contracts in each period for each scenario stochastic linear and nonlinear programming has been used as a framework for modeling and solution results are exposited for a norwegian power producer participating in nord pool the nordic power exchange

forward contracts for the operation of an electricity industry under spot pricing a study is reported of the use of forward contracts as risk instruments for electricity industries operating under spot pricing forward contracts involve financial transactions or commitments which relate to a physical trade at a later time instance price setting and appropriate participant responses are discussed simulation studies are used to demonstrate that forward contracts offer participants an opportunity to reduce their risk exposure without removing the incentive to respond to higher spot prices

financial risk management in a competitive electricity market this paper proposes solutions for electricity producers in the field of financial risk management for electric energy contract evaluation the efficient frontier is used as a tool to identify the preferred portfolio of contracts each portfolio has a probability density function for the profit for important scheduling policies closed form solutions are found for the amount of futures contracts that correspond to the efficient frontier production scheduling must consider resource constraints it is found that without resource constraints the portfolio with the highest expected profit can be preferred even for a risk averse decision maker when resource constraints are present portfolios not corresponding to the maximum expected profit criteria will more frequently be preferred

genco s integrated trading decision making to manage multimarket uncertainties fossil fuel generation companies gencos trade in multiple uncertain energy markets fuel and carbon markets on upstream side while electricity market on downstream side global economic and environmental benefits lead these markets to pursue overlapping goals making them highly interactive gencos may identify optimal trading strategies for upstream and downstream trading in an integrated framework to manage an overall secure and profitable position further severe unpredictability of energy market prices may necessitate a genco to make trading plans which perform better meeting its goals under severe uncertainty of involved markets this paper proposes information gap decision theory igdt based approach to select three interrelated trading portfolios in an integrated framework results from a realistic case study provide a comprehensive decision insight to address risk averse and risk seeking behavior of genco explicitly highlighting importance of co variation in prices of interactive markets

genco s risk constrained hydrothermal scheduling this paper presents a stochastic midterm risk constrained hydrothermal scheduling algorithm in a generation company genco the objective of a genco is to maximize payoffs and minimize financial risks when scheduling its midterm generation of thermal cascaded hydro and pumped storage units the proposed schedule will be used by the genco for bidding purposes to the iso the optimization model is based on stochastic price based unit commitment the proposed genco solution may be used to schedule midterm fuel and natural water inflow resources for a few months to a year the proposed stochastic mixed integer programming solution considers random market prices for energy and ancillary services as well as the availability of natural water inflows and generators in monte carlo scenarios financial risks associated with uncertainties are considered by applying expected downside risks which are incorporated explicitly as constraints variable time steps are adopted to avoid the exponential growth in solution time and memory requirements when considering midterm constraints a single water to power conversion function is used instead of several curves for representing water head and discharge parameters piecewise linearized head dependant water to power conversion functions are used for computational efficiency illustrative examples examine gencos midterm generation schedules risk levels fuel and water usage and hourly generation dispatches for bidding in energy and ancillary services markets the paper shows that gencos could decrease their financial risks by adjusting expected payoffs

optimal integrated generation bidding and scheduling with risk management under a deregulated daily power market in the deregulated power industry a generation company genco sells energy and ancillary services primarily through bidding at a daily market developing effective strategies to optimize hourly bid curves for a hydrothermal power system to maximize profits becomes one of the most important tasks of a genco this paper presents a unified framework for optimizing energy and reserve bidding strategies under a deregulated market in view of high volatilities of market clearing prices mcp the hourly mcps and reserve prices are modeled as discrete random variables whose probability mass functions are predicted with a classification based neural network approach the mean variance method is applied to manage bidding risks where a risk penalty term related to mcp and reserve price variances is added to the objective function to avoid buying too much power from the market at high prices a genco may also require covering at least a certain percentage of its own customer load this self scheduling requirement is modeled similar to the system demand in traditional unit commitment problems the formulation is a stochastic mixed integer optimization with a separable structure an optimization based algorithm combining lagrangian relaxation and stochastic dynamic programming is presented to optimize bids for both energy and reserve markets numerical testing based on an 11 unit system in new england market shows that the method can significantly reduce profit variances and thus reduce bidding risks near optimal energy and reserve bid curves are obtained in 4 5 minutes on a 600 hz pentium iii pc efficient for daily use

a multimarket decision making framework for genco considering emission trading scheme in a multimarket environment a genco produces electricity subject to a number of factors including physical and environmental constraints together with trading strategies in the electricity market em fuel market fm and carbon market cm to assist a genco to maximize its profits from em fm and cm this paper proposes a dynamic decision making model with two consecutive stages fuzzy differential evolution algorithm is used to solve this decision making problem taking transactions in the three interactive markets into account the proposed model has been tested for a genco consisting of seven thermal units and a wind farm a rational tradeoff between the profit making and emission reduction has been demonstrated by the genco using the proposed model indicating a well alignment with the intended goal of the introducing emission trading scheme ets

optimal price and quantity determination for retail electric power contracts considering the viewpoint of a retailer this paper analyzes the problem of setting up contracts on both the supplier and end user sides to maximize profits while maintaining an acceptable level of settlement risk the proposed stochastic optimization model can assist retailers with these efforts and guide them in their contractual arrangements a realistic example illustrates the capabilities of the methodology proposed

the risk in hedge fund strategies theory and evidence from trend followers hedge fund strategies typically generate option like returns linear factor models using benchmark asset indices have difficulty explaining them following the suggestions in glosten and jagannathan 1994 this article shows how to model hedge fund returns by focusing on the popular trend following strategy we use lookback straddles to model trend following strategies and show that they can explain trend following funds returns better than standard asset indices though standard straddles lead to similar empirical results lookback straddles are theoretically closer to the concept of trend following our model should be useful in the design of performance benchmarks for trend following funds

risk based electricity procurement for large consumers in a competitive electricity market consumers seek strategies to procure their energy needs from different resources pool bilateral contracts and their own generation facilities at minimum cost while controlling the risk in this paper a novel method is proposed to address the electricity procurement problem of large consumers using the concept of information gap decision theory the method can be used as a tool for assessing the risk levels considering whether a large consumer is risk taking or risk averse regarding its midterm procurement strategies in the proposed method procurement decisions are evaluated by means of two criteria the first criterion is the robustness of the decision against experiencing high procurement costs and the second one is the opportunity of taking advantage of low procurement costs the pool price is considered an uncertain variable and it is assumed that the large consumer has an estimate of the prices in this study two models of uncertainty are addressed for the pool price based on the concept of weighted mean squared error using a variance covariance matrix and the expected procurement cost is modeled using a joint normal probability distribution function a case study illustrates the working of the proposed technique

modeling long term dynamics of electricity markets in the last decade many countries have restructured their electricity industries by introducing competition in their power generation sectors although some restructuring has been regarded as successful the short experience accumulated with liberalized power markets does not allow making any founded assertion about their long term behavior long term prices and long term supply reliability are now center of interest this concerns firms considering investments in generation capacity and regulatory authorities interested in assuring the long term supply adequacy and the stability of power markets in order to gain significant insight into the long term behavior of liberalized power markets in this paper a simulation model based on system dynamics is proposed and the underlying mathematical formulations extensively discussed unlike classical market models based on the assumption that market outcomes replicate the results of a centrally made optimization the approach presented here focuses on replicating the system structure of power markets and the logic of relationships among system components in order to derive its dynamical response the simulations suggest that there might be serious problems to adjust early enough the generation capacity necessary to maintain stable reserve margins and consequently stable long term price levels because of feedback loops embedded in the structure of power markets and the existence of some time lags the long term market development might exhibit a quite volatile behavior by varying some exogenous inputs a sensitivity analysis is carried out to assess the influence of these factors on the long run market dynamics

risk constrained self scheduling of hydro wind units for short term electricity markets considering intermittency and uncertainty abstract in view of the intermittency and uncertainty associated with both the electricity production sector of restructured power system and their competitive markets it is necessary to develop an appropriate risk managing scheme so that it is desirable to trade off between optimum utilization of intermittent generation resources i e renewable energy resources uncertain market prices and related risks in order to maximize participants benefits and minimize the corresponding risks in the multi product market environment the main goal of this paper is to investigate risk management by introducing a novel multi risk index to quantify expected downside risk edr which is caused by both the wind power and market price uncertainties value at risk var method is used to assess the mentioned risk issue by the proposed weighted edr so that an optimal trade off between the profit and risk is made for the system operations also the roulette wheel mechanism is employed for random market price scenario generation wherein the stochastic procedure is converted into its respective deterministic equivalents moreover the autoregressive integrated moving average arima model is employed to characterize the stochastic wind farm wf generation by predetermined mean level and standard deviation of wind behavior as well as temporal correlation the problem is formulated as a mixed integer stochastic framework for a hydro wind power system scheduling and tested on a generation company genco

electricity procurement for large consumers based on information gap decision theory in the competitive electricity market consumers seek strategies to meet their electricity needs at minimum cost and risk this paper provides a technique based on information gap decision theory igdt to assess different procurement strategies for large consumers supply sources include bilateral contracts a limited self generating facility and the pool it is considered that the pool price is uncertain and its volatility around the estimated value is modeled using an igdt model the proposed method does not minimize the procurement cost but assesses the risk aversion or risk taking nature of some procurement strategies with regard to the minimum cost using this method the robustness of experiencing costs higher than the expected one is optimized and the related strategy is determined the proposed method deals with optimizing the opportunities to take advantage of low procurement costs or low pool prices a case study is used to illustrate the proposed technique

an econometric analysis of emission allowance prices knowledge of the statistical distribution of the prices of emission allowances and their forecastability are crucial in constructing among other things purchasing and risk management strategies in the emissions constrained markets this paper analyzes the two emission permits markets co2 in europe and so2 in the us and investigates a model for dealing with the unique stylized facts of this type of data its effectiveness in terms of model fit and out of sample value at risk forecasting as compared to models commonly used in risk forecasting contexts is demonstrated

robust self scheduling under price uncertainty using conditional value at risk in a deregulated power industry power producing companies bid in the hour ahead and day ahead power markets in an attempt to maximize their profit for a successful bidding strategy each power producing company has to generate bidding curves derived from an optimal self schedule this self schedule is commonly obtained from a profit maximizing optimal power flow model based on predicted locational marginal prices lmps however at the time of self scheduling the predicted values of the lmps are largely uncertain therefore it is desired to produce robust self schedules that can be used to lessen the risk resulting from exposure to fluctuating prices in portfolio optimization theory methods of risk management include value at risk var and conditional value at risk cvar cvar is known to be a more consistent measure of risk than var in fact whilst cvar is the mean excess loss the var provides no indication on the extent of losses that might be suffered beyond the amount indicated by this measure this research proposes a method for robust self scheduling based on cvar it will be shown that polynomial interior point methods can be used to obtain the robust self schedules from a second order cone program the obtained schedules provide a compromise solution between maximum profit and minimum risk simulation results on a standard ieee bus test system will be used to demonstrate the scheduling model based on cvar

financial bilateral contract negotiation in wholesale electricity markets using nash bargaining theory bilateral contracts are important risk hedging instruments constituting a major component in the portfolios held by many electric power market participants however bilateral contract negotiation is a complicated process as it involves risk management strategic bargaining and multi market participation this study analyzes a financial bilateral contract negotiation process between a generation company and a load serving entity in a wholesale electric power market with congestion managed by locational marginal pricing nash bargaining theory is used to model a pareto efficient settlement point the model predicts negotiation outcomes under various conditions and identifies circumstances in which the two parties might fail to reach an agreement both analysis and simulation are used to gain insight regarding how these negotiation outcomes systematically vary in response to changes in the participants risk preferences and price biases

large and small baseload power plants drivers to define the optimal portfolios despite the growing interest in small medium sized power plants smpp international literature provides only studies related to portfolios of large plants in infinite markets grids with no particular attention given to base load smpp this paper aims to fill this gap investigating the attractiveness of smpp portfolios respect to large power plant portfolios the analysis includes nuclear coal and combined cycle gas turbines ccgt of different plant sizes the mean variance portfolio theory mvp is used to define the best portfolio according to internal rate of return irr and levelised unit electricity cost luec considering the life cycle costs of each power plant carbon tax electricity price and grid dimension the results show how large plants are the best option for large grids while smpp are as competitive as large plants in small grids in fact in order to achieve the highest profitability with the lowest risk it is necessary to build several types of different plants and in case of small grids this is possible only with smpp a further result is the application of the framework to european oecd countries and the united states assessing their portfolios

time varying fund manager skill we propose a new definition of skill as general cognitive ability to pick stocks or time the market we find evidence for stock picking in booms and market timing in recessions moreover the same fund managers that pick stocks well in expansions also time the market well in recessions these fund managers significantly outperform other funds and passive benchmarks our results suggest a new measure of managerial ability that weighs a fund s market timing more in recessions and stock picking more in booms the measure displays more persistence than either market timing or stock picking alone and predicts fund performance

market integration among electricity markets and their major fuel source markets dynamic price information flows among u s electricity wholesale spot prices and the prices of the major electricity generation fuel sources natural gas uranium coal and crude oil are studied multivariate time series methods applied to weekly price data show that in contemporaneous time peak electricity prices move natural gas prices which in turn influence crude oil in the long run price is discovered in the fuel sources market except uranium as these prices are weakly exogenous in a reduced rank regression representation of these energy prices

income risk of eu coal fired power plants after kyoto coal fired power plants enjoy a significant advantage relative to gas plants in terms of cheaper fuel cost this advantage may erode or turn into disadvantage depending on co2 emission allowance price financial risks are further reinforced when the price of electricity is determined by natural gas fired plants marginal costs we aim to empirically assess the risks in eu coal plants margins up to the year 2020 parameter values are derived from actual market data monte carlo simulation allows compute the expected value and risk profile of coal plants earnings future allowance prices may spell significant risks on utilities balance sheets

application of information gap decision theory to risk constrained self scheduling of gencos in a competitive electricity market a generation company genco optimizes its operation schedules referred to as self scheduling in order to maximize its profit however various sources of uncertainty such as market price fluctuations or forced outage of generating units may impact the genco s profit in this paper a non probabilistic information gap model is proposed to model the uncertainties in short term scheduling of a genco the self scheduling problem is formulated for risk neutral risk averse and risk seeker gencos robustness of the decisions against low market prices are evaluated using a robustness model furthermore windfall higher profit due to unpredicted higher market prices is modeled using an opportunity function the proposed models are applied to a 54 unit thermal genco

integrated risk management for an electricity producer abstract electricity suppliers face two sources of risk uncertainty of spot prices and uncertainty of production costs uncertainty in selling the output is usually solved by signing forwards and the two sources of risk are dealt with separately however managing the integrated risk optimally is the direction we will suggest we intend to analyse the problem a power producer is confronted to upon acting in a market where spot and forward agreements are available since forwards allow to sell production in advance at a given price but do not hedge against cost volatility the total risk can be reduced by selling also in the spot market the analysis is further detailed to encompass the spread option inherent in electricity production we find a benchmark value for forwards to sign and the optimal spot forward combinations the analysis is carried out by accounting for market figures for input and output variables in the german market eex

risk constrained self scheduling of a thermal power producer this paper addresses the self scheduling problem of a price taker power producer it focuses on risk modeling emphasizing the tradeoff existing between maximum profit and minimum risk the paper analyzes a self scheduling model that considers simultaneously profit and risk this model is formulated as a mixed integer quadratic programming problem which is solved using commercially available software relevant results from a realistic case study are discussed

a review on risk constrained hydropower scheduling in deregulated power market in deregulated power market hydro producer has in principle no other objective than to produce electricity and sell with maximum profit with lower market risk attention must focus on profit uncertainty caused by uncertainty in spot prices and reservoir inflow the purpose of this review is to assess the state of the art in hydropower operations considering profit risk under uncertainty and consider future directions for additional research and application physical and financial tools to hedge risk in bilateral market and risk assessment methods are all discussed in detail furthermore production resources can also be used to manage risk to a certain extent this concept when be integrated with variety of risk management methods under stochastic optimal framework has operational significance for hydro producer participating in electricity market

energy security and improvements in the function of diversity indices taiwan energy supply structure case study abstract facing the risk and uncertainty inherent in social and economic development many countries have adopted an energy supply diversification strategy in their energy security policy to control the energy import risk and cost to ensure that sufficient energy can be obtained at an affordable price level diversification helps the energy system respond to external changes and shocks diversification can also lessen the vulnerability of a single energy source to supply shocks and the market power of various energy supply sources this study develops the diversity reliability index and co vary diversity reliability index based on the hirschman herfindahl and shannon wiener indices investigates the contribution of different energy sources to the energy system and analyzes the covariance between various energies and their impacts on energy security to determine the impact of energy diversity in reducing the risk of energy supply shortages and cost fluctuations

electricity markets volatility estimates regularities and risk management applications the recent deregulation of the market for electric power in many parts of the us and canada has expanded the set of potential tools for managing the types of risks faced by both generators and consumers of electric power in particular manufacturing and other firms whose operations are powered by electricity now face on a continuing basis the engineering management decisions concerning whether they should buy or produce electricity and if they are to buy or sell electricity what types of contracts are optimum these types of risk management decisions typically involve futures forwards options and other financial derivatives the price and volatility of electric power are known to play an essential role in determining which of these instruments should be used however electricity as a commodity possesses certain special features not shared by other commodities and hence its risk properties are not yet well understood in this paper we consider and test certain hypotheses about the properties of electricity price using recent market data we find that electricity prices possess certain volatility and other systematic properties that can be characterized by the type and method of delivery of electricity these properties can be used by firms in formulating their optimal demand and supply schedules of electric power

house prices borrowing constraints and monetary policy in the business cycle i develop a general equilibrium model with sticky prices credit constraints nominal loans and asset prices changes in asset prices modify agents borrowing capacity through collateral value changes in nominal prices affect real repayments through debt deflation monetary policy shocks move asset and nominal prices in the same direction and are amplified and propagated over time the financial accelerator is not constant across shocks nominal debt stabilises supply shocks making the economy less volatile when the central bank controls the interest rate i discuss the role of equity debt indexation and household and firm leverage in the propagation mechanism finally i find that monetary policy should not target asset prices as a means of reducing output and inflation volatility

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

behavior of the firm under regulatory constraint a recent article by akira takayama discusses an earlier paper by harvey averch and leland l johnson on fair rate of return regulation of public utilities although takayama p 255 agrees with averch and johnson s general conclusions that a firm will tend to increase its investment with the introduction of an active constraint on its rate of return he criticizes the a j argument as being confusing ambiguous and in error takayama then attempts a clarification and presents a new formulation which leads to the a j result quoted above this comment will discuss several of takayama s criticisms in addition to showing that the so called a j effect cannot be derived from the basic assumptions made by both averch and johnson and takayama 1 we will show that the very assumptions used to prove the a j effect by defining the region of x require an assumption that the a j effect exists in the first place

a new index for electricity spot markets abstract different indexes are used in electricity markets worldwide to represent the daily behavior of spot prices however the peculiarities of these markets require a careful choice of the index based on mathematical formulation and its statistical properties choosing a bad index may influence the financial policies of market players since derivative pricing and hedging performance can be deeply affected in this paper with an initial theoretical analysis we intend to show that the most widely used indexes simple arithmetic average and weighted average with current volumes are poor representatives of the spot market we will then perform an analysis of the hedging strategy on a derivative instrument an asian option written on a reference index the resulting simulations applied to omel spain and eex germany are sufficiently clear cut to suggest that the decision to adopt an index to represent properly a market must be taken very carefully finally we will propose a new index fast index and after comparing it with the previous indexes will show that both theoretically and practically this index can be taken as a good electricity market synthetic indicator

negotiating bilateral contracts in electricity markets in mixed pool bilateral electricity markets participants can sign forward bilateral contracts several months in advance of its delivery in addition generators may sell to and loads may buy from the pool at the spot price through the day ahead or balancing markets forward bilateral contracts have the advantage of price predictability in comparison with the uncertain spot price however the risk is that such a contract commits the partners to a price that may be disadvantageous compared to the spot price here we propose a systematic negotiation scheme through which a generator and load can reach a mutually beneficial and risk tolerable forward bilateral contract either physical or financial under this approach the generator and load respond rationally to a stream of bilateral bids counter bids and offers counter offers considering their respective benefits while accounting for the risks incurred by the prediction uncertainty in the pool spot price and other market parameters over the length of the contract each negotiating party can choose its own definition of risk which can be influenced by regret value at risk or dispersion from the mean numerical tests show that this flexible negotiating approach can be readily put into practice

weekly self scheduling forward contracting and pool involvement for an electricity producer an adaptive robust optimization approach abstract this paper addresses the optimization under uncertainty of the self scheduling forward contracting and pool involvement of an electricity producer operating a mixed power generation station which combines thermal hydro and wind sources and uses a two stage adaptive robust optimization approach in this problem the wind power production and the electricity pool price are considered to be uncertain and are described by uncertainty convex sets to solve this problem two variants of a constraint generation algorithm are proposed and their application and characteristics discussed both algorithms are used to solve two case studies based on two producers each operating equivalent generation units differing only in the thermal units characteristics their market strategies are investigated for three different scenarios corresponding to as many instances of electricity price forecasts the effect of the producers approach whether conservative or more risk prone is also investigated by solving each instance for multiple values of the so called budget parameter it was possible to conclude that this parameter influences markedly the producers strategy in terms of scheduling profit forward contracting and pool involvement these findings are presented and analyzed in detail and an attempted rationale is proposed to explain the less intuitive outcomes regarding the computational results these show that for some instances the two variants of the algorithms have a similar performance while for a particular subset of them one variant has a clear superiority

should central banks respond to movements in asset prices in recent decades asset booms and busts have been important factors in macroeconomic fluctuations in both industrial and developing countries in light of this experience how if at all should central bankers respond to asset price volatility we have addressed this issue in previous work bernanke and gertler 1999 the context of our earlier study was the relatively new but increasingly popular monetary policy framework known as inflation targeting see e g bernanke and frederic mishkin 1997 in an inflation targeting framework publicly announced medium term inflation targets provide a nominal anchor for monetary policy while allowing the central bank some flexibility to help stabilize the real economy in the short run the inflation targeting approach gives a specific answer to the question of how central bankers should respond to asset prices changes in asset prices should affect monetary policy only to the extent that they affect the central bank s forecast of inflation to a first approximation once the predictive content of asset prices for inflation has been accounted for there should be no additional response of monetary policy to assetprice fluctuations in use now for about a decade inflationtargeting has generally performed well in practice however so far this approach has not often been stress tested by large swings in asset prices our earlier research employed simulations of a small calibrated macroeconomic model to examine how an inflation targeting policy defined as one in which the central bank s instrument interest rate responds primarily to changes in expected inflation might fare in the face of a boom and bust cycle in asset prices we found that an aggressive inflationtargeting policy rule in our simulations one in which the coefficient relating the instrument interest rate to expected inflation is 2 0 substantially stabilizes both output and inflation in scenarios in which a bubble in stock prices develops and then collapses as well as in scenarios in which technology shocks drive stock prices intuitively inflation targeting central banks automatically accommodate productivity gains that lift stock prices while offsetting purely speculative increases or decreases in stock values whose primary effects are through aggregate demand conditional on a strong policy response to expected inflation we found little if any additional gains from allowing an independent response of central bank policy to the level of asset prices in our view there are good reasons outside of our formal model to worry about attempts by central banks to influence asset prices including the fact that as history has shown the effects of such attempts on market psychology are dangerously unpredictable hence we concluded that inflationtargeting central banks need not respond to asset prices except insofar as they affect the inflation forecast in the spirit of recent work on robust control the exercises in our earlier paper analyzed the performance of policy rules in worst case discussants robert shiller yale university glenn rudebusch federal reserve bank of san francisco kenneth rogoff harvard university

combined pool bilateral dispatch i performance of trading strategies this three paper series deals with the dispatch of power networks under mixed pool bilateral trading the major questions examined are i to what degree does the relative level of pool versus bilateral trading influence performance in terms of individual power levels costs prices revenues and expenditures ii what is the comparative performance of mixed trading with firm and nonfirm bilateral contracts under various curtailment strategies and iii is the revenue derived from the pool and bilateral trading consistent with the corresponding unbundled costs the above issues are sequentially addressed in each of the three parts the eventual goal of these results is to help generator and load serving entities choose appropriate relative levels of pool versus bilateral trades while considering risk economic performance as well as physical constraints this paper proposes a one step optimal power flow model that dispatches the pool in combination with the privately negotiated bilateral contracts while minimizing cost and accounting for both losses and congestion in part i notions of pool bilateral demand and generation as well as a number of technical and economic performance measures for each competing entity are defined this dissection of total and individual financial measures according to pool or bilateral trading allows the market participant to evaluate the profitability of each component of its chosen pool bilateral mix a number of simulation results illustrate the effect of varying the relative levels of pool bilateral trading on the values of individual performance measures

an experimental study on emission trading behaviors of generation companies the overall performance of emission trading et a market based emission regulation tool strongly relies on participants participation and responses in order to improve market design it is important for policy makers to understand the participants trading behaviors in different market environments however human behaviors cannot be easily modeled with conventional analytical methods due to its bounded rationality characteristics in this paper based on the complementary features between experimental and agent based computational methods a hybrid interactive simulation methodology is proposed to solve human behaviors related problems human subjected experiment based on european union emissions trading system price data in 2006 is conducted the results show that there is no fixed emission trading interval for generation companies and the strategic behaviors of market participants are observed major driving factors of emission trading are categorized into emission price emission quantity and time related factors which are in accordance with empirical analysis results on eu ets 2005 2006 transaction dataset furthermore more human subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis based on thousands of samples obtained the joint influences of driving factors on emission trading behaviors are analyzed the quantitative analysis results obtained can reflect the trading patterns of human participants which provide basis for constructing computer agents that can act as useful substitutes for human participants

inflation targeting what inflation rate to target abstract in an economy with nominal rigidities in both an intermediate good sector and a finished good sector and thus with a natural distinction between cpi and ppi inflation rates a benevolent central bank faces a tradeoff between stabilizing the two measures of inflation a final output gap and unique to our model a real marginal cost gap in the intermediate sector so that optimal monetary policy is second best we discuss how to implement the optimal policy with minimal information requirement and evaluate the robustness of these simple rules when the central bank may not know the exact sources of shocks or nominal rigidities a main finding is that a simple hybrid rule under which the short term interest rate responds to cpi inflation and ppi inflation results in a welfare level close to the optimum whereas policy rules that ignore ppi inflation or ppi sector shocks can result in significant welfare losses

risk aversion and technology mix in an electricity market this article analyzes the eff ect of risk and risk aversion on the long term equilibrium technology mix in an electricity market it develops a model where fi rms can invest in baseload plants with a fi xed variable cost and peak plants with a random variable cost and demand for electricity varies over time but is perfectly predictable at equilibrium the electricity price is partly determined by the random variable cost and the returns from the two kinds of plants are negatively correlated when the variable cost of the peak technology is high the return of peak plants is low but the return to baseload plants is high risk averse fi rms reduce the capacity of the riskiest technology and develop the capacity of the other compared to risk neutral fi rms in the particular case where a risk neutral fi rm invests heavily in baseload technology and only sparely in peak capacity a risk averse fi rm would invest less in baseload increase peak capacity and increase total installed capacity

valuing risky projects option pricing theory and decision analysis in the academic literature and professional practice there are a number of alternative and apparently competing methods for valuing risky projects in this paper we compare and contrast three different approaches risk adjusted discount rate analysis option pricing analysis and decision analysis focusing on the last two we show that in contrast to some of the claims made in the real options literature when both option pricing and decision analysis methods are correctly applied they must give consistent results we also explore ways in which option pricing and decision analysis methods can be profitably integrated in particular we show how option pricing techniques can be used to simplify decision analysis when some risks can be hedged by trading and conversely how decision analysis techniques can be used to extend option pricing techniques to problems with incomplete securities markets

fuel mix diversification incentives in liberalized electricity markets a mean variance portfolio theory approach monte carlo simulations of gas coal and nuclear plant investment returns are used as inputs of a mean variance portfolio optimization to identify optimal base load generation portfolios for large electricity generators in liberalized electricity markets we study the impact of fuel electricity and co2 price risks and their degree of correlation on optimal plant portfolios high degrees of correlation between gas and electricity prices as observed in most european markets reduce gas plant risks and make portfolios dominated by gas plant more attractive long term power purchase contracts and or a lower cost of capital can rebalance optimal portfolios towards more diversified portfolios with larger shares of nuclear and coal plants

conference on housing stability and the macroeconomy international perspectives this article provides an introduction to the jmcb special issue on housing bubbles the global financial crisis and the ensuing recessions in countries that experienced housing busts we focus on five themes that are important for policymakers and researchers alike the domestic and international factors driving housing booms and busts the relevance of the housing sector for the real economy how monetary policy should react to housing booms and busts how housing and mortgage finance reform could affect financial stability and the broad lessons learned for macroeconomics and macroprudential policy

day ahead financial loss gain modeling and prediction for a generation company in an electricity market the main goal of a generation company genco is to maximize its profit while encountering the uncertainty of the electricity price forecast different risk measures have been proposed to cope with this source of uncertainty however those are usually before the fact performance indices and cannot give a measure for the financial loss gain flg of a genco considering the electricity prices actually realized in the market this paper focuses on this matter the time series of flg is first constructed given the real conditions of the electricity market then the flg time series is quantized using silhouette criterion and k means clustering approach subsequently based on the historical values of the quantized flg time series and relevant exogenous variables its day ahead values are predicted the method proposed for day ahead flg prediction consist of conditional mutual information and sequential forward search as the feature selection technique and extreme learning machine as the forecasting engine the effectiveness of the whole proposed approach including the flg time series construction quantization approach and the prediction method is shown for a typical genco using the real data of the pjm and ontario electricity markets

partial carbon permits allocation of potential emission trading scheme in australian electricity market emission trading is widely considered to be the most effective policy to minimize the overall costs for co2 abatement however the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon intensive industries in order to offset the reduction in profits this paper aims to analyze these potential profit impacts and the possible compensation to affected generation companies through modeling the australian national electricity market under a potential emission trading scheme historical emission based and historical generation based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated two carbon permit price scenarios are used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price

diversity and ignorance in electricity supply investment addressing the solution rather than the problem the need for diversity has been cited as a rationale for important recent investment decisions in the uk electricity supply sector it is invoked as the principal argument for continuing major government intervention yet there exists no formal analytical basis for the concept why is diversity good what is meant by diversification what is it that must be diversified what trade off should be struck between diversity and other measures of performance the resulting ambiguity causes confusion and is vulnerable to special pleading despite this this paper argues that diversification offers a superior alternative to probabilistic approaches towards uncertainty and ignorance in electricity supply investments and proposes a formal analytical approach a quantitative index of diversity is borrowed from information theory and used to derive a value for the government s implicit willingness to pay for diversity in the uk electricity supply mix this is then applied in a pilot exercise to the problem of optimizing the diversity and financial performance of the supply portfolio although provisional the results are relevant to the government s forthcoming nuclear review particularly in assessing the relative levels of support for nuclear power and renewable energy

optional forward contracts for electric power markets this paper extends the idea of callable forward contracts which are potentially useful as demand side interruptible load contracts to their supply side analogues together these contracts allow market participants to take advantage of flexibility in generation or consumption to obtain a monetary benefit while simultaneously removing the risk of market price fluctuations this paper also considers the effects of strategic behavior on the part of market participants in their contract sales purchase decisions

do hedge funds deliver alpha a bayesian and bootstrap analysis abstract using a robust bootstrap procedure we find that top hedge fund performance cannot be explained by luck and hedge fund performance persists at annual horizons moreover we show that bayesian measures which help overcome the short sample problem inherent in hedge fund returns lead to superior performance predictability sorting on bayesian alphas relative to ols alphas yields a 5 5 per year increase in the alpha of the spread between the top and bottom hedge fund deciles our results are robust and relevant to investors as they are neither confined to small funds nor driven by incubation bias backfill bias or serial correlation

risk assessment in energy trading this paper provides a state of the art summary of risk assessment in energy trading techniques from financial engineering are needed by electric energy companies to manage price risk these tools are needed by suppliers distributors and traders in a competitive electric power marketplace tools that have been adapted to the specific environment of the electric power system include portfolio analysis and hedging instruments this paper provides a comprehensive critical literature survey of what has been applied to date in the power markets and which areas continue to need additional research one example market scenario is used throughout the paper to demonstrate the usefulness of the risk assessment methods

a decision support system based on particle swarm optimization for multiperiod hedging in electricity markets this paper proposes a particle swarm optimization pso approach to support electricity producers for multiperiod optimal contract allocation the producer risk preference is stated by a utility function u expressing the tradeoff between the expectation and variance of the return variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors a certain confidence level alpha is associated to each forecasted scenario interval the proposed model makes use of contracts with physical spot and forward and financial options settlement pso performance was evaluated by comparing it with a genetic algorithm based approach this model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers moreover it can easily be adapted to the use of other type of contracts

performance characteristics of hedge funds and commodity funds natural vs spurious biases it is well known that the pro forma performance of a sample of investment funds contains biases these biases are documented in brown goetzmann ibbotson and ross 1992 using mutual funds as subjects the organization structure of hedge funds as private and often offshore vehicles makes data collection a much more onerous task amplifying the impact of performance measurement biases theis paper reviews these biases in hedge funds we also propose using funds of hedge funds to measure aggregate hedge fund performance based on the idea that the investment experience of hedge fund investors can be used to estimate the performance of hedge funds

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

tacit collusion in repeated auctions uniform versus discriminatory within an infinitely repeated game i compare the level and conduct of collusion under uniform and discriminatory auctions for this purpose i characterize the optimal collusive schemes and sets of sustainable profits under these auction formats i show that uniform auctions facilitate collusion more than discriminatory auctions the optimal penal code is equally severe under the two formats but bidders deviation incentives are weaker in uniform auctions given that the payoff irrelevant bids can be used to relax the enforcement problem this discussion is particularly relevant after the recent reforms in the electricity industry in england wales and elsewhere

a medium term decision model for discos forward contracting and tou pricing time varying prices by motivating customers to reduce their consumption in peak periods propel the electricity industry towards a higher efficiency compared to that of common flat prices focusing on time of use tou sale prices this paper establishes a stochastic model for the medium term decision making problem faced by a distribution company disco the developed model deals with the disco s decisions on the level of involvement in forward contracts and in the pool as well as the sale prices offered to customers the demand response to tou prices is captured using a price elasticity matrix pem the objective is to maximize the disco s profit while the exposure risk imposed by uncertainties is limited to a given level the model is formulated as a mixed integer linear programming milp problem that can be effectively solved via commercial software packages a typical finnish 20 kv urban distribution network is used to demonstrate the effectiveness of the developed model

power portfolio optimization in deregulated electricity markets with risk management in a deregulated electric power system multiple markets of different time scales exist with various power supply instruments a load serving entity lse has multiple choices from these instruments to meet its load obligations in view of the large amount of power involved the complex market structure the risks in such volatile markets the stringent constraints to be satisfied and the long time horizon a power portfolio optimization problem is of critical importance for an lse to serve its load maximize its profit and manage its risks in this paper a midterm power portfolio optimization problem with risk management is presented key instruments are considered risk terms based on semi variances of spot market transactions are introduced and penalties on load obligation violations are added to the objective function to improve algorithm convergence and constraint satisfaction to overcome the inseparability of the resulting problem a surrogate optimization framework is developed enabling a decomposition and coordination approach numerical testing results show that our method effectively provides decisions for various instruments to maximize profit and manage risks and it is computationally efficient

managing the financial risks of electricity producers using options abstract electricity producers participating in electricity markets face risks pertaining to both selling prices and the availability of the production units among electricity derivatives options represent an adequate instrument to manage these risks in this paper we propose a multi stage stochastic model to determine the optimal selling strategy of a risk averse electricity producer including options forward contracts and pool trading a detailed case study highlights the advantages of an option vs a forward contract to hedge against the financial risks related to pool prices and unexpected unit failures

hedging with futures contracts in a deregulated electricity industry this paper is a statistical study of direct and cross hedging strategies using futures contracts in an electricity market a comparison of the strategies is based on the standard deviation or risk of the values of the hedging positions results indicate that the use of electricity futures contracts is superior to using other related futures contracts such as crude oil

sentiment hedging how hedge funds adjust their exposure to market sentiment abstract we investigate a new facet of hedging ability among hedge fund managers using a sentiment exposure model we find evidence that fund managers adjust the market exposure of their portfolios to changes in market sentiment out of sample evidence indicates that hedge funds having the highest negative sentiment exposure outperform funds having the highest positive sentiment exposure by 1 7 2 4 per year the results remain persistent for both the sub period analysis and the analysis excluding crisis periods we also find that a hedge fund s willingness to take on sentiment exposure decreases with fund age and fund size and increases with incentive fees our findings remain robust even after controlling for hedge fund data biases as well as using alternative sentiment measures

the hedging performance of electricity futures on the nordic power exchange the nordic power exchange nord pool the first multinational exchange for electricity trading has existed since january 1996 spot and futures contracts are traded on this exchange and its typical characteristics are very high volatilities as well as non normally distributed returns this article looks at electricity futures and how they can be used for short term hedging of positions taken in the spot market it studies the minimum variance hedge ratio and how it can be estimated in different ways the traditional naive hedge and the ols hedge are compared out of sample to more elaborate moving average and garch hedges and the empirical results indicate some gains from hedging with futures despite the lack of straight forward arbitrage possibilities in the electricity market furthermore we find a slightly better performance of the simple ols hedge compared to the conditional hedges

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

hedging strategies in energy markets the case of electricity retailers as market intermediaries electricity retailers buy electricity from the wholesale market or self generate for re sale on the retail market electricity retailers are uncertain about how much electricity their residential customers will use at any time of the day until they actually turn switches on while demand uncertainty is a common feature of all commodity markets retailers generally rely on storage to manage demand uncertainty on electricity markets retailers are exposed to joint quantity and price risk on an hourly basis given the physical singularity of electricity as a commodity in the literature on electricity markets few articles deal on intra day hedging portfolios to manage joint price and quantity risk whereas electricity markets are hourly markets the contributions of the article are twofold first we define through a var and cvar model optimal portfolios for specific hours 3 am 6 am 12 pm based on electricity market data from 2001 to 2011 for the french market we prove that the optimal hedging strategy differs depending on the cluster hour secondly we demonstrate the significantly superior efficiency of intra day hedging portfolios over daily therefore weekly and yearly portfolios over a decade 2001 2011 our results clearly show that the losses of an optimal daily portfolio are at least nine times higher than the losses of optimal intra day portfolios

bayesian methods for dynamic multivariate models if dynamic multivariate models are to be used to guide decisionmaking it is important that probability assessments of forecasts or policy projections be provided when identified bayesian vector autoregression var models are presented with error bands in the existing literature both conceptual and numerical problems have not been dealt with in an internally consistent way in this paper the authors develop methods to introduce prior information in both reduced form and structural var models without introducing substantial new computational burdens their approach makes it feasible to use a single large dynamic framework for example twenty variable models for tasks of policy projections copyright 1998 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

risk and profit in self scheduling for gencos this letter addresses the risk based self scheduling problem of a price taker generation company in the day ahead competitive electricity markets the letter analyzes a self scheduling model that accounts for profit and risk simultaneously the effect of risk is explicitly modeled in the self scheduling problem taking into account the variance of the market clearing prices the tradeoff of maximum profit versus minimum risk is properly addressed

free euas and fuel switching abstract this paper focuses on the impact of euas on the optimal policy of a competitive electricity producer the effect of grandfathering is consistently shown to introduce significant distortions to the system it is theoretically shown that there is a threshold value of carbon price so that for prices above this the eua becomes an incentive for reduced production rather than a penalty for inefficient producers these theoretical results are supported by the data of one producer from italy and one from germany furthermore the empirical evidence concludes that the high level of free allowances may generate a shift in production from less to more polluting technologies

spatial dependencies of wind power and interrelations with spot price dynamics abstract wind power has seen strong growth over the last decade and increasingly affects electricity spot prices in particular prices are more volatile due to the stochastic nature of wind such that more generation of wind energy yields lower prices therefore it is important to assess the value of wind power at different locations not only for an investor but for the electricity system as a whole in this paper we develop a stochastic simulation model that captures the full spatial dependence structure of wind power by using copulas incorporated into a supply and demand based model for the electricity spot price this model is calibrated with german data we find that the specific location of a turbine i e its spatial dependence with respect to the aggregated wind power in the system is of high relevance for its value many of the locations analyzed show an upper tail dependence that adversely impacts the market value therefore a model that assumes a linear dependence structure would systematically overestimate the market value of wind power in many cases this effect becomes more important for increasing levels of wind power penetration and may render the large scale integration into markets more difficult

fuzzy self scheduling for gencos this letter presents a comprehensive fuzzy approach for maximizing a genco s profit in a competitive electricity market considering uncertainties the market uncertainties considered are the demand spinning and nonspinning reserves market prices and probability that reserves are called and generated the proposed technique produces an optimal profit fuzzy function that reflects the market uncertainties and helps for hedging risks

using renewables to hedge against future electricity industry uncertainties an australian case study abstract a generation portfolio modelling was employed to assess the expected costs cost risk and emissions of different generation portfolios in the australian national electricity market nem under highly uncertain gas prices carbon pricing policy and electricity demand outcomes were modelled for 396 possible generation portfolios each with 10 000 simulations of possible fuel and carbon prices and electricity demands in 2030 the lowest expected cost generation portfolio includes 60 renewable energy increasing the renewable proportion to 75 slightly increased expected cost by 0 2 mwh but significantly decreased the standard deviation of cost representing the cost risk increasing the renewable proportion from the present 15 to 75 by 2030 is found to decrease expected wholesale electricity costs by 17 mwh fossil fuel intensive portfolios have substantial cost risk associated with high uncertainty in future gas and carbon prices renewables can effectively mitigate cost risk associated with gas and carbon price uncertainty this is found to be robust to a wide range of carbon pricing assumptions this modelling suggests that policy mechanisms to promote an increase in renewable generation towards a level of 75 by 2030 would minimise costs to consumers and mitigate the risk of extreme electricity prices due to uncertain gas and carbon prices

price maker self scheduling in a pool based electricity market a mixed integer lp approach this paper addresses the self scheduling problem faced by a price maker to achieve maximum profit in a pool based electricity market an exact and computationally efficient mixed integer linear programming milp formulation of this problem is presented this formulation models precisely the price maker capability of altering market clearing prices to its own benefits through price quota curves no assumptions are made on the characteristics of the pool and its agents a realistic case study is presented and the results obtained are analyzed in detail

optimal self scheduling of a thermal producer in short term electricity markets by milp this paper addresses the problem of the self scheduling of a thermal electricity producer in day ahead energy and reserves markets a 0 1 mixed integer linear formulation of the producer self scheduling problem is provided which allows a realistic and accurate modeling of the unit s operating phases a novel unit start up modeling is presented in which three different start up types are modeled hot warm and cold each with distinct start up cost synchronization time soak time and predefined start up power output trajectories all dependent on the unit s prior reservation time test results address the effect of the energy and reserves market clearing prices on the producer units day ahead commitment status and profits

risk constrained bidding and offering strategy for a merchant compressed air energy storage plant electricity price forecasts are imperfect therefore a merchant energy storage facility requires a bidding and offering strategy for purchasing and selling the electricity to manage the risk associated with price forecast errors this paper proposes an information gap decision theory igdt based risk constrained bidding offering strategy for a merchant compressed air energy storage caes plant that participates in the day ahead energy markets considering price forecasting errors price uncertainty is modeled using igdt the igdt based self scheduling formulation is then used to construct separate hourly bidding and offering curves the theoretical approach to develop the proposed strategy is presented and validated using numerical simulations

a monte carlo based decision support tool for assessing generation portfolios in future carbon constrained electricity industries abstract this paper presents a novel decision support tool for assessing future generation portfolios in an increasingly uncertain electricity industry the tool combines optimal generation mix concepts with monte carlo simulation and portfolio analysis techniques to determine expected overall industry costs associated cost uncertainty and expected co 2 emissions for different generation portfolio mixes the tool can incorporate complex and correlated probability distributions for estimated future fossil fuel costs carbon prices plant investment costs and demand including price elasticity impacts the intent of this tool is to facilitate risk weighted generation investment and associated policy decision making given uncertainties facing the electricity industry applications of this tool are demonstrated through a case study of an electricity industry with coal ccgt and ocgt facing future uncertainties results highlight some significant generation investment challenges including the impacts of uncertain and correlated carbon and fossil fuel prices the role of future demand changes in response to electricity prices and the impact of construction cost uncertainties on capital intensive generation the tool can incorporate virtually any type of input probability distribution and support sophisticated risk assessments of different portfolios including downside economic risks it can also assess portfolios against multi criterion objectives such as greenhouse emissions as well as overall industry costs

a robust linear approach for offering strategy of a hybrid electric energy company this paper presents a new approach for determining the day ahead bidding strategies of a large scale hybrid electric energy company the company has both energy generation and energy retailing businesses in a competitive electricity market demand response programs are also considered in the retail side of the company in order to hedge the risk of participation in wholesale market this paper introduces a max min bilevel mathematical programming with equilibrium constraint model for offering a strategy that manages the risk of uncertain forecasted rivals bids by robust optimization the max min bilevel model is converted to its equivalent single level optimization using karush kuhn tucker optimality conditions the duality theory is utilized to find the equivalent ordinary maximization model of the max min problem strong duality theory and big m method are also used to linearize the final model of offering strategy applicability of the proposed approach is shown by implementing it on the ieee 118 bus test system

managing price risk in a multimarket environment in a competitive electricity market a generation company genco can manage its trading risk through trading electricity among multiple markets such as spot markets and contract markets the question is how to decide the trading proportion of each market in order to maximize the genco s profit and minimize the associated risk based on the mean variance portfolio theory this paper proposes a sequential optimization approach to electric energy allocation between spot and contract markets taking into consideration the risks of electricity price congestion charge and fuel price especially the impact of the fuel market on electric energy allocation is analyzed and simulated with historical data in respect of the electricity market and other fuel markets in the u s simulation results confirm that the proposed analytic approach is consistent with intuition and therefore reasonable and feasible for a genco to make a trading plan involving risks in an electricity market

on timing and selectivity the dichotomy between timing ability and the ability to select individual assets has been widely used in discussing investment performance measurement this paper discusses the conceptual and econometric problems associated with defining and measuring timing and selectivity in defining these notions we attempt to capture their intuitive interpretation we offer two basic modeling approaches which we term the portfolio approach and the factor approach we show how the quality of timing and selectivity information can be identified statistically in a number of simple models and discuss some of the econometric issues associated with these models in particular a simple quadratic regression is shown to be valid in measuring timing information

optimal response of a thermal unit to an electricity spot market this paper addresses the optimal response of a thermal unit to an electricity spot market the objective is to maximize the unit profit from selling both energy and spinning reserve in the spot market the paper proposes a 0 1 mixed integer linear programming approach that allows a rigorous modeling of i nonconvex and nondifferentiable operating costs ii exponential start up costs iii available spinning reserve taking into account ramp rate restrictions and iv minimum up and down time constraints this approach overcomes the modeling limitations of dynamic programming approaches and is computationally efficient results from realistic case studies are reported