reconciling social welfare agent profits and consumer payments in electricity pools under a common condition called profit suboptimality market equilibrium cannot be reached in electricity pools in other words no system marginal price exists for which the profit driven independent generators would self schedule to levels that exactly meet the demand on the other hand although a centrally imposed generation schedule satisfies power balance it may force some agents to operate at a profit below what they could achieve under self scheduling this paper examines these incompatible goals and proposes a conflict resolution scheme based on the notion of generalized uplift functions these functions are defined such that they i change the offered generation cost characteristics so as to increase the system marginal price thus forcing the consumers to compensate the generators for part of their combined loss of profit ii execute an equitable transfer of revenues among the generators so that these also participate in any loss of profit compensation iii ensure market equilibrium at the centralized minimum cost solution and iv ensure that the net sum of the uplifts adds up to zero

efficient mechanisms for bilateral trading we consider bargaining problems between one buyer and one seller for a single object the seller s valuation and the buyer s valuation for the object are assumed to be independent random variables and each individual s valuation is unknown to the other we characterize the set of allocation mechanisms that are bayesian incentive compatible and individually rational and show the general impossibility of ex post efficient mechanisms without outside subsidies for a wide class of problems we show how to compute mechanisms that maximize expected total gains from trade and mechanisms that can maximize a broker s expected profit journal of economic literature classification number 026

optimal bidding strategies and modeling of imperfect information among competitive generators the emerging electricity market behaves more like an oligopoly than a perfectly competitive market due to special features such as a limited number of producers large investment size barrier to entry transmission constraints and transmission losses which discourage purchase from distant suppliers this makes it practicable for only a few independent power suppliers to service a given geographic region and in this imperfect market each power supplier can increase its own profit through strategic bidding the profit of each supplier is influenced to varying extents by differences in the degree of imperfection of knowledge of rival suppliers a new framework to build bidding strategies for power suppliers in an electricity market is presented in this paper it is assumed that each supplier bids a linear supply function and that the system is dispatched to minimize customer payments each supplier chooses the coefficients in the linear supply function to maximize benefits subject to expectations about how rival suppliers will bid a stochastic optimization formulation is developed and two methods proposed for describing and solving this problem a numerical example serves to illustrate the essential features of the approach and the results are used to investigate the potential market power

evaluating a stochastic programming based bidding model for a multireservoir system hydropower producers need to schedule when to release water from reservoirs and participate in wholesale electricity markets where the day ahead production is physically traded a mixed integer linear stochastic model for bid optimization and short term production allocation is developed and tested through a simulation procedure implemented for a complex real life river system the stochastic bid model sees uncertainty in both spot market prices and inflow to the reservoirs the same simulation procedure is also implemented for a practice based deterministic heuristic method similar to what is currently used for bid determination in the industry and the results are compared the stochastic approach gives improvements in terms of higher obtained average price and higher total value than the deterministic alternative it also performs well in terms of startup costs in the presence of river flow travel delay the practice based method is even more outperformed by the stochastic model

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

min max regret bidding strategy for thermal generator considering price uncertainty the electricity price volatility brings challenges to bidding strategies in the electricity markets in this paper we propose a minimax regret approach for a market participant to obtain an optimal bidding strategy and the corresponding self scheduled generation plans motivated by recently proposed robust optimization approaches our approach relies on the confidence intervals of price forecasts rather than point estimators we reformulate the minimax regret model as a mixed integer linear program milp and solve it by the benders decomposition algorithm moreover we design a bidding strategy based on the price forecast confidence intervals to generate the offer curve finally we numerically test the minimax regret approach in comparison with the robust optimization approach on three types of thermal generators by using real electricity price data from pjm to verify the effectiveness of our proposed approach

a generalized class of locational pricing mechanisms for the electricity markets abstract in this paper we consider a new class of mechanisms l p r which serves as a generalization of locational marginal pricing lmp and uniform pricing cp mechanisms we analyze a space of possible mechanisms defined by the l p r class in the context of fairness system balancing cost and market signals we show that lmp is the only mechanism that produces signals of good quality on the other hand the uniform price mechanism enables minimization of system balancing costs and thereby maximization of economic surplus which is allocated under competitive rules lmp generally works better than cp in terms of fairness however we show that there are other possibilities for constructing new and interesting mechanisms in the l p r class we present an exemplary mechanism lp which acts as a trade off between lmp and cp and presents sustainable properties the numerical experiments we provide reveal that lp has interesting features finally we claim that none of the following mechanisms lmp cp and lp is dominated in l p r space in terms of considered quality measures

marginal loss modeling in lmp calculation this paper discusses the pricing of marginal transmission network losses in the locational marginal pricing approach recently deployed in the iso new england iso ne standard market design smd project implemented by alstom s t d energy automation and information eai business the traditional loss model is studied and a new model is proposed the new model achieves more defendable and predictable market clearing results by introducing loss distribution factors to explicitly balance the consumed losses in the lossless dc power system model the distributed market slack reference is also introduced and discussed the lmp components produced by the two models are studied and compared under changes in slack reference numerical examples are presented to further compare the two models

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

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

bidding strategies for electricity producers in a competitive electricity marketplace this paper presents a methodology for the development of bidding strategies for electricity producers in a competitive electricity marketplace initially the problem is modeled as a two level optimization problem where at the first level a market participant tries to maximize his expected profit under the constraint that at the second level an independent system operator dispatches power solving an optimal power flow problem that minimizes total system cost it is assumed that each supplier bids a linear supply function and chooses his bidding strategy based on probabilistic estimates of demand and rival behavior monte carlo simulation is used to calculate the expected profit and genetic algorithms are employed to find the optimal strategy subsequently the formulation is expanded to account for different market participants risk profiles it is shown that risk aversion may influence the optimal bidding strategy of an individual

optimal response of a power generator to energy agc and reserve pool based markets the electric power industry all over the world is undertaking major regulatory and operational changes the understanding of power supply as a public service is being replaced by the notion that competitive markets constitute a more appropriate framework to supply reliable and cheap electric energy to consumers as mandated by ferc orders 888 and 889 this paper considers simultaneously energy agc and reserve markets in a pool framework it provides a tool to enable a power generator to determine optimally its degree of involvement in each one of these markets a real world case study is analyzed and results are presented

winds of change how high wind penetrations will affect investment incentives in the gb electricity sector abstract wind power is widely expected to expand rapidly in britain over the next decade large amounts of variable wind power on the system will increase market risks with prices more volatile and load factors for conventional thermal plant lower and more uncertain this extra market risk may discourage investment in generation capacity financial viability for thermal plant will be increasingly dependent on price spikes during periods of low wind increased price risk will also make investment in other forms of low carbon generation e g nuclear power more challenging a number of policies can reduce the extent to which generators are exposed to market risks and encourage investment however market risks play a fundamental role in shaping efficient investment and dispatch patterns in a liberalised market therefore measures to improve price signals and market functioning such as a stronger carbon price and developing more responsive demand are desirable however the scale of the investment challenge and increased risk mean targeted measures to reduce although not eliminate risk exposure such as capacity mechanisms and fixed price schemes may have increasing merit the challenge for policy is to strike the right balance between market and planned approaches

commercial policy and the domestic carrying trade in this paper the authors examine the economic consequences of existing u s restrictions on trade in domestic water transportation cabotage services they briefly trace the history of u s policy on cabotage the authors also use an applied general equilibrium model of the united states to analyze the effects of the jones act on welfare and on production trade and employment in important upstream and downstream sectors the economic effects of u s maritime policies rank them with u s trade policies like the mfa the automobile and steel quotas of the mid 1980s and agricultural import restraints coauthors are hugh m arce kenneth a reinert and joseph e flynn

gaming and price spikes in electric power markets many challenging issues arise under the newly deregulated competitive electric power markets instead of centralized decision making in a vertically integrated environment as in the past decision making is now decentralized and driven by market forces gaming and price spikes have been observed in almost every electricity market but explicit analysis of these phenomena is rare in this paper the authors study historical bidding behavior to see how power suppliers and demand service providers were actually bidding in the california day ahead energy market based on their observations they formulate a prisoner s dilemma matrix game and introduce the notion of opportunistic tacit collision to explain strategic bidding behaviors in which suppliers withhold generation capacity from the market to drive up prices this explanation is applicable with or without market power transmission constraints and insufficient supply and is only enhanced by these factors their analysis is generally applicable to any uniform price electricity market in which there is significant insensitivity to price on the demand side

pay as bid versus marginal pricing part ii market behavior under strategic generator offers as the arguments for and against the use of pay as bid pab or marginal pricing mp in electricity pools tend to be qualitative we compare the quantitative behavior of the two markets assuming that generators submit the best strategic offers that correspond to the specified pricing method in part i of this two part study assuming that the system marginal costs for pab and mp are random with known probability density functions we develop generator strategic offers by maximizing the corresponding expected values of the generator profits over the offer parameters in part ii relations are established between the system marginal costs for each market type and a common random demand thus allowing the two markets to be compared through the expected values and variances of the individual generation profits and of the consumer payments this comparison demonstrates both theoretically and through simulation that 1 he expected values of the individual generator profits as well as of the consumer payments are the same under mp and pab and 2 the variances of the individual generator profits and of the consumer payments however are larger under mp than under pab the primary conclusion is then that although mp and pab yield identical expected generator profits and consumer payments the risk of not meeting these expected values is greater under mp than under pab

revenue adequate bidding strategies in competitive electricity markets energy trading in a competitive electricity market can be modeled as a two level optimization at the top level a centralized economic dispatch ced uses a priority list method to solve the fundamental problem of reliable market clearing with price discovery the lower level consists of a set of decentralized bidding db subproblems the db model uses a self unit scheduling simulator based on parametric dynamic programming to produce hourly bid curves for the central dispatch coordinator unit operating constraints and costs such as the unit minimum up and minimum down times ramp rates and the unit start up no load and sunk capital costs are internalized in the bid curves through the simulator a special algorithm is presented to solve the revenue adequacy problem for marginal units both ced and db models are based on the revenue maximization in contrast with the cost minimization criteria used in the conventional unit commitment uc the proposed method has been tested in a study case and some interesting results have been demonstrated

application of games with incomplete information for pricing electricity in deregulated power pools the authors present a game theory approach to the problem of pricing electricity in deregulated marketplaces they assume that an independent operator receives bids by pool participants and defines transactions among participants by looking for the minimum price that satisfies the demand in the pool the competition among pool participants is modeled as a noncooperative game with incomplete information they assume that each pool participant knows its own operation costs but does not know his opponents operation costs the game with incomplete information is transformed into a game with complete but imperfect information and solved using the nash equilibrium idea the approach presented in this paper is geared towards providing support for pricing electricity in deregulated pools

regulatory performance analysis case study britain s electricity industry the success or failure of the privatisation and liberalisation of electricity supply industries has more often been judged in terms of process than of outcomes in this paper in contrast this performance is assessed in terms of the performance of its associated system of regulatory governance taking the uk s electricity supply industry between 1989 and 2000 as case study initially a vertical cross section of the regulation system gives a finding matrix for the various stakeholders involved and identifying winners and losers from the standpoint of funding flows next a horizontal cross section provides the environmental distributive allocative dynamic and productive efficiencies grid for this system the survey shows that the performance of the british esi regulation system produced benefits although not for all stakeholders and not as fairly as possible the chosen path did not seem sustainable and failed to respect intergenerational transfers as a way of fostering sustainability and equity it was unable to underpin simultaneous improvements in efficiencies over time while redistributing industry s funding flows among the players in a regressive manner

a new regional policy for britain fothergill s 2005 a new regional policy for britain regional studies 39 659 667 britain s labour government is setting new directions for uk regional policy and proposing radical changes in european union regional policy as well this paper provides a critical review of the three uk government reports that underpin the new approach it argues that the new approach takes insufficient account of accumulated knowledge or of the long history of uk regional policy the new approach also prioritizes the devolution of decision making over positive discrimination from the centre in favour of less prosperous areas the paper concludes that the three reports are taking uk regional policy into uncharted territory

nash equilibrium bidding strategies in a bilateral electricity market this paper examines bidding strategies in a bilateral market in which generating companies submit bids to loads a load accepts electricity delivery from the generator with the lowest bid at its bid price as long as this price is not higher than the load s willingness to pay necessary and sufficient conditions of nash equilibrium ne bidding strategy are derived based on a generic generating cost matrix and the loads willingness to pay vector the study shows that in any ne efficient allocation is achieved furthermore all nash equilibria are revenue equivalent for the generators based on the necessary and sufficient conditions this problem is formulated as an optimal assignment problem network optimization techniques are applied to calculate ne bid prices for the generators

the economics of new labour policy and performance this paper reviews the foundations of new labour s economic policies and the performance of the economy since 1997 it argues that new labour s policies have evolved from thatcherism and that it has largely embraced the tenets of neo liberalism new labour has rejected most aspects of keynesianism and its policies have eschewed the use of active demand management policies but it has been the high levels of demand in particular consumption expenditure that have driven economic growth in the uk and which have ensured that as yet new labour has not faced the problems of dealing with a major economic downturn

cabotage sabotage the curious case of the jones act abstract this paper examines the economic implications of the jones act which restricts domestic waterborne shipments to american vessels since the passage of this cabotage law a century ago a plausibly exogenous rise in foreign competition has contributed to the closure of most american shipyards and to a decline in american built ships thus the jones act requirements have become more onerous over time the results show that domestic shipments are less likely to be transported via water than imports of the same good into the same state exploiting the decline in jones act eligible vessels over time additional results show that this cabotage law has disproportionately decreased domestic water trade especially in coastal states these findings support common but to date unverified claims that the jones act impedes domestic trade

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

an ordinal optimization based bidding strategy for electric power suppliers in the daily energy market the deregulation and reconstruction of the electric power industry worldwide raises many challenging issues related to the economic and reliable operation of electric power systems traditional unit commitment or hydrothermal scheduling problems have been integrated with generation resource bidding but the development of optimization based bidding strategies is only at a very preliminary stage this paper presents a bidding strategy based on the theory of ordinal optimization that the ordinal comparisons of performance measures are robust with respect to noise and modeling error and the problems become much easier if the optimization goal is softened from asking for the best to good enough solution the basic idea is to use an approximate model that describes the influence of bidding strategies on the market clearing prices mcp a nominal bid curve is obtained by solving optimal power generation for a given set of mcps via lagrangian relaxation then n bids are generated by perturbing the nominal bid curve the ordinal optimization method is applied to isolate a good enough set s that contains some good bids with high probability by performing rough evaluation the best bid is then selected by solving full hydrothermal scheduling or unit commitment problems for each of the bids in s using ordinal optimization approach we are able to obtain a good enough bidding strategy with reasonable computational effort numerical results using historical mcps from the california market and a generation company with 10 units show that the ordinal optimization based method is efficient

strategic bidding for minimum power output in the competitive power market as deregulation of the power industry is becoming a reality there has been an intense interest in the strategic bidding for suppliers to optimize their benefits the benefit gained by a supplier is related not only to its energy price bid curve but also to its submitted operational parameters such as minimum output etc this is especially so when market size is limited because of a limited number of competitors in the market itself or due to the transmission capacity constraints this paper addresses the study of strategic bidding for minimum output in a deregulated environment the impact of minimum output bids on the market result is analyzed a criterion with regard to minimum output bid to assess the outcome of competition among suppliers is derived the method to optimize the benefit from a supplier s viewpoint by adjusting the bids for the minimum output and price is proposed it is shown that an individual supplier can optimize its own benefit by fine tuning its minimum output and price when there are only a few suppliers dominating the market a fairly thorough theoretical analysis of the bidding for minimum output is illustrated with a numerical example

price taker offering strategy in electricity pay as bid markets the recent increase in the deployment of renewable energy sources may affect the offering strategy of conventional producers mainly in the balancing market the topics of optimal offering strategy and self scheduling of thermal units have been extensively addressed in the literature the feasible operating region of such units can be modeled using a mixed integer linear programming approach and the trading problem as a linear programming problem however the existing models mostly assume a uniform pricing scheme in all market stages while several european balancing markets e g in germany and italy are settled under a pay as bid pricing scheme the existing tools for solving the trading problem in pay as bid electricity markets rely on nonlinear optimization models which combined with the unit commitment constraints result in a mixed integer nonlinear programming problem in contrast we provide a linear formulation for that trading problem then we extend the proposed approach by formulating a two stage stochastic problem for optimal offering in a two settlement electricity market with a pay as bid pricing scheme at the balancing stage the resulting model is mixed integer and linear the proposed model is tested on a realistic case study against a sequential offering approach showing the capability of increasing profits in expectation

genetic algorithm evolution of utility bidding strategies for the competitive marketplace this paper describes an environment in which distribution companies discos and generation companies gencos buy and sell power via double auctions implemented in a regional commodity exchange the electric utilities profits depend on the implementation of a successful bidding strategy in this research a genetic algorithm evolves bidding strategies as gencos and discos trade power a framework in which bidding strategies may be tested and modified is presented this simulated electric commodity exchange can be used offline to predict whether bid strategies will be profitable and successful it can also be used to experimentally verify how bidding behavior affects the competitive electric marketplace

comprehensive bidding strategies with genetic programming finite state automata this research is an extension of the authors previous work in double auctions aimed at developing bidding strategies for electric utilities which trade electricity competitively the improvements detailed in this paper come from using data structures which combine genetic programming and finite state automata termed gp automata the strategies developed by the method described here are adaptive reacting to inputs whereas the previously developed strategies were only suitable in the particular scenario for which they had been designed the strategies encoded in the gp automata are tested in an auction simulator the simulator pits them against other distribution companies distcos and generation companies gencos buying and selling power via double auctions implemented in regional commodity exchanges the gp automata are evolved with a genetic algorithm so that they possess certain characteristics in addition to designing successful bidding strategies whose usage would result in higher profits the resulting strategies can also be designed to imitate certain types of trading behaviors the resulting strategies can be implemented directly in online trading or can be used as realistic competitors in an off line trading simulator

production cost minimization versus consumer payment minimization in electricity pools algorithms that involve some kind of optimization have been adopted by several electricity pools as a tool to clear the market traditionally this kind of models were used on a cost minimizing basis but recent papers have pointed out that alternative dispatches may be obtained that even with higher production costs result in lower electricity prices for consumers this paper studies this new payment minimization approach including the long term economic signals that it provides and their impact on future investments the authors results show that minimizing consumer payment results in discriminatory scheduling for certain generation resources and may cause in the long run higher prices for consumers

unemployment wage bargaining and capital labour substitution many economists believe that capital accumulation technical progress and labour force expansion have no lasting effect on unemployment this view rests on the empirically doubtful assumption that the elasticity of substitution between labour and capital is equal to unity i e production is cobb douglas using a simple model based on the work of layard nickell and jackman this paper demonstrates that with a lower elasticity of substitution the equilibrium unemployment rate is affected by all of the above factors it considers briefly how capital accumulation may be endogenised and what long run implications this has for unemployment copyright 1999 by oxford university press

optimal electricity supply bidding by markov decision process the bidding decision making problem is studied from a supplier s viewpoint in a spot market environment the decision making problem is formulated as a markov decision process a discrete stochastic optimization method all other suppliers are modeled by their bidding parameters with corresponding probabilities a systematic method is developed to calculate transition probabilities and rewards a simplified market clearing system is also included in the implementation a risk neutral decision maker is assumed the optimal strategy is calculated to maximize the expected reward over a planning horizon simulation cases are used to illustrate the proposed method

a continuous strategy game for power transactions analysis in competitive electricity markets this article presents a game theory application for analyzing power transactions in a deregulated energy marketplace such as poolco where participants especially generating entities maximize their net profits through optimal bidding strategies i e bidding prices and bidding generations in this article the electricity market for power transactions is modeled as a noncooperative game with complete information where the solution is determined in a continuous strategy domain having recourse to the nash equilibrium idea in order to provide more apprehensible analysis we suggest a new hybrid solution approach employing a two dimensional graphical approach as well as an analytical method finally the proposed approach is demonstrated on a sample power system

pay as bid versus marginal pricing part i strategic generator offers as the arguments for and against the use of pay as bid pab or marginal pricing mp in electricity pools tend to be qualitative we compare the quantitative behavior of the two markets assuming that generators submit the best strategic offers that correspond to the specified pricing method in part i of this two part study assuming that the system marginal costs for pab and mp are random with known probability density functions we develop generator strategic offers by maximizing the corresponding expected values of the generator profits over the offer parameters in part ii relations are established between the smcs for each market type and a common random demand thus allowing the two markets to be compared through the expected values and variances of the individual generation profits and of the consumer payments this comparison demonstrates both theoretically and through simulation that 1 the expected values of the individual generator profits as well as of the consumer payments are the same under mp and pab and 2 the variances of the individual generator profits and of the consumer payments however are larger under mp than under pab the primary conclusion is then that although mp and pab yield identical expected generator profits and consumer payments the risk of not meeting these expected values is greater under mp than under pab

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

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

bidding of price taker power generators in the deregulated turkish power market abstract in deregulated power markets power firms bid into the day ahead power market either with buy offers or sell offers the auction mechanism and competition determine the equilibrium price and quantity for each hour if the bid price of a company is below the market clearing price then the offer of the company is accepted and rewarded with the market price a company can be a price maker or price taker depending on the capacity it offers to the market a price taker unit must determine the right offer that will maximize their profit given price uncertainty and blind auction rules this paper first examines power supply in the turkish electricity market and bidding process then a marginal cost based monte carlo method is developed to determine hourly and block bidding strategies of price taker units historical market prices are then implemented in a normal distribution to generate hourly price scenarios a solution methodology is developed that maximizes the expected profit of each hourly and block bidding strategy over price scenarios the generator is able to both evaluate the hourly bidding and block bidding strategies and find the best bidding strategy that will be submitted to the market using the proposed methodology the model is illustrated for two coal units in turkish power market and the results are presented

a new pricing scheme for a multi period pool based electricity auction a new pricing scheme is proposed for determining the social welfare distribution in a centralized pool based auction in the context of solving the unit commitment problems under competition a significant contribution of this paper over previous publications on this subject is the inclusion of the price responsive demand side for the multi period auctions with dynamic commitment characteristics the model allows every thermal unit and every consumer to obtain individual maximum profits and at the same time it gives the market coordinator an adequate tool for solving the ensuing technologically constrained unit commitment problem with fair market clearing the pricing model is in the form of a mixed linear programming model that minimizes the sum of the compensation costs the accompanying case study illustrates the approach proposed

draining the pool the reform of electricity trading in england and wales abstract the british government has decided to abolish the pool the electricity spot market in england and wales and replace it with a series of bilateral markets the government believes that the pool has been biased against coal fired generators and that its price setting rule all generators are paid the bid of the marginal unit has inflated the level of prices in practice many of the perceived problems in the pool are the result of market power not the basic design of the pool which is capable of sending the right price signals to generators efficient bilateral markets could well produce similar results prices could rise however if buyers are more risk averse and less good at trading than sellers

optimal risk constrained participation of industrial cogeneration systems in the day ahead energy markets abstract this paper presents an optimal bidding strategy for industrial consumers with cogeneration facilities power only and heat only units to participate in day ahead electricity market a information gap decision theory igdt technique is implemented for determining the optimal bidding strategies considering market price uncertainty igdt evaluates the robustness opportunity of optimal bidding strategy under market price uncertainty considering the consumer choice of taking risk averse or risk taking decisions it is confirmed that the risk averse or risk taking decisions might affect the expected profit and bidding curve of the consumers moreover demand response dr program has been implemented in order to serve the power and heat demands of the consumer with minimum cost in the proposed dr program the total power and heat demand of consumer will be supplied without any curtailed load the responsive load can vary in different time intervals in addition it is assumed that the consumer will pay receive for increased reduced consumption which is proportional to the day ahead market price in this paper the heat power dual dependency characteristic in different types of combined heat and power chp units is taken into account and all technical constraints of generation units are satisfied in addition a heat buffer tank with the ability of heat storage is incorporated in the proposed framework the verification of the proposed method is demonstrated using the simulation of a case study

regulatory restrictions and energy the impact of the jones act on spot gasoline prices the purpose of the present study is to estimate the effects of the jones act on spot gasoline prices although the jones act pertains to the domestic shipment of all types of goods the present study will only focus on gasoline the present study will use data obtained from the energy information administration in order to determine if the price of gasoline declined during jones act waiver periods looking at daily prices the results regarding the effects of the jones act on spot gasoline prices are mixed when using a t test the results indicated either that there was no significant difference or that prices were actually higher during the waiver periods when using a first order autoregressive model it was found that prices were lower during the 2005 waiver period but higher during the 2012 waiver given these inconclusive results it is not possible to conclude that the jones act restrictions contribute to higher gasoline prices

thermal power generation scheduling by simulated competition this paper presents a new method for solving the unit commitment problem by simulation of a competitive market where power is traded through a power exchange px procedures for bidding and market clearing are described the market clearing process handles the spinning reserve requirements and power balance simultaneously the method is used on a standard unit commitment problem with minimum up down times start up costs and spinning reserve requirement taken into account comparisons with solutions provided by lagrangian relaxation genetic algorithms and chao an li s unit decommitment procedure demonstrate the potential benefits of this new method the motivation for this work was to design a competitive electricity market suitable for thermal generation scheduling however performance in simulations of the proposed market has been so good that it is presented here as a solving technique for the unit commitment problem

optimal flexible operation of a co _ 2 capture power plant in a combined energy and carbon emission market since co2 capture and storage ccs is a viable co2 abatement option co2 capture power plants ccpps could become a significant part of the future generation mix this paper investigates the flexibility of the operation of a representative post combustion ccpp with ancillary facilities a mathematical model of the operation of such a plant is developed this model quantifies the relation between the major operating characteristics of a ccpp including efficiency penalty capacity penalty net power output and net co2 emission on this basis a profit maximization model is proposed this model would help a ccpp decide its power output schedule co2 capture schedule and bidding strategies in response of volatile power and carbon prices in a day ahead energy market and a cap and trade carbon emission market the validity and the usefulness of the proposed model are demonstrated using numerical results

transaction analysis in deregulated power systems using game theory the electric power industry is in transition to a deregulated marketplace for power transactions in this environment all power transactions are made based on price rather than cost a regional power pool is noted as the most straightforward path to a deregulated electricity marketplace however many questions remain unanswered regarding the economics of power pool participation in a deregulated energy marketplace participants are interested in maximizing their own profits regardless of the system wide profits it is perceived that competition will reduce the price of electricity for retail customers however the key issue for participants is related with the price definition to remain competitive in this paper the authors use game theory to simulate the decision making process for defining offered prices in a deregulated environment the outcome of this study may be used by power pool coordinators to discourage unfair coalitions a modified ieee 30 bus power system is used as a deregulated power pool to illustrate the main features of the proposed method

the merit order effect in the italian power market the impact of solar and wind generation on national wholesale electricity prices abstract italy promoted one of the most generous renewable support schemes worldwide which resulted in a high increase of solar power generation we analyze the italian day ahead wholesale electricity market finding empirical evidence of the merit order effect over the period 2005 2013 an increase of 1 gwh in the hourly average of daily production from solar and wind sources has on average reduced wholesale electricity prices by respectively 2 3 mwh and 4 2 mwh and has amplified their volatility the impact on prices has decreased over time in correspondence with the increase in solar and wind electricity production we estimate that over the period 2009 2013 solar production has generated higher monetary savings than wind production mainly because the former is more prominent than the latter however in the solar case monetary savings are not sufficient to compensate the cost of the related supporting schemes which are entirely internalized within end user tariffs causing a reduction of the consumer surplus while the opposite occurs in the case of wind

a study of basic bidding strategy in clearing pricing auctions this paper models bidding behaviors of suppliers in electricity auction markets under clearing pricing rule and with some simplified bidding assumptions optimal bidding strategy of suppliers is derived by solving a set of differential equations that specify the necessary conditions for bidders to maximize their expected payoffs the derived result indicates that bidders have incentives to mark up their bids above their costs of production the amount of markup depends on the probability of winning below and on the margin that are computed from the cost distribution of all suppliers market demand and the number of suppliers participating in the auction

the merit order effect a detailed analysis of the price effect of renewable electricity generation on spot market prices in germany the german feed in support of electricity generation from renewable energy sources has led to high growth rates of the supported technologies critics state that the costs for consumers are too high an important aspect to be considered in the discussion is the price effect created by renewable electricity generation this paper seeks to analyse the impact of privileged renewable electricity generation on the electricity market in germany the central aspect to be analysed is the impact of renewable electricity generation on spot market prices the results generated by an agent based simulation platform indicate that the financial volume of the price reduction is considerable in the short run this gives rise to a distributional effect which creates savings for the demand side by reducing generator profits in the case of the year 2006 the volume of the merit order effect exceeds the volume of the net support payments for renewable electricity generation which have to be paid by consumers

risk constrained profit maximization in day ahead electricity market the deregulation of the electricity industry has caused for the generation company genco the need of tools for measuring and managing the risk beyond the classical problem of generating unit scheduling in this paper a probabilistic framework for the problem of managing risk faced by gencos trading in day ahead energy market is proposed in particular a stochastic forecast of electricity price and the technical features of hydrothermal units are considered the approach is based on an optimization procedure for maximizing expected profits in the presence of risk constraints conditional value at risk for the distribution of daily profit is used as risk measure

optimization based bidding strategies in the deregulated market with the deregulation of electric power systems market participants are facing an important task of bidding energy to an independent system operator iso this paper presents a model and a method for optimization based bidding and self scheduling where a utility bids part of its energy and self schedules the rest as in new england the model considers iso bid selections and uncertain bidding information of other market participants with appropriately simplified bidding and iso models closed form iso solutions are first obtained these solutions are then plugged into the utility s bidding and self scheduling model which is solved by using lagrangian relaxation testing results show that the method effectively solves the problem with reasonable amount of cpu time

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

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

auction design in day ahead electricity markets competition in day ahead electricity markets has been established through auctions where generators and loads bid prices and quantities different approaches have been discussed regarding the market auction design multi round auctions despite its implementation complexity allow market participants to adapt their successive bids to market prices considering their operational and economic constraints however most of the day ahead electricity market implementations use noniterative single round auctions this paper presents a market simulator to compare both auction models different auction alternatives such as the spanish single round auction that takes into account special conditions included in the generator bids and multi round auctions with different stopping rules are analyzed the results and acquired experience in the simulation of the spanish market started in january 1998 are presented hourly market prices average daily price price demand correlation and several economic efficiency indicators such as generator surplus consumer surplus and social welfare are compared to derive conclusions regarding the performance of the auction alternatives

measuring the efficiency of grid companies in china a bootstrapping non parametric meta frontier approach abstract the unbundling policy reform of power industry has become a worldwide trend since 1980s there are quite many studies focusing on the efficiency changes of transmission and distribution sectors in european and latin american countries while few reports on china s issues have been found this paper employs a nonparametric frontier estimation combined with meta frontier and bootstrapping method to measure the productive efficiency and changes in the decomposition index for china s 31 grid companies from 2004 to 2013 in addition it further employs tobit regression model to analyze the influences of relevant policies and heterogeneity factors such as natural conditions and economic development the results indicate that the grouping approach extends the application of non parametric data envelopment analysis which may decrease the assumption of homogeneity for the decision making units and provide better efficiency estimates by increasing the credibility of the evaluation results overall productive efficiency increased during the whole study period with technology progress the main contributor to this improvement efficiency trends fluctuated with changes in economic development and power supply situation and the economics and customer density had positive effects on productive efficiency while the influence of clean power proportion is negative

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

on intermittent renewable generation the stability of australia s national electricity market abstract energy only markets have an inherently unstable equilibrium even under ideal conditions because participants are unable to optimise voll events the addition of intermittent renewable generation is thought to make conditions harder in this article optimal voll events in an islanded nem region is modelled by substituting high price caps for boiteux capacity charges then analysing the impact of adding progressively more variable renewable energy vre up to 35 market share spot market conditions prove stable and tractable provided thermal plant exit and adjust perfectly but vre asset allocation is important absent highly elastic demand or ultra low cost storage solar pv market share has economic limits because the technology rapidly cannibalises itself furthermore as vre rises in imperfectly interconnected regions a tipping point appears to exist where hedge markets enter an unstable zone through shortages of asset backed firm intra regional swaps and caps government initiated cfds for vre need to be designed carefully to ensure any instability is not exacerbated by extracting contracts from an already shortening hedge market

transportation costs and international trade in the second era of globalization while the precise causes of postwar trade growth are not well understood declines in transport costs top the lists of usual suspects however there is remarkably little systematic evidence documenting the decline this paper brings to bear an eclectic mix of data in order to provide a detailed accounting of the time series pattern of shipping costs the ad valorem impact of ocean shipping costs is not much lower today than in the 1950s with technological advances largely trumped by adverse cost shocks in contrast air shipping costs have dropped an order of magnitude and airborne trade has grown rapidly as a result as a result international trade has also experienced a significant rise in speed

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

price taker bidding strategy under price uncertainty this paper provides a framework to obtain the optimal bidding strategy of a price taker producer an appropriate forecasting tool is used to estimate the probability density functions of next day hourly market clearing prices this probabilistic information is used to formulate a self scheduling profit maximization problem that is solved taking advantage of its particular structure the solution of this problem allows deriving a simple yet informed bidding rule results from a realistic case study are discussed in detail

market based coordination of thermostatically controlled loads part i a mechanism design formulation this paper focuses on the coordination of a population of thermostatically controlled loads tcls with unknown parameters to achieve group objectives the problem involves designing the device bidding and market clearing strategies to motivate self interested users to realize efficient energy allocation subject to a peak energy constraint this coordination problem is formulated as a mechanism design problem and we propose a mechanism to implement the social choice function in dominant strategy equilibrium the proposed mechanism consists of a novel bidding and clearing strategy that incorporates the internal dynamics of tcls in the market mechanism design and we show it can realize the team optimal solution this paper is divided into two parts part i presents a mathematical formulation of the problem and develops a coordination framework using the mechanism design approach part ii presents a learning scheme to account for the unknown load model parameters and evaluates the proposed framework through realistic simulations

a simulation model for a competitive generation market the simulation of a wholesale electricity market should go beyond a simple optimization based on the operating costs of the generating units a model of a competitive electricity market must consider the market structure the strategy of the market participants and any other factor that lead to prices different from costs this paper presents cosmee a model of a wholesale electricity market based on simple bids that estimates expected bid prices and quantities system hourly prices and generation schedules taking into account the bidding strategies of generators and the structure of the market the model reflects the profit maximizing behavior of the market agents subject to different types of constraints cosmee has been used to simulate a real wholesale market

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

bidding strategy design for different types of electric power market participants this paper presents a methodology to design an optimal bidding strategy for a generator according to his or her degree of risk aversion a typical thermal plant is assumed to be located in the ontario electricity system to apply this methodology for two types of participants risk averse and risk seeker

strategic bidding for price taker hydroelectricity producers this paper introduces a stochastic programming model that integrates strategic bids or offers for electricity in quantities and prices in a deregulated electricity market the model is designed to maximize the profits of a producer of electricity who manages a series of power plants along a river the model is compared with a previously reported stochastic model where the bidding process is disregarded the superiority of the new model is empirically demonstrated on historical data

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

economics for the future many of us decided to study economics because we were interested in issues such aseconomic growth unemployment poverty discrimination and social exclusion andfor much of the history of the discipline economists grappled with explaining suchphenomena and hopefully helped to improve the development of economic policy but now the discipline is increasingly dominated by other concerns such asmathematical rigour and econometric modelling the new york times columnist michael weinstein recalled how his passion to learn economics which was driven bya desire to understand the causes of poverty and the impact of technical change wasimmediately quelled when on his rst day as a graduate student at the massachusettsinstitute of technology the professor announced that all of economics is a subset ofthe theory of separating hyperplans weinstein 2000 the subject has become soobscure that even orthodox economists are bemoaning its intellectual poverty accordingtomiltonfriedman economicshasbecomeincreasinglyanarcanebranchof mathematics rather than dealing with real economic problems friedman 1999 p 137 and robert solow has observed today if you ask a mainstream economista question about almost any aspect of economic life the response will be suppose wemodel that situation and see what happens modern mainstream economics consistsof little else but examples of this process quoted in lawson 2005 so many economists are now increasingly engaged in research and teaching that isdisconnected from the issues that in uence people s lives as long 2005 observed academiceconomistsremainhiddenintheirivorytowers theyareneitherhouseholdnames nor a signi cant presence in newspaper commentary pages how strange fora country that produced the most famous economists of all time adam smith davidricardo and john maynard keynes but not all remain hidden in their ivory towers there are a few who do in uence the policy domain but they come from the intel lectually narrow sect of neoclassical economics and there are those from the neo classical sect who have managed to in ltrate popular culture by throwing away themaths packaging their prose in airport style books and entertaining their readerswithstories about why sumo wrestlers cheat and why drug pushers live with their mums see levitt and dubner 2005 as modern orthodox economics moves away from reality there is both theopportunity and the need for heterodox economics to orientate economics towards

generation scheduling and the switching curve law the switching curve law is an analytic procedure derived from the dual of the lagrangian relaxation approach to the static unit commitment suc problem it is argued here that this analytic result provides a new understanding of the general unit commitment problem not otherwise possible from numerical methods moreover the suc problem and the analytic results presented here have direct applications in the scheduling of hydro generation in power plants and in the interpretation of the pricing rules of the england and wales power pool

strategic bidding in an energy brokerage this focus of this paper is to present a framework in which strategies may developed for the individual participants in an energy brokerage such a framework is expected to be important with the increasing interest in electricity market structures such as the energy brokerage that deregulation has produced to illustrate the process of strategic bidding a simple suboptimal bidding strategy is presented with the aid of a numerical example the bidding strategy must be suboptimal because of the amount of unknown information about the other bidders further tuning of this strategy will be required before application to realistic scenarios

the nature of heterodox economics heterodoxy serves as an umbrella term to cover the coming together of separate projects or traditions in answering the question what distinguishes heterodoxy from the orthodoxy the author argues that matters of ontology are central in answering the question how are the various traditions that make up the modern heterodoxy to be distinguished from each other the author defends criteria other than varying commitments to specific substantive theories policy measures or techniques or basic units of analysis

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