keep or return managing ordering and return policies in start up companies abstract start up companies are considered an important factor in the success of a nation s economy we are interested in the decisions for long term survival of these firms when they have considerable cash restrictions in this paper we analyse several inventory control models to manage inventory purchasing and return policies the markov decision models are formulated for both established companies that look at maximising average profit and start up companies that look at maximising their long term survival probability we contrast both objectives and present properties of the policies and the survival probabilities we find that start up companies may need to be riskier if the return price is very low but there is a period where a start up firm becomes more cautious than an established company and there is a point as it accumulates capital where it starts behaving as an established firm we compare the various models and give conditions under which their policies are equivalent

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

optimal alliance strategies among retailers under energy deviation settlement mechanism in china s forward electricity market an energy deviation settlement eds mechanism has been proposed to settle the deviation between consumption and energy contracts in china s forward electricity market due to high eds costs of electricity retailers a deviation mutual insurance dmi mechanism is put forward representing an option for electricity retailers to reduce the deviation through contract transfer given this background an optimal alliance strategy is proposed to maximize the utility of different types of individual electricity retailers the alliance income model considers the eds cost reduction and the cooperation cost analyzed based on transaction cost theory tct an improved income allocation method which considers distribution indexes selected by game theory and resource dependence theory rdt is proposed to ensure the comprehensive and reasonable allocation of alliance income considering the expected income and financial risk an evaluation model using conditional value at risk cvar as the financial risk assessment indicator is presented to measure the stability of alliances simulations based on data from a provincial electricity market in china illustrate that the eds costs of retailers can be reduced by selecting appropriate partners for alliance in addition the proposed alliance income allocation method is more reasonable and more targeted than the cooperative game based methods

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

fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market in a deregulated electricity market generators have to optimally bid to maximize their profit under incomplete information of other competing generators this paper addresses an optimal bidding strategy of a thermal generator in a uniform price spot market considering a precise model of nonlinear operating cost function and minimum up down constraints of unit commitment the bidding behaviors of other competing generators are described using normal probability distribution function bidding strategy of a generator for each trading period in a day ahead market is solved by fuzzy adaptive particle swarm optimization fapso where inertia weight is dynamically adjusted using fuzzy evaluation fapso can dynamically follow the frequently changing market demand and supply in each trading interval the effectiveness of the proposed approach is tested with examples and the results are compared with the solutions obtained using genetic algorithm ga approach and other versions of pso

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

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

overeducation undereducation and the theory of career mobility the theory of career mobility sicherman and galor journal of political economy 98 1 169 92 1990 claims that wage penalties for overeducated workers are compensated by better promotion prospects sicherman journal of labour economics 9 2 101 22 1991 was able to confirm this theory in an empirical study using panel data however the only retest using panel data so far robst eastern economic journal 21 539 50 1995 produced rather ambiguous results in the present paper random effects models to analyse relative wage growth are estimated using data from the german socio economic panel it is found that overeducated workers in germany have markedly lower relative wage growth rates than adequately educated workers the results cast serious doubt on whether the career mobility model is able to explain overeducation in germany the plausibility of the results is supported by the finding that overeducated workers have less access to formal and informal on the job training which is usually foun

should start up companies be cautious inventory policies which maximise survival probabilities new start up companies which are considered to be a vital ingredient in a successful economy have a different objective than established companies they want to maximise their chance of long term survival we examine the implications for their operating decisions of this different criterion by considering an abstraction of the inventory problem faced by a start up manufacturing company the problem is modelled under two criteria as a markov decision process the characteristics of the optimal policies under the two criteria are compared it is shown that although the start up company should be more conservative in its component purchasing strategy than if it were a well established company it should not be too conservative nor is its strategy monotone in the amount of capital it has available the models are extended to allow for interest on investment and inflation

pre ipo operational and financial decisions many owners of growing privately held firms make operational and financial decisions in an effort to maximize the expected present value of the proceeds from an initial public offering ipo we ask what is the right time to make an ipo and how should operational and financial decisions be coordinated to increase the likelihood of a successful ipo financial and operational decisions in this problem are linked because adequate financial capital is crucial for operational decisions to be feasible and operational decisions affect the firm s access to financial resources the ipo event is treated as a stopping time in an infinite horizon discounted markov decision process unlike traditional stopping time models at every stage the model includes other decisions such as production sales and loan size the results include 1 characterization of an optimal capacity expansion policy 2 sufficient conditions for a monotone threshold rule to yield an optimal ipo decision and 3 algorithmic implications of results in 1 and 2

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

generator bidding in oligopolistic electricity markets using optimal control fundamentals and application in this paper optimal control is applied to study generator bidding in an oligopolistic electricity market the repeated bidding process in hourly based real time electricity markets is modeled as a dynamic feedback system an optimal control problem is then formulated to explore individual generator s long term multiperiod optimization behavior particularly in our formulation the periodic property of the system demand is considered several lemmas are included for concerning system stability based on the necessary conditions for optimality from the pontryagin maximum principle a sweeping method is proposed and an optimal state feedback control rule is then obtained via backward induction numerical results suggest that the generator who unilaterally applies optimal control for generation decisions will obtain more profits a sensitivity analysis is also performed identifying these market factors that affect the performance of optimal control

games with incomplete and asymmetric information in poolco markets strategic equilibrium solutions as a result of gaming by electricity market participants have been the subject of various recent studies yet in many of the tools developed for finding those strategic solutions either information is regarded as being complete or the complexities of the transmission system are disregarded since the assumption of complete information is neither absolutely correct nor absolutely necessary for the more complex models these models need to be extended the strategic solutions attained in these incomplete information or bayesian games are bayesian nash equilibria the central thrust of the paper is on bringing the representation of incomplete and asymmetric information in strategic games together with some of the transmission system constraints for this purpose the paper focuses on a specific strategic game model referred to as the iwm numerical examples using the ieee 57 bus system that deal with the power flow equality constraints are presented as an illustration of the concepts behind the proposed bayesian games

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

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

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

a scenario simulation approach for market power analysis in hydrothermal systems this paper focuses on markets based on physical bilateral contracts combined with a power exchange px the main feature to be analyzed is the behavior of the hydrothermal system where one or more companies have a big reservoir with capacity for inter annual regulation in this work inter annual regulation means the ability of a reservoir to allocate water from consecutive years the use of the reservoirs in a context of a power exchange market with competition is the main objective of this study results on the chilean central interconnected system using stochastic dynamic programming are presented and discussed

a two tier matrix game approach for obtaining joint bidding strategies in ftr and energy markets transmission congestion results in revenue collected from the loads that often exceeds the payment made to the generators the financial transmission right ftr is an instrument that allows the independent system operator iso to redistribute the excess revenue congestion charge among the market participants generators and loads hold ftrs to hedge against congestion charges since the revenues from the ftr and energy markets are interdependent each generator and load must have a joint bidding strategy for ftr and energy markets to maximize its total payoff this paper presents a two tier matrix game model that allows the generators and loads to obtain joint bidding strategies the model also allows the isos and the market designers to assess the impact of various network configurations and rules on market performance e g market power the two tier model considers multiple participants multidimensional bid vectors network contingencies and varying demand we conducted a numerical validation of our model by analyzing the three node network studied by joskow and tirole 2000 additional numerical studies were conducted on a five node network to examine the impact of holding ftrs on the bidding strategies in the energy market and joint payoffs of the participants

purchase allocation and demand bidding in electric power markets the purchase allocation problem is one of the most important problems faced by an electric energy service provider under new market environments the optimal purchase allocation problem for dual electric power markets and demand bid generation are discussed in this paper the price volatility is explicitly considered in purchase allocation problems and the sequential nature is modeled by conditional stochastic characteristics an analytical solution for the optimal allocation is derived with given demand and statistical characteristics of the market prices the method for generating demand bids for purchaser based on market allocation and price forecasting is then developed the numerical simulations for market allocation and bid generation are demonstrated based on the actual data of the u s california power market

agent based modeling in electrical energy markets using dynamic bayesian networks due to uncertainties in generation and load optimal decision making in electrical energy markets is a complicated and challenging task participating agents in the market have to estimate optimal bidding strategies based on incomplete public information and private assessment of the future state of the market to maximize their expected profit at different time scales in this paper we present an agent based model to address the problem of short term strategic bidding of conventional generation companies gencos in a power pool based on the proposed model each genco agent develops a private probabilistic model of the market using dynamic bayesian networks employs an online learning algorithm to train the model sparse bayesian learning and infers the future state of the market to estimate the optimal bidding function we show that by using this multiagent framework the agents will be able to predict and adapt to approximate nash equilibrium of the market through time using local reasoning and incomplete publicly available data the model is implemented in matlab and is tested on four test case systems two generic systems with 5 and 15 genco agents and two ieee benchmarks 9 bus and 30 bus systems both the day ahead da and hour ahead ha bidding schemes are implemented the results show a drop in market power in the 15 agent system compared to 5 agent system along with a pareto superior equilibrium in the ha scheme compared to the da scheme which corroborates the validity of the proposed decision making model also the simulations show that introduction of an ha decision making stage as an uncertainty containment tool leads to a more stable and less volatile price signal in the market which consequently results in flatter and improved profit curves for the gencos

gaming the uniform price spot market quantitative analysis in this paper a range of indices have been derived to quantify the incentives to game the uniform price spot market the gain of a portfolio generator due to withdrawing capacity and or inflating the price bid has been found to be proportional to the in merit market share of the gaming generator and the inverse combined price elasticity of supply demand a gaming index has also been derived as a modification of the herfindahl hirschman index which takes into account the reduction in the number of competitors as the demand goes up and the benefit the portfolio generators take from gaming price elastic loads reduce gaming opportunities but do not remove them application of the indices has been illustrated using the the spot market in england and wales the results seem to confirm practical evidence from many countries that spot markets are vulnerable to gaming

capacity investment timing by start ups and established firms in new markets we analyze the competitive capacity investment timing decisions of both established firms and start ups entering new markets which have a high degree of demand uncertainty firms may invest in capacity early when uncertainty is high or late when uncertainty has been resolved possibly at different costs established firms choose an investment timing and capacity level to maximize expected profits whereas start ups make those choices to maximize the probability of survival when a start up competes against an established firm we find that when demand uncertainty is high and costs do not decline too severely over time the start up takes a leadership role and invests first in capacity whereas the established firm follows by contrast when two established firms compete in an otherwise identical game both firms invest late we conclude that the threat of firm failure significantly impacts the dynamics of competition involving start ups this paper was accepted by yossi aviv operations management

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

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 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