strategic offering for a wind power producer as a result of subsidies and technological maturity renewable electricity producers have grown in some jurisdictions to clearly dominant positions in the market under this context we propose an offering strategy for a wind power producer with market power that participates in the day ahead market as a price maker and in the balancing market as a deviator uncertainty pertaining to wind power production and balancing market price is represented through a set of correlated scenarios the proposed model is a stochastic mathematical program with equilibrium constraints mpec that can be recast as a tractable mixed integer linear programming milp problem which is solvable using available optimization software results from an illustrative example and two case studies show the effectiveness of the proposed model

risk assessment of virtual power plants offering in energy and reserve markets distributed energy resources ders can be integrated into a single entity namely virtual power plant vpp the integration enables them to participate in competitive wholesale electricity markets according to the different types of uncertainty faced by different ders this integration can also act as a risk hedging mechanism providing a surplus profit in this paper using a two stage stochastic programming approach risk averse optimal offering model for a vpp trading in a joint market of energy and spinning reserve service is presented the conditional value at risk cvar is used to control the risk of profit variability uncertainties involved in generation of renewables consumption of loads calls for reserve service as well as prices in the day ahead market the spinning reserve market rm and the balancing real time market bm are taken into consideration this paper assesses how total and surplus profits of vpp are affected by risk aversion participation in the rm and the pricing system in the bm for this purpose the role of the integration and the trends of energy and reserve transactions under both single and dual pricing systems in the bm are addressed in detail through a numerical study

energy market design for renewable resources imbalance settlements and efficiency robustness tradeoffs this paper examines optimal energy market bidding behavior for stochastic resources when energy imbalance payments are determined endogenously from market clearing conditions some electricity markets require wind producers to settle day ahead offers based on their real time energy production this is usually justified by economic and cost causation principles without considering the effects of these policies on curtailment and system balancing costs under these real time settlement requirements demand and wind forecast errors impact the effective penalty price so strategic behavior affects the penalty price paid by producers this paper formulates a two stage game where producers place energy bids in the day ahead market and then choose a curtailment level in real time and it derives the symmetric subgame perfect equilibrium strategy for producers it details how supply curve convexity and resource correlation can lead wind producers to bid conservatively even when they are not risk averse the results show that market based pricing mechanisms improve tradeoffs between system efficiency and mean squared bid error as compared to the case where penalty levels are determined by alternative policies suggesting additional benefits of market based penalty prices beyond those previously studied

intraday trading of wind energy in this paper we tackle the problem of a wind power producer participating in a short term electricity market that allows for the continuous but potentially illiquid intraday trading of energy considering the realistic case of a wind farm operating in the western danish price area of nord pool we build a simple but effective algorithm for the wind power producer to fully benefit from the elbas intraday market we then investigate the sensitivity of the obtained benefits to the maximum volume of energy the wind power producer is willing to trade in the intraday market the ultimate aim of the trade either to decrease energy imbalances or to increase profits and to the installed capacity of the wind farm our numerical results reveal that the wind power producer can substantially increase his revenues by partaking in the intraday market but with diminishing returns to scale a result that we attribute to the low liquidity of elbas

optimal offering and operating strategies for wind storage systems with linear decision rules the participation of wind farm energy storage systems wf ess in electricity markets calls for an integrated view of day ahead offering strategies and real time operation policies such an integrated strategy is proposed here by co optimizing offering at the day ahead stage and operation policy to be used at the balancing stage linear decision rules are seen as a natural approach to model and optimize the real time operation policy these allow enhancing profits from balancing markets based on updated information on prices and wind power generation our integrated strategies for wf ess in electricity markets are optimized under uncertainty in both wind power and price predictions the resulting stochastic optimization problem readily yields optimal offers and linear decision rules by adding a risk aversion term in form of conditional value at risk into the objective function the optimization model additionally provides flexibility in finding a trade off between profit maximization and risk management uncertainty in wind power generation as well as day ahead and balancing prices takes the form of scenario sets permitting to reformulate the optimization problem as a linear program case studies validate the effectiveness of the strategy proposed by highlighting and quantifying benefits w r t other existing strategies

trading wind generation in short term energy markets even with state of the art forecasting methods the short term generation of wind farms cannot be predicted with a high degree of accuracy in a market situation these forecasting errors lead to commercial risk through imbalance costs when advance contracting this situation is one that needs to be addressed due to the steady increase in the amount of grid connected wind generation combined with the rise of deregulated market orientated electricity systems in the presence of imbalance prices and uncertain generation a method is required to determine the optimum level of contract energy to be sold on the advance markets such a method is presented here using markov probabilities for a wind farm and demonstrates substantial reductions in the imbalance costs the effect of market closure delays and forecasting window lengths are also shown

offering strategy of wind power producer a multi stage risk constrained approach given the significant amount of installed generation capacity based on wind power and also due to current economic downturn the subsidies and incentives that have been widely used by wind power producers to recover their investment costs have decreased and are even expected to disappear in the near future in these conditions wind power producers need to develop offering strategies to make their investments profitable counting solely on the market this paper proposes a multi stage risk constrained stochastic complementarity model to derive the optimal offering strategy of a wind power producer that participates in both the day ahead and the balancing markets uncertainties concerning wind power productions market prices demands bids and rivals offers are efficiently modeled using a set of scenarios the conditional value at risk metric is used to model the profit risk associated with the offering decisions the proposed model is recast as a tractable mixed integer linear programming program solvable using available branch and cut algorithms results of a case study are reported and discussed to show the effectiveness and applicability of the proposed approach

demand response application by strategic wind power producers this paper considers a wind power producer playing strategically in a day ahead market while willing to set demand response dr contracts with a dr aggregator to this end a bilevel problem including a single leader and two followers is formulated the wind power producer is the leader aiming at maximizing its profit through offering into a day ahead market and clearing its deviation in a balancing market the strategic behavior of the producer in the day ahead market is modeled through the market clearing process follower 1 in addition the dr aggregator behavior is modeled through a revenue function in which the aggregator is able to sell its dr product to the wind power producer other competitors and the day ahead market follower 2 the overall problem is a stochastic mathematical program with equilibrium constraints mpec in which wind power production and imbalance prices are associated with uncertainty the problem is then transformed into a linear programming approach a case of the nordic market is chosen to assess the validity of the given problem

a stochastic multi layer agent based model to study electricity market participants behavior this paper presents a new stochastic multi layer agent based model to study the behavior of electricity market participants the wholesale market players including renewable power producers are modeled in the first layer of the proposed multi agent environment the players optimize bidding offering strategies to participate in the electricity markets in the second layer responsive customers including plug in electric vehicle pev owners and consumers who participate in demand response dr programs are modeled as independent agents the objective of the responsive customers is to increase their benefit while retaining welfare the interaction between market players in day ahead and real time markets is modeled using an incomplete information game theory algorithm due to the uncertainties of resources and customers behavior the model is developed using a stochastic framework a case study containing wind power producers wpps pev aggregators and retailers providing dr is considered to demonstrate the usefulness and proficiency of the proposed multi layer agent based model

minimization of imbalance cost trading wind power on the short term power market present power markets are designed for trading conventional generation for wind generation to participate in a short term energy market lengthy wind power production forecasts are required although wind speed forecasting techniques are constantly improving wind speed forecasts are never perfect and resulting wind power forecast errors imply imbalance costs for wind farm owners in this paper a new method for minimization of imbalance costs is developed stochastic programming is used to generate optimal wind power production bids for a short term power market a wind power forecast error is represented as a stochastic process the imbalance costs resulting from this strategy are then compared to the case when wind power production bids on a short term power market are based directly on a wind speed forecast

wind power offering strategy in day ahead markets employing demand response in a two stage plan this paper deals with wind power offering strategies in day ahead markets a new plan is proposed in which a wind power producer participates in the day ahead market while employing demand response dr to smooth its power variations in this context a new dr scheme is presented through which the wind power producer is able to achieve dr by establishing various dr agreements with dr aggregators the proposed offering plan involves two stages the first stage clears on the day ahead market the wind power producer decides on day ahead offers as well as dr agreements with the aggregator the second stage takes place on the balancing market in a successive approach the wind power producer determines its energy trading for each period until the whole day is covered additionally proper dr agreements for each period are confirmed here the proposed plan is formulated in a stochastic programming approach where its validity is assessed on a case of the nordic market

application of fuzzy q learning for electricity market modeling by considering renewable power penetration abstract by increasing renewable resource penetration the need for developing fast and reliable market modeling approaches in the presence of these resources has gained greater attention in this paper fuzzy q learning approach is proposed for hour ahead electricity market modeling in presence of renewable resources the proposed approach is implemented on ieee 30 bus test system the effectiveness of the proposed approach is evaluated and compared with q learning approach for both normal and stressful cases simulation results indicate that the proposed approach is able to model electricity market for a range of continuous multidimensional renewable power penetration in considerably less iterations compared with q learning approach moreover the probability of finding nash equilibrium is becoming higher by using fuzzy q learning approach while the other indices such as average social welfare average of locational marginal prices lmps and average standard of deviation of lmps do not change considerably

optimal offering and operating strategy for a large wind storage system as a price maker wind farms and energy storage systems are playing increasingly more important roles in power systems which makes their offering nonnegligible in some markets from the perspective of wind farm energy storage systems wf ess this paper proposes an integrated strategy of day ahead offering and real time operation policies to maximize their overall profit as participants with large capacity in electricity markets can influence cleared prices by strategic offering a large scaled wf ess is assumed to be a price maker in day ahead markets correspondingly the strategy considers influence of offering quantity on cleared day ahead prices and adopts linear decision rules as the real time control strategy these allow enhancing overall profits from both day ahead and balancing markets the integrated price maker strategy is formulated as a stochastic programming problem where uncertainty of wind power generation and balancing prices are taken into account in the form of scenario sets permitting to reformulate the optimization problem as a linear program case studies validate the effectiveness of the proposed strategy by highlighting and quantifying benefits comparing with the price taker strategy and also show the profit enhancement brought to the distributed resources

trading wind generation from short term probabilistic forecasts of wind power due to the fluctuating nature of the wind resource a wind power producer participating in a liberalized electricity market is subject to penalties related to regulation costs accurate forecasts of wind generation are therefore paramount for reducing such penalties and thus maximizing revenue despite the fact that increasing accuracy in spot forecasts may reduce penalties this paper shows that if such forecasts are accompanied with information on their uncertainty i e in the form of predictive distributions then this can be the basis for defining advanced strategies for market participation such strategies permit to further increase revenues and thus enhance competitiveness of wind generation compared to other forms of dispatchable generation this paper formulates a general methodology for deriving optimal bidding strategies based on probabilistic forecasts of wind generation as well as on modeling of the sensitivity a wind power producer may have to regulation costs the benefits resulting from the application of these strategies are clearly demonstrated on the test case of the participation of a multi mw wind farm in the dutch electricity market over a year

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

strategic wind power investment this paper considers the problem of identifying the optimal investment of a strategic wind power investor that participates in both the day ahead da and the balancing markets this investor owns a number of wind power units that jointly with the newly built ones allow it to have a dominant position and to exercise market power in the da market behaving as a deviator in the balancing market in which the investor buys sells its production deviations the model is formulated as a stochastic complementarity model that can be recast as a mixed integer linear programming milp model a static approach is proposed focusing on a future target year whose uncertainties pertaining to demands wind power productions and balancing market prices are precisely described the proposed model is illustrated using a simple example and two case studies

equilibria in an oligopolistic electricity pool with stepwise offer curves we study the equilibria reached by strategic producers in a pool based network constrained electricity market the behavior of each producer is modeled by a mathematical program with equilibrium constraints mpec whose objective is maximizing profit and whose complementarity constraints describe market clearing the joint solution of all these mpecs constitutes an equilibrium problem with equilibrium constraints epec the equilibria associated with the epec are analyzed by solving the strong stationarity conditions of all mpecs which can be linearized without approximation by mixed integer linear programming milp techniques the resulting mixed integer linear conditions can be reformulated as an optimization problem that allows establishing diverse objectives to differentiate among alternative equilibria

monetary policy during transition progress and pitfalls in central and eastern europe 1990 6 monetary policy rarely accomplished stabilization if sound fiscal policy had not already been established distinctions between different nominal anchors can be exaggerated any credit crunch owed more to micro distortions than to tight money initial stabilization did not always precede the resumption of growth but there is little evidence that more gradual transition would have made disinflation easier thereafter countries usually experienced large capital inflows whatever the ostensible exchange rate regime fiscal tightening and more exchange rate flexibility offer a sounder eventual response poor incentives and corporate governance in banks should have been anticipated subsequent improvements were slow and costly large investments in writing off bad debt and providing adequate resources for supervision made monetary transmission more reliable and hardened budget constraints through which the price mechanism could improve efficiency copyright 1997 by oxford university press

a game theoretical pricing mechanism for multi area spinning reserve trading considering wind power uncertainty the rapid development of wind power has led to an increased demand for spinning reserve in power systems today however one of the most severe challenges to china s power systems is the mismatch between wind power installation capacity and the capability for supplying spinning reserve within each independently operated provincial power system coordinating the spinning reserve across multiple areas would providentially improve the accommodation of wind power this paper proposes a game theoretical model for spinning reserve trading between provincial systems that treat spinning reserve as a commodity based on the incomplete information the trading price is calculated by satisfying the bayesian nash equilibrium and then the trading quantity is determined this ensures that both the buyer and the seller are able to maximize their expected profit case studies are performed using a 2 bus interconnected system and a 3 area ieee rts system the results show that the proposed model is valid and effective

modeling the impact of a wind power producer as a price maker wind energy is present in many countries throughout the world the main types of wind sales in electricity markets are via regulated tariffs or pool based markets production companies choose cost effective options for selling wind energy and some markets like the irish electricity market use regulated tariffs to remunerate wind production this paper aims to provide some answers to explain what effect wind offers may have in an electricity market if wind power producers participated in the day ahead market without receiving any premium or aid a price maker optimization model is used to detect its effect on prices the model encompasses energy offers by other technologies using residual demand curves and detailed modeling of wind imbalances it is observed that wind acting as price maker reduces electricity prices and the imbalance penalties help the system operator to reduce imbalances a realistic case study using data from the irish electricity market illustrates the methodology used comparing the effect of imbalance penalties in the models in terms of profit and total imbalance of the system

relative sources of european regional productivity convergence a bootstrap frontier approach enflo k and hjertstrand p relative sources of european regional productivity convergence a bootstrap frontier approach regional studies this paper addresses the issue of western european regional productivity growth and convergence by means of data envelopment analysis dea decomposing labour productivity into efficiency change technical change and capital accumulation the decomposition shows that most regions have fallen behind the production frontier in efficiency and that capital accumulation has had a diverging effect on the labour productivity distribution using bootstrapping methods the paper also accounts for the inherent bias and the stochastic elements in the efficiency estimation it is found that the relative ranking of the efficiency scores remains stable after the bias correction even after controlling for spatially correlated measurement errors and that the dea successfully identifies the regions on the production frontier as significantly more efficient than other regions enfl

operational strategies for a portfolio of wind farms and chp plants in a two price balancing market in this paper we explore the portfolio effect of a system consisting of a combined heat and power chp plant and a wind farm the goal is to increase the overall profit of the portfolio by reducing imbalances and consequently their implicit penalty in a two price balancing market for electricity we investigate two different operational strategies which differ in whether the chp plant and the wind farm are operated jointly or independently and we evaluate their economic performance on a real case study based on a chp wind system located in the western part of denmark we present a comprehensive mathematical model for describing the different heat and power production units of the chp plant and suggest different ways of determining its operation in a setup with two trading floors a day ahead market and a balancing market we build a simulation framework that runs in a rolling horizon fashion so that forecasts for heat demand wind power production and market prices are updated at each iteration we conclude that the portfolio strategy is the most profitable due to the two price structure of the balancing market this encourages producers to handle their imbalances outside the market

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