purchase bidding strategies of an energy coalition with demand response capabilities the implementation of demand response programs drp has gained interest as a means to alleviate energy consumption during peak hours two explanations account for the success of such programs which involve both utilities and electricity consumers with the latter often organized into coalitions the system operator meets its goal of reducing the load peak simultaneously electricity consumers achieve economic benefits when reducing consumption during peak hours in this paper a monte carlo based algorithm has been proposed for the formulation of multiple purchase offers in the day ahead energy market daem by coalitions in which consumers vary in their sensitivity to drp manifesting different responsiveness to hourly tariffs based on the hourly market clearing prices being able to monitor how coalition members use air conditioning in the presence of variable hourly energy tariffs the coordinator can then define a purchase bidding strategy depending on how price sensitive the coalition is simulation results show that the presence of a price sensitive demand leads not only to a subsequent reduction in energy prices during peak hours but also leads to a decrease in their inter hour volatility

the demand elasticity impacts on the strategic bidding behavior of the electricity producers due to the oligopoly structure of the electricity markets and to the constraints imposed by the transmission network the producers may exert market power by strategically bidding higher than their marginal costs this brings market performance far from the perfect competition equilibrium with higher market clearing price and extra surpluses obtained by the producers demand elasticity can significantly affect the market performance contributing to mitigate the strategic bidding behavior of the producers compared to other commodity markets demand elasticity in the electricity markets is low but even a small increase can result in appreciable improvement of the market performance the network constraints of the power system play a specific role in determining the oligopoly equilibrium of the gaming behavior of the electricity producers the model of supply function equilibrium is first applied to a simple three bus test system for a conceptual analysis and then to a standard ieee 30 bus test system the effects of an increase of demand elasticity are assessed resorting to a set of proper quantitative indexes

evaluating the impacts of priority dispatch in the european electricity market this paper compares the impact of the nodal pricing and european market coupling organizations on different economic agents of the power system under two main wind policies under the priority dispatch policy transmission system operators tsos must accommodate all wind energy produced which thus has the priority over energy produced by conventional plants in the no priority dispatch policy tsos can decide not to inject all potential wind power in the grid in order to limit congestion problems the effects of these two wind policies are measured by developing simple stochastic programming models that consider cases with different wind penetration levels existing capacities and endogenous investments as well as assumptions on the eu ets our computational experiments show that when there is priority dispatch nodal pricing and market coupling evolve in a similar way as long as wind penetration is not too high in contrast a significant increase of wind penetration causes the collapse of the market coupling organization while nodal pricing continues to perform well on the other hand no priority dispatch removes most of the problems resulting from market coupling which still exhibits a slightly lower efficiency than nodal pricing these outcomes do not depend on the contextual assumptions fixed capacities vs investment eu ets vs non eu ets that characterize the several cases analyzed this suggested that our policy conclusions are robust furthermore our models overestimate the flexibility of conventional plants which means that these conclusions would likely be reinforced with a more detailed model

optimal clustering of time periods for electricity demand side management several pure binary integer optimization models are developed for clustering time periods by similarity for electricity utilities seeking assistance with pricing strategies the models include alternative objectives for characterizing various notions of within cluster distances admit as feasible only clusters that are contiguous and allow for circularity where time periods at the beginning and end of the planning cycle may be in the same cluster restrictions upon cluster size may conveniently be included without the need of additional constraints the models are populated with a real world dataset of electricity usage for 93 buildings and solutions and run times attained by conventional optimization software are compared with those by dynamic programming or by a greedy algorithm applicable to one of the models that run in polynomial time the results provide time of use segments that an electricity utility may employ for selective pricing for peak and off peak time periods to influence demand for the purpose of load leveling

the indian gaming regulatory act and its effects on american indian economic development the indian gaming regulatory act igra passed by the us congress in 1988 was a watershed in the history of policymaking directed toward reservation resident american indians igra set the stage for tribal government owned gaming facilities it also shaped how this new industry would develop and how tribal governments would invest gaming revenues since then indian gaming has approached commercial state licensed gaming in total revenues gaming operations have had a far reaching and transformative effect on american indian reservations and their economies specifically indian gaming has allowed marked improvements in several important dimensions of reservation life for the first time some tribal governments have moved to fiscal independence native nations have invested gaming revenues in their economies and societies often with dramatic effect

effect of inter temporal factors on the real time pricing of electricity a rigorous theoretical framework for the solution of the price formation and optimal dispatch problems taking into account intertemporal dependence of demand under spot and dynamic tariffs is presented the case in which a common tariff is to be applied to all customers is examined the difference in system response for spot and day ahead dynamic tariffs is illustrated simulation examples of a variety of event types are provided

coordinated price maker operation of large energy storage units in nodal energy markets in this paper a new optimization framework is proposed to coordinate the operation of large price maker and geographically dispersed energy storage battery systems in a nodal transmission constrained energy market the energy storage units are assumed to be investor owned and independently operated seeking to maximize their total profit various design factors are taken into consideration such as the location size efficiency and charge and discharge rates of the energy storage units as well as the joint impact of the energy storage operations on the locational marginal prices while the formulated optimization problem is originally nonlinear and hard to solve nonlinearities are tackled both in the objective function and in the constraints and the problem is transformed into a tractable mixed integer linear program for which the global optimal solutions are found for the charge and discharge schedules of each energy storage unit both deterministic and stochastic design scenarios are addressed various case studies are presented it is observed that transmission line congestion is often but not always desirable for the coordinated storage systems locational diversity practice of arbitrage robust design self scheduling versus economic bidding and the overall power system performance are also investigated

market clearing with stochastic security part i formulation the first of this two paper series formulates a stochastic security constrained multi period electricity market clearing problem with unit commitment the stochastic security criterion accounts for a pre selected set of random generator and line outages with known historical failure rates and involuntary load shedding as optimization variables unlike the classical deterministic reserve constrained unit commitment here the reserve services are determined by economically penalizing the operation of the market by the expected load not served the proposed formulation is a stochastic programming problem that optimizes concurrently with the pre contingency social welfare the expected operating costs associated with the deployment of the reserves following the contingencies this stochastic programming formulation is solved in the second companion paper using mixed integer linear programming methods two cases are presented a small transmission constrained three bus network scheduled over a horizon of four hours and the ieee reliability test system scheduled over 24 h the impact on the resulting generation and reserve schedules of transmission constraints and generation ramp limits of demand side reserve of the value of load not served and of the constitution of the pre selected set of contingencies are assessed

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

price based unit commitment with wind power utilization constraints this paper proposes an optimal bidding strategy for independent power producers ipps in the deregulated electricity market the ipps are assumed to be price takers whose objectives are to maximize their profits considering price and wind power output uncertainties while ensuring high wind power utilization the problem is formulated as a two stage stochastic price based unit commitment problem with chance constraints to ensure wind power utilization in our model the first stage decision includes unit commitment and quantity of electricity submitted to the day ahead market the second stage decision includes generation dispatch actual usage of wind power and amount of energy imbalance between the day ahead and real time markets the chance constraint is applied to ensure a certain percentage of wind power utilization so as to comply with renewable energy utilization regulations finally a sample average approximation saa approach is applied to solve the problem and the computational results are reported for the proposed saa algorithm showing the sensitivity of the total profit as the requirement of wind power utilization changes

stochastic security for operations planning with significant wind power generation in their attempt to cut down on greenhouse gas emissions from electricity generation several countries are committed to install wind power generation up to and beyond the 10 20 penetration mark however the large scale integration of wind power represents a challenge for power system operations planning because wind power 1 cannot be dispatched in the classical sense and 2 its output varies as weather conditions change this warrants the investigation of alternative short term power system operations planning methods capable of better coping with the nature of wind generation while maintaining or even improving the current reliability and economic performance of power systems to this end this paper formulates a short term forward electricity market clearing problem with stochastic security capable of accounting for nondispatchable and variable wind power generation sources the principal benefit of this stochastic operation planning approach is that when compared to a deterministic worst case scenario planning philosophy it allows greater wind power penetration without sacrificing security

modeling load recovery impact for demand response applications in this paper we develop a novel mathematical model to describe the localized economic impact of load recovery by electricity consumers following demand response dr event participation in this model termed securitization cost and benefit associated with an amount of load recovery change exponentially with time varying stochastic discount rates these volatile rates which can be estimated using the principle of brownian motion are further examined via case studies on the roy billinton test system a formal application of the proposed recovery model to the crucial task of global dr scheduling is then studied the study indicates that it is possible to optimize a dr schedule to cater for uncertainty in the subsequent load recovery

household and industrial electricity demand in europe abstract this paper examines the electricity demand and its determinants in 29 european countries during the liberalization of the electricity market based on panel data for these countries for the years 1995 2015 and using a dynamic partial adjustment model price elasticities are estimated for both residential and industrial electricity demand these elasticities and effects of other variables on electricity consumption are estimated using both gmm generalized method of moments and ml maximum likelihood approaches it is found that the price elasticities are very small especially in the short run while the income elasticities are relatively large especially for households and in the long run

uncertainty management in the unit commitment problem uncertainty in power systems operations has been traditionally managed by multistage decision making and operating reserve requirements a familiar example of multistage decisions is day ahead unit commitment and real time economic dispatch an alternate approach for managing uncertainty is a stochastic formulation which allows the explicit modeling of the sources of uncertainty this paper compares stochastic and reserve methods and evaluates the benefits of a combined approach for the efficient management of uncertainty in the unit commitment problem numerical studies show that unit commitment solutions obtained for the combined approach are robust and superior with respect to the traditional approach in terms of both economics and reliability metrics

stochastic unit commitment with uncertain demand response although demand response dr encourages customers to voluntarily schedule electricity consumption based on price signals the response from the consumer side could be uncertain due to a variety of reasons in this letter we study the stochastic unit commitment problem with uncertain demand response to enhance the reliability unit commitment process for independent system operators isos we use a stochastic representation of dr by scenario and each scenario corresponds to a price elastic demand curve contingency constraints are considered and in addition a chance constraint is applied to ensure the loss of load probability lolp lower than a pre defined risk level finally a sample average approximation saa method is applied to solve the problem

the influential factors of china s regional energy intensity and its spatial linkages 1988 2007 abstract a large amount of literature on china s energy intensity seldom considers the regional differences of energy intensity inside china and the spatial effects based on spatial statistics methods this paper explores the regional imbalance of china s provincial energy intensity and the spatially correlation of energy intensity among provinces using spatial panel data models this paper finds that gdp per capita transportation infrastructure the level of marketization and scientific and technological input significantly reduce the energy intensity the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption significantly expand the energy intensity meanwhile the coefficient of the ratio of export to gdp is not significant then the spillover and convergence of china s regional energy intensity have been tested the results indicate that the spillover effect between the eastern and western china is remarkable and there exist absolute convergence of provincial energy intensity moreover gdp per capita transportation infrastructure the level of marketization and scientific technological input are conducive to conditional convergence after the spatial effects are controlled according to the empirical results this paper proposes some policy suggestions on reducing china s energy intensity

spatial econometric analysis of foreign direct investment determinants in russian regions using a spatial autoregressive model of cross sectional and panel data we study the determinants and dominant strategies of foreign direct investment fdi inflows into russia before and after the 1998 financial crisis the important determinants of fdi inflows into russian regions since the start of transition appear to be market size the presence of large cities and sea ports oil and gas availability proximity to european market and political and legislative risks since 1998 it appears the importance of big cities the sakhalin region oil and gas resources proximity to european markets and legislation and political risks has increased our results also reveal a shift from horizontal fdi strategy to vertical fdi strategy in the post crisis period using a multiple spatial lags approach we show that neighbouring port endowed regions tend to have emerged in the post crisis era as competitors for fdi

the effect of demand elasticity on security prices for the poolco and multi lateral contract models optimum power flows and security constrained power flows assume that customer demand is a fixed quantity in the new competitive environment it is necessary to assume that demand is elastic and will vary as a function of price a critical element in any competitive model whether it be a poolco or a multi lateral contract model is for a system operator to ensure reliability and feasibility of the power system operating point security pricing for feasibility was first advanced by schweppe et al for the case of line flow constraints for a poolco model using geometry this paper generalizes the approach to include any security constraint for a general competitive model the paper discusses interpretations of security pricing and its possible implementation in energy management systems

a new stepwise power tariff model and its application for residential consumers in regulated electricity markets stepwise power tariff spt which has been put into practice is a crucial way for energy saving and environment protecting in this paper a new optimal model of spt based on residential demand response model is presented the optimal decision is proposed to restrain high electricity consumption as well as safeguard benefits of both supply and demand sides as a result the objective is designed to minimize electricity consumption and constraints are taken into consideration thoroughly including acceptable index of consumers average price sales profit of power providers and basic electricity demand which serves as a foundation for smooth implement of spt to solve the constrained optimal problem genetic algorithm ga is employed the effectiveness of the model and algorithm is investigated and demonstrated based on real data of 300 residents by a numerical example the study shows that the method can reduce power consumption obviously with little sacrifice of the benefits of consumers and power providers

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

bureaucrats and indians principal agent relations and efficient management of tribal forest resources abstract under pl 638 indian tribes can contract with the bureau of indian affairs to take over the management and marketing of tribal timber resources when indian principals replace bureau agents in these tasks both the quantity of timber harvested relative to potential and the price obtained increase tribes participation in the program is related both to their levels of economic development and the pecuniary gains available from improved incentives

the complex integration strategies of multinationals and cross country dependencies in the structure of foreign direct investment abstract the behavior of many multinational enterprises is not well described by existing models of foreign direct investment fdi firms often follow strategies that involve vertical integration in some countries and horizontal integration in others a strategy known as complex integration this paper presents a three country model that is used to analyze why firms might follow a strategy of complex integration my analysis reveals that complex integration strategies create complementarities between potential host countries that have important implications for the structure of fdi the analysis also shows that falling transport cost between countries may increase the importance of complex integration strategies

the incentive announcement effect of demand response on market power mitigation in the electricity market abstract the incentive announcement effect of demand response dr which can mitigate exercising of market power was assessed using a game theory method to analyze player behaviors the profit functions of generation companies dr consumers and normal consumers were formulated and their best response functions derived because the implementation method of a pre announced dr incentive with game theory has not been studied before a sequential game that can make a normal consumer a leader was considered the leader s strategy i e the incentive level is determined by a neutral agent to prevent overuse of the demand resource with this procedure market equilibrium can be obtained by solving the stackelberg game an analytical solution for market equilibrium was derived for a simple case simulations were used to confirm the maintenance of tendencies in a more complex case as a result the market clearing price was reduced and a fairer distribution of the surplus was achieved compared with the result of the oligopoly condition case this indicates that the exercise of market power is affected by the incentive announcement procedure and market power can be mitigated

uniform marginal pricing for the remuneration of distribution networks a new method for distribution access via uniform pricing for the remuneration of distribution networks is presented the proposed approach merges in a unified framework the investments the optimal network operation requirements the effect of the price elasticity of demand and the application of hourly pricing for demand side management purposes hourly uniform marginal prices understood as tariffs of use of the network are obtained from maximum social welfare condition sending efficient signals to the utility and consumers related to the optimal operation of the grid and use of the energy at peak and valley hours this method is used in the context of a performance based ratemaking regulation to get model companies from operational optimized real networks capital fees are integrated in the marginal tariff of use by means of the new replacement value concept broadly used in yardstick competition the model is stated as a mixed integer linear optimization problem suitable to be solved through well known linear programming tools the methodology has been successfully tested in a 42 bus test distribution network

stochastic security constrained unit commitment this paper presents a stochastic model for the long term solution of security constrained unit commitment scuc the proposed approach could be used by vertically integrated utilities as well as the isos in electricity markets in this model random disturbances such as outages of generation units and transmission lines as well as load forecasting inaccuracies are modeled as scenario trees using the monte carlo simulation method for dual optimization coupling constraints among scenarios are relaxed and the optimization problem is decomposed into deterministic long term scuc subproblems for each deterministic long term scuc resource constraints represent fuel and emission constraints in the case of vertically integrated utilities and energy constraints in the case of electricity markets lagrangian relaxation is used to decompose subproblems with long term scuc into tractable short term mip based scuc subproblems without resource constraints accordingly penalty prices lagrangian multipliers are signals to coordinate the master problem and small scale subproblems computational requirements for solving scenario based optimization models depend on the number of scenarios in which the objective is to minimize the weighted average generation cost over the entire scenario tree in large scale applications the scenario reduction method is introduced for enhancing a tradeoff between calculation speed and accuracy of long term scuc solution numerical simulations indicate the effectiveness of the proposed approach for solving the stochastic security constrained unit commitment

expected value and chance constrained stochastic unit commitment ensuring wind power utilization this paper proposes an expected value and chance constrained stochastic optimization approach for the unit commitment problem with uncertain wind power output in the model the utilization of wind power can be adjusted by changing the utilization rate in the proposed expected value constraint meanwhile the chance constraint is used to restrict the probability of load imbalance then a sample average approximation saa method is used to transform the objective function the expected value constraint and the chance constraint into sample average reformulations furthermore a combined saa framework that considers both the expected value and the chance constraints is proposed to construct statistical upper and lower bounds for the optimization problem finally the performance of the proposed algorithm with different utilization rates and different risk levels is tested for a six bus system a revised ieee 118 bus system is also studied to show the scalability of the proposed model and algorithm

factoring the elasticity of demand in electricity prices as electricity markets are liberalized consumers become exposed to more volatile electricity prices and may decide to modify the profile of their demand to reduce their electricity costs this paper analyzes the effect that the market structure can have on the elasticity of the demand for electricity it then describes how the consumers behavior can be modeled using a matrix of self and cross elasticities it is shown how these elasticities can be taken into consideration when scheduling generation and setting the price of electricity in a pool based electricity market these concepts are illustrated using a 26 generator system

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

value of price responsive load for wind integration in unit commitment the ability of load to respond to short term variations in electricity prices plays an increasingly important role in balancing short term supply and demand especially during peak periods and in dealing with fluctuations in renewable energy supplies however price responsive load has not been included in standard models for defining the optimal scheduling of generation units in short term here elasticities are included to adjust the demand profile in response to price changes including cross price elasticities that account for load shifts among hours the resulting peak reductions and valley fill alter the optimal unit commitment enhancing demand response also increases the amount of wind power that can be economically injected further wind power uncertainty can be managed at a lower cost by adjusting electricity consumption in case of wind forecast errors which is another way in which demand response facilitates the integration of intermittent renewables

a single settlement energy only electric power market for unpredictable and intermittent participants we discuss a stochastic programming based method for scheduling electric power generation subject to uncertainty such uncertainty may arise from either imperfect forecasting or moment to moment fluctuations and on either the supply or the demand side the method gives a system of locational marginal prices that reflect the uncertainty and these may be used in a market settlement scheme in which payment is for energy only we show that this scheme is revenue adequate in expectation

dynamic pricing design for demand response integration in power distribution networks this paper presents optimal pricing design for demand response dr integration in the distribution network in particular we study the energy scheduling problem for a load serving entity lse that serves two types of loads namely inflexible and flexible loads inflexible loads are charged under a regular pricing tariff while flexible loads enjoy a dynamic pricing tariff that ensures cost saving for them moreover flexible loads are assumed to be aggregated by several dr aggregators the interaction between the lse and its customers is formulated as a bilevel optimization problem where the lse is the leader and dr aggregators are the followers the optimal solution of this problem corresponds to the optimal pricing tariff for flexible loads the key advantage of the proposed model is that it can be readily implemented thanks to its compatibility with existing pricing structures in the retail market extensive numerical results show that the proposed approach provides a win win solution for both the lse and its customers

a real options evaluation model for the diffusion prospects of new renewable power generation technologies this study presents a policy planning model that integrates learning curve information on renewable power generation technologies into a dynamic programming formulation featuring real options analysis the model recursively evaluates a set of investment alternatives on a year by year basis thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of renewable power generation technologies price uncertainty is introduced through stochastic processes for the average wholesale price of electricity and for input fuel prices demand for electricity is assumed to be increasingly price sensitive as the electricity market deregulation proceeds reflecting new options of consumers to react to electricity price changes such as time of use pricing unbundled electricity services and choice of supplier the empirical analysis is based on data for the turkish electricity supply industry apart from general implications for policy making it provides some interesting insights about the impact of uncertainty and technical change on the diffusion of various emerging renewable energy technologies

optimal opt in residential time of use contract based on principal agent theory installation of smart meters is increasing world wide opening the possibility to implement time of use tou tariffs to moderate peak loads this study is focused on the design of an optimal opt in residential tou tariff residential consumers are assumed to act in their private interests and maximize utility in response to tariffs the regulated utility has a broader interest in maximizing societal welfare however the regulated utility cannot impose household behavior and cannot directly observe household type instead the regulated utility must offer contract options including both a tou tariff and current pricing to allow households to self select the plan best suited to their interests this paper proposes a simple flexible household utility function that can be calibrated with minimal data to describe diverse household behaviors and reveal household responses to different prices an optimal pricing model is designed for the regulated utility taking account of asymmetric information and potential household opportunistic behavior using economic constructs from principal agent theory the pricing model ensures participation among households most aligned with the regulated utility s desire the pricing model can also be extended for competitive markets a case study is performed to demonstrate the benefit the optional tou tariff can realize

decentralized participation of flexible demand in electricity markets part i market mechanism in the deregulated power systems setting the realization of the significant demand flexibility potential should be coupled with its integration in electricity markets centralized market mechanisms raise communication computational and privacy issues while existing dynamic pricing schemes fail to realize the actual value of demand flexibility in this two part paper a novel day ahead pool market mechanism is proposed combining the solution optimality of centralized mechanisms with the decentralized demand participation structure of dynamic pricing schemes and based on lagrangian relaxation lr principles part i presents the theoretical background algorithmic approaches and suitable examples to address challenges associated with the application of the mechanism and provides an implementation framework non convexities in reschedulable demand participants price response and their impacts on the ability of the basic lr structure to reach feasible market clearing solutions are identified and a simple yet effective lr heuristic method is developed to produce both feasible and high quality solutions by limiting the concentrated shift of reschedulable demand to the same low priced time periods

to harvest or not to harvest an analysis of cutting behavior on federal timber sales contracts economists have studied the environmental consequences and competitiveness of alternative sales techniques for publicly owned na tural resources little attention has been paid however to fluctuat ions in revenues resulting from the option nature of public resourc e sales this paper analyzes how the contract structure of public res ource sales leads to defaults and thereby revenue instability data on u s forest service timber sales contracts which were modified i n the early 1980s in response to default problems suggest that reduc ed contract lengths and more stringent requirements for extensions in crease harvest rates but that price adjustment clauses are unlikely to have similar effects copyright 1988 by mit press

estimating deficit probabilities with price responsive demand in contract based electricity markets studies that estimate deficit probabilities in hydrothermal systems have generally ignored the response of demand to changing prices in the belief that such response is largely irrelevant we show that ignoring the response of demand to prices can lead to substantial over or under estimation of the probability of an energy deficit to make our point we present an estimation of deficit probabilities in chile s central interconnected system between 2006 and 2010 this period is characterized by tight supply fast consumption growth and rising electricity prices when the response of demand to rising prices is acknowledged forecasted deficit probabilities and marginal costs are shown to be substantially lower

wheeling of power under deregulated environment of power system a bibilographical survey the power industry is moving rapidly from regulated conventional setup to a deregulated environment there is an urgent need to keep a track of international experiences and activities taking place in this emerging field this paper gives a bibliographical survey and general backgrounds of research and development in the field of power system wheeling under deregulated environment based on over 170 published articles wheeling in general and the establishment of wheeling rate in particular are subjects of extensive debate today this will be very much useful for all countries especially those developing countries which are moving toward the unbundling of electricity supply industry esi the collected literature have been divided into many section so that new researchers do not face any difficulty for obtaining literature particularly in the area of wheeling of power under deregulated environment

the impact of systematic changes in weather on the supply and demand of beverages abstract weather especially temperature plays an important role in determining the supply and demand for a vast variety of goods and services though the economic impact of extreme weather is often measured and reported the impact of systematic changes in weather such as long term temperature trends or short term heat and cold waves are seldom quantified in this research we posit that such systematic changes in temperature impact the profitability of firms and we develop a two step econometric model to quantify the impact the proposed two step model estimates the impact that temperature has on the profitability of firms and partitions this impact into a long term and a short term component we take up the case of six liquid refreshment beverages lrbs for illustrating the impact that systematic change in temperature has on demand the results indicate that a the demand for lrbs is increasing by about 0 21 63 million gallons from year to year on account of the rising temperature trend and b there is an asymmetry in the effect of a heat wave versus a cold wave on beverage demand i e the weekly demand for beverages increases by about 2 1 per degree on account of a heat wave while the weekly demand decreases by only 0 4 per degree on account of a cold wave we also illustrate how these estimates of the impact of the long term temperature trends could be linked to strategic decisions such as facility location and the estimates of the impact of the short term temperature aberrations could be linked to operational decisions such as the inventory order quantity in a week when there is a temperature aberration

coupon incentive based demand response theory and case study this paper presents the formulation and critical assessment of a novel type of demand response dr program targeting retail customers such as small medium size commercial industrial and residential customers who are equipped with smart meters yet still face a flat rate enabled by pervasive mobile communication capabilities and smart grid technologies load serving entities lses could offer retail customers coupon incentives via near real time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping and or price spikes this scheme is referred to as coupon incentive based demand response cidr in contrast to the real time pricing or peak load pricing dr programs cidr continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response theoretical analysis shows the benefits of the proposed scheme in terms of social welfare consumer surplus lse profit the robustness of the retail electricity rate and readiness for implementation the pros and cons are discussed in comparison with existing dr programs numerical illustration is performed based on realistic supply and demand data obtained from the electric reliability council of texas ercot

in search of spatial interdependence of us outbound fdi in the mena region the paper investigates the spatial interdependence of us mne investments in the mena region given the variations in resource endowments governance structures and degree of infrastructure availability in mena countries one would expect these variables to affect an mne s choice of fdi location we do find that domestic non spatial factors such as own country inflation and governance measured by bureaucratic quality as well as infrastructure affect a host country s inward fdi we also found that only one measure of natural resource endowment that is oil and gas exports were instrumental in attracting fdi this non spatial result is generally robust and invariant to the two methodologies employed in this study that is the spatially autoregressive sar model and the spatial durbin model sdm we found that neighbouring countries infrastructure availability measured either by electricity used or energy used affected fdi inflows in a host country however this spatial impact was found only in the sdm model the spatial effects of neighbouring countries economic and political conditions and resource endowments were however not observed on a host country s inward fdi the insignificance of both the surrounding market potential and the spatially weighted fdi suggests a purely horizontal motive of mne investments in the mena region

spatial interdependence in us outward fdi into africa latin america and the caribbean the existing literature on spatial interdependence in fdi flows has primarily focused on developed economies as hosts with these hosts economically tied together via good infrastructure and historically strong significant trade flows in contrast we explicitly test for the presence of spatial interdependence in developing hosts africa latin american and the caribbean where such ties are not as strong for us outbound fdi between 1995 and 2007 our empirical results confirm third country effects do matter even when controlling for spatial and time period fixed effects based on the signs of the market potential and spatial lag coefficients we find us fdi strategies into these regions as consistent with complex vertical specialisation

economic valuation of reserves in power systems with high penetration of wind power this paper proposes a methodology to determine the required level of spinning and nonspinning reserves in a power system with a high penetration of wind power the computation of the required reserve levels and their costs is achieved through a stochastic programming market clearing model spanning a daily time horizon this model considers the network constraints and takes into account the cost of both the load shedding and the wind spillage the methodology proposed is illustrated using an example and a realistic case study some conclusions are finally drawn

multi stage robust unit commitment considering wind and demand response uncertainties with the increasing penetration of wind power into the power grid maintaining system reliability has been a challenging issue for isos rtos due to the intermittent nature of wind power in addition to the traditional reserves provided by thermal hydro and gas generators demand response dr programs have gained much attention recently as another reserve resource to mitigate wind power output uncertainty however the price elastic demand curve is not exactly known in advance which provides another dimension of uncertainty to accommodate the combined uncertainties from wind power and dr we allow the wind power output to vary within a given interval with the price elastic demand curve also varying in this paper we develop a robust optimization approach to derive an optimal unit commitment decision for the reliability unit commitment runs by isos rtos with the objective of maximizing total social welfare under the joint worst case wind power output and demand response scenario the problem is formulated as a multi stage robust mixed integer programming problem an exact solution approach leveraging benders decomposition is developed to obtain the optimal robust unit commitment schedule for the problem additional variables are introduced to parameterize the conservatism of our model and avoid over protection finally we test the performance of the proposed approach using a case study based on the ieee 118 bus system the results verify that our proposed approach can accommodate both wind power and demand response uncertainties and demand response can help accommodate wind power output uncertainty by lowering the unit load cost

fdi in space spatial autoregressive relationships in foreign direct investment abstract there are a number of theoretical reasons why foreign direct investment fdi into a host country may depend on the fdi in proximate countries such spatial interdependence has been largely ignored by the empirical fdi literature with only a couple recent papers accounting for such issues in their estimation this paper conducts a general examination of spatial interactions in empirical fdi models using data on us outbound fdi activity we find that estimated relationships of traditional determinants of fdi are surprisingly robust to inclusion of terms to capture spatial interdependence even though such interdependence is estimated to be significant however we find that both the traditional determinants of fdi and the estimated spatial interdependence are quite sensitive to the sample of countries one examines

foreign direct investment in china a spatial econometric study foreign direct investment fdi began to flow into china with advent of reforms in 1978 following a period of relatively slow growth fdi inflows to china picked up after 1990 as china surpassed every other nation but the united states in attracting foreign investment in particular coastal regions of china have received the bulk of fdi inflows to the country in this paper we use province level data to explain the pattern of fdi location across china we build upon previous research introducing new potential determinants using more recent fdi data and incorporating spatial econometric techniques in doing so we test for potential econometric problems arising from the spatial pattern of the data and correct for them by running more appropriate models we find that economic size labor productivity and coastal location attract fdi while higher wages and illiteracy rates deter it the transportation infrastructure variables we try are not found to have statistically significant relationships with the level of fdi inflows across provinces this abstract was borrowed from another version of this item

the location choices of foreign investors a district level analysis in india this paper analyzes the determinants of the location choices made by foreign investors at the district level in india to gauge the relative importance of economic geography factors local business conditions and the presence of previous foreign investors we employ a discretechoice model and poisson regressions to control for the potential violation of the assumption of independence of irrelevant alternatives our sample includes about 19 500 foreign investment projects approved in 447 districts from 1991 2005 we find that foreign investors strongly prefer locations where other foreign investors are they are also attracted to industrially diverse locations and those with better infrastructure we conclude that the concentration of fdi in a few locations could fuel regional divergence in post reform india

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

economic and environmental implications of different approaches to hedge against wind production uncertainty in two settlement electricity markets a pjm case study abstract this paper examines the economic and environmental outcomes of four two settlement electricity market clearing designs the first design corresponds to a deterministic market clearing dmc similar to the mechanism currently used in organized wholesale electricity markets in the united states the other three designs account for the day ahead da wind power production uncertainty into the da market mechanisms either implicitly or explicitly an augmented deterministic market clearing admc design introduces da ramp capability products these products ensure adequate and ramp feasible electricity generation capacity commitments in the da stage to cope with the real time realization of wind power generation a hybrid deterministic market clearing hdmc design augments admc by explicitly integrating a characterization of wind power production uncertainty into the residual unit commitment ruc process which is run after the da market is closed using stochastic programming the last design referred to as stochastic market clearing smc uses stochastic optimization to explicitly account for wind power production uncertainty in the da market clearing mechanisms i e da unit commitment and economic dispatch the four market clearing designs are assessed by simulating the electricity market operations of a test system and comparing their results in terms of operating costs prices costs and revenues of different types of producers consumer s payments integration of wind power and air emissions the test system has 12 of the capacity of pjm s fossil fired power generation fleet and uses data on coincident demand and wind power production from the bonneville power administration bpa system during years 2010 2014 the simulations are performed hourly for a whole year results show that smc is superior as its costs reductions are more than two times the improvements attained by admc and hdmc also smc results in electricity prices that are better aligned with operation costs cuts the spread between the day ahead and real time prices by 40 reduces out of market short term revenue sufficiency payments by 58 reduces co2 emissions by 3 52 and decreases power plants cycling hdmc is a distant second best market design relative to dmc it achieves a reduction in total costs that is less than half the reduction achieved by smc a reduction in out of market payments that is 80 of the reduction attained by smc and an increase in wind power integration that is

wind power trading under uncertainty in lmp markets this paper presents a new model for optimal trading of wind power in day ahead da electricity markets under uncertainty in wind power and prices the model considers settlement mechanisms in markets with locational marginal prices lmps where wind power is not necessarily penalized from deviations between da schedule and real time rt dispatch we use kernel density estimation to produce a probabilistic wind power forecast whereas uncertainties in da and rt prices are assumed to be gaussian utility theory and conditional value at risk cvar are used to represent the risk preferences of the wind power producers the model is tested on real world data from a large scale wind farm in the united states optimal da bids are derived under different assumptions for risk preferences and deviation penalty schemes the results show that in the absence of a deviation penalty the optimal bidding strategy is largely driven by price expectations a deviation penalty brings the bid closer to the expected wind power forecast furthermore the results illustrate that the proposed model can effectively control the trade off between risk and return for wind power producers operating in volatile electricity markets

risk constrained bidding strategy with stochastic unit commitment this paper develops optimal bidding strategies based on hourly unit commitment in a generation company genco that participates in energy and ancillary services markets the price based unit commitment problem with uncertain market prices is modeled as a stochastic mixed integer linear program the market price uncertainty is modeled using the scenario approach monte carlo simulation is applied to generate scenarios scenario reduction techniques are applied to reduce the size of the stochastic price based unit commitment problem and postprocessing is applied based on marginal cost of committed units to refine bidding curves the financial risk associated with market price uncertainty is modeled using expected downside risk which is incorporated explicitly as a constraint in the problem accordingly the proposed method provides a closed loop solution to devising specific strategies for risk based bidding in a genco illustrative examples show the impact of market price uncertainty on genco s hourly commitment schedule and discuss the way gencos could decrease financial risks by managing expected payoffs

financial determinants of domestic investment in sub saharan africa evidence from panel data abstract this study investigates the effects of financial development on domestic investment in a sample of 30 sub saharan african countries it is based on a dynamic serial correlation investment model including various indicators of financial development controlling for country specific fixed effects and nonfinancial factors of investment the results indicate a positive relationship between domestic investment total investment and private investment and various indicators of financial development higher financial development leads to higher future levels of investment implying a potent long run effect of financial development on domestic investment the findings imply that financial development can stimulate economic growth through capital accumulation

impact of market timing on the profit of a risk averse load aggregator non generating resources such as thermostatically controlled loads tcls can arbitrage energy prices and provide balancing reserves when aggregated due to their thermal energy storage capacity an aggregator that wants to quantify this flexibility in order to place optimal bids before gate closure would be affected by market timing the market timing can be quantified by parameters such as lead time and contract period this paper explores the impact of market timing on tcl aggregate power consumption and reserve capacity bids and quantifies trade offs between market timing and flexibility we first optimize the power consumption and reserve capacity offers at given lead times and contract periods varying from 24 hours ahead to real time we then introduce uncertainty in prices and tcl availability formulate a two stage chance constrained optimization problem of a risk averse aggregator implement it on a rolling horizon basis and evaluate how the trade offs change the results show that shorter lead times and contract periods positively impact tcl profit as well as flexibility if the prediction horizon is sufficiently long the proposed method can be used by aggregators to decide on optimal bids and incentive payments to consumers to reward flexibility