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

dispatchability maximization for co optimized energy and reserve dispatch with explicit reliability guarantee in this paper we consider dispatchability as the set of all admissible nodal wind power injections that will not cause infeasibility in real time dispatch rtd our work reveals that the dispatchability of the affine policy based rtd af rtd is a polytope whose coefficients are linear functions of the generation schedule and the gain matrix of affine policy two mathematical formulations of the dispatchability maximized energy and reserve dispatch dm erd are proposed the first one maximizes the distance from the forecast to the boundaries of the dispatchability polytope subject to the available production cost or reserve cost provided the forecast value and variance of wind power the generalized gauss inequality ggi is adopted to evaluate the probability of infeasible rtd without the exact probability distribution of wind power combining the first formulation and the ggi approach the second one minimizes the total cost subject to a desired reliability level through dispatchability maximization efficient convex optimization based algorithms are developed to solve these two models different from the conventional robust optimization method our model does not rely on the specific uncertainty set of wind generation and directly optimizes the uncertainty accommodation capability of the power system the proposed method is also compared with the affine policy based robust energy and reserve dispatch ar erd case studies on the pjm 5 bus system illustrate the proposed concept and method experiments on the ieee 118 bus system demonstrate the applicability of our method on moderate sized systems and its scalability to large dimensional uncertainty

reserve requirements to efficiently manage intra zonal congestion the security constrained unit commitment problem schedules power generators to minimize cost subject to operating constraints reserve requirements ensure that reserve is available to re balance the system following a contingency but do not guarantee reliability in congested systems where reserve activation is limited by transmission constraints operators improve reserve deliverability by limiting power flows and controlling the location of reserves zones are used to address inter zonal congestion but operators lack efficient ways to handle intra zonal congestion we propose dynamically relating minimum reserve levels to transmission stress in a computationally efficient approach that complements existing reserve models from the literature analysis on the ieee rts 96 test case demonstrates the approach can mitigate intra zonal congestion more economically than traditional reserve policies

a convex model of risk based unit commitment for day ahead market clearing considering wind power uncertainty the integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors in the market clearing process the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment uc model this paper presents a novel risk based day ahead unit commitment ruc model that considers the risks of the loss of load wind curtailment and branch overflow caused by wind power uncertainty these risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast and are considered in both the objective functions and the constraints the ruc model is shown to be convex and is transformed into a mixed integer linear programming milp problem using relaxation and piecewise linearization the proposed ruc model is tested using a three bus system and an ieee rts79 system with wind power integration the results show that the model can dynamically schedule the spinning reserves and hold the transmission capacity margins according to the uncertainty of the wind power a comparison between the results of the ruc a deterministic uc and two scenario based uc models shows that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense

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

microgrid based co optimization of generation and transmission planning in power systems this paper presents an algorithm for the microgrid planning as an alternative to the co optimization of generation and transmission expansion planning in electric power systems the integration of microgrids in distribution systems will offer a decentralized control of local resources for satisfying the network reliability and the power quality required by local loads the objective in this paper is to minimize the total system planning cost comprising investment and operation costs of local microgrids the co optimized planning of large generating units and transmission lines and the expected cost of unserved energy the cost of unserved energy reflects the cost of load shedding which is added to the objective function for reliability considerations the microgrid based co optimization planning problem is decomposed into a planning problem and annual reliability subproblem the optimal integer planning decisions calculated in the planning problem will be examined against the system reliability limits in the subproblem and the planning decisions will be revised using proper feasibility cuts if the annual reliability limits are violated numerical simulations demonstrate the effectiveness of the proposed microgrid based co optimization planning in power systems and explore the economic and reliability merits of microgrid planning as compared to grid based generation and transmission upgrades

deliverable robust ramping products in real time markets the increasing penetration of variable energy resources has led to more uncertainties in power systems flexible ramping products frp have been adopted by several electricity markets to manage the uncertainties we reveal that neglected line congestion for frp may not only cause infeasibility but also result in a failure of cost recovery to address the deliverability issues on frp this paper proposes a new concept deliverable robust ramping products drrp in real time markets the drrp includes generation ramping reserve and generation capacity reserve the drrp is deliverable and immunized against any predefined uncertainty it also fully addresses the bid cost recovery issue caused by the line congestion in existing frps the prices of drrp are derived within the affine adjustable robust optimization aaro framework these prices can be used to identify valuable reserves among available reserves and quantify the values of flexible resources that provide reserves this paper also proposes a general approach to obtaining the time decoupled prices for drrp and generation which can be used for the market settlement of the first interval only in real time markets simulations on a 3 bus system and the ieee 118 bus system are performed to illustrate the concept of drrp and the advantages of drrp compared to existing frp

fuel resource scheduling the long term problem long term fuel scheduling addresses the problem of optimum allocation of fuels to various generating units using mixed and shared fuels subject to yearly and monthly constraints inventory constraints generation availability constraints and load require ments both energy and capacity this problem is formulated as a network flow optimization problem feasibility of the resulting fuel consumption plan with respect to fuel constraints and its optimality with respect to cost of fuel purchases are assured by the network flow algorithm feasibility with respect to the load duration and generation availability are assured by defining appropriate fictitious branches in the network the limits on these branches are calculated by the method of cumulants the unit priorities in each month are modified by an iterative procedure on the basis of both fuel price and fuel availability as seen by the network flow algorithm results for a 17 unit 17 contract test system over one year are presented to illustrate various aspects of the long term fuel resource scheduling problem

robust security constrained unit commitment and dispatch with recourse cost requirement with increasing renewable energy resources price sensitive loads and electric vehicle charging stations in the power grid uncertainties on both power generation and consumption sides become critical factors in the security constrained unit commitment scuc problem recently worst scenario based robust optimization approaches are employed to consider uncertainties this paper proposes a non conservative robust scuc model and an effective solution approach the contributions of this paper are three fold first the commitment and dispatch solution obtained in this paper can be directly used in day ahead market as it overcomes two issues conservativeness and absence of robust dispatch which are the two largest obstacles to applying robust scuc in real markets secondly a new concept recourse cost requirement similar to reserve requirement is proposed to define the upper bound of re dispatch cost when uncertainties are revealed thirdly a novel decomposition approach is proposed to effectively address the well known computational challenge in robust approaches simulation results on the ieee 118 bus system validate the effectiveness of the proposed novel model and solution approach

market based generation and transmission planning with uncertainties this paper presents a stochastic coordination of generation and transmission expansion planning model in a competitive electricity market the monte carlo simulation method is applied to consider random outages of generating units and transmission lines as well as inaccuracies in the long term load forecasting the scenario reduction technique is introduced for reducing the computational burden of a large number of planning scenarios the proposed model assumes a capacity payment mechanism and a joint energy and transmission market for investors costs recovery the proposed approach simulates the decision making behavior of individual market participants and the iso it is an iterative process for simulating the interactions among gencos transcos and iso the iterative process might be terminated by the iso based on a pre specified stopping criterion the case studies illustrate the applications of proposed stochastic method in a coordinated generation and transmission planning problem when considering uncertainties

willingness to pay lip service applying a neuroscience based method to wtp for green electricity abstract consumers willingness to pay wtp represents a central question for providers of renewable energy most studies on the subject have used contingent valuation and choice experiments however all empirical designs employed to date suffer from serious biases such as strategic behavior we introduce a novel neuroscience based approach to renewable energy related wtp research neuropricing which eliminates some of these biases we tested consumers wtp for two different green electricity products and found wtp for these products to be about 15 above that for a non green electricity tariff our results suggest that neuropricing is indeed able to eliminate strategic behavior moreover this approach allows for wtp studies with much smaller sample sizes than traditional methods require the neuroscience methodology can be used by researchers and marketers alike not only for testing the effects of product attributes on wtp but also for evaluating wtp effects of specific messages in marketing communications thus it may lead to a better understanding of consumer behavior and hence facilitate more successful marketing of renewable energy

a hybrid model for day ahead price forecasting this paper presents a hybrid time series and adaptive wavelet neural network awnn model for the day ahead electricity market clearing price forecast instead of using price series one period continuously compounded return series is used to achieve more attractive statistical properties the autoregressive moving average with exogenous variables armax model is used to catch the linear relationship between price return series and explanatory variable load series the generalized autoregressive conditional heteroscedastic garch model is used to unveil the heteroscedastic character of residuals and awnn is used to present the nonlinear nonstationary impact of load series on electricity prices the monte carlo method is adopted to generate more evenly distributed random numbers used for time series and awnn models to accelerate the convergence several criteria such as average mean absolute percentage error amape and the variance of forecast errors are used to assess the model and measure the forecasting accuracy illustrative price forecasting examples of the pjm market are presented to show the efficiency of the proposed method

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

entering a preannounced currency band abstract krugman 1988 provides a stationary characterisation of an exchange rate inside a currency band here we derive the non stationary solution for a floating exchange rate which applies when currency dealers expect such a band to be imposed at a known future date

exploitation of electric drive vehicles in electricity markets the paper presents the optimization algorithm which may eventually be used by electric energy suppliers to coordinate charging and discharging of electric drive vehicles edvs exploited in electricity markets the research is focused on a day ahead market and a provision of system regulation in an ancillary service market the proposed optimization minimizes the charging costs that can be partly compensated with profits obtained from participation in the energy markets due to the stochastic nature of transportation patterns the monte carlo simulation is applied to model uncertainties presented by numerous scenarios to reduce the problem complexity the simulated driving patterns are not individually considered in the optimization but clustered into fleets using the gams scenred tool uncertainties of energy requirements in the market and energy prices are presented by statistical central moments that are further considered in hong s 2 point 1 estimation method in order to define points considered in the optimization finally each energy supplier has to offer competitive energy prices to edv users for transportation due to uncertainties the final prices cannot be deterministically calculated thus the paper proposes the risk based approach applying value at risk case studies illustrate the application of the proposed optimization in achieving competitive prices for edv users

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

monte carlo computation of power generation production costs under operating constraints this paper highlights the need for considering the stochastic processes associated with the frequency and duration of generating unit outages for assessing the mean and variance of production costs under operating constraints a numerical example based on a markov model is given to show that monte carlo estimates of these quantities may be incorrect if only the forced outage rates are used in place of the stochastic parameters underlying the outage frequency and duration additionally it describes a variance reduction procedure whereby the monte carlo estimates can be obtained with a much smaller sample size than would be required otherwise a numerical example is given for a small system

market implications and pricing of dynamic reserve policies for systems with renewables static reserve policies are used within security constrained unit commitment scuc and security constrained economic dispatch sced to ensure reliability a common policy is that 10 min reserve must exceed the largest contingency however this condition does not guarantee reliability because voltage and thermal limits can hinder reserve deliverability many operators use zonal reserve markets to ensure reserves are dispersed across the grid such zonal models attempt to anticipate transmission bottlenecks which is a difficult task when the future system state is uncertain this paper examines the market implications of dynamic reserve policies used to mitigate uncertainty from renewable resources and contingencies we study the market implications of policies recently proposed in the literature such as hourly zones within day ahead scuc and an algorithm that formally disqualifies reserves that are expected to be undeliverable a locational reserve pricing scheme is also proposed in connection with scenario based reserve disqualification analysis on the rts 96 test case shows that dynamic zones and reserve disqualification along with the proposed compensation scheme help direct reserve payments toward resources that more effectively respond to contingencies

impact of natural gas system on risk constrained midterm hydrothermal scheduling this paper studies the impact of natural gas ng contracts and constraints on a genco s midterm risk constrained hydrothermal scheduling problem the ng contracts and constraints are modeled as a set of linear equations the proposed model utilizes the stochastic price based unit commitment pbuc the pbuc hourly solution considers uncertainties of market prices for energy and ancillary services uncertainties of natural water inflows and random ng infrastructure interruptions in monte carlo scenarios illustrative examples analyze the genco s risk levels when considering midterm schedules for generating units target payoffs and usages of water inflow ng and other thermal resources simulation results show that a genco s midterm schedules and financial risks could be impacted significantly with the consideration of ng contracts and constraints

strong formulations for multistage stochastic self scheduling unit commitment with the increasing penetration of renewable energy into the power grid system the volatility of real time electricity prices increases significantly this brings challenges for independent power producers to provide optimal bidding strategies the traditional approaches of only attending the day ahead market might not be profitable enough without taking advantage of real time price volatility in this paper we study the optimal bidding strategies for the independent power producers utilizing self scheduling strategies to participate in the real time market considering real time electricity price volatility with the objective of maximizing the total expected profit considering the correlations of renewable energy generation outputs among different time periods the correlations of real time prices are captured in our modeling framework in which we explore a multistage stochastic scenario tree to formulate the price uncertainties accordingly the derived multistage stochastic self scheduling unit com

security constrained resource planning in electricity markets we propose a security based competitive generation resource planning model in electricity markets the objective of the model is to introduce the impact of transmission security in a multi genco generation resource planning the proposed approach is based on effective decomposition and coordination strategies lagrangian relaxation and benders decomposition are applied to decompose the original planning problem into tractable optimization subproblems locational price signal and security signal are defined for the simulation of competition among gencos and the coordination of security between gencos and the regulatory body iso the numerical examples exhibit the effectiveness of the proposed generation planning model in electricity markets

financial risk evaluation in stochastic pbuc this letter is an extension of our previous price based unit commitment pbuc work this letter identifies a frontier relationship between payoff and risk this letter also discusses the calculation of target value and risk using payoff risk frontiers test results show a trade off between risk minimization and payoff maximization for power generation utilities

stochastic multiobjective market clearing of joint energy and reserves auctions ensuring power system security in this paper a stochastic multiobjective framework is proposed for day ahead joint market clearing the proposed multiobjective framework can concurrently optimize competing objective functions including augmented generation offer cost and security indices overload index voltage drop index and voltage stability margin besides system uncertainties including generating units and branches contingencies and load uncertainty are explicitly considered in the stochastic market clearing scheme the solution methodology consists of two stages which firstly employs roulette wheel mechanism and monte carlo simulation mcs for random adaptive 24 h scenario generation wherein the stochastic multiobjective market clearing procedure is converted into its respective deterministic equivalents scenarios in the second stage for each deterministic scenario a multiobjective mathematical programming mmp formulation based on the epsiv constrained approach is implemented for provision of spinning reserve sr and nonspinning reserve nsr as well as energy the mmp formulation of the market clearing process is optimized while meeting ac power flow constraints and expected interruption cost eic the ieee 24 bus reliability test system rts 24 bus is used to demonstrate the performance of the proposed method

hourly electricity demand response in the stochastic day ahead scheduling of coordinated electricity and natural gas networks this paper studies the role of hourly economic demand response in the optimization of the stochastic day ahead scheduling of electric power systems with natural gas transmission constraints the proposed coordinated stochastic model referred to as egtran considers random outages of generating units and transmission lines and random errors in forecasting the day ahead hourly loads the monte carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties the nonlinear natural gas network constraints are linearized and incorporated into the stochastic model numerical results demonstrate the benefits of applying the hourly economic demand response for enhancing the scheduling coordination of natural gas and electricity networks it is demonstrated that electricity demand response would offer a less volatile hourly load profile and locational marginal prices and less dependence on natural gas constraints for the optimal operation of electric power systems the proposed model for egtran could be applied by grid operators for the hourly commitment and dispatch of power system units

large scale integration of deferrable demand and renewable energy sources we present a stochastic unit commitment model for assessing the impacts of the large scale integration of renewable energy sources and deferrable demand in power systems in terms of reserve requirements we analyze three demand response paradigms for assessing the benefits of demand flexibility the centralized co optimization of generation and demand by the system operator demand bids and the coupling of renewable resources with deferrable loads we motivate coupling as an alternative for overcoming the drawbacks of the two alternative demand response options and we present a dynamic programming algorithm for coordinating deferrable demand with renewable supply we present simulation results for a model of the western electricity coordinating council