The Experts below are selected from a list of 9858 Experts worldwide ranked by ideXlab platform

Chung-li Tseng - One of the best experts on this subject based on the ideXlab platform.

  • variable capacity utilization ambient temperature shocks and Generation Asset valuation
    Research Papers in Economics, 2009
    Co-Authors: Chung-li Tseng, Wei Zhu, Alexandre Dmitriev
    Abstract:

    This paper discusses Generation Asset valuation in a framework where capital utilization decisions are endogenous. We use real options approach for valuation of natural gas fuelled turbines. Capital utilization choices that we explore include turning on/off the unit, operating the unit at increased firing temperatures (overfiring), and conducting preventive maintenance. Overfiring provides capacity enhancement which comes at the expense of reduced maintenance interval and increased costs of part replacement. We consider the costs and benefits of overfiring in attempt to maximize the Asset value by optimally exercising the overfire option. In addition to stochastic processes governing prices, we incorporate an exogenous productivity shock: ambient temperature. We consider how variation in ambient temperature affects the Asset value through its effect on gas turbine’s productivity.

  • a framework using two factor price lattices for Generation Asset valuation
    Operations Research, 2007
    Co-Authors: Chung-li Tseng, Kyle Y Lin
    Abstract:

    In this paper, we use a real-options framework to value a power plant. The real option to commit or decommit a generating unit may be exercised on an hourly basis to maximize expected profit while subject to intertemporal operational constraints. The option-exercising process is modeled as a multistage stochastic problem. We develop a framework for generating discrete-time price lattices for two correlated Ito processes for electricity and fuel prices. We show that the proposed framework exceeds existing approaches in both lattice feasibility and computational efficiency. We prove that this framework guarantees existence of branching probabilities at all nodes and all stages of the lattice if the correlation between the two Ito processes is no greater than 4/√35 ≈ 0.676. With price evolution represented by a lattice, the valuation problem is solved using stochastic dynamic programming. We show how the obtained power plant value converges to the true expected value by refining the price lattice. Sensitivity analysis for the power plant value to changes of price parameters is also presented.

  • short term Generation Asset valuation a real options approach
    Operations Research, 2002
    Co-Authors: Chung-li Tseng, Graydon Barz
    Abstract:

    This paper discusses using real options to value power plants with unit commitment constraints over a short-term period. We formulate the problem as a multistage stochastic problem and propose a solution procedure that integrates forward-moving Monte Carlo simulation with backward-moving dynamic programming. We assume that the power plant operator maximizes expected profit by deciding in each hour whether or not to run the unit, that a certain lead time for commitment and decommitment decisions is necessary to start up and shut down a unit, and that these commitment decisions, once made, are subject to physical constraints such as minimum uptime and downtime. We also account for the costs associated with starting up and shutting down a unit. Last, we assume that there are hourly markets for both electricity and the fuel used by the generator and that their prices follow Ito processes. Using numerical simulation, we show that failure to consider physical constraints may significantly overvalue a power plant.

  • a stochastic model for a price based unit commitment problem and its application to short term Generation Asset valuation
    2002
    Co-Authors: Chung-li Tseng
    Abstract:

    In this paper, we model the unit commitment problem as a multi-stage stochastic programming problem under price and load uncertainties. We assume that there are hourly spot markets for both electricity and fuel consumed by the generators. In each time period, the operator needs to determine which units are to be scheduled so as to maximize the profit while meeting the demand. Assuming that the price and load uncertainties can be represented by a scenario tree, we develop a unit decommitment method using dynamic programming to solve this problem. When there is only one unit under consideration, we show that a scenario tree can be converted to a lattice that allows branch recombination, which may greatly reduce the size of state space. This one-unit problem can be used to value a Generation Asset over a short-term period. In conclusion, we present our numerical results.

  • Short-Term Generation Asset Valuation
    Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, 1999
    Co-Authors: Chung-li Tseng, Graydon Barz
    Abstract:

    We present a method for valuing a power plant over a short term period using Monte Carlo simulation. The power plant valuation problem is formulated as a multi stage stochastic problem. We assume there are hourly markets for both electricity and the fuel used by the generator, and their prices follow some Ito processes. At each hour, the power plant operator must decide to run or not to run the unit so as to maximize expected profit. A certain lead time for commitment decision is necessary to start up a unit. The commitment decision, once made, is subject to physical constraints such as minimum uptime and downtime constraints. The generator's startup cost, is also taken into account in our model. The Monte Carlo method is employed not only in forward moving simulation, but also backward moving recursion of dynamic programming. We demonstrate through numerical tests how the physical constraints affect a power plant value.

Mohammad Shahidehpour - One of the best experts on this subject based on the ideXlab platform.

  • risk constrained Generation Asset arbitrage in power systems
    IEEE Transactions on Power Systems, 2007
    Co-Authors: Mohammad Shahidehpour
    Abstract:

    A competitive generating company (GENCO) can maximize its payoff by optimizing its Generation Assets. This paper considers the GENCO's arbitrage problem using stochastic price-based unit commitment while considering the associated risks. The GENCO may consider arbitrage opportunities in purchases from qualifying facilities (QFs) as well as simultaneous trades with spot markets for energy, ancillary services, fuel, and emission allowance. The tradeoff between maximizing expected payoffs and minimizing risks due to market price uncertainties is modeled explicitly by including the expected downside risk as a constraint. The downside risk is defined as the unfulfilled profit. The Monte Carlo simulation is applied to generate scenarios, and scenario reduction techniques are applied to reduce the number of scenarios while maintaining a good approximation of the exact solution. The proposed case studies illustrate the significance of arbitrage in multi-commodity markets and the importance of considering the uncertainty of market prices.

  • Generation Asset valuation and risk analysis
    2002
    Co-Authors: Mohammad Shahidehpour, Hatim Yamin
    Abstract:

    In this chapter, Generation Asset valuation (short term) and Generation capacity valuation (long term) are discussed. To consider the impact of risk, the concept of VaR is utilized.

  • market operations in electric power systems forecasting scheduling and risk management
    2002
    Co-Authors: Mohammad Shahidehpour, Hatim Yamin, Zuyi Li
    Abstract:

    Preface. Chapter 1: Market Overview in Electric Power Systems. Chapter 2: Short-Term Load Forecasting. Chapter 3: Electricity Price Forecasting. Chapter 4: Price-Based Unit Commitment. Chapter 5: Arbitrage in Electricity Markets. Chapter 6: Market Power Analysis Based on Game Theory. Chapter 7: Generation Asset Valuation and Risk Analysis. Chapter 8: Security-Constrained Unit Commitment. Chapter 9: Ancillary Services Auction Market Design. Chapter 10: Transmission Congestion Management and Pricing. Appendix A: List of Symbols. Appendix B: Mathematical Derivation. Appendix C: RTS Load Data. Appendix D: Example Systems Data. Appendix E: Game Theory Concepts. Appendix F: Congestion Charges Calculation. References. Index.

Graydon Barz - One of the best experts on this subject based on the ideXlab platform.

  • short term Generation Asset valuation a real options approach
    Operations Research, 2002
    Co-Authors: Chung-li Tseng, Graydon Barz
    Abstract:

    This paper discusses using real options to value power plants with unit commitment constraints over a short-term period. We formulate the problem as a multistage stochastic problem and propose a solution procedure that integrates forward-moving Monte Carlo simulation with backward-moving dynamic programming. We assume that the power plant operator maximizes expected profit by deciding in each hour whether or not to run the unit, that a certain lead time for commitment and decommitment decisions is necessary to start up and shut down a unit, and that these commitment decisions, once made, are subject to physical constraints such as minimum uptime and downtime. We also account for the costs associated with starting up and shutting down a unit. Last, we assume that there are hourly markets for both electricity and the fuel used by the generator and that their prices follow Ito processes. Using numerical simulation, we show that failure to consider physical constraints may significantly overvalue a power plant.

  • Short-Term Generation Asset Valuation
    Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences, 1999
    Co-Authors: Chung-li Tseng, Graydon Barz
    Abstract:

    We present a method for valuing a power plant over a short term period using Monte Carlo simulation. The power plant valuation problem is formulated as a multi stage stochastic problem. We assume there are hourly markets for both electricity and the fuel used by the generator, and their prices follow some Ito processes. At each hour, the power plant operator must decide to run or not to run the unit so as to maximize expected profit. A certain lead time for commitment decision is necessary to start up a unit. The commitment decision, once made, is subject to physical constraints such as minimum uptime and downtime constraints. The generator's startup cost, is also taken into account in our model. The Monte Carlo method is employed not only in forward moving simulation, but also backward moving recursion of dynamic programming. We demonstrate through numerical tests how the physical constraints affect a power plant value.

Jianhui Wang - One of the best experts on this subject based on the ideXlab platform.

  • risk constrained Generation Asset scheduling for price takers in the electricity markets
    Hawaii International Conference on System Sciences, 2008
    Co-Authors: Jianhui Wang
    Abstract:

    A risk-constrained Generation Asset scheduling model for Generation companies (GENCOs) in the electricity markets is proposed in this paper. The model embodies the arbitrage opportunities for GENCOs through an optimization procedure. The risk exposure of GENCOs is managed by explicitly adding the downside risk constraints into the optimization problem. To avoid the inaccuracy of downside risk, the variance of expected profit is calculated to measure the fluctuation of GENCO's profit. The sensitivity of GENCOs' profit to risk is also calculated in the form of Sharpe ratio. The downside risk constraint will keep tightening iteratively until the risk exposure tolerance is satisfied. Consequently the profit and risk will be balanced automatically.

Guoji Sun - One of the best experts on this subject based on the ideXlab platform.

  • optimization based Generation Asset allocation for forward and spot markets
    IEEE Transactions on Power Systems, 2008
    Co-Authors: Xiaohong Guan, Feng Gao, Guoji Sun
    Abstract:

    One of the most important daily decisions that a Genco has to make is to allocate Generation Assets between the forward and spot markets. That is, how much capacity should be contracted in the forward market and how much should be kept to bid in spot market? This paper focuses on Generation Asset allocation between monthly forward contracts, such as bilateral contracts, futures contracts, options contracts, and daily spot markets, considering operating costs and constraints of generating units, as well as spot price risk. The problem is to find the optimal hedging position based on the known forward price and the forecasted hourly spot prices and is formulated based on the model of PJM market. The double dynamic programming method is applied to solve this optimal Asset allocation problem with all the short-term operating constraints of the generating unit satisfied. Three types of forward contracts that are commonly used in practice are considered and their impact on Generation Asset allocation are analyzed and compared. The analytic relationship between the optimal contract quantity of a particular type and spot Generation is established. Based on this relationship, the applicability and characteristics of a particular type of contract are discussed. Furthermore, with the solution to the Generation Asset allocation problem as a basis, the pricing strategy in the forward market is analyzed. A Nash game model is established and an iterative process is developed to determine the equilibrium pricing of futures contracts. Numerical testing shows that the method for the Generation Asset allocation is effective. Various factors influencing the decision of Generation Asset allocation and the relationship between futures contract price and spot price are tested and analyzed.