Robust Optimization Based Economic Dispatch for Managing System Ramp Requirement

Thatte, Anupam A.; Sun, Xu Andy; Xie, Le

doi:10.1109/hicss.2014.295

Cited by 29 publications

(20 citation statements)

References 15 publications

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“…When this happens, the system can just rely on other products in the electricity market such as frequency regulation with a higher cost. In MISO, the price associated with system power balance constraint violation is assumed to be the value of lost load, which is $3500/MWh [11].…”

Section: B Impact Of Ev Participation In the Ramp Marketmentioning

confidence: 99%

“…Congcong et al [10] proposed a new principle to determine the amount of ramp capacity needed. Thatte et al [11] and Wang and Hobbs [12] compared the performances of the flexible ramping product model, traditional dispatch model, stochastic model, and robust model. However, few efforts have been devoted to discuss EV provision of the flexible ramping product.…”

Section: Introductionmentioning

confidence: 99%

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Impact on Power System Flexibility by Electric Vehicle Participation in Ramp Market

Zhang

Kezunovic

2016

IEEE Trans. Smart Grid

121

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This paper investigates electric vehicle (EV) participation in the flexible ramp market. We take into account EV stochastic mobility, and evaluate impact on power system reliability and flexibility. Based on dynamic programming, the model deals with uncertainties and variations of net load, as well as EV charging requirements. Markov process is utilized in estimating the aggregated power capacity of EVs. Two participation modes are analyzed: 1) EV direct provision of the ramp product; and 2) EV cooperation with the conventional generator. Moreover, we propose new indices to evaluate power system flexibility. Finally, numerical experiments are conducted to validate the proposed approach and illustrate how EV involvement into the ramp market can improve power system reliability and flexibility.Index Terms-Electric vehicles (EVs), flexibility, ramp market, reliability. NOMENCLATURE λVehicles' arrival rate into the traffic system. μ Reciprocal of the mean travel time. m Maximum capacity of the traffic system. γ ij Transition rate from states i to j of the Markov model. E sij+ Discharging energy per electric vehicle (EV) under states i or j. ω sij+ Mean discharging energy per EV under states i or j. σ sij+ Standard deviation of the discharging energy per EV under states i or j. F Z+ (z) Cumulative distribution of EV available discharging energy. f Z+ (z) Probability density function of EV aggregated discharging energy. C EVtotal EV total battery capacity in one large area. q Average energy per EV to finish the travel. Energy + EV aggregated energy capacity for discharging. Energy − EV aggregated energy capacity for charging. h Time duration of EV charging/discharging. Power + EV aggregated discharging power. r + max Maximum discharging rate for one EV. p max EV+ EV aggregated discharging power capacity. p EVG+ EV discharging power in the market. p EVG− EV charging power in the market. FRU EV EV ramp up service in the market. FRD EV EV ramp down service in the market. G Set of the on-line generators. p t Gi Generation output of generator i at time t. FRU t i Ramp up service of generator i at time t. FRD t i Ramp down service of generator i at time t. C FRUi Cost of the ramp up service for generator i. C FRDi Cost of the ramp down service for generator i. C EVG Cost for EVs to provide the energy service. C EVFRU Cost for EVs to provide the ramp up service. C EVFRD Cost for EVs to provide the ramp down service. P t L Net load at time t. p t EVG− EV charging load at time interval t. p min Gi Minimum generation of the generator i. p max Gi Maximum generation of the generator i. R i Ramp rate of the generator i. t Time interval period to ramp up or down. D t FRU System requirement for ramping up at time t. D t FRD System requirement for ramping down at time t. E t EV EV aggregated energy at time t. η + EV discharging coefficient. η − EV charging coefficient. C req EV energy requirement.

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Section: B Impact Of Ev Participation In the Ramp Marketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact on Power System Flexibility by Electric Vehicle Participation in Ramp Market

Zhang

Kezunovic

2016

IEEE Trans. Smart Grid

121

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“…In [7], both the deterministic and stochastic model is evaluated in designing the market for flexible ramping product. In [8], a robust economic dispatch model is developed with ramping capability requirement and compared with the deterministic model. In this paper, a new stochastic economic dispatch with flexible ramping product is developed, and transmission constraints are included in the model to guarantee deliverability.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic look-ahead economic dispatch with flexible ramping product

Zhang

McCalley

2015

2015 IEEE Power &Amp; Energy Society General Meeting

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Under high penetration of wind and solar, the power system is facing the challenge to maintain the balance between generation and load due to the insufficient ramping capability. To resolve this challenge, a new market mechanism was proposed by many ISOs to add the flexible ramping constraints or flexible ramping product to maintain the ramping flexibility in addressing net load uncertainty and variability. The current market practice is mainly based on a deterministic method for identifying the ramping capability requirement, which may increasingly result in insufficient ramping capability. This situation can be further aggravated if deliverability of the flexible ramping product is overly constrained by insufficient transmission. In this paper, a stochastic economic dispatch model with flexible ramping product, accounting for deliverability constraints, is developed to effectively handle the net load uncertainty and variability.

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“…In [19], both the deterministic and stochastic models are evaluated in designing the market for flexible ramping products. In [20], a robust economic dispatch model is developed with ramping capability requirement and compared with the deterministic model. A stochastic dayahead scheduling model is developed in [21], where the flexible ramping product is scheduled in the day-ahead for managing the variability of renewable generation.…”

Section: Introductionmentioning

confidence: 99%

Generation Ramping Valuation in Day-Ahead Electricity Markets

Parvania

Scaglione

2016

2016 49th Hawaii International Conference on System Sciences (HICSS)

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In this paper, we first introduce a variational formulation of the Unit Commitment (UC) problem, in which generation and ramping trajectories of the generating units are continuous time signals and the generating units cost depends on the three signals: the binary commitment status of the units as well as their continuous-time generation and ramping trajectories. We assume such bids are piecewise strictly convex time-varying linear functions of these three variables. Based on this problem derive a tractable approximation by constraining the commitment trajectories to switch in a discrete and finite set of points and representing the trajectories in the function space of piece-wise polynomial functions within the intervals, whose discrete coefficients are then the UC problem decision variables. Our judicious choice of the signal space allows us to represent cost and constraints as linear functions of such coefficients; thus, our UC models preserves the MILP formulation of the UC problem. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total dayahead and real-time operation cost, and the number of ramping scarcity events in the real-time operations.

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Robust Optimization Based Economic Dispatch for Managing System Ramp Requirement

Cited by 29 publications

References 15 publications

Impact on Power System Flexibility by Electric Vehicle Participation in Ramp Market

Impact on Power System Flexibility by Electric Vehicle Participation in Ramp Market

Stochastic look-ahead economic dispatch with flexible ramping product

Generation Ramping Valuation in Day-Ahead Electricity Markets

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