Optimal participation and risk mitigation of wind generators in an electricity market

Dukpa, A.; Duggal, Inderjeet; Venkatesh, Bala; Chang, Ly-Yu

doi:10.1049/iet-rpg.2009.0016

Cited by 115 publications

(56 citation statements)

References 11 publications

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“…Eqs. (8) and (10) are used to compute the load curtailment and its probability that results in loss of load due to forecast errors and unit i outage. The EENS (MWh) term that incorporates the combined forecast error and unit outages at hour t is formulated as…”

Section: Formulation Of Expected Energy Not Served (Eens)mentioning

confidence: 99%

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Incorporating forecast uncertainties into EENS for wind turbine studies

Toh¹,

Gooi²

2011

Electric Power Systems Research

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Section: Formulation Of Expected Energy Not Served (Eens)mentioning

confidence: 99%

“…The ARMA technique is capable of producing a least-error in short-term wind speed forecasts [6,7]. However, the wind and load forecasts are not 100% reliable [8,9]. Therefore, additional tech- * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Incorporating forecast uncertainties into EENS for wind turbine studies

Toh¹,

Gooi²

2011

Electric Power Systems Research

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“…Morais et al [11] proposed the classical unit commitment. Dukpa et al [12] proposed a new optimal participation strategy for a wind power generator that employs an energy storage device for participating in a day-ahead unit commitment process considering stochastic power output.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty

Mohammadi¹,

Mozafari²,

Soleymani³

2014

Turk J Elec Eng & Comp Sci

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Abstract:This paper analyzes the behavior of Hong's point estimate method to account for uncertainties in probabilistic energy management systems to optimize the operation of a microgrid (MG). These uncertainties may arise from different sources, such as the market prices, load demands, and electric power generation of wind farms and photovoltaic systems.Point estimate methods constitute a remarkable tool to handle stochastic power system problems because good results can be achieved using the same routines as those corresponding to deterministic problems, while keeping the computational burden low. The problem is formulated as a nonlinear constraint optimization problem to minimize the total operating cost. Weibull, beta, and normal distributions are used to model the uncertain input variables in this study. Moreover, the firefly algorithm is applied to achieve optimal operational planning with regard to cost minimization. The efficiency of Hong's point estimate method is validated on a typical MG. Results for the case study are presented and compared against those obtained from the Monte Carlo simulation. Specifically, this paper shows that the use of the 2m+1 scheme provides the best performance when a high number of random variables, both continuous and discrete, are considered.

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“…Optimal trading strategies for wind generation that consider the risk to power producers have been extensively researched [10][11][12][13][14][15][16][17][18][19][20]. In [10], different risk management approaches for wind trading in the electricity market were discussed, and the utility function method was considered to be more effective than the mean variance model.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], different risk management approaches for wind trading in the electricity market were discussed, and the utility function method was considered to be more effective than the mean variance model. Xue et al [11] defined the power producer's attitude towards risk as the membership function based on the fuzzy set theory, and subsequently proposed a multi-objective optimal bidding strategy; this fuzzy optimization method was extended to coordinated trading of wind generators and an energy storage device (ESD) in [12], which took into account the risk quantified by computing expected energy not served (EENS). Bidding strategies based on utility and the conditional value at risk (CVaR) were derived to study the optimal bidding strategies for WPPs in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

Zhang

Gao

et al. 2012

Energies

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Wind Power Producers (WPPs) seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-maximization strategy (EPS), the chance-constrained programming-based strategy (CPS) and the multi-objective bidding strategy (ECPS). Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets.Keywords: wind power; bidding; day-ahead electricity market; risk; chance-constrained programming; multi-objective optimization

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Optimal participation and risk mitigation of wind generators in an electricity market

Cited by 115 publications

References 11 publications

Incorporating forecast uncertainties into EENS for wind turbine studies

Incorporating forecast uncertainties into EENS for wind turbine studies

Optimal operation management of microgrids using the point estimate method and firefly algorithm while considering uncertainty

Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

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