Reserves in Stochastic Unit Commitment: An Irish System Case Study

Lowery, Colm; O’Malley, Mark

doi:10.1109/tste.2014.2364520

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…Two wind farms are implemented with buses 5 and 14 in IEEE 14‐bus system, while they are located at buses 5 and 83 in IEEE‐118 bus system. The normal distribution is typically used to characterise the forecast wind power error [27, 28]. Consequently, assuming the forecast wind power at next operation point follows the normal distribution with standard deviations equals to 20%.…”

Section: Resultsmentioning

confidence: 99%

“…For

i \in N

, P LN i consists of the summation of its forecasted output P pre i and the error Δ P pre i at the i th node. Since the P pre i is given by a known forecast value and the Δ P pre i is typically characterised by the normal distribution [27, 28], the P LN i (

i \in N

) follows the normal distribution. Similarly, for

i \in D

, the rated active load P LN i in P LN and the rated reactive load Q LN i in Q LN at the i th node follow the normal distribution [29]

bold-italicM = {[\begin{matrix} {bold-italicP}_{LN_LB}^{normalT} & {bold-italicQ}_{LN_LB}^{normalT} & {bold-italicP}_{LN_LE}^{normalT} & {bold-italicQ}_{LN_LE}^{normalT} \end{matrix}]}^{T}

is defined to represent border and external random variables P LN_LB , Q LN_LB , P LN_LE and Q LN_LE .…”

Section: Probabilistic Characteristics and Correlations Of Equivalentmentioning

confidence: 99%

See 1 more Smart Citation

Equivalent model considering frequency characteristics and renewable uncertainties for probabilistic power flow

Lin

Kamel

et al. 2018

IET Generation, Transmission & Distribution

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Section: Resultsmentioning

confidence: 99%

“…For

i \in N

i \in N

) follows the normal distribution. Similarly, for

i \in D

, the rated active load P LN i in P LN and the rated reactive load Q LN i in Q LN at the i th node follow the normal distribution [29]

bold-italicM = {[\begin{matrix} {bold-italicP}_{LN_LB}^{normalT} & {bold-italicQ}_{LN_LB}^{normalT} & {bold-italicP}_{LN_LE}^{normalT} & {bold-italicQ}_{LN_LE}^{normalT} \end{matrix}]}^{T}

is defined to represent border and external random variables P LN_LB , Q LN_LB , P LN_LE and Q LN_LE .…”

Section: Probabilistic Characteristics and Correlations Of Equivalentmentioning

confidence: 99%

Equivalent model considering frequency characteristics and renewable uncertainties for probabilistic power flow

Lin

Kamel

et al. 2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…We use this definition to identify if a prediction convex hull generated for time t includes the measurement recorded at the same time. y ∈ C if there is a solution for the following linear programming problem arg θ min e ⊺ θ (6) subject to Aθ = y Bθ = 1 θ ≥ 0…”

Section: B Prediction Convex Hullsmentioning

confidence: 99%

“…For example, temporal uncertainty forecast is a key requirement for multi-period operational problems such as unit-commitment and state of charge of energy storage [5]. Stochastic programming as one of the most common optimization techniques in power systems applications uses scenarios as inputs to find optimal solutions in uncertain environments [6], [7]. However, stochastic programming holds a number of pitfalls in a practical context including heavy computational burden and the need for hard-to-obtain probability distributions [8].…”

Section: Introductionmentioning

confidence: 99%

Polyhedral Predictive Regions for Power System Applications

Golestaneh¹,

Gooi

2019

IEEE Trans. Power Syst.

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Despite substantial improvement in the development of forecasting approaches, conditional and dynamic uncertainty estimates ought to be accommodated in decision-making in power system operation and market, in order to yield either costoptimal decisions in expectation, or decision with probabilistic guarantees. The representation of uncertainty serves as an interface between forecasting and decision-making problems, with different approaches handling various objects and their parameterization as input. Following substantial developments based on scenario-based stochastic methods, robust and chanceconstrained optimization approaches have gained increasing attention. These often rely on polyhedra as a representation of the convex envelope of uncertainty. In the work, we aim to bridge the gap between the probabilistic forecasting literature and such optimization approaches by generating forecasts in the form of polyhedra with probabilistic guarantees. For that, we see polyhedra as parameterized objects under alternative definitions (under L1 and L∞ norms), the parameters of which may be modelled and predicted. We additionally discuss assessing the predictive skill of such multivariate probabilistic forecasts. An application and related empirical investigation results allow us to verify probabilistic calibration and predictive skills of our polyhedra.

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“…Restrepo and Galiano [18] proposed an approach, where reserve should cover the single largest unit plus the demand forecast error within a predefined probability. Lowery and OMalley [19] applied both deterministic and stochastic UC modelling approaches to examine different deterministic rules. They also investigated the differences between explicit and implicit modelling of reserve constraints.…”

Section: Introductionmentioning

confidence: 99%