Hybrid Space-Filling Designs for Computer Experiments

Johnson, Rachel T.; Montgomery, Douglas C.; Kennedy, Kathryn

doi:10.1007/978-3-7908-2846-7_19

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Note that two-thirds of the independent variables are electricity-price sensitive. Arzamasov et al [24] sampled these variables, throughout their respective feasible spaces, using a random Latin hypercube simulation (LHS) design with no specific dependencies applied among the variables (dependent or independent) [40,41], viz. participants j = 1 to 4 were allowed to vary independently of each other throughout the constrained (Table 1) feasible spaces.…”

Section: Decentralised and Idealised Power Grid Defined And Quantifiedmentioning

confidence: 99%

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Wood

2020

Technol Econ Smart Grids Sustain Energy

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The stability of decentralized electricity grids is influenced by real-time electricity prices and the cost sensitivity and reaction times of power producers and consumers. The decentral smart grid control (DSGC) system is designed to provide demand-side control of decentralized electricity grids by linking real-time electricity prices to changes in grid frequency over the time scale of a few seconds. This stimulates electricity demand-side consumption / production on similar time scales. Grid stability of DSGC systems can be simulated by considering a wide range of assumptions for the electricity volumes consumed / produced (P) by each grid participant, their cost-sensitivity (G) and reaction times (Tau) to changing grid conditions. Such a simulation (10,000 cases) published for a simple four-node star decentralized grid configuration with randomized values for P, G and Tau quantifies dynamic grid stability (Stb in) in terms of grid mechanical and pricing influences. This study applies an optimized data-matching machine-learning algorithm, the, transparent open box (TOB) learning network to predict Stb in (ranging from −0.0808 to +0.1094 s −2) for this published simulation from its independent variables. TOB manages to predict Stb in to a high degree of accuracy (RMSE~0.016 s −2 ; R 2~0 .85) for this grid configuration in which independent variables P, G and Tau are poorly correlated with Stb in. By involving average G and Tau values for the three consumers as input variables TOB prediction accuracy is further improved (RMSE~0.0075 s −2 ; R 2~0 .90). The study highlights the importance of compound feature selection when predicting grid stability of decentralized electricity grids.

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Section: Decentralised and Idealised Power Grid Defined And Quantifiedmentioning

confidence: 99%