Peak-load management in steel plants

Ashok, S.

doi:10.1016/j.apenergy.2005.05.002

Cited by 179 publications

(78 citation statements)

References 7 publications

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“…Demand-side energy management usually concerns (i) load-shifting of energy consumption at di erent time points and/or (ii) load-adjustment by increasing or decreasing overall energy consumption [Stadler et al, 2009, Strengers, 2008, Ashok, 2006. Although demand-side energy management is not a recently introduced idea [Schweppe et al, 1989], nowadays it is an established operation of the Smart Power Grid because of 6.1 Demand-side Energy Management the broad adoption of micro-generation using renewable energy resources and enabling control technologies in households such as smart sensors.…”

Section: Research Backgroundmentioning

confidence: 99%

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Multi-level reconfigurable self-organization in overlay services

Pournaras¹

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Section: Research Backgroundmentioning

confidence: 99%

“…Note that demand-side energy management can also be applied in various domains of industrial consumers as illustrated by Paulus and Borggrefe [2011], Ashok [2006] and Middelberg et al [2009].…”

Section: Research Backgroundmentioning

confidence: 99%

Multi-level reconfigurable self-organization in overlay services

Pournaras¹

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“…Several articles [7][8][9][10][11][12] aim to model the flexibility contribution together with the computation of possible economic benefits and the feasibility of the DR programmes. All these studies stress the difficulty on assessing the internal flexibility of industrial consumers, because a detailed knowledge of the industrial process is required; in addition, each electro-intensive industry must be analysed at a time, and the effective DR programmes to be applied should be adapted specifically.…”

Section: List Of Symbolsmentioning

confidence: 99%

“…Consequently, the least-squares optimisation can be stated as (10), where the continuation value is estimated as C t,i = D d=1 α * g,t b g (S t,i ), being b g a set of basis function (e.g., g=2 implies the square value of S t,i ) and α * g,t , the solution obtained from the minimisation problem.…”

Section: American Option Valuementioning

confidence: 99%

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

Malpica

Frías

2019

IEEE Trans. Power Syst.

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Abstract-This paper presents an original methodology to compute a financial product that could enhance the demand side participation in ancillary services, specially for industrial consumers. The financial product consists in an american option on the Spanish secondary reserve market for the following day, where the buyer has the right but not the obligation to offer part of its capacity to the system operator. Considering this approach, an industrial consumer would receive an economic incentive to offer its flexibility to the system without changing its production planning, paying an upfront premium. The computation of the american option is leveraged on a Monte Carlo simulation approach where the random paths are obtained from a machine learning model. The machine learning model attempts to forecast the 24-hour secondary band prices of the following day using a combination of different algorithms; the output of the model is used as a baseline to perform the Monte Carlo simulation that computes the option value. LIST OF SYMBOLSt, τ time -hour of the day d day T tenor of the american option, i.e. 24 hours i index of the random path I total number of random paths g index of basis function G total number of basis functions β r coefficient vector of ridge regression γ shrinkage parameter of ridge penalty K strike -electricity valuation α g,t coefficient of basis function g at hour t S t secondary band price prediction at hour t S t secondary band price at hour t Y t error term at hour t X t feature vector at hour t Z t logarithm of the error term at hour t W t input feature vector of the second layer at hour t V t option value at hour t V t,i option value at hour t for random path i V t+∆t,i simulated continuation value at hour t for random path i h t payoff of the option at hour t S t,i secondary band price at hour t for random path i C t continuation value at hour t C t,i estimation of the continuation value at hour t for random path i

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“…In [26] an open loop optimal control model is applied to a steel plant (arch furnace) in India that is charged on both TOU and MD. However, the MD charge component is not included in the objective function of the optimal control model.…”

Section: Open Loop Optimal Control Models With Tou Charges Onlymentioning

confidence: 99%

A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges

2011

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SUMMARYThe aim of this research is to affirm the application of closed-loop optimal control for load shifting in plants with electricity tariffs that include time-of-use (TOU) and maximum demand (MD) charges. The water pumping scheme of the Rietvlei water purification plant in the Tshwane municipality (South Africa) is selected for the case study. The objective is to define and simulate a closed-loop load shifting (scheduling) strategy for the Rietvlei plant that yields the maximum potential cost saving under both TOU and MD charges. SLEUTELWOORDEModel voorspellende beheer, las verskuiwing, maksimum aanvraag, optimale beheer.

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Peak-load management in steel plants

Cited by 179 publications

References 7 publications

Multi-level reconfigurable self-organization in overlay services

Multi-level reconfigurable self-organization in overlay services

Valuation of an American Option for the Spanish Secondary Reserve Market Using a Machine Learning Model

A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges

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