Probabilistic determination of pilot points for zonal voltage control

Amraee, Turaj; Soroudi, Alireza; Ranjbar, A.M.

doi:10.1049/iet-gtd.2011.0334

Cited by 41 publications

(23 citation statements)

References 24 publications

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“…A reasonable trade off must be respected between the loss of the information and decreasing the computational burden [2]. The scenario reduction technique is carried out via the following steps [14,15]:…”

Section: Scenario Based Decision Makingmentioning

confidence: 99%

Decision making under uncertainty in energy systems: State of the art

Soroudi¹,

Amraee²

2013

Renewable and Sustainable Energy Reviews

422

226

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The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time.

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Section: Scenario Based Decision Makingmentioning

confidence: 99%

Decision making under uncertainty in energy systems: State of the art

Soroudi¹,

Amraee²

2013

Renewable and Sustainable Energy Reviews

422

226

View full text Add to dashboard Cite

show abstract

“…The cases with more than 150 scenarios provide more stable and improved results in most cases. Although there are mechanisms to make efficient scenarios in stochastic programming [29,49,50], such programming is not considered here, because the random samples are enough to provide effective results with our data. Finally, Figures 8-10 compare the performance of the proposed optimization to other baselines.…”

Section: Energy Reduction Ratiomentioning

confidence: 99%

Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers

Kang

Lee

2017

Energies

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Abstract:Demand response is nowadays considered as another type of generator, beyond just a simple peak reduction mechanism. A demand response service provider (DRSP) can, through its subcontracts with many energy customers, virtually generate electricity with actual load reduction. However, in this type of virtual generator, the amount of load reduction includes inevitable uncertainty, because it consists of a very large number of independent energy customers. While they may reduce energy today, they might not tomorrow. In this circumstance, a DSRP must choose a proper set of these uncertain customers to achieve the exact preferred amount of load curtailment. In this paper, the customer selection problem for a service provider that consists of uncertain responses of customers is defined and solved. The uncertainty of energy reduction is fully considered in the formulation with data-driven probability distribution modeling and stochastic programming technique. The proposed optimization method that utilizes only the observed load data provides a realistic and applicable solution to a demand response system. The performance of the proposed optimization is verified with real demand response event data in Korea, and the results show increased and stabilized performance from the service provider's perspective.

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“…Demand values are modeled using a normal distribution function with a known mean and variance. It is assumed that the load and wind power generation scenarios are independent so the scenarios are combined to construct the whole set of scenarios as follows [28].…”

Section: Problem Formulation a Uncertainty Modeling Of Wind Powmentioning

confidence: 99%