A robust approach to schedule flexible ramp in Real-Time Electricity Market considering Demand Response

Heidarabadi, Houman; Hosseini, Seyed Hamid; Ranjbar, Hossein

doi:10.1109/iraniancee.2017.7985216

Cited by 2 publications

(1 citation statement)

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“…The problem is modelled as a bi-level optimization problem which is solved in two iterative steps [29]. In the first step, genetic algorithm (GA) determines the worst situation of uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A Robust Model for Daily Operation of Grid-connected Microgrids During Normal Conditions

Ranjbar

Safdarian

2019

Scientia Iranica

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Microgrids (MGs) are designed to be able to serve hosting critical load in island-mode during major events. However, during normal condition when they are in grid-connected mode, MGs may have opportunity to achieve monetary profits through optimizing operation of energy resources and their participation in wholesale markets. This paper proposes a model to optimize MGs participation in the markets and operation of energy resources. Since MGs usually host renewable energy resources, making decision without considering the uncertainties may prone MGs to risk. So, the model considers uncertainties associated with generation of renewable DGs, demand, and market prices via robust optimization technique. The model is formulated as a bi-level max-min optimization problem. The problem is solved in two iterative steps. In the first step, a genetic algorithm (GA) finds the worst situation of uncertain parameters such that MG profit is minimized. Then, a mixed-integer linear problem is solved to maximize the profit over MG decision variables considering the values determined in the first step. The steps are iterated to converge to the best solution. To verify performance of the approach, it is applied to a typical MG and the results are reported.

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Section: Introductionmentioning

confidence: 99%