Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles

Sedighizadeh, Mostafa; Esmaili, Masoud; Mohammadkhani, Nahid

doi:10.1016/j.jclepro.2018.05.103

Cited by 133 publications

(73 citation statements)

References 36 publications

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“…AUGMECON has been employed in various applications and systems, including supply chain management (Torabi et al 2013;Bootaki et al 2014Bootaki et al , 2016Canales-Bustos et al 2017;Musavi and Bozorgi-Amiri 2017;Ravat et al 2017;Vieira et al 2017;Sazvar et al 2018;Ehrenstein et al 2019;Oiu et al 2019;Shekarjan et al 2019;Xin et al 2019), energy planning (Hombach and Walther 2015;Tartibu et al 2015;Arancibia et al 2016;Mohammadkhani et al 2018;Rabbani et al 2018;Sedighizadeh et al 2018;Razm et al 2019), waste management Mavrotas et al 2015a, b;Inghels et al 2016), portfolio analysis (Xidonas et al 2010(Xidonas et al , 2011 Mavrotas and Florios (2013) further extended this algorithm in AUGMECON 2, by introducing a bypass coefficient as well as a type of lexicographic optimisation to all objective functions, the order of which was insignificant in AUGMECON:…”

Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multiobjective linear programming problems, by drawing from the weaknesses of AUG-MECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

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Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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“…37 The solar irradiance is normalized by dividing into the standard irradiance (G 0 ) according to (1), and the solar clearness (k t ) index is defined. 38 The Beta distribution is one of the most popular distributions for statistical modeling of the solar clearness index, [39][40][41] as shown in (2).…”

Section: Scenario Generation Using Mcsmentioning

confidence: 99%

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Faraji

Hashemi‐Dezaki

Ketabi

2020

Energy Science & Engineering

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“…The normal probability density function is used to generate the load and the day-ahead market price scenarios. Finally, by using the method of Reference [42], the number of scenarios is reduced to 20. Figures 2 and 3 show the basic load and day-ahead market price.…”

Section: Simulation Informationmentioning

confidence: 99%

Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

Zhao

et al. 2020

Energies

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More and more attention has been paid to the development of renewable energy in the world. Microgrids with flexible regulation abilities provide an effective way to solve the problem of renewable energy connected to power grids. In this article, an optimization strategy of a microgrid participating in day-ahead market operations considering demand responses is proposed, where the uncertainties of distributed renewable energy generation, electrical load, and day-ahead market prices are taken into account. The results show that, when the microgrid implements the demand response, the operation cost of the microgrid decreases by 4.17%. Meanwhile, the demand response program can transfer the peak load of the high-price period to the low-price period, which reduces the peak valley difference of the load and stabilizes the load curve. Finally, a sensitivity analysis of three factors is carried out, finding that, with the increase of the demand response adjustable ratio or the maximum capacity of the electrical storage devices, the operation cost of the microgrid decreases, while, with the increase of the demand response cost, the operation cost of the microgrid increases and, finally, tends to the cost without the demand response. The sensitivity analysis reveals that the demand response cost has a reasonable pricing range to maximize the value of the demand response.

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Stochastic multi-objective energy management in residential microgrids with combined cooling, heating, and power units considering battery energy storage systems and plug-in hybrid electric vehicles

Cited by 133 publications

References 36 publications

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

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