A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems

Good, Nicholas; Ceseña, Eduardo A. Martínez; Heltorp, Christopher; Mancarella, Pierluigi

doi:10.1016/j.energy.2018.02.089

Cited by 61 publications

(93 citation statements)

References 34 publications

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“…In [24], TE technology is employed for the integration of several villages to manage the energy sharing between villages with the aim of rural network electrification. In addition, a novel TE framework is presented in [25] for technical and economic analysis of distributed multi-energy systems with the aim of profit sharing between commercial participants. In [26], the features of TE and demand response programs are described to investigate the effectiveness of TE technology in establishing dynamic energy balance in the power grid.…”

Section: Introductionmentioning

confidence: 99%

Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

Daneshvar

Mohammadi‐Ivatloo

Abapour

et al. 2020

Journal of Modern Power Systems and Clean Energy

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In recent years, the advent of microgrids with numerous renewable energy sources has created some fundamental challenges in the control, coordination, and management of energy trading between microgrids and the power grid. To respond to these challenges, some techniques such as the transactive energy (TE) technology are proposed to control energy sharing. Therefore, this paper uses TE technology for energy exchange control among the microgrids, and applies three operation cases for analyzing the energy trading control of four and ten microgrids with the aim of minimizing the energy cost of each microgrid, respectively. In this regard, Monte Carlo simulation and fast forward selection (FFS) methods are respectively exerted for scenario generation and reduction in uncertainty modeling process. The first case is assumed that all microgrids can only receive energy from the network and do not have any connection with each other. In order to maximize the energy cost saving of each microgrid, the second case is proposed to provide a positive percentage of cost saving for microgrids. All microgrids can also trade energy with each other to get the most benefit by reducing the dependency on the main grid. The third case is similar to the second case, but its target is to indicate the scalability of the models based on the proposed TE technology by considering ten commercial microgrids. Finally, the simulation results indicate that microgrids can achieve the positive amount of cost saving in the second and third cases. In addition, the total energy cost of microgrids has been reduced in comparison with the first case.

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Section: Introductionmentioning

confidence: 99%

Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

Daneshvar

Mohammadi‐Ivatloo

Abapour

et al. 2020

Journal of Modern Power Systems and Clean Energy

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“…As shown, in all cases, the energy storage could be a a feasible solution for the mentioned problems [13]. Many are the technologies for this purpose, some of them relatively old and wasteful, like pumping water for its storage [14].…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

et al. 2019

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The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of 3.73 with the validation dataset.

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“…In any case, it is a difficult task to match the generation with the demand, which makes energy storage a desirable option in this case [7]. For all of the aforementioned reasons, it is justified that the energy storage systems are desirable in all of the different cases when working with electric power [8]. There are many technologies available for this purpose, even though some of them are relatively old and inefficient, such as pumping water into a dam [9].…”

Section: Introductionmentioning

confidence: 99%

Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

Sousa

Aláiz‐Moretón

Quintián

et al. 2020

Energy

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Energy storage is one of the challenges of the electric sector. There are several different technologies available for facing it, from the traditional ones to the most advanced. With the current trend, it is mandatory to develop new energy storage systems that allow optimal efficiency, something that does not happen with traditional ones. Another feature that new systems must meet is to envisage the behavior of energy generation and consumption. With this aim, the present research deals the hydrogen consumption prediction of a fuel cell based system thanks a hybrid intelligent approach implementation.

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A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems

Abstract: A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems. Energy.

Cited by 61 publications

References 34 publications

Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

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