Day-ahead economic optimization of energy use in an olive mill

Báez-González, Pablo; Real, Alejandro J. del; Carlini, Miguel Ángel Ridao; Bordons, Carlos

doi:10.1016/j.conengprac.2016.05.019

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…15 A sustainable SM process for extracting olive oil has also been proposed. 57 Energy saving/efficiency is the technology due to which the energy required to provide a product and service can be reduced. Various studies have been done to decrease the use of energy in manufacturing systems.…”

Section: Technology Clustersmentioning

confidence: 99%

Smart manufacturing: Characteristics, technologies and enabling factors

Mittal

Khan

Romero

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

386

261

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The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of ''semantic similarity'' to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

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Section: Technology Clustersmentioning

confidence: 99%

Smart manufacturing: Characteristics, technologies and enabling factors

Mittal

Khan

Romero

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part B:

386

261

View full text Add to dashboard Cite

show abstract

“…In another survey, an electric system composed of photovoltaic panels, electric storage, electric vehicles, controllable and non-controllable loads, is optimised [5]. MES made up of smart manufacture of an olive mill is controlled in [6]. In this work,carriers which composed the latter system were electricity, natural gas while the energy resources were wind, biomass and sun.…”

Section: Introductionmentioning

confidence: 99%

“…Some papers deal with physical representation as state-space in order to control the system with an optimisation [21]. Two main energy state-space model concepts have been identified in literature for the energy systems control, namely the Prosumer Node (PN) [4,22,23] and the Energy Hub (EH) [14,6,21,24,25].…”

Section: Introductionmentioning

confidence: 99%

“…It has been employed in optimal power flow with MES [8,30]. EH optimisation with a state-space model has been proposed in literature [6,14,21,24]. The main advantages of EH state-space model may be mentioned as following: the state-space model can be used for the optimisation process in order to complete economic optimisation, grid exchange and controllable equipment are modelled in distinct carrier representation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Blaud

Haurant

Claveau

et al. 2020

International Journal of Electrical Power & Energy Systems

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The present study deals with Multi-Energy Systems (MES) modelling and advanced control with Economic Model Predictive Control (EMPC). MES provide energy flexibility, efficiency, and adaptability thanks to several energy carriers. MES are identified as a lever for integrating renewable energy. A MES novel formulation technique called Multi-Prosumer Node (MPN) is developed in this paper. MPN makes possible the modeling of MES, considering MES dynamics, several energy carriers, converters, on-grid, and off-grid. In addition, this MES modeling approach is compatible with predictive control strategies like the EMPC. In fact, EMPC is able to take into account loads, weather, renewable power and energy grid cost predictions to minimise economic costs. A real case study is implemented to examine MPN capabilities, which it is composed of renewable generators, loads, storages from two-energy carriers. Two real scenarios have been developed in order to represent realistic winter and summer cases. Simulation results, thanks to modelling with MPN and EMPC advanced control, demonstrate that the node is optimally controlled, devices dynamics are considered on a minute scale, and energy conversion from one carrier to another one is taken into account while economic cost minimisation is performed. The gained results indicate that the presented MPN modelling and optimisation approach reduces economic cost by 8.21% in winter case and 84.24% in summer case compared to the benchmarks which are composed of rule-based control.

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“…A method is proposed in Báez-González et al (2016) to minimize the energy cost associated to olive oil production in a day-ahead market.…”

Section: Introductionmentioning

confidence: 99%

Cost-optimal model predictive scheduling of freezers

Bálint

Fodor

Hangos

et al. 2018

Control Engineering Practice

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A cost-optimal model predictive scheduling algorithm is presented that operates in a day-ahead market. The underlying optimizer is a heuristic branch and bound algorithm that finds the constrained optimal scheduling of a freezer with respect to hourly changing energy price. The method is also able to iteratively re-estimate the heat capacity of the freezer. Simulation experiments were performed on a freezer model identified from measurement data. Results show that the proposed algorithm successfully decreased the cost of operation, however the computational complexity increases when the price is growing. The proposed method can be generalized for home appliances of different kind.

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Day-ahead economic optimization of energy use in an olive mill

Cited by 11 publications

References 27 publications

Smart manufacturing: Characteristics, technologies and enabling factors

Smart manufacturing: Characteristics, technologies and enabling factors

Modelling and control of multi-energy systems through Multi-Prosumer Node and Economic Model Predictive Control

Cost-optimal model predictive scheduling of freezers

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