Multi-carrier optimal power flow of energy hubs by means of ANFIS and SQP

Kampouropoulos, Konstantinos; Andrade, Fabio; Sala, Enric; Espinosa, Antonio García; Romeral, L.

doi:10.1109/iecon.2016.7793570

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In this section, a case study based on a real manufacturing plant of the automotive sector, located in Catalonia, at the south-west of Europe, is exposed. Most industrial SMEs, have higher thermal consumption than electrical consumption [78] and are characterised by a diversity of processes and equipment that enable the incorporation of energy assets to interconnect the different sides of the industry, increasing the robustness of the energy system [79]. Especially, in the automotive industry, electricity consumption accounts for approximately 22% of total primary energy used [80], and the determinant resource depends highly on the type of process performed, being natural gas the primary energy source for the industrial entity considered in this article.…”

Section: Case Studymentioning

confidence: 99%

Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach

et al. 2021

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Section: Case Studymentioning

confidence: 99%

Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach

et al. 2021

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“…In this section, a case study for an industrial SME of the automotive sector is presented in which the methodology exposed in the previous section is applied. Industrial SMEs, in contrast with other entities in the tertiary and residential sector, have higher thermal consumption than electrical consumption [40][41][42] and are characterized by a diversity of processes and equipment that enable the incorporation of different energy assets to interconnect the different energy carriers present in the industry, increasing the robustness of the energy system [43]. Additionally, the load pattern of industrial SMEs is much more predictable than in other sectors, as it is strongly affected by production and varies only slightly with daily human behaviour [44].…”

Section: Case Studymentioning

confidence: 99%

Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis

et al. 2021

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Industrial SMEs may take the decision to invest in energy efficient equipment to reduce energy costs by replacing or upgrading their obsolete equipment or due to external socio-political and legislative pressures. When upgrading their energy equipment, it may be beneficial to consider the adoption of new energy strategies rising from the ongoing energy transition to support green transformation and decarbonisation. To face this energy-investment decision-making problem, a set of different economic and environmental criteria have to be evaluated together with their associated risks. Although energy-investment problems have been treated in the literature, the incorporation of both quantitative and qualitative risks for decision-making in SMEs has not been studied yet. In this paper, this research gap is addressed, creating a framework that considers non-risk criteria and quantitative and qualitative risks into energy-investment decision-making problems. Both types of risks are evaluated according to their probability and impact on the company’s objectives and, additionally for qualitative risks, a fuzzy inference system is employed to account for judgmental subjectivity. All the criteria are incorporated into a single cost–benefit analysis function, which is optimised along the energy assets’ lifetime to reach the best long-term energy investment decisions. The proposed methodology is applied to a specific industrial SME as a case study, showing the benefits of considering these risks in the decision-making problem. Nonetheless, the methodology is expandable with minor changes to other entities facing the challenge to invest in energy equipment or, as well, other tangible assets.

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“…To improve a large-scale power system stability the authors make a coordination of ANFIS and type-2 fuzzy logic system-power system stabilizer [13]. ANFIS and other method were used to calculate the short-term demand forecasting of a multi-carrier energy system and to optimize its energy flow [14]. There is other methods that were used in power system, for example a modified models of PSO algorithm was used to get the estimation solution of power system state [15], in reference [16], the authors used the continuous Newton's for power flow analysis, reference [17] proposed the firefly algorithm for solving load flow problem and authors of paper [18] discussed the optimal power flow based congestion management using enhanced genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

Fikri¹,

Sabri²,

Cheddadi³

2020

IJECE

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In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System using real electrical networks. To do that we used few significant discrepancies. These methods are characterized by giving results in real time. To make this comparison successful, we implemented these two methods, to predict the voltage magnitudes and the voltage phase angles, on two Moroccan electrical networks. The results of the comparison show that the method of Adaptive Neuro-Fuzzy Inference System have more advantages than the method of Artificial Neural Network.

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Multi-carrier optimal power flow of energy hubs by means of ANFIS and SQP

Cited by 3 publications

References 17 publications

Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach

Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach

Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

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