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Algorithms
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Optimization in Renewable Energy Systems
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Optimization in Renewable Energy Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 7391

Special Issue Editors

Prof. Dr. Cristina Requejo

E-Mail Website
Guest Editor
Departamento de Matemática, Universidade de Aveiro, 3810-193 Aveiro, Portugal
Interests: applications of mixed integer linear programming in health care; green energy and smart grids; communications networks
Special Issues, Collections and Topics in MDPI journals
Dr. Adelaide Cerveira

E-Mail Website
Guest Editor
Departamento de Matemática, Universidade de Trás os Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: optimization and applications; power cables; power grids; project management; reactive power; renewable energy sources; smart power grids

Special Issue Information

Dear Colleagues,

There is an urgent need to develop sustainable energy systems and encourage carbon footprint reduction. Wind and solar power are driving a clean energy revolution, and renewable energy is booming as innovation reduces costs and begins to deliver on the promise of a clean energy future. Renewables are increasingly replacing “dirty” fossil fuels in the energy sector, offering the benefit of lower carbon emissions and other types of pollution.

The increasing use of solar and wind energy gives rise to a great diversity of optimization problems, such as telecommunications infrastructures, electrical and transport networks, and support systems. In particular, these networks and systems are increasingly converging and becoming very large networks and systems.

This Special Issue aims to explore emerging concerns arising from the integration and transformation of the existing energy system and to give an overview of the wide spectrum of interesting algorithms, optimization problems, mixed integer linear programming models, and studies related to solution algorithms and their applications in renewable energy.

Manuscripts regarding new and innovative research proposals, algorithms and ideas of computer science, computational mathematics, optimization models, artificial intelligence, automation and control systems, theory, methods, interdisciplinary applications, data and information systems, and software engineering are particularly welcome.

This Special Issue aims to explore the emerging concerns arising from integrating and transforming the existing energy system, with particular emphasis on algorithms applied to solving problems in this area.

Prof. Dr. Cristina Requejo
Dr. Adelaide Cerveira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • renewable energy
  • virtual power plants
  • algorithm engineering
  • approximation algorithms
  • iterative methods and algorithms
  • performance and testing of algorithms
  • optimization
  • operational research
  • machine learning
  • mathematical programming
  • combinatorial optimization
  • discrete mathematics and graph theory
  • metaheuristics and matheuristics
  • modelling
  • networks
  • communication and data networks
  • uncertainty data
  • production planning
  • scheduling
  • transport
  • timetabling

Published Papers (4 papers)

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Research

14 pages, 6333 KiB  
Article
Self-Sustainability Assessment for a High Building Based on Linear Programming and Computational Fluid Dynamics
by Carlos Oliveira, José Baptista and Adelaide Cerveira
Algorithms 2023, 16(2), 107; https://doi.org/10.3390/a16020107 - 13 Feb 2023
Cited by 2 | Viewed by 1390
Abstract
With excess energy use from non-renewable sources, new energy generation solutions must be adopted to make up for this excess. In this sense, the integration of renewable energy sources in high-rise buildings reduces the need for energy from the national power grid to [...] Read more.
With excess energy use from non-renewable sources, new energy generation solutions must be adopted to make up for this excess. In this sense, the integration of renewable energy sources in high-rise buildings reduces the need for energy from the national power grid to maximize the self-sustainability of common services. Moreover, self-consumption in low-voltage and medium-voltage networks strongly facilitates a reduction in external energy dependence. For consumers, the benefits of installing small wind turbines and energy storage systems include tax benefits and reduced electricity bills as well as a profitable system after the payback period. This paper focuses on assessing the wind potential in a high-rise building through computational fluid dynamics (CFD) simulations, quantifying the potential for wind energy production by small wind turbines (WT) at the installation site. Furthermore, a mathematical model is proposed to optimize wind energy production for a self-consumption system to minimize the total cost of energy purchased from the grid, maximizing the return on investment. The potential of a CFD-based project practice that has wide application in developing the most varied processes and equipment results in a huge reduction in the time and costs spent compared to conventional practices. Furthermore, the optimization model guarantees a significant decrease in the energy purchased at peak hours through the energy stored in energy storage systems (ESS). The results show that the efficiency of the proposed model leads to an investment amortization period of 7 years for a lifetime of 20 years. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems)
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15 pages, 4154 KiB  
Article
Variational Bayes to Accelerate the Lagrange Multipliers towards the Dual Optimization of Reliability and Cost in Renewable Energy Systems
by Pavlos Nikolaidis
Algorithms 2023, 16(1), 20; https://doi.org/10.3390/a16010020 - 29 Dec 2022
Viewed by 1367
Abstract
Renewable energy sources are constantly increasing in the modern power systems. Due to their intermittent and uncertain potential, increased spinning reserve requirements are needed to conserve the reliability. On the other hand, each action towards efficiency improvement and cost reduction contradicts the participation [...] Read more.
Renewable energy sources are constantly increasing in the modern power systems. Due to their intermittent and uncertain potential, increased spinning reserve requirements are needed to conserve the reliability. On the other hand, each action towards efficiency improvement and cost reduction contradicts the participation of variable resources in the energy mix, requiring more accurate tools for optimal unit commitment. By increasing the renewable contribution, not only does the overall system inertia decrease with the decreasing conventional generation, but more generators that are expensive are also introduced. This work provides a radically different approach towards a tractable optimization task based on the framework of Lagrange relaxation and variational Bayes. Following a dual formulation of reliability and cost, the Lagrange multipliers are accelerated via a machine learning mechanism, namely, variational Bayesian inference. The novelty in the proposed approach stems from the employed acquisition function and the effect of the Gaussian process. The obtained results show great improvements compared with the Lagrange relaxation alternative, which can reach over USD 1 M in production cost credits at the least number of function evaluations. The proposed hybrid method promises global solutions relying on a proper acquisition function that is able to move towards regions with minimum objective value and maximum uncertainty. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems)
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11 pages, 1670 KiB  
Article
MIRROR: Methodological Innovation to Remodel the Electric Loads to Reduce Economic OR Environmental Impact of User
by Michela Chimienti, Ivan Danzi, Donato Impedovo, Giuseppe Pirlo, Gianfranco Semeraro and Davide Veneto
Algorithms 2023, 16(1), 1; https://doi.org/10.3390/a16010001 - 20 Dec 2022
Cited by 1 | Viewed by 1343
Abstract
Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodologies do not produce energy consistently due to [...] Read more.
Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodologies do not produce energy consistently due to weather factors. This results in a significant commitment of the user who must appropriately distribute loads in the most productive time slots. In this paper, a comparison is made between two methods of predicting solar energy production, one statistical and the other meteorological. For this work, a system capable of presenting the scheduling of household appliances is tested. The system is able to predict the energy consumption of the users and the energy production of the solar system. The system is tested using data from three different users, and the mean percentage of consumption reduction is about 77.73%. This is achieved through optimized programming of appliance use that also considers user comfort. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems)
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21 pages, 5158 KiB  
Article
Indoor Comfort and Energy Consumption Optimization Using an Inertia Weight Artificial Bee Colony Algorithm
by Farah Nur Arina Baharudin, Nor Azlina Ab. Aziz, Mohamad Razwan Abdul Malek, Anith Khairunnisa Ghazali and Zuwairie Ibrahim
Algorithms 2022, 15(11), 395; https://doi.org/10.3390/a15110395 - 25 Oct 2022
Cited by 2 | Viewed by 2195
Abstract
A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators [...] Read more.
A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators helps in maximizing the user’s comfort level. Nonetheless, the energy consumption of these appliances needs to be taken into consideration to minimize the operational cost and at the same time provide an environmentally friendly system. Comfort level maximization and energy consumption minimization are optimization problems. This issue is becoming more important due to the lifestyle changes caused by the COVID-19 pandemic that resulted in more time spent at home and indoors. Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. The multi-objective problem is tackled as a weighted single objective optimization problem. The proposed solution is tested using a dataset of 48 environmental conditions. The results of the simulation show that the IW-ABC performs better than the original ABC and other benchmark algorithms and the IW-ABC with linear increasing inertia weight has the most improved convergence behavior. Full article
(This article belongs to the Special Issue Optimization in Renewable Energy Systems)
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