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Processes
Special Issues
Bioinspired Computation for Sustainable Energy Systems
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Bioinspired Computation for Sustainable Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 8775

Special Issue Editors

Dr. Eneko Osaba

E-Mail Website
Guest Editor
Tecnalia Research & Innovation, 48160 Derio, Spain
Interests: bioinspired optimization; combinatorial optimization; artificial intelligence; metaheuristics; swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Diana Manjarres

E-Mail Website
Guest Editor
Tecnalia Research & Innovation, 48160 Derio, Spain
Interests: sustainable energy systems; heuristic techniques; multiobjective optimization; time series forecasting; artificial intelligence

Special Issue Information

Dear Colleagues,

Bioinspired computation has arisen as one of the most studied and quickly-growing topics in artificial intelligence. Some of the main influences behind the conception of this topic are the well-known Genetic Algorithm and the Ant Colony Optimization and Particle Swarm Optimization algorithms. These methods, and many similar ones, lit the fuse of the success of this area of knowledge, serving as the origin and principal inspiration of their subsequent research. This success has led to the proposal of additional novel algorithms, inspired by different sources such as the behavioral patterns of animals, social and political behaviors or physical processes. The proposal of new solvers evidences the capability of this type of algorithms to reach a near-optimal performance over a wide range of academic and real-world problems.

In the context of sustainable energy systems, the high increase of energy demand in the last century has led to a development of new renewable and sustainable energy sources, such as solar photovoltaic, wind, biomass, and geothermal energy, among others. In order to enhance the performance of these systems, modeling and optimization techniques have arisen to provide efficient solutions for optimal energy production, planning, storage, distribution, etc. In this regard, bioinspired computation tecniques are considered as a key enabling technology for coping with the above-mentioned challenges.

This Special Issue aims at disseminating the latest findings and research achievements in the areas of bioinspired optimization and sustainable energy systems, with the intention to balance between theoretical research ideas and their practicability as well as industrial applicability. To this end, scholars and practitioners from academia and industrial fields are invited to submit high-quality original contributions to this Special Issue.

Dr. Eneko Osaba
Dr. Diana Manjarres
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. Processes 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 2400 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

  • Bioinspired computation
  • Metaheuristics
  • Optimization of energy systems
  • Energy storage optimization
  • Optimization modeling of energy systems planning
  • Model-based decision support tools in sustainable energy

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Published Papers (3 papers)

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Research

28 pages, 2622 KiB  
Article
Effects of Dynamic Pricing on the Design and Operation of Distributed Energy Resource Networks
by Tim Sidnell, Bogdan Dorneanu, Evgenia Mechleri, Vassilios S. Vassiliadis and Harvey Arellano-Garcia
Processes 2021, 9(8), 1306; https://doi.org/10.3390/pr9081306 - 28 Jul 2021
Cited by 4 | Viewed by 2425
Abstract
This paper presents a framework for the use of variable pricing to control electricity imported/exported to/from both fixed and unfixed residential distributed energy resource (DER) network designs. The framework shows that networks utilizing much of their own energy, and importing little from the [...] Read more.
This paper presents a framework for the use of variable pricing to control electricity imported/exported to/from both fixed and unfixed residential distributed energy resource (DER) network designs. The framework shows that networks utilizing much of their own energy, and importing little from the national grid, are barely affected by dynamic import pricing, but are encouraged to sell more by dynamic export pricing. An increase in CO2 emissions per kWh of energy produced is observed for dynamic import and export, against a baseline configuration utilizing constant pricing. This is due to feed-in tariffs (FITs) that encourage CHP generation over lower-carbon technologies. Furthermore, batteries are shown to be expensive in systems receiving income from FITs and grid exports, but for the cases when they sell to/buy from the grid using dynamic pricing, their use in the networks becomes more economical. Full article
(This article belongs to the Special Issue Bioinspired Computation for Sustainable Energy Systems)
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19 pages, 2729 KiB  
Article
A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data
by Rami Al-Hajj, Ali Assi, Mohamad Fouad and Emad Mabrouk
Processes 2021, 9(7), 1187; https://doi.org/10.3390/pr9071187 - 8 Jul 2021
Cited by 21 | Viewed by 2881
Abstract
The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate [...] Read more.
The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency. Full article
(This article belongs to the Special Issue Bioinspired Computation for Sustainable Energy Systems)
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22 pages, 568 KiB  
Article
Optimal Non-Convex Combined Heat and Power Economic Dispatch via Improved Artificial Bee Colony Algorithm
by Abbas Rabiee, Mohammad Jamadi, Behnam Mohammadi-Ivatloo and Ali Ahmadian
Processes 2020, 8(9), 1036; https://doi.org/10.3390/pr8091036 - 25 Aug 2020
Cited by 18 | Viewed by 2646
Abstract
It is well accepted that combined heat and power (CHP) generation can increase the efficiency of power and heat generation at the same time. With the increasing penetration of CHPs, determination of economic dispatch of power and heat becomes more complex and challenging. [...] Read more.
It is well accepted that combined heat and power (CHP) generation can increase the efficiency of power and heat generation at the same time. With the increasing penetration of CHPs, determination of economic dispatch of power and heat becomes more complex and challenging. The CHP economic dispatch (CHPED) problem is a challenging optimization problem due to non-linearity and non-convexity in both objective function and constraints. Hence, in this paper a novel meta-heuristic algorithm, namely improved artificial bee colony (IABC) algorithm is proposed to solve the CHPED problem. The valve-point effects, power losses as well as the feasible operation region of CHP units are taken into account in the proposed CHPED problem model and the optimal dispatch of power/heat outputs of CHP units is determined via the proposed IABC algorithm. The proposed algorithm is applied on three test systems, in which two of them are large-scale CHPED benchmarks. The obtained results and comprehensive comparison with available methods, demonstrate the superiority of the proposed algorithm for dealing with non-convex and constrained CHPED problem. Full article
(This article belongs to the Special Issue Bioinspired Computation for Sustainable Energy Systems)
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