Special Issue "Planning and Economics of Electric Energy Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy Economics and Policy".

Deadline for manuscript submissions: 20 April 2021.

Special Issue Editor

Prof. Dr. Luis Baringo
Website
Guest Editor
Escuela Técnica Superior de Ingeniería Industrial, University of Castilla–La Mancha, 13071 Ciudad Real, Spain
Interests: electric energy systems; robust optimization; stochastic programming; electricity markets; wind energy; power systems; operations research
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Special Issue Information

Dear colleagues,

In recent decades, there has been an important increase in the use of renewable energy sources aiming at reducing the greenhouse gas emissions. In this vein, many countries are still implementing new actions to further reduce these emissions, such as the progressive replacement of combustion-engine vehicles by electric vehicles, the transition to fully renewable electric energy systems, and the development of new technologies that allow storinf energy in large quantities. All these actions will change the way that electric energy systems are operated, both from a technical and a economical point of view. Thus, new approaches are needed for the planning and economics of future electric energy systems.
Topics of interest for this Special Issue include but are not limited to the following:

  • Transmission expansion planning to enable a high penetration of electric vehicles and renewable energies;
  • Generation expansion planning in fully renewable electric energy systems;
  • Generation and tranmission expansion planning in power systems considering storage facilities;
  • New methods to account for uncertainties in the planning and economics of electric energy systems.

Prof. Dr. Luis Baringo
Guest Editor

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 papers will be 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. Energies is an international peer-reviewed open access semimonthly 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 2000 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
  • power systems
  • planning, economics
  • uncertainty
  • electric vehicles
  • stochastic programming
  • robust optimization

Published Papers (2 papers)

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Research

Open AccessArticle
A New Approach to Optimal Location and Sizing of DSTATCOM in Radial Distribution Networks Using Bio-Inspired Cuckoo Search Algorithm
Energies 2020, 13(18), 4615; https://doi.org/10.3390/en13184615 - 04 Sep 2020
Cited by 3
Abstract
This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM [...] Read more.
This article proposes a new approach based on a bio-inspired Cuckoo Search Algorithm (CSA) that can significantly envisage with several issues for optimal allocation of distribution static compensator (DSTATCOM) in Radial Distribution System (RDS). In the proposed method, optimal locations of the DSTATCOM are calculated by using the Loss Sensitivity Factor (LSF). The optimal size of the DSTATCOM is simulated by using the newly developed CSA. In the proposed method, load flow calculations are performed by using a fast and efficient backward/forward sweep algorithm. Here, the mathematically formed objective function of the proposed method is to reduce the total system power losses. Standard 33-bus and 69-bus systems have been used to show the effectiveness of the proposed CSA-based optimization method in the RDS with different load models. The simulated results confirm that the optimal allocation of DSTATCOM plays a significant role in power loss minimization and enhanced voltage profile. The placement of DSTATCOM in RDS also plan an important role for minimizing uncertainties in the distribution level. The proposed method encourages one to use renewable-based resources, which results in affordable and clean energy. Full article
(This article belongs to the Special Issue Planning and Economics of Electric Energy Systems)
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Open AccessArticle
Planning Under Uncertainty Applications in Power Plants Using Factored Markov Decision Processes
Energies 2020, 13(9), 2302; https://doi.org/10.3390/en13092302 - 06 May 2020
Cited by 1
Abstract
Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its importance and usage in power plants and power systems. [...] Read more.
Due to its ability to deal with non-determinism and partial observability, represent goals as an immediate reward function and find optimal solutions, planning under uncertainty using factored Markov Decision Processes (FMDPs) has increased its importance and usage in power plants and power systems. In this paper, three different applications using this approach are described: (i) optimal dam management in hydroelectric power plants, (ii) inspection and surveillance in electric substations, and (iii) optimization of steam generation in a combined cycle power plant. For each case, the technique has demonstrated to find optimal action policies in uncertain settings, present good response and compilation times, deal with stochastic variables and be a good alternative to traditional control systems. The main contributions of this work are as follows, a methodology to approximate a decision model using machine learning techniques, and examples of how to specify and solve problems in the electric power domain in terms of a FMDP. Full article
(This article belongs to the Special Issue Planning and Economics of Electric Energy Systems)
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