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Energies
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Electricity Markets: Modelling, Simulation and Analysis
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Electricity Markets: Modelling, Simulation and Analysis

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

Deadline for manuscript submissions: closed (24 December 2022) | Viewed by 4020

Special Issue Editors

Dr. Cristian Bovo

E-Mail Website
Guest Editor
Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, 27100 Pavia, Italy
Interests: electric power system; planning and operation of electric power systems; optimization; electricity markets; smart grids
Dr. Ilea Valentin

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: power systems operation; power systems dynamics; power systems optimization; electricity markets; smart grids

Special Issue Information

Dear Colleagues,

Due to decarbonization efforts to dampen the effects of global warming, power systems have seen an increased penetration of nonprogrammable renewable energy sources; hence, the need for flexibility services is becoming crucial. However, on the other hand, the decarbonization efforts also mean an increase in the availability of storage systems, in the role of the demand of providing flexibility services and, in general, an increased presence of flexibility providers connected to distribution networks. These factors are determining the revolution in the context of electricity markets towards flexibility-orientated ones.

Moreover, it is also necessary to consider that electricity systems are increasingly evolving towards a scenario characterized by the presence of power plants having cost structures significantly different from traditional ones, as well as the presence of local energy communities integrating different primary energy sources.

All this requires a thorough review of market rules, both regarding the day-ahead and intraday markets, as well as the ancillary service markets.

These changes raise policy and regulatory issues requiring addressal if innovations are to become business as usual within the power grid.

This Special Issue focuses on the modelling, simulation and analysis of novel electric energy and ancillary service market models and schemes. In particular, we invite articles concerning:

  • Local electricity markets (managed by distribution system operators, DSOs);
  • Coordination of the DSO-TSO (transmission system operator) markets;
  • New market products;
  • Transactive energy drivers;
  • New market participation rules for RES;
  • Coordination between traditional power plants and RES at market level;
  • Smart pricing to manage renewable generation.

Authors are encouraged to use their articles to describe their models and algorithms and to show what implications they have regarding the need to change existing policies and regulatory frameworks. Documents supporting their proposals through a quantitative analysis using models applied to real-world scenarios are particularly encouraged.

Dr. Cristian Bovo
Dr. Ilea Valentin
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. 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 2600 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

  • local electricity markets
  • smart pricing
  • ancillary service markets
  • aggregators
  • TSO-DSO coordination
  • nonprogrammable renewable energy sources
  • electricity market structure
  • local energy community

Published Papers (2 papers)

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Research

17 pages, 2371 KiB  
Article
The Negative Impact of Electrical Energy Subsidies on the Energy Consumption—Case Study from Jordan
by Aiman Albatayneh, Adel Juaidi and Francisco Manzano-Agugliaro
Energies 2023, 16(2), 981; https://doi.org/10.3390/en16020981 - 15 Jan 2023
Cited by 5 | Viewed by 2116
Abstract
Many developing countries subsidise energy (petroleum fuel products, natural gas and electricity), which was reflected in an extra pressure on the national budget, and this will support inefficient use of energy. In this study, the effects of electrical energy subsidies on the total [...] Read more.
Many developing countries subsidise energy (petroleum fuel products, natural gas and electricity), which was reflected in an extra pressure on the national budget, and this will support inefficient use of energy. In this study, the effects of electrical energy subsidies on the total electrical energy consumption in the residential sector were examined. Data on more than 260,000 Jordanian ordinary customers were collected, and the energy consumption of more than 1000 energy-extra subsidised Irbid District Electricity Distribution Company (IDECO) staff members was recorded over a 2-year period (2017 and 2018). These two groups were compared to examine the consequences of subsidising energy on the energy consumption and the consumption behaviour in the residential sector. The analysis revealed that ordinary householders consume around 296 kWh/month, while for the subsidised group 615 kWh/month was noted. Energy consumption increased during the summer and winter months, especially in the subsidised group, due to the heavy reliance on mechanical systems for cooling and heating. Electricity full price (without any subsidies) can be a very effective way to control the demand profile. It can be structured to encourage customers (generally those that have significant electricity demand) to reduce their total usage as well as peak demand (thus reducing the pressure on the grid and the power plant) by charging them full electricity prices. Full article
(This article belongs to the Special Issue Electricity Markets: Modelling, Simulation and Analysis)
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17 pages, 3307 KiB  
Article
Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models
by Hasan Murat Ertuğrul, Mustafa Tevfik Kartal, Serpil Kılıç Depren and Uğur Soytaş
Energies 2022, 15(20), 7512; https://doi.org/10.3390/en15207512 - 12 Oct 2022
Cited by 5 | Viewed by 1445
Abstract
The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily [...] Read more.
The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed. Full article
(This article belongs to the Special Issue Electricity Markets: Modelling, Simulation and Analysis)
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