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Energy Transition: Decentralization, Electric Vehicles, and Local Energy Markets

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (29 October 2023) | Viewed by 17221

Special Issue Editors

GECAD–Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), 4200-072 Porto, Portugal
Interests: computational intelligence; energy resource management; energy systems simulation; evolutionary computation; local energy markets; multi-agent systems; smart grids
Special Issues, Collections and Topics in MDPI journals
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-465 Porto, Portugal
Interests: artificial intelligence; demand response; electric vehicles; electricity markets; power and energy systems; renewable and sustainable energy; smart grids
Special Issues, Collections and Topics in MDPI journals
School of Energy Engineering, São Paulo State University (UNESP), Rosana, Brazil
Interests: power systems; distribution networks; smart grids; distributed energy resources

Special Issue Information

Dear Colleagues,

The power industry is on the move for a significant transformation motivated by the clean energy and zero carbon emissions acts. Moreover, the world will likely face a faster energy transition due to the COVID-19 pandemic. The proliferation of distributed energy resources (DERs) in distribution networks, namely PV panels and electric vehicles, is transforming conventional centralized management into a decentralized, bottom-up, localized control model. Local markets are emerging as a promising solution to address the problem of large amounts of energy resources at this level. Trading of energy and flexibility for local agents can be achieved with adequate coordination at the distribution grid; however, this transition is not possible without solving new technical and economic challenges. The new paradigm is calling for innovative ideas and solutions with a highly interdisciplinary research scope.

This Special Issue invites original research papers for publication focusing on topics of interest including but limited to the following:

  • Pricing, market clearing, and validation methods in local electricity markets;
  • Local market architecture, business models, cost–benefit analysis, and energy policies for the adoption of DER;
  • Coordination and interactions between markets at different levels, e.g., local, distribution, and wholesale markets;
  • Modelling and coordination of different actors interacting at the different levels of the energy chain, e.g., local, distribution, and transmission levels;
  • Flexibility services for DSO, TSO, and balancing responsible parties (i.e., grid service trading);
  • Distributed ledger technology (including blockchain) for peer-to-peer energy markets and transactive energy;
  • Classical and modern optimization methods for scalable management and control of large-scale DER;
  • Modern ICT to implement decentralized energy systems in the smart grid paradigm;
  • Decentralized electric vehicle management and scheduling models;
  • Local electricity market models for electric vehicles;
  • Smart contracts for electric vehicles.

Dr. João Soares
Dr. Fernando Lezama
Prof. Dr. Zita Vale
Dr. John Fredy Franco
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.

Published Papers (9 papers)

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Research

21 pages, 12420 KiB  
Article
Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques
Energies 2023, 16(18), 6414; https://doi.org/10.3390/en16186414 - 05 Sep 2023
Cited by 1 | Viewed by 1131
Abstract
Renewable energy (RE) sources, such as wind, geothermal, bioenergy, and solar, have gained interest in developed regions. The rapid expansion of the economies in the Middle East requires massive increases in electricity production capacity, and currently fossil fuel reserves meet most of the [...] Read more.
Renewable energy (RE) sources, such as wind, geothermal, bioenergy, and solar, have gained interest in developed regions. The rapid expansion of the economies in the Middle East requires massive increases in electricity production capacity, and currently fossil fuel reserves meet most of the power station demand. There is a considerable measure of unpredictability surrounding the locations of the concerned regions where RE can be used to generate electricity. This makes forecasting difficult for the investor to estimate future electricity production that could be generated in each area over the course of a specific period. Energy production forecasting with complex time-series data is a challenge. However, artificial neural networks (ANNs) are well suited for handling nonlinearity effectively. This research aims to investigate the various ANN models capable of providing reliable predictions for sustainable sources of power such as wind and solar. In addition to the ANN models, a state-of-the-art ensemble learning approach is used to improve the accuracy of predictions further. The proposed strategies can forecast RE generation accurately over short and long time frames, relying on historical data for precise predictions. This work proposes a new hybrid ensemble framework that strategically combines multiple complementary machine learning (ML) models to improve RE forecasting accuracy. The ensemble learning (EL) methodology outperforms long short-term memory (LSTM), light gradient boosting machine (LightGBM), and sequenced-GRU in predicting wind power (MAE: 0.782, MAPE: 0.702, RMSE: 0.833) and solar power (MAE: 1.082, MAPE: 0.921, RMSE: 1.055). It achieved an impressive R2 value of 0.9821, indicating its superior accuracy. Full article
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14 pages, 352 KiB  
Article
Large Neighborhood Search for Electric Vehicle Fleet Scheduling
Energies 2023, 16(12), 4576; https://doi.org/10.3390/en16124576 - 07 Jun 2023
Viewed by 812
Abstract
This work considers the problem of planning how a fleet of shared electric vehicles is charged and used for serving a set of reservations. While exact approaches can be used to efficiently solve small to medium-sized instances of this problem, heuristic approaches have [...] Read more.
This work considers the problem of planning how a fleet of shared electric vehicles is charged and used for serving a set of reservations. While exact approaches can be used to efficiently solve small to medium-sized instances of this problem, heuristic approaches have been demonstrated to be superior in larger instances. The present work proposes a large neighborhood search approach for solving this problem, which employs a mixed integer linear programming-based repair operator. Three variants of the approach using different destroy operators are evaluated on large instances of the problem. The experimental results show that the proposed approach significantly outperforms earlier state-of-the-art methods on this benchmark set by obtaining solutions with up to 8.5% better objective values. Full article
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16 pages, 6027 KiB  
Article
Empirical Study of Stability and Fairness of Schemes for Benefit Distribution in Local Energy Communities
Energies 2023, 16(4), 1756; https://doi.org/10.3390/en16041756 - 09 Feb 2023
Cited by 1 | Viewed by 1224
Abstract
The concept of local energy communities is receiving increasing attention. However, the question of how to distribute the benefit of a community among its members is still open. It is commonly desired that the benefit distribution is fair and stable. While benefit distribution [...] Read more.
The concept of local energy communities is receiving increasing attention. However, the question of how to distribute the benefit of a community among its members is still open. It is commonly desired that the benefit distribution is fair and stable. While benefit distribution schemes such as the nucleolus, Shapley value and Shapley-core are known to perform well in terms of fairness and stability, studies have shown that none of them can guarantee full fairness and stability at the same time. However, the existing studies neglect the temporal component. Hence, in order to gain more insights into the stability and fairness of the three aforementioned distributions in practice, we investigate their performance over time in simulation experiments on real-world data from Australian households. In about 90% of the cases, the Shapley value yielded a reasonably stable distribution, while the nucleolus yielded a reasonably fair distribution in about 75% of the cases. Furthermore, the experiments show an impact of the community size on the stability and fairness of the investigated distributions. One can conclude that for small communities, the Shapley value is the best choice, but that the nucleolus and Shapley–core become more and more attractive with increasing size of the community. Full article
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12 pages, 1528 KiB  
Article
A Robust Model for Portfolio Management of Microgrid Operator in the Balancing Market
Energies 2023, 16(4), 1700; https://doi.org/10.3390/en16041700 - 08 Feb 2023
Cited by 1 | Viewed by 962
Abstract
The stochastic nature of renewable energy resources and consumption has the potential to threaten the balance between generation and consumption as well as to cause instability in power systems. The microgrid operators (MGOs) are financially responsible for compensating for the imbalance of power [...] Read more.
The stochastic nature of renewable energy resources and consumption has the potential to threaten the balance between generation and consumption as well as to cause instability in power systems. The microgrid operators (MGOs) are financially responsible for compensating for the imbalance of power within their portfolio. The imbalance of power can be supplied by rescheduling flexible resources or participating in the balancing market. This paper presents a robust optimization (RO)-based model to maintain the balance of a portfolio according to uncertainties in renewable power generation and consumption. Furthermore, load reduction (LR) and battery energy storage (BES) are considered flexible resources of the MGO on the consumption side. The model is formulated based on the minimax decision rule that determines the minimum cost of balancing based on the worst-case realizations of uncertain parameters. Through the strong duality theory and big-M theory, the proposed minimax model is transformed into a single-level linear maximization problem. The proposed model is tested on a six-node microgrid test system. The main contributions of the proposed model are presenting a robust model for portfolio management of MGO and using BES and LR to improve the flexibility of microgrid. Simulation results demonstrate that using LR and BES could decrease the balancing cost. However, the optimal portfolio management to compensate for the imbalance of power is highly dependent on the risk preferences of MGO. Full article
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19 pages, 6557 KiB  
Article
Techno-Economic Analysis of Renewable-Energy-Based Micro-Grids Considering Incentive Policies
Energies 2022, 15(21), 8285; https://doi.org/10.3390/en15218285 - 06 Nov 2022
Cited by 13 | Viewed by 2162
Abstract
Renewable-energy-based microgrids (MGs) are being advocated around the world in response to increasing energy demand, high levels of greenhouse gas (GHG) emissions, energy losses, and the depletion of conventional energy resources. However, the high investment cost of the MGs besides the low selling [...] Read more.
Renewable-energy-based microgrids (MGs) are being advocated around the world in response to increasing energy demand, high levels of greenhouse gas (GHG) emissions, energy losses, and the depletion of conventional energy resources. However, the high investment cost of the MGs besides the low selling price of the energy to the main grid are two main challenges to realize the MGs in developing countries such as Iran. For this reason, the government should define some incentive policies to attract investor attention to MGs. This paper aims to develop a framework for the optimal planning of a renewable energy-based MG considering the incentive policies. To investigate the effect of the incentive policies on the planning formulation, three different policies are introduced in a pilot system in Iran. The minimum penetration rates of the RESs in the MG to receive the government incentive are defined as 20% and 40% in two different scenarios. The results show that the proposed incentive policies reduce the MG’s total net present cost (NPC) and the amount of carbon dioxide (CO2) emissions. The maximum NPC and CO2 reduction in comparison with the base case (with incentive policies) are 22.87% and 56.13%, respectively. The simulations are conducted using the hybrid optimization model for electric renewables (HOMER) software. Full article
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25 pages, 7166 KiB  
Article
On the Importance of Grid Tariff Designs in Local Energy Markets
Energies 2022, 15(17), 6209; https://doi.org/10.3390/en15176209 - 26 Aug 2022
Cited by 7 | Viewed by 1610
Abstract
Local Energy Markets (LEMs) were recently proposed as a measure to coordinate an increasing amount of distributed energy resources on a distribution grid level. A variety of market models for LEMs are currently being discussed; however, a consistent analysis of various proposed grid [...] Read more.
Local Energy Markets (LEMs) were recently proposed as a measure to coordinate an increasing amount of distributed energy resources on a distribution grid level. A variety of market models for LEMs are currently being discussed; however, a consistent analysis of various proposed grid tariff designs is missing. We address this gap by formulating a linear optimization-based market matching algorithm capable of modeling a variation of grid tariff designs. A comprehensive simulative study is performed for yearly simulations of a rural, semiurban, and urban grids in Germany, focusing on electric vehicles, heat pumps, battery storage, and photovoltaics in residential and commercial buildings. We compare energy-based grid tariffs with constant, topology-dependent and time-variable cost components and power-based tariffs to a benchmark case. The results show that grid tariffs with power fees show a significantly higher potential for the reduction of peak demand and feed-in (30–64%) than energy fee-based tariffs (8–49%). Additionally, we show that energy-based grid tariffs do not value the flexibility of assets such as electric vehicles compared to inflexible loads. A postprocessing of market results valuing the reduction of power peaks is proposed, enabling a compensation for the usage of asset flexibility. Full article
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19 pages, 597 KiB  
Article
An Examination of Consumers’ Opinions toward Adopting Electric Vehicles in the United Arab Emirates: On the Effects of Functional and Symbolic Values
Energies 2022, 15(16), 6068; https://doi.org/10.3390/en15166068 - 21 Aug 2022
Cited by 2 | Viewed by 1860
Abstract
The aim of this study was to examine consumers’ opinions toward adopting electric vehicles (EVs) for light-duty transport in the United Arab Emirates (UAE) from the functional value (i.e., the utility or benefit attained by consumers from the functions or tangible features associated [...] Read more.
The aim of this study was to examine consumers’ opinions toward adopting electric vehicles (EVs) for light-duty transport in the United Arab Emirates (UAE) from the functional value (i.e., the utility or benefit attained by consumers from the functions or tangible features associated with EVs) and symbolic value (i.e., the social meaning that consumers associate with EVs) perspectives. The primary research question was as follows: To what extent do functional and symbolic values affect consumers’ opinions toward adopting EVs in the UAE? The objectives were to determine if relationships exist between gender, age, and residency and the functional and symbolic values of consumers’ opinions toward adopting EVs. A survey of 5459 people was conducted in 14 cities across the seven emirates (Abu Dhabi, Ajman, Dubai, Fujairah, Ras Al Khaimah, Sharjah, and Umm Al Quwain) to test the relationship. The results revealed that females, respondents aged 20–29, and residents living in Abu Dhabi City found more appealing functional and symbolic values regarding EVs. Full article
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19 pages, 1954 KiB  
Article
A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows
Energies 2022, 15(7), 2372; https://doi.org/10.3390/en15072372 - 24 Mar 2022
Cited by 13 | Viewed by 2612
Abstract
Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with [...] Read more.
Transportation has been incorporating electric vehicles (EVs) progressively. EVs do not produce air or noise pollution, and they have high energy efficiency and low maintenance costs. In this context, the development of efficient techniques to overcome the vehicle routing problem becomes crucial with the proliferation of EVs. The vehicle routing problem concerns the freight capacity and battery autonomy limitations in different delivery-service scenarios, and the challenge of best locating recharging stations. This work proposes a mixed-integer linear programming model to solve the electric location routing problem with time windows (E-LRPTW) considering the state of charge, freight and battery capacities, and customer time windows in the decision model. A clustering strategy based on the k-means algorithm is proposed to divide the set of vertices (EVs) into small areas and define potential sites for recharging stations, while reducing the number of binary variables. The proposed model for E-LRPTW was implemented in Python and solved using mathematical modeling language AMPL together with CPLEX. Performed tests on instances with 5 and 10 clients showed a large reduction in the time required to find the solution (by about 60 times in one instance). It is concluded that the strategy of dividing customers by sectors has the potential to be applied and generate solutions for larger geographical areas and numbers of recharging stations, and determine recharging station locations as part of planning decisions in more realistic scenarios. Full article
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33 pages, 961 KiB  
Article
Smart Charging of Electric Vehicles Considering SOC-Dependent Maximum Charging Powers
Energies 2021, 14(22), 7755; https://doi.org/10.3390/en14227755 - 18 Nov 2021
Cited by 8 | Viewed by 2173
Abstract
The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for [...] Read more.
The aim of this work is to schedule the charging of electric vehicles (EVs) at a single charging station such that the temporal availability of each EV as well as the maximum available power at the station are considered. The total costs for charging the vehicles should be minimized w.r.t. time-dependent electricity costs. A particular challenge investigated in this work is that the maximum power at which a vehicle can be charged is dependent on the current state of charge (SOC) of the vehicle. Such a consideration is particularly relevant in the case of fast charging. Considering this aspect for a discretized time horizon is not trivial, as the maximum charging power of an EV may also change in between time steps. To deal with this issue, we instead consider the energy by which an EV can be charged within a time step. For this purpose, we show how to derive the maximum charging energy in an exact as well as an approximate way. Moreover, we propose two methods for solving the scheduling problem. The first is a cutting plane method utilizing a convex hull of the, in general, nonconcave SOC–power curves. The second method is based on a piecewise linearization of the SOC–energy curve and is effectively solved by branch-and-cut. The proposed approaches are evaluated on benchmark instances, which are partly based on real-world data. To deal with EVs arriving at different times as well as charging costs changing over time, a model-based predictive control strategy is usually applied in such cases. Hence, we also experimentally evaluate the performance of our approaches for such a strategy. The results show that optimally solving problems with general piecewise linear maximum power functions requires high computation times. However, problems with concave, piecewise linear maximum charging power functions can efficiently be dealt with by means of linear programming. Approximating an EV’s maximum charging power with a concave function may result in practically infeasible solutions, due to vehicles potentially not reaching their specified target SOC. However, our results show that this error is negligible in practice. Full article
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