Artículos de revistas: "ELECTRICITY MARKET,AGGREGATOR,ELECTRIC VEHICLE"

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Rashidizadeh-Kermani, Homa, Hamid Najafi, Amjad Anvari-Moghaddam y Josep Guerrero. "Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets". Energies 11, n.º 9 (12 de septiembre de 2018): 2413. http://dx.doi.org/10.3390/en11092413.

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This paper proposes the problem of decision making of an electric vehicle (EV) aggregator in a competitive market in the presence of different uncertain resources. In the proposed model, a bi-level problem is formulated where, in the upper-level, the objective of the aggregator is to maximize its expected profit through its interactions and, in the lower-level, the EV owners minimize their payments. Therefore, the objectives of the upper and the lower-level are contrary. To solve the obtained nonlinear bi-level program, Karush-Kuhn-Tucker (KKT) optimality conditions and strong duality are applied to transform the initial problem into a linear single-level problem. Moreover, to deal with various uncertainties, including market prices, EVs charge/discharge demands and the prices offered by rivals, a risk measurement tool is incorporated into the problem. The proposed model is finally applied to a test system and its effectiveness is evaluated. Simulation results show that the proposed approach has the potential to offer significant benefits to the aggregator and EV owners for better decision-making in an uncertain environment. During different situations, it is observed that with increasing risk-aversion factor, as the aggregator tries to hedge against volatilities, its purchases from day-ahead and negative balancing markets decreases significantly. However, the participation of EV aggregator in the positive balancing market increases accordingly to make more profit.

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2

Gomes, Isaias, Rui Melicio y Victor Mendes. "Comparison between Inflexible and Flexible Charging of Electric Vehicles—A Study from the Perspective of an Aggregator". Energies 13, n.º 20 (19 de octubre de 2020): 5443. http://dx.doi.org/10.3390/en13205443.

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This paper is about the problem of the management of an aggregator of electric vehicles participating in an electricity market environment. The problem consists in the maximization of the expected profit through a formulation given by a stochastic programming problem to consider the uncertainty faced by the aggregator. This uncertainty is due to the day-ahead market prices and the driving requirements of the owners of the vehicles. Depending on the consent of the owners, inflexible charging to flexible charging is considered. Thus, the aggregator can propose different profiles and charging periods to the owners of electric vehicles. Qualitatively, as expected, the more flexible the vehicle owners, the higher the expected profit. The formulation, however, offers more to the aggregator and provides the ability to quantify the influence of consent of favorable driving requirements in the expected profit, allowing the aggregator to consider rewarding the owners of vehicles with more flexibility. Case studies addressed are for comparison of the influence of owners having inflexibility, partial flexibility, or flexibility in the expected profit of the aggregator.

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3

Essiet, Ima O. y Yanxia Sun. "Maximizing Demand Response Aggregator Compensation through Optimal RES Utilization: Aggregation in Johannesburg, South Africa". Applied Sciences 10, n.º 2 (14 de enero de 2020): 594. http://dx.doi.org/10.3390/app10020594.

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This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during winter in Johannesburg is considered for the simulation using a genetic algorithm (GA). Results obtained demonstrate the effectiveness of demand response aggregators in maximizing the benefits on both sides of the electricity supply chain.

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4

Clairand, Jean-Michel. "Participation of Electric Vehicle Aggregators in Ancillary Services Considering Users’ Preferences". Sustainability 12, n.º 1 (18 de diciembre de 2019): 8. http://dx.doi.org/10.3390/su12010008.

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Growing environmental concerns have contributed to urban transit alternatives, such as Electric Vehicles (EVS). As a result, the market for EVs is growing each year, which are a solution to mitigate these concerns. All authors have read and agreed to the published version of the manuscript. Although EVs present several environmental advantages, a massive introduction of them could generate power systems issues. Several works have proposed strategies to mitigate those issues. Since EVs posses batteries with significant capacity, they could provide services to the power grid, such as ancillary services. Thus, this paper presents a methodology where EVs could participate in Secondary frequency response through an EV aggregator. Moreover, EV user’s preferences are taken into account to facilitate their participation. The case study of Quito, Ecuador is considered. The results of this methodology show that the EV aggregator has a significant potential for providing electricity market regulation services, especially in view of the use of V2G mode.

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5

Park Lee, Esther H., Zofia Lukszo y Paulien Herder. "Conceptualization of Vehicle-to-Grid Contract Types and Their Formalization in Agent-Based Models". Complexity 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3569129.

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Fuel cell electric vehicles (FCEVs) have the potential to be used as flexible power plants in future energy systems. To integrate FCEVs through vehicle-to-grid (V2G), agreements are needed between the FCEV owners and the actor that coordinates V2G on behalf of them, usually considered the aggregator. In this paper, we argue that, depending on the purpose of providing V2G and the goal of the system or the aggregator, different types of contracts are needed, not currently considered in the literature. We propose price-based, volume-based, and control-based contracts. Using agent-based modeling and simulation we show how price-based contracts can be applied for selling V2G in the wholesale electricity market and how volume-based contracts can be used for balancing the local energy supply and demand in a microgrid. The models can provide a base to explore strategies in the market and to improve performance in a system highly dependent on V2G.

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6

Ni, Feng, Linfang Yan, Ke Wu, Mengxuan Shi, Jianyu Zhou y Xia Chen. "Hierarchical Optimization of Electric Vehicle System Charging Plan Based on the Scheduling Priority". Journal of Circuits, Systems and Computers 28, n.º 13 (4 de febrero de 2019): 1950221. http://dx.doi.org/10.1142/s0218126619502219.

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Unregulated charging of a large number of electric vehicles (EVs) simultaneously may cause electricity shortages and price spikes in the power market. This paper deals with the optimization of EV charging strategy using the priority sequence. Firstly, the evaluation indices such as the schedulable interval ratio, the emergency probability and the battery losses are proposed. Then a comprehensive evaluation system for the EV scheduling priority is established by adopting the entropy weight method to incorporate multiple indices. Based on the scheduling priority obtained, a double-hierarchical optimal model is proposed, taking into account the constraints such as the demand of the EV owner. Its upper objective aims to minimize the sum of the square of deviation between the actual and the required schedulable capacity of EV aggregator over every interval. The lower one minimizes the sum of EV scheduling priority sequence over the scheduling interval. Case studies with 100 EVs show that the hierarchical optimization model can assist EV aggregator in making effective charging scheme. It is also observed that better flexibility for dispatching EVs can be achieved using multiple indices with weights.

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7

Perez-Diaz, Alvaro, Enrico Harm Gerding y Frank McGroarty. "Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators". Journal of Artificial Intelligence Research 67 (5 de marzo de 2020): 437–70. http://dx.doi.org/10.1613/jair.1.11573.

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Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited to the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark.

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8

-Guarin, J. Garcia, W. Infante, J. Ma, D. Alvarez y S. Rivera. "Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations". International Journal of Electrical and Computer Engineering (IJECE) 10, n.º 5 (1 de octubre de 2020): 5093. http://dx.doi.org/10.11591/ijece.v10i5.pp5093-5107.

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Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources of smart microgrids. These resources should support other services, for example, the supply of energy at peak hours. This study addresses the formulation of a decision matrix based on operating conditions of electric vehicles and examines economically viable alternatives for a battery swapping station. The decision matrix is implemented to manage the swapping, charging, and discharging of electric vehicles. Furthermore, this study integrates a smart microgrid model to assess the operational strategies of the aggregator, which can act like a prosumer by managing both electric vehicle battery swapping stations and energy storage systems. The smart microgrid model proposed includes elements used for demand response and generators with renewable energies. This model investigates the effect of the wholesale, local and electric-vehicle markets. Additionally, the model includes uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions, and load forecasting. This article also analyzes how both the user and the providers maximize their economic benefits with the hybrid optimization algorithm called variable neighborhood search - differential evolutionary particle swarm optimization. The strategy to organize the infrastructure of these charging stations reaches a reduction of 72% in the overall cost. This reduction percentage is obtained calculating the random solution with respect to the suboptimal solution.

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9

Zheng, Yanchong, Hang Yu, Ziyun Shao y Linni Jian. "Day-ahead bidding strategy for electric vehicle aggregator enabling multiple agent modes in uncertain electricity markets". Applied Energy 280 (diciembre de 2020): 115977. http://dx.doi.org/10.1016/j.apenergy.2020.115977.

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10

Gonzalez Vaya, Marina y Goran Andersson. "Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty". IEEE Transactions on Power Systems 30, n.º 5 (septiembre de 2015): 2375–85. http://dx.doi.org/10.1109/tpwrs.2014.2363159.

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11

Thomas, Polly y Prabhakar Karthikeyan S. "Development of a Self-Coordinated Algorithm for Demand Side Management in the Case of Aggregated Electric Vehicle in a Grid Integrated System". International Journal of Engineering & Technology 7, n.º 4.10 (2 de octubre de 2018): 182. http://dx.doi.org/10.14419/ijet.v7i4.10.20832.

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Electric Vehicles (EV) are now a days proposed to serve the electric power grid bi-directionally by means of consuming energy from grid and also by injecting back the captive energy within the EV battery upon grid requirements. Thus EV and its known variants like Battery Electric Vehicles (BEV) and Plug-in Hybrid-Electric Vehicle (PHEV) possess unique rewards compared to the conventional fossil fueled vehicle. The increasing number of EVs integration with electricity network could have a significant knock on the performance and planning of a power system especially in the demand side management. The recent studies made by the National laboratory of U S Department of Energy clearly mentions the risks involved in EV integration in terms of its peak demand profile and spinning reserve profile. The work in this paper investigates the behavior of different types of EVs & its impact on the load profile in a grid connected system in terms of EV capacity, EV charging levels and EV penetration time. The charging profile thus obtained for the above different cases clearly conveys very significant and relevant information regarding its influence on the peak time demand. The peak time period is extended to late hours respective of the different charging conditions which has a definite impact of DSM. Also, an intelligent algorithm is developed to take care of the Demand Side Management (DSM) issues. For the same, the algorithm inputs the grid as well as the vehicle parameters. The uniqueness of the proposed algorithm is in its ability to avoid the communication complexities with the Independent System Operator (ISO) & aggregator. The work is done after studying relevant market models of EVs having different similar or different characteristics.

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12

Garcia-Guarin, Julian, David Alvarez, Arturo Bretas y Sergio Rivera. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints". Energies 13, n.º 17 (3 de septiembre de 2020): 4567. http://dx.doi.org/10.3390/en13174567.

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Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

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13

Ferreira, Joao y Ana Martins. "Building a Community of Users for Open Market Energy". Energies 11, n.º 9 (4 de septiembre de 2018): 2330. http://dx.doi.org/10.3390/en11092330.

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Energy markets are based on energy transactions with a central control entity, where the players are companies. In this research work, we propose an IoT (Internet of Things) system for the accounting of energy flows, as well as a blockchain approach to overcome the need for a central control entity. This allows for the creation of local energy markets to handle distributed energy transactions without needing central control. In parallel, the system aggregates users into communities with target goals and creates new markets for players. These two approaches (blockchain and IoT) are brought together using a gamification approach, allowing for the creation and maintenance of a community for electricity market participation based on pre-defined goals. This community approach increases the number of market players and creates the possibility of traditional end users earning money through small coordinated efforts. We apply this approach to the aggregation of batteries from electrical vehicles so that they become a player in the spinning reserve market. It is also possible to apply this approach to local demand flexibility, associated with the demand response (DR) concept. DR is aggregated to allow greater flexibility in the regulation market based on an OpenADR approach that allows the turning on and off of predefined equipment to handle local microgeneration.

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14

Vagropoulos, Stylianos I. y Anastasios G. Bakirtzis. "Optimal Bidding Strategy for Electric Vehicle Aggregators in Electricity Markets". IEEE Transactions on Power Systems 28, n.º 4 (noviembre de 2013): 4031–41. http://dx.doi.org/10.1109/tpwrs.2013.2274673.

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15

Aliasghari, Parinaz, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Ali Ahmadian y Ali Elkamel. "Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market". Energies 13, n.º 7 (7 de abril de 2020): 1771. http://dx.doi.org/10.3390/en13071771.

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Selecting an appropriate contract price between electric vehicle aggregators and electric vehicle owners is an uncertain, multi-criteria decision-making issue. In addition, the results can cause strong conflict due to different aims: the optimal value for increasing electric vehicle aggregator (EVA) profit negatively affects the cost for owners. The value of the contract price can change the optimal scheduling of EVAs in the day-ahead market. Taking into consideration this context, the current paper proposes to solve the multi-objective scheduling problem of an aggregator with a goal programming approach. The presented approach sets a satisfaction level for each goal according to decision-makers’ preference. Numerical results illustrate the validity of this approach to balance different performance measures. Furthermore, optimal scheduling of electric vehicle aggregators in the day-ahead market is created.

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16

Guo, Yunpeng, Weijia Liu, Fushuan Wen, Abdus Salam, Jianwei Mao y Liang Li. "Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets". Energies 10, n.º 1 (23 de enero de 2017): 144. http://dx.doi.org/10.3390/en10010144.

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17

Foley, Aoife, Barry Tyther, Patrick Calnan y Brian Ó Gallachóir. "Impacts of Electric Vehicle charging under electricity market operations". Applied Energy 101 (enero de 2013): 93–102. http://dx.doi.org/10.1016/j.apenergy.2012.06.052.

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18

Su, Jun, T. T. Lie y Ramon Zamora. "A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market". Applied Energy 275 (octubre de 2020): 115406. http://dx.doi.org/10.1016/j.apenergy.2020.115406.

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19

Correa-Florez, Carlos, Andrea Michiorri y Georges Kariniotakis. "Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets". Energies 12, n.º 6 (15 de marzo de 2019): 1019. http://dx.doi.org/10.3390/en12061019.

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Active participation of end users in energy markets is identified as one of the major challenges in the energy transition context. One option to bridge the gap between customers and the market is aggregators of smart homes or buildings. This paper presents an optimization model from the standpoint of an aggregator of residential prosumers who have PV panels, electric water heaters, and batteries installed at home level. This aggregator participates in the day-ahead energy market to minimize operation costs by controlling the settings of flexible devices. Given that energy prices, PV production, and demand have uncertain behavior, appropriate models should be used to include these effects. In the present work, Adjustable Robust Optimization (ARO) is used to include uncertainty in the optimization model, and a comparative study of modifications to this formulation is carried out to determine its potential and limitations. The comparative analysis is performed from the point of view of average cost and risk, after performing Monte Carlo simulation. Simulations show the advantages of using an ARO framework when compared to deterministic approaches and also allow us to conclude about the advantages of using the proposed alternative formulation to find more attractive solutions for an aggregator.

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20

Han, Bing, Shaofeng Lu, Fei Xue y Lin Jiang. "Day-ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market". IET Generation, Transmission & Distribution 13, n.º 12 (18 de junio de 2019): 2517–25. http://dx.doi.org/10.1049/iet-gtd.2018.6951.

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21

Byk, Felix y Lyudmila Myshkina. "The element of digital transformation of regional network – an aggregator". E3S Web of Conferences 139 (2019): 01013. http://dx.doi.org/10.1051/e3sconf/201913901013.

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PJSC “Rosseti” is forced to find new ways of getting additional income. It’s caused by the insufficient financing of network. But a lot of known and supported by network organizations initiatives are leading to the increase of network component in electricity cost for consumers. By the way the new subject, called demand side response aggregator in the UPS, is appeared in power engineering. This problem can be partially solved by the large regional network companies in case of them being an aggregator. At the same time, the efficiency of the aggregator function increases in case of using local power systems as a subject of the retail market as a regulatory resource. It’ll let to avoid the decrease of the power consumption and transmitted power volume, to increase the reliability and the quality of power supply due to reduce of power losses in network. It’s obvious, that the appropriate automation is needed as an element of the digital transformation of the electric network.

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22

Ding, Zhaohao, Ying Lu, Kexing Lai, Ming Yang y Wei-Jen Lee. "Optimal coordinated operation scheduling for electric vehicle aggregator and charging stations in an integrated electricity-transportation system". International Journal of Electrical Power & Energy Systems 121 (octubre de 2020): 106040. http://dx.doi.org/10.1016/j.ijepes.2020.106040.

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23

Zhang, Xian, Jiefeng Hu, Huaizhi Wang, Guibin Wang, Ka Wing Chan y Jing Qiu. "Electric Vehicle Participated Electricity Market Model Considering Flexible Ramping Product Provisions". IEEE Transactions on Industry Applications 56, n.º 5 (septiembre de 2020): 5868–79. http://dx.doi.org/10.1109/tia.2020.2995560.

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24

Jin, Chenrui, Jian Tang y Prasanta Ghosh. "Optimizing Electric Vehicle Charging With Energy Storage in the Electricity Market". IEEE Transactions on Smart Grid 4, n.º 1 (marzo de 2013): 311–20. http://dx.doi.org/10.1109/tsg.2012.2218834.

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25

Clairand, Jean-Michel, Javier Rodríguez-García y Carlos Álvarez-Bel. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation". Energies 11, n.º 11 (16 de noviembre de 2018): 3188. http://dx.doi.org/10.3390/en11113188.

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Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both aWorld Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO2 emissions

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Tseng, Huei Ru. "Threshold-Based Privacy-Preserving Key Management Scheme for Vehicle-to-Grid Networks". Applied Mechanics and Materials 479-480 (diciembre de 2013): 978–82. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.978.

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The concept of vehicle-to-grid (V2G) is that electric vehicles (EVs) communicate with the smart grid to sell demand response services by delivering electricity into the grid. Due to the scale of the network, the speed of the vehicles, their geographic positions, and the very sporadic connectivity between them, V2G communications have the crucial requirements of fast session key establishment. In this paper, we propose a threshold-based privacy-preserving key management scheme for V2G networks, which utilizes the threshold-based secret sharing and symmetric key technique to protect the identities of the EV owners and to establish the shared session key between the aggregator and the vehicle. The proposed scheme can achieve the property of identity privacy, confidentiality of the communications, and known-key security.

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27

Jeon, Wooyoung, Sangmin Cho y Seungmoon Lee. "Estimating the Impact of Electric Vehicle Demand Response Programs in a Grid with Varying Levels of Renewable Energy Sources: Time-of-Use Tariff versus Smart Charging". Energies 13, n.º 17 (24 de agosto de 2020): 4365. http://dx.doi.org/10.3390/en13174365.

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An increase in variable renewable energy sources and soaring electricity demand at peak hours undermines the efficiency and reliability of the power supply. Conventional supply-side solutions, such as additional gas turbine plants and energy storage systems, can help mitigate these problems; however, they are not cost-effective. This study highlights the potential value of electric vehicle demand response programs by analyzing three separate scenarios: electric vehicle charging based on a time-of-use tariff, smart charging controlled by an aggregator through virtual power plant networks, and smart control with vehicle-to-grid capability. The three programs are analyzed based on the stochastic form of a power system optimization model under two hypothetical power system environments in Jeju Island, Korea: one with a low share of variable renewable energy in 2019 and the other with a high share in 2030. The results show that the cost saving realized by the electric vehicle demand response program is higher in 2030 and a smart control with vehicle-to-grid capability provides the largest cost saving. When the costs of implementing an electric vehicle demand response are considered, the difference in cost saving between the scenarios is reduced; however, the benefits are still large enough to attract customers to participate.

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28

Rücker, Fabian, Michael Merten, Jingyu Gong, Roberto Villafáfila-Robles, Ilka Schoeneberger y Dirk Uwe Sauer. "Evaluation of the Effects of Smart Charging Strategies and Frequency Restoration Reserves Market Participation of an Electric Vehicle". Energies 13, n.º 12 (16 de junio de 2020): 3112. http://dx.doi.org/10.3390/en13123112.

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The emergence of electric vehicles offers the opportunity to decarbonize the transportation and mobility sector. With smart charging strategies and the use of electricity generated from renewable sources, electric vehicle owners can reduce their electricity bill as well as reduce their carbon footprint. We investigated smart charging strategies for electric vehicle charging at household and workplace sites with photovoltaic systems. Furthermore, we investigated the participation of an electric vehicle in the provision of positive automatic frequency restoration reserve (aFRR) in Germany from 30 October 2018 to 31 July 2019. We find that the provision of positive aFRR in Germany returns a positive net return. The positive net return is, however, not sufficient to cover the current investment cost for a necessary control unit. For home charging, we find that self-sufficiency rates of up to 48.1% and an electricity cost reduction of 17.6% for one year can be reached with unidirectional smart charging strategies. With bidirectional strategies, self-sufficiency rates of up to 56.7% for home charging and electricity cost reductions of up to 26.1% are reached. We also find that electric vehicle (EV) owners who can charge at their workplace can reduce their electricity cost further. The impact of smart charging strategies on battery aging is also discussed.

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29

Liu, Weijia, Fushuan Wen, Zhaoyang Dong y Ivo Palu. "Development of robust participation strategies for an aggregator of electric vehicles in multiple types of electricity markets". Energy Conversion and Economics 1, n.º 2 (27 de octubre de 2020): 104–23. http://dx.doi.org/10.1049/enc2.12010.

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30

Wang, Yue, David Infield y Simon Gill. "Smart charging for electric vehicles to minimise charging cost". Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 231, n.º 6 (23 de enero de 2017): 526–34. http://dx.doi.org/10.1177/0957650916688409.

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This paper assumes a smart grid framework where the driving patterns for electric vehicles are known, time variations in electricity prices are communicated to householders, and data on voltage variation throughout the distribution system are available. Based on this information, an aggregator with access to this data can be employed to minimise electric vehicles charging costs to the owner whilst maintaining acceptable distribution system voltages. In this study, electric vehicle charging is assumed to take place only in the home. A single-phase Low Voltage (LV) distribution network is investigated where the local electric vehicles penetration level is assumed to be 100%. Electric vehicle use patterns have been extracted from the UK Time of Use Survey data with a 10-min resolution and the domestic base load is generated from an existing public domain model. Apart from the so-called real time price signal, which is derived from the electricity system wholesale price, the cost of battery degradation is also considered in the optimal scheduling of electric vehicles charging. A simple and effective heuristic method is proposed to minimise the electric vehicles’ charging cost whilst satisfying the requirement of state of charge for the electric vehicles’ battery. A simulation in OpenDSS over a period of 24 h has been implemented, taking care of the network constraints for voltage level at the customer connection points. The optimisation results are compared with those obtained using dynamic optimal power flow.

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31

Lim, Keumju, Justine Jihyun Kim y Jongsu Lee. "Forecasting the future scale of vehicle to grid technology for electric vehicles and its economic value as future electric energy source: The case of South Korea". Energy & Environment 31, n.º 8 (7 de enero de 2020): 1350–66. http://dx.doi.org/10.1177/0958305x19898283.

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With the world seeking ways to cope with climate change, the interest in and demand for electric vehicles are increasing as part of the efforts to resolve the issue of fine dust, especially in South Korea. The Korean government has consistently announced plans to promote electric vehicles as a means of transportation by providing benefits such as subsidies for electric vehicle purchase and expansion of charging infrastructure. Meanwhile, as electric vehicles continue to grow in number, the energy industry has become attentive to its role as a resource for power generation through vehicle to grid technology. This study analyzes electric vehicle consumer preferences using the discrete choice experiment (DCE) and found that there exists a clear nested structure in Korean consumers’ choice of vehicle. The study also estimates the amount of vehicle to grid electricity supply in the power market and calculates not only national but also individual economic benefit of electric vehicle owners participating in vehicle to grid services based on the estimated amount of electricity supplied. The results of scenario analysis indicate that the estimated electric vehicle supply in Korea will be about 560,000 units cumulatively and that the vehicle to grid electric vehicle power supply scale will reach 1.81 GW by 2030. The estimation shows that the economic benefit of vehicle to grid at the national power market level is 50.9 billion KRW per year, while the economic benefit at an individual level (per vehicle) is 104,151 KRW.

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Tseng, Huei Ru. "Cryptanalysis and Improvement of a Unique Batch Authentication Protocol for Vehicle-to-Grid Communications". Applied Mechanics and Materials 284-287 (enero de 2013): 3380–84. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3380.

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The concept of vehicle-to-grid (V2G) is that electric vehicles (EVs) communicate with the smart grid to sell demand response services by delivering electricity into the grid. By letting EVs discharge during peak hours and charge during off-peak hours, V2G networks could bring numerous social and technical benefits to the smart grid. Due to the scale of the network, the speed of the vehicles, their geographic positions, and the very sporadic connectivity between them, V2G communications have the crucial requirements of fast authentication. In 2011, Guo et al. proposed a unique batch authentication protocol for V2G communications. There are three parties in V2G communications, including an aggregator, a smart grid control centre, and individual EVs. The proposed idea is that instead of verifying each packet for each vehicle, the aggregator verifies the received batch of packets with only one signature verification. They used DSA signature for the batch authentication and claimed their protocol is strong enough to defend against security attacks. In this paper, we investigate the security of Guo et al.’s protocol. More precisely, we show that any attackers can easily forge signatures satisfying the batch verification criterion without the knowledge of the signer’s private key. To remedy the security flaw of Guo et al.’s protocol, we proposed a simple and secure improvement of Guo et al.’s protocol. The key point of the improved protocol is that we make multiple signatures in order. The attacker or the dishonest signer cannot transpose these digital signatures such that the aggregator passes the validation of the batch verifying multiple digital signatures. Based on Guo et al.’s protocol, the security of our proposed protocol is the same as that of their protocol except that our improved protocol has no security flaw of their protocol. Therefore, the improved protocol is secure to detect forged multiple signatures.

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33

Jursova, Simona, Dorota Burchart-Korol y Pavlina Pustejovska. "Carbon Footprint and Water Footprint of Electric Vehicles and Batteries Charging in View of Various Sources of Power Supply in the Czech Republic". Environments 6, n.º 3 (26 de marzo de 2019): 38. http://dx.doi.org/10.3390/environments6030038.

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In the light of recent developments regarding electric vehicle market share, we assess the carbon footprint and water footprint of electric vehicles and provide a comparative analysis of energy use from the grid to charge electric vehicle batteries in the Czech Republic. The analysis builds on the electricity generation forecast for the Czech Republic for 2015–2050. The impact of different sources of electricity supply on carbon and water footprints were analyzed based on electricity generation by source for the period. Within the Life Cycle Assessment (LCA), the carbon footprint was calculated using the Intergovernmental Panel on Climate Change (IPCC) method, while the water footprint was determined by the Water Scarcity method. The computational LCA model was provided by the SimaPro v. 8.5 package with the Ecoinvent v. 3 database. The functional unit of study was running an electric vehicle over 100 km. The system boundary covered an electric vehicle life cycle from cradle to grave. For the analysis, we chose a vehicle powered by a lithium-ion battery with assumed consumption 19.9 kWh/100 km. The results show that electricity generated to charge electric vehicle batteries is the main determinant of carbon and water footprints related to electric vehicles in the Czech Republic. Another important factor is passenger car production. Nuclear power is the main determinant of the water footprint for the current and future electric vehicle charging, while, currently, lignite and hard coal are the main determinants of carbon footprint.

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34

Rauma, Kalle, Alexander Funke, Toni Simolin, Pertti Järventausta y Christian Rehtanz. "Electric Vehicles as a Flexibility Provider: Optimal Charging Schedules to Improve the Quality of Charging Service". Electricity 2, n.º 3 (24 de junio de 2021): 225–43. http://dx.doi.org/10.3390/electricity2030014.

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The sub-aggregation of electric vehicles provides significant potential to power systems in the form of ancillary services. This means with smart charging it is possible to shift loads from peak to off-peak hours. For the flexibility from privately owned electric vehicles to be offered to the electricity market, customer participation is crucial; however, the impacts of sub-aggregation on customers have not been studied thoroughly. In this paper, charging data covering over 80,000 real-world charging sessions from various commercial charging sites are introduced and the charging characteristics are analyzed. Importantly, a method for finding an optimal pattern for load reduction with the least impact on customers is presented. We suggest an optimal schedule for load reduction from the customer viewpoint at different types of charging sites, including public car parks, offices, residential sites, and shopping centers. The findings indicate that residential and office charging sites offer the greatest potential for load reduction with the least impact on customers. The most flexibility is available during peak charging hours, which on average are at 08:00 at car parks, 07:30 at office sites, 19:00 at residential sites, and 10:00 at shopping centers.

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35

Hanauer, Dieter. "Mode 2 Charging—Testing and Certification for International Market Access". World Electric Vehicle Journal 9, n.º 2 (20 de julio de 2018): 26. http://dx.doi.org/10.3390/wevj9020026.

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This paper provides an analysis of the current IEC 62752 standard. Establishing measures to protect against electric shock is one of the major tasks in the development of safe electric appliances. For electric vehicles this is very important too, because they are movable and in a public area most of the time. Even during recharge of the batteries, it is very likely that the electric vehicle is accessible to playing children or other people. IEC 62752 is a standard for a product which connects the electric vehicle with a standard household socket. This connection is required to provide electricity to the on-board charger of the electric vehicle and is called Mode 2 charging. In this article, the complexity of worldwide standardization for eMobility products is shown. Because the development of these products is still going on, some special requirements of IEC 62752 are explained, and some unique tests are described to help development engineers to design a safe, reliable, and durable product.

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Falkoni, Anamarija, Antun Pfeifer y Goran Krajačić. "Vehicle-to-Grid in Standard and Fast Electric Vehicle Charging: Comparison of Renewable Energy Source Utilization and Charging Costs". Energies 13, n.º 6 (22 de marzo de 2020): 1510. http://dx.doi.org/10.3390/en13061510.

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Croatia aims to achieve 10% of its energy production from the renewable energy sources in the total energy consumption in the transport sector. One of the ways to achieve this goal is by the use of electric vehicles. This work comparatively analyses the financial and social aspects of vehicle-to-grid charging in standard and fast charging mode, their impact on the renewable electricity production and the total electricity consumption regulated through variable electricity prices. Data were taken for the wider urban area of the Dubrovnik region. The assumption is that the Dubrovnik region will be self-sufficient by the year 2050 with 100% renewable electricity production and that all conventional vehicles will be replaced by electric vehicles. This work aims to show that the fast charging based on 10 min time steps offers more opportunities for flexibility and utilization of renewable generation in the energy system than the standard charging based on hourly time step. The results of this work showed the opposite, where in most of the scenarios standard charging provided better results. Replacement of the existing two tariff model in electricity prices with variable electricity prices contributes to the stability of the energy system, providing better regulation of charging and higher opportunities for renewable electricity utilization in standard and fast charging and reduction of charging costs. According to the financial aspects, fast charging is shown to be more expensive, but for the social aspects, it provides electric vehicles with more opportunities for better competition in the market.

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37

Soares, João, Tiago Pinto, Fernando Lezama y Hugo Morais. "Survey on Complex Optimization and Simulation for the New Power Systems Paradigm". Complexity 2018 (1 de agosto de 2018): 1–32. http://dx.doi.org/10.1155/2018/2340628.

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This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

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38

Chen, Lidan, Yao Zhang y Antonio Figueiredo. "Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network". Energies 12, n.º 10 (23 de mayo de 2019): 1981. http://dx.doi.org/10.3390/en12101981.

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Electric vehicles (EVs) can be regarded as a kind of demand response (DR) resource. Nevertheless, the EVs travel behavior is flexible and random, in addition, their willingness to participate in the DR event is uncertain, they are expected to be managed and utilized by the EV aggregator (EVA). In this perspective, this paper presents a composite methodology that take into account the dynamic road network (DRN) information and fuzzy user participation (FUP) for obtaining spatio-temporal projections of demand response potential from electric vehicles and the electric vehicle aggregator. A dynamic traffic network model taking over the traffic time-varying information is developed by graph theory. The trip chain based on housing travel survey is set up, where Dijkstra algorithm is employed to plan the optimal route of EVs, in order to find the travel distance and travel time of each trip of EVs. To demonstrate the uncertainties of the EVs travel pattern, simulation analysis is conducted using Monte Carlo method. Subsequently, we suggest a fuzzy logic-based approach to uncertainty analysis that starts with investigating EV users’ subjective ability to participate in DR event, and we develop the FUP response mechanism which is constructed by three factors including the remaining dwell time, remaining SOC, and incentive electricity pricing. The FUP is used to calculate the real-time participation level of a single EV. Finally, we take advantage of a simulation example with a coupled 25-node road network and 54-node power distribution system to demonstrate the effectiveness of the proposed method.

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39

Holland, Stephen P., Erin T. Mansur y Andrew J. Yates. "The Electric Vehicle Transition and the Economics of Banning Gasoline Vehicles". American Economic Journal: Economic Policy 13, n.º 3 (1 de agosto de 2021): 316–44. http://dx.doi.org/10.1257/pol.20200120.

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Electric vehicles have a unique potential to transform personal transportation. We analyze this transition with a dynamic model capturing falling costs of electric vehicles, decreasing pollution from electricity, and increasing vehicle substitutability. Our calibration to the US market shows a transition from gasoline vehicles is not optimal at current substitutability: a gasoline vehicle production ban would have large deadweight loss. At higher substitutability, a ban can reduce deadweight loss from vehicle mix and adoption timing inefficiencies. A cumulative gasoline vehicle production quota has smaller deadweight loss, and an electric vehicle purchase subsidy is more robust to regulator misperceptions about substitutability. (JEL H23, L51, L62, L94, Q53)

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40

Kern, Timo, Patrick Dossow y Serafin von Roon. "Integrating Bidirectionally Chargeable Electric Vehicles into the Electricity Markets". Energies 13, n.º 21 (6 de noviembre de 2020): 5812. http://dx.doi.org/10.3390/en13215812.

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Replacing traditional internal combustion engine vehicles with electric vehicles (EVs) proves to be challenging for the transport sector, particularly due to the higher initial investment. As EVs could be more profitable by participating in the electricity markets, the aim of this paper is to investigate revenue potentials when marketing bidirectionally chargeable electric vehicles in the spot market. To simulate a realistic marketing behavior of electric vehicles, a mixed integer linear, rolling horizon optimization model is formulated considering real trading times in the day-ahead and intraday market. Results suggest that revenue potentials are strongly dependent on the EV pool, the user behavior and the regulatory framework. Modeled potential revenues of EVs of current average size marketed with 2019 German day-ahead prices are found to be at around 200 €/EV/a, which is comparable to other findings in literature, and go up to 500 €/EV/a for consecutive trading in German day-ahead and intraday markets. For future EVs with larger batteries and higher efficiencies, potential revenues for current market prices can reach up to 1300 €/EV/a. This study finds that revenues differ widely for different European countries and future perspectives. The identified revenues give EV owners a clear incentive to participate in vehicle-to-grid use cases, thereby increasing much needed flexibility for the energy system of the future.

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41

Maeng, Kyuho, Sungmin Ko, Jungwoo Shin y Youngsang Cho. "How Much Electricity Sharing Will Electric Vehicle Owners Allow from Their Battery? Incorporating Vehicle-to-Grid Technology and Electricity Generation Mix". Energies 13, n.º 16 (17 de agosto de 2020): 4248. http://dx.doi.org/10.3390/en13164248.

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Global trends and prospects of environmentally friendly transportation have helped to popularize electric vehicles (EVs). With the spread of EVs, vehicle-to-grid (V2G) technology is gaining importance for its role in connecting the electricity stored in the battery of EVs to a grid-like energy storage system (ESS). Electricity generation mix and battery for V2G energy storage have a decisive effect on the stabilization of a V2G system, but no attempt has been made. Therefore, this study analyzes consumer preference considering the electricity generation mix and battery for the V2G. We conduct a conjoint survey of a 1000 South Koreans and employ the multiple discrete-continuous extreme value model. The results show that drivers prefer plug-in hybrid- and battery EVs to other vehicles. Additionally, findings show that driver’s utility changes at 27.9% of the battery allowance for V2G system and it becomes positive after 55.7%. Furthermore, we conduct a scenario analysis considering the electricity generation mix (more traditional vs. renewable) and battery allowance. Based on this analysis, we suggest some policies and corporate strategies to support the success of the V2G market depending on energy policies and battery allowance level.

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42

Wu, Meng Jie, Qiang Qiang Liao, Guo Ding Zhou, Xiao Lin Liu y Shen Yang Zhang. "Technical Economic Analysis on V2G Mode Shifting Peak Load". Advanced Materials Research 860-863 (diciembre de 2013): 1105–9. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.1105.

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As the electric vehicles (EVs) such as hybrid, battery, and fuel cell cars have become common in market, there is an opportunity to apply vehicle-to-grid (V2G) power. This article introduces the concept of V2G and uses equations to calculate the capacity for grid power from defined electric drive vehicles. Further this paper evaluates the revenue and costs for V2G mode from selling electricity into market. The results show that developing V2G mode provides electric vehicles owners and electric utilities additional revenue, stability and reliability of the electric grid, lower-cost storage for intermittent power resources.

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43

Yang, Shengjie, Jiangang Yao, Tong Kang y Xiangqian Zhu. "Dynamic operation model of the battery swapping station for EV (electric vehicle) in electricity market". Energy 65 (febrero de 2014): 544–49. http://dx.doi.org/10.1016/j.energy.2013.11.010.

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Ligen, Yorick, Heron Vrubel y Hubert Girault. "Mobility from Renewable Electricity: Infrastructure Comparison for Battery and Hydrogen Fuel Cell Vehicles". World Electric Vehicle Journal 9, n.º 1 (24 de mayo de 2018): 3. http://dx.doi.org/10.3390/wevj9010003.

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This work presents a detailed breakdown of the energy conversion chains from intermittent electricity to a vehicle, considering battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs). The traditional well-to-wheel analysis is adapted to a grid to mobility approach by introducing the intermediate steps of useful electricity, energy carrier and on-board storage. Specific attention is given to an effective coupling with renewable electricity sources and associated storage needs. Actual market data show that, compared to FCEVs, BEVs and their infrastructure are twice as efficient in the conversion of renewable electricity to a mobility service. A much larger difference between BEVs and FCEVs is usually reported in the literature. Focusing on recharging events, this work additionally shows that the infrastructure efficiencies of both electric vehicle (EV) types are very close, with 57% from grid to on-board storage for hydrogen refilling stations and 66% for fast chargers coupled with battery storage. The transfer from the energy carrier at the station to on-board storage in the vehicle accounts for 9% and 12% of the total energy losses of these two modes, respectively. Slow charging modes can achieve a charging infrastructure efficiency of 78% with residential energy storage systems coupled with AC chargers.

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45

Vardanyan, Yelena y Henrik Madsen. "Optimal Coordinated Bidding of a Profit Maximizing, Risk-Averse EV Aggregator in Three-Settlement Markets Under Uncertainty". Energies 12, n.º 9 (9 de mayo de 2019): 1755. http://dx.doi.org/10.3390/en12091755.

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This paper develops a two-stage stochastic and dynamically updated multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an electric vehicle (EV) aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at risk (T-CVaR) is applied to model the risk of trading in different markets. The objective of two-stage SD-MILP is modeled as a convex combination of the expected profit and the T-CVaR hourly risk measure. When day-ahead, intra-day and real-time market prices and fleet mobility are uncertain, the proposed two-stage SD-MILP model yields optimal EV charging/discharging plans for day-ahead, intra-day and real-time markets at per device level. The degradation costs of EV batteries are precisely modeled. To reflect the continuous clearing nature of the intra-day and real-time markets, rolling planning is applied, which allows re-forecasting and re-dispatching. The proposed two-stage SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs. Furthermore, the model statistics and computation time are recorded while simulating the developed algorithm with 5000 EVs.

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46

Wu, Hongyu, Mohammad Shahidehpour, Ahmed Alabdulwahab y Abdullah Abusorrah. "A Game Theoretic Approach to Risk-Based Optimal Bidding Strategies for Electric Vehicle Aggregators in Electricity Markets With Variable Wind Energy Resources". IEEE Transactions on Sustainable Energy 7, n.º 1 (enero de 2016): 374–85. http://dx.doi.org/10.1109/tste.2015.2498200.

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47

Gope, Sadhan, Arup Kumar Goswami y Prashant Kumar Tiwari. "Impact Assessment of Bi-directional Solar Electric Vehicle in Competitive Electricity Market under Congested Transmission System". Procedia Computer Science 143 (2018): 653–62. http://dx.doi.org/10.1016/j.procs.2018.10.451.

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48

Kozlovskyi, Serhii, Vladyslav Bolhov, Allam Yousuf, Albina Batechko, Larisa Hlushchenko y Nataliia Vitka. "Marketing analysis of the electromobile market as a factor in the innovation of the national economy". Innovative Marketing 15, n.º 1 (18 de marzo de 2019): 42–53. http://dx.doi.org/10.21511/im.15(1).2019.04.

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The marketing development of the electric vehicle market can be considered as a key element of innovative changes in the national economy. Object of research is the development of the electric vehicle market. The purpose of this article is the theoretical substantiation and development of guidelines for determining the rating of countries by the level of development of this market as a determining factor in the innovative development of the national economy. In the study, expert survey methods, logical generalization and comparison of results, statistical analysis and graphical presentation of results were used. The study conducted a marketing analysis of sales and production of electric cars, government programs to stimulate them, existing rating indicators of countries for the development of the electric vehicle market, and based on the results, a method for determining the integrated rating indicator of national economy innovativeness was developed. The article established that in countries that are world economic leaders, the growing interest of consumers and manufacturers of cars to electric vehicles, which is actively supported by government programs and incentives. It is proposed to use a integrated indicator of innovative development of the national economy, which are directly related to the market of electric vehicles. This indicator consists of the following factors: share of investments in the development of branches related to the national production of electric vehicles; level of growth of electric transport in the country’s total fleet; the share of electric vehicles in total number of cars produced in the country; level of increase in the amount of electricity produced based on RES in total; infrastructure development; level of state support for the market. The use of these integrated factors in marketing analysis will determine the level of the world leader in the country, its innovative development.

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49

Lee, Jong-Uk, Young-Min Wi, Youngwook Kim y Sung-Kwan Joo. "Optimal Coordination of Charging and Frequency Regulation for an Electric Vehicle Aggregator Using Least Square Monte-Carlo (LSMC) with Modeling of Electricity Price Uncertainty". Journal of Electrical Engineering and Technology 8, n.º 6 (1 de noviembre de 2013): 1269–75. http://dx.doi.org/10.5370/jeet.2013.8.6.1269.

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50

Zagrajek, Krzysztof, Józef Paska, Łukasz Sosnowski, Konrad Gobosz y Konrad Wróblewski. "Framework for the Introduction of Vehicle-to-Grid Technology into the Polish Electricity Market". Energies 14, n.º 12 (20 de junio de 2021): 3673. http://dx.doi.org/10.3390/en14123673.

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Vehicle-to-grid (V2G) technology is one of the advanced solutions that uses electric vehicles (EV) to balance electricity demand in the power system. It can be particularly useful in analyzing and then mitigating the risk of not delivering electricity to the end user. Therefore, it is necessary to analyze the possibility of operation of this technology in the legal framework. The article presents the analysis of the legal status in Poland, referring to the documents of the European Union and domestic legislation. Potential changes in Polish energy law that could facilitate the implementation of V2G technology are also proposed. In addition, the authors suggested the principles for the use of this technology, formulating a mechanism called the V2G Program. Within this Program, the V2G Service was defined and a business model of its implementation by a participant of the V2G Program (uEV) was presented. In addition, an uEV selection algorithm is provided so that the mathematical model of the V2G Service can be validated. Based on the performed simulations, it can be concluded that the implementation of the V2G Program requires significant changes in the Polish energy law, but it is feasible from the technical point of view.

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