Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends

Ouramdane, Oussama; Elbouchikhi, Elhoussin; Amirat, Yassine; Gooya, Ehsan Sedgh

doi:10.3390/en14144166

Cited by 69 publications

(42 citation statements)

References 197 publications

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“…The DC network of the hybrid microgrid is a natural place for connecting photovoltaic panels [17,18], fuel cells [19], wind turbine generation sets equipped with DC generators [19], battery energy storage systems [20], and supercapacitors. The ability to integrate AC and DC networks within a hybrid microgrid can also contribute to the development of V2G technology [21][22][23].…”

Section: Review Of Knowledge In the Field Of Hybrid Ac/dc Microgridsmentioning

confidence: 99%

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Rokicki

2021

Energies

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The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

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Section: Review Of Knowledge In the Field Of Hybrid Ac/dc Microgridsmentioning

confidence: 99%

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Rokicki

2021

Energies

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“…Additionally, an optimal coordination between the charging processes needs to be guaranteed in order to significantly reduce possible negative effects on the main power grid, while improving the power supply reliability and the resource allocation efficiency (see, for instance, Ref. [3] for a recent review on the topic). All these aspects therefore motivate the present work, so that the main objective is the design of an efficient and flexible power allocation strategy able to take into account not only the battery dynamics to improve the charging process but also charging time limits to allow electric bus fleets to perform their daily service without interruptions.…”

Section: Motivation and Objectivementioning

confidence: 99%

Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants

et al. 2021

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Recently, the introduction of electric vehicles has given rise to a new paradigm in the transportation field, spurring the public transport service in the direction of using completely electric bus fleets. In this context, one of the main challenges is that of guaranteeing an optimal scheduling of the charging process, while reducing the power supply requested from the main grid, and improving the efficiency of the resource allocation. Therefore, in this paper, a power allocation strategy is proposed in order to optimize the charging of electric bus fleets, while fulfilling the limitation imposed on the maximum available power, as well as ensuring limited charging times. Specifically, relying on real bus charging scenarios, a charging optimization algorithm based on a Nonlinear Additive Increase Multiplicative Decrease (NAIMD) strategy is proposed and discussed. This approach is designed on the basis of real charging power curves related to the batteries of the considered vehicles. Moreover, the adopted NAIMD algorithm allows us to minimize the sum of charging times in the presence of saturation constraints in a distributed way and with a small amount of aggregated data sent over the communication network. Finally, an extensive simulation campaign is illustrated, showing the effectiveness of the proposed approach both in allocating the power resources and in sizing the maximum power capacity of charging plants in progress.

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“…(4) EVs that are supplying power to the grid may end at any time. These uncertainties lead to the fact that the power level (rated power) of the microgrid is not fixed, bringing about challenges for the DC/AC inverter control strategies [6]. Specifically, the commonly used inverter control techniques for the microgrid include droop control and virtual synchronous generator (VSG) control [7,8].…”

Section: Introductionmentioning

confidence: 99%

Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems

Liang

Niu

2022

Energies

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Regarding the microgrid with large-scale electric vehicle (EV) energy storage systems working at the vehicle-to-grid (V2G) mode, uncertain factors (e.g., the number of EVs feeding the microgrid shifts frequently) make the system unfixed, leading to the fact that it is difficult to precisely determine the real-time droop coefficients of the system, thereby degrading the performance of the traditional inverter control strategies that rely on the droop coefficients. To solve the problem, this paper proposes an errorless-control-targeted double control loop (DCL) technique based on robust MPC to control the microgrid with EV energy storage systems without using droop coefficients. Firstly, the structure of the DCL method is developed, with each component in the structure detailed. Compared to the traditional control strategies, the novel one regards the frequency, voltage, and currents as the control objectives instead of active/inactive power. It deserves to be mentioned that the frequency and voltage are regulated by proportional-integral controllers, while the currents are regulated by the finite control set model predictive control (FCS-MPC) method. Secondly, the impacts of system parameter uncertainties on the prediction accuracy of the FCS-MPC controller are analyzed clearly, illustrating that it is necessary to develop effective techniques to enhance the robustness of the controller. Thirdly, sliding mode observers (SMO) based on a novel hyperbolic function are constructed to detect the real-time disturbances, which can be used to generate voltage compensations by using automatic disturbance regulators. Then, the voltage compensations are adopted to establish a modified predicting plant model (PPM) used for the FCS-MPC controller. By using the proposed SMO-based disturbance detection and compensation techniques, the MPC controller gains a strong robustness against parameter uncertainties. Finally, a simulation is conducted on a microgrid system to verify the effectiveness of the proposed techniques, and the obtained results are compared with the traditional virtual synchronous machine (VSG) strategy relying on droop coefficients.

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Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends

Cited by 69 publications

References 197 publications

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants

Robust Errorless-Control-Targeted Technique Based on MPC for Microgrid with Uncertain Electric Vehicle Energy Storage Systems

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