International audience

The main challenge in hybrid wind-battery systems is the battery cost including investment and replacement. Thus, numerous studies have been conducted on cost minimization. Previous studies often focused on the battery sizing, while the battery performance has also the significant effect on the battery replacement and in turn battery cost. In this paper, a new approach is proposed to improve the battery performance and lifetime. We proposed an extended two-set battery model with new scenarios and formulation to achieve a balance between batteries and managing their power during prediction error. In this model, shallow charge/discharge cycles are eliminated, and batteries experience complete cycles. Furthermore, the battery availability is increased. The simulation results for a case study are presented and the results show the effectiveness of the proposed approach in comparison of conventional models.

Smart charging/discharging of Plug-in Electric Vehicles (PEVs) is definitely able to bring a significant cost benefit for both EV station owners and EV owners as well. To this end, this paper develops a Mamdani-based fuzzy strategy with the inputs including the forecasted and real PV generation data, electricity price, and the instantaneous state-of-charging (SOC). Moreover, the output power of PEVs is considered as the output of the proposed strategy. In order to attain further smooth and accurate power sharing for PEVs, varied output rules are assigned commensurate with the rated power of each PEVs. In addition, another fuzzy strategy is developed using the instantaneous SOC error to establish a specified SOC for PEVs at the departure time. While two PV panel profiles are taken into account, Simulation in MATLAB/Simulink environment presents accurate and acceptable results from SOCs of PEVs, PEVs output power, prices and grid power.

This is a fact that the battery life is inversely related to the number of discharge cycles. However, these days, the bidirectional smart charging scenario of plug-in electric vehicles (PEVs) has become a hot trend, using them as a storage source at peak load or price. Therefore, this paper aims to present a simple and novel technique in predictive-based linear programming for limiting the number of discharge cycles of PEV. In this regard, a specified number of cycles is defined by the user such that the predictive algorithm tries to use these cycles in the most optimal way. In this paper, first, the effect of the discharging cycle on the battery life available in the literature is presented. Then, the photovoltaic-assisted charging station configuration and desired optimization algorithm are introduced. Following, the main novelty of this work, i.e. a linear technique for limiting the number of discharging cycles will be described. Finally, the simulation results are provided to validate the performance of the proposed method.

The complementary energy exchange between residential microgrids aims at decreasing the dependence on the main grid, reducing the size and cost of the energy storage units, and increasing energy efficiency. In this regard, the use of a power router interface to connect the microgrids to the power system is necessary for controlling bidirectional power and data flow, which will build the core of the future Energy Internet. In fact, the power router is served as an energy exchange center that enables energy flow between microgrids. One of the most important factors in determining multimicrogrid system performance is the energy routing algorithm strategy. In this article, a new routing algorithm is proposed based on the graph theory. The objective of the proposed algorithm is to minimize the overall power losses with respect to congestion and reliability. Therefore, the best set of loads, optimal sources, and no-congestion minimum loss paths are determined. Various analysis cases have been investigated to confirm the performance and flexibility of the proposed algorithm. A novel case study with multisources and multiloads is addressed, which has not been previously analyzed with the application of power routers. In addition, the developed algorithm is compared with other existing algorithms in terms of power loss minimization, different scenarios, congestion management, computation time, and complexity.