A case study is presented to show the feasibility and effectiveness of the put forward method. By taking advantage of the differential evolution (DE) algorithms and a newly developed crisscross optimization algorithm, a new multi-objective hybrid optimization algorithm is put forward to deal with the problem including effectively dealing with the inequality and equality constraints. The problem to be solved is a challenging multi-objective non-linear problem. A new strategy is put forward for dynamic economic/emission dispatch (DEED) with the consideration of PEVs for the purpose to shave the peak and fill the valley in this paper, and the influences brought from different loads of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) on DEED problem are discussed. Once PEVs continue the random charging behavior, there will be a higher difference of peak-valley and bigger burden on the grid. Finally, future trends and investigation occasions in this field of study are discussed.ĭue to the significant uncertainty of charging time and charging power consumption, the large increase in plug-in electric vehicles (PEVs) may create a major influences on the power system: According to people’s living habits, PEVs are basically charged during peak load periods (after work). Also, different types of uncertain variables in recent studiesĪnd mathematical methods for optimizing the relevant objective functions of EVs charging are reviewed The presence of different distributed generation (DG) such as RESs and a diverse storage system such asĮVs would have an important role in the future smart grid, thus, this paper aims to illustrate further research opportunities in this particular field. Moreover, with knowing this fact that, microgrid with

Ones that focused on stochastic energy scheduling. Pros and cons of integrating EVs to the grid and recent investigations in EV energy scheduling especially This regard, EVs coordinated energy scheduling is inevitable. Scenario with uncoordinated EV charging which can impose a huge amount of excess load on the grid. Load leveling, peak shaving, RESs support, GHG emission saving and so on. Coordinated charging of EVs can bring some benefits by itself such as voltage and frequency regulation, spinning reserve, Transportation sector and renewable energy sources (RESs) in the power generation. In the transportation sector have stimulated global trends to employ the electric vehicles (EVs) in the This factor alone is expected to increase the participation rate of GVs by a significant margin, and ensure economic load dispatch.Įnergy crisis and global warming due to fossil fuel implementation in the energy production sector and In contrast, our proposed system ensures that no GVs are at loss when discharging to the grid. Simulation results show that up to 52% of GVs might be discharging at a net loss.

With this model in place, fuel and emissions cost has been reduced, as shown using particle swarm optimization.

In this study, we present a system model, for GVs to act as distributed storage devices, which mitigates concerns over battery lifetime, and provides GV owners with a transparent cost-benefit analysis of their participation in the vehicle-to-grid discharge program. These issues reduce the participation rate of GVs in the vehicle-to-grid discharge program. However, unsupervised use of GVs as storage devices presents new challenges due to concerns over battery lifetime and cost effectiveness of this two-way power transfer. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs), have become an option as storage devices. The intermittent nature of renewable energy sources (RESs) and unpredictable variable load demands have necessitated the inclusion of energy storage devices in the smart grid environment.

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