Discharging Of Plug-in Electric Vehicles Research Articles

An optimal solution to unit commitment problem of realistic integrated power system involving wind and electric vehicles using chaotic slime mould optimizer

Plug-in electric vehicles (PEVs) could be integrated into power networks to meet rising demand as well as provide mobile storage to help the electric grid operate more efficiently. The most efficient charging and discharging of PEVs are required for the effective utilization of this potential. PEVs with poor charging management may see a spike in peak demand, resulting in increased generation. To take advantage of off-peak charging benefits and avoid load shedding, PEVs charging and discharging must be intelligently scheduled. This paper offers a solution to optimal generation scheduling and the impact of vehicle to grid (V2G) operation in the presence of wind as a renewable energy source using the chaotic slime mould algorithm (CSMA). Further, the effectiveness of the proposed simulation results for a 10-unit system incorporating V2G operation has been compared with other well-known optimization techniques such as harmony search algorithm (HAS), chemical reaction optimization(CRO), genetic algorithm and artificial neural network(GA-ANN), particle swarm optimization (PSO), and cuckoo search (CS). The comparative analysis of the results reveals a significant cost savings in power generation.

A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies

The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, the uncertainty of RGs has also exerted significant challenges to the grid operation and control. Therefore, dynamic power system scheduling to accommodate the intermittent RGs and mass roll-out of PEVs has become extremely important. In this paper, a novel power system rescheduling strategy is proposed to tackle this problem. Considering the uncertainty of the wind energy, a set of indices according to different wind power application scenarios is proposed to initiate a rescheduling scheme for power generations. In addition, a social learning particle swarm optimization algorithm based on real-value and binary parallel is proposed to schedule the output of generator units and the charging and discharging of the PEV. The effectiveness of the proposed active rescheduling framework and solving algorithm has been verified by extensive experiments considering different number of generating units and scenarios, achieving up to over 5.3% cost reduction. The experimental results have also shown that through expropriate management of the charging and discharging of PEVs would be significantly alleviate the negative impact on the grid stability caused by the intermittent wind power generations.

A novel strategy for maximization of plug-In electric vehicle’s storage utilization for grid support with consideration of customer flexibility

The increasing penetration of Plug-In-Electric Vehicles (PEV) in the existing electric distribution network needs strategic scheduling of their charging. On the other side, PEV’s storage can be utilized for grid support through Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) functionalities. In this work, a multi-agent-based PEV control strategy is proposed for load flattening with consideration of voltage regulation and customer revenue. The power system is viewed as a multi-agent-based energy management system (MAEMS) consisting of three layers: Central Agent (CA), Aggregator Agent (AA) and Distributor Agent (DA). This work mainly focuses on only DA level where load flattening using PEV’s storage is the main objective. Zones of energy need for load flattening are identified where charging and discharging of PEVs is required in order to achieve load flattening. In each zone, the total available energy from PEVs is distributed optimally among all the intervals using Water Filling Algorithm (WFA). During Optimal Enrgy Distribution (OED) using WFA, uncertainty of PEV availability for grid support has been taken into account for estimating the available PEVs energy capacity in each zone (charging and discharging). Multi-Objective Genetic Algorithm (MOGA) is used for setting optimal power transaction (OPT) between the grid and PEVs with two objectives: load flattening and voltage regulation. Pareto-front obtained from MOGA is used to decide OPT to achieve flat load profile by ensuring bus voltage limits. Further, an Adaptive Neuro-Fuzzy Inference System (ANFIS) based vehicle prioritization is implemented in order to maximize storage usage and to minimize cost of charging (CoC). The results show the impact of OED and OPT on load flattening and voltage regulation ahave been studied. Different cases are studied in ANFIS prioritization and the resultant effect on total PEV power availability and CoC are also studied.

Modelling and control of a bidirectional single-phase single-stage converter for plug-in electric vehicles

In this paper, the modelling and control of a bidirectional single-phase single-stage converter for plug-in electric vehicles (PEVs) is presented. A nonlinear sliding mode controller with boundary layer solution and a redefined sliding manifold is proposed to enforce a pseudo-resistive relation between the input voltage and current. Based on the pseudo-resistive relationship obtained between the input voltage and current, sliding mode control approach is utilised to regulate the input resistance of the converter to a desired value, hence allowing charging and discharging of PEVs. Numerical simulation results are presented that evaluate performance of the proposed modelling and feedback control scheme.

Modelling and control of a bidirectional single-phase single-stage converter for plug-in electric vehicles

In this paper, the modelling and control of a bidirectional single-phase single-stage converter for plug-in electric vehicles (PEVs) is presented. A nonlinear sliding mode controller with boundary layer solution and a redefined sliding manifold is proposed to enforce a pseudo-resistive relation between the input voltage and current. Based on the pseudo-resistive relationship obtained between the input voltage and current, sliding mode control approach is utilised to regulate the input resistance of the converter to a desired value, hence allowing charging and discharging of PEVs. Numerical simulation results are presented that evaluate performance of the proposed modelling and feedback control scheme.

A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles

Significant penetration of renewable generations (RGs) and mass roll-out of plug-in electric vehicles (PEVs) will pay a vital role in delivering the low carbon energy future and low emissions of greenhouse gas (GHG) that are responsible for the global climate change. However, it is of considerable difficulties to precisely forecast the undispatchable and intermittent wind and solar power generations. The uncoordinated charging of PEVs imposes further challenges on the unit commitment in modern grid operations. In this paper, all these factors are comprehensively investigated for the first time within a novel hybrid unit commitment framework, namely UCsRP, which considers a wide range of scenarios in renewable generations and demand side management of dispatchable PEVs load. UCsRP is however an extremely challenging optimisation problem not only due to the large scale, mixed integer and nonlinearity, but also due to the double uncertainties relating to the renewable generations and PEV charging and discharging. In this paper, a meta-heuristic solving tool is introduced for solving the UCsRP problem. A key to improve the reliability of the unit commitment is to generate a range of scenarios based on multiple distributions of renewable generations under different prediction errors and extreme predicted value conditions. This is achieved by introducing a novel multi-zone sampling method. A comprehensive study considering four different cases of unit commitment problems with various weather and season scenarios using real power system data are conducted and solved, and smart management of charging and discharging of PEVs are incorporated into the problem. Test results confirm the efficacy of the proposed framework and new solving tool for UCsRP problem. The economic effects of various scenarios are comprehensively evaluated and compared based on the average economic cost index, and several important findings are revealed.

Evaluation of plug-in electric vehicles impact on cost-based unit commitment

Incorporating plug in electric vehicles (PEVs) to power systems may address both additional demand as well as mobile storage to support electric grid spatially. Better utilization of such potential depends on the optimal scheduling of charging and discharging PEVs. Charging management malfunction of PEVs may increase the peak load which leads to additional generation. Therefore, charging and discharging of PEVs must be scheduled intelligently to prevent overloading of the network at peak hours, take advantages of off peak charging benefits and delaying any load shedding. A charging and discharging schedule of PEVs with respect to load curve variations is proposed in this paper. The proposed methodology incorporates integrated PEVs; the so-called parking lots; into the unit commitment problem. An IEEE 10-unit test system is employed to investigate the impacts of PEVs on generation scheduling. The results obtained from simulation analysis show a significant techno-economic saving.