A unit commitment model for optimal vehicle-to-grid operation in a power system

Egbue, Ona; Uko, Charles; Aldubaisi, Ali; Santi, Enrico

doi:10.1016/j.ijepes.2022.108094

Cited by 26 publications

(3 citation statements)

References 38 publications

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“…V2G technology, also referred to as Vehicle-Grid Integration (VGI), enables the transfer of surplus power from electric vehicles to the smart grid. This innovative approach aids in meeting electricity demands during the high usage periods, and serves as an additional energy source when weather-dependent renewable sources are inaccessible (Egbue et al, 2022). The system power quality is impacted by EV charging which affects different sections of the distribution grid.…”

Section: Introductionmentioning

confidence: 99%

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Srihari,

Krishnam Naidu,

Falkowski-Gilski

et al. 2024

Front. Energy Res.

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Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

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Section: Introductionmentioning

confidence: 99%

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Srihari,

Krishnam Naidu,

Falkowski-Gilski

et al. 2024

Front. Energy Res.

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“…Reference [11] proposed an approach to solving the problem of stochastic unit commitment by predicting the high penetration of solar resources without considering the outage of lines and power plants. In [12], a simple unit commitment with the EV system connected to the smart grid was proposed without considering the effects of renewable energy resources, uncertainty, and energy storage systems in the grid. In [13], a hierarchical modeling strategy based on the mixed-integer linear programming (MILP) model was proposed to solve the short-term optimal timing problem of hydro units by dividing it into two subsections, i.e., load distribution and unit commitment.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Security-constrained Unit Commitment Considering Electric Vehicles, Energy Storage Systems, and Flexible Loads with Renewable Energy Resources

Gholami Trojani,

Samiei Moghaddam,

Mohamadi Baigi

2023

Journal of Modern Power Systems and Clean Energy

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In this paper, a new formulation for modeling the problem of stochastic security-constrained unit commitment along with optimal charging and discharging of large-scale electric vehicles, energy storage systems, and flexible loads with renewable energy resources is presented. The uncertainty of renewable energy resources is considered as a scenario-based model. In this paper, a multi-objective function which considers the reduction of operation cost, no-load and startup/shutdown costs, unserved load cost, load shifting, carbon emission, optimal charging and discharging of energy storage systems, and power curtailment of renewable energy resources is considered. The proposed formulation is a mixed-integer linear programming (MILP) model, of which the optimal global solution is guaranteed by commercial solvers. To validate the proposed formulation, several cases and networks are considered for analysis, and the results demonstrate the efficiency.

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“…The target of unit commitment (UC) is to reduce the operation cost of power systems while ensuring high power supply reliability (Egbue et al, 2022 ). For many years, UC has been regarded as one of the most important control process of power systems.…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots

et al. 2022

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With the increasing penetration of renewable energy, uncertainty has become the main challenge of power systems operation. Fortunately, system operators could deal with the uncertainty by adopting stochastic optimization (SO), robust optimization (RO) and distributionally robust optimization (DRO). However, choosing a good decision takes much experience, which can be difficult when system operators are inexperienced or there are staff shortages. In this paper, a decision-making approach containing robotic assistance is proposed. First, advanced clustering and reduction methods are used to obtain the scenarios of renewable generation, thus constructing a scenario-based ambiguity set of distributionally robust unit commitment (DR-UC). Second, a DR-UC model is built according to the above time-series ambiguity set, which is solved by a hybrid algorithm containing improved particle swarm optimization (IPSO) and mathematical solver. Third, the above model and solution algorithm are imported into robots that assist in decision making. Finally, the validity of this research is demonstrated by a series of experiments on two IEEE test systems.

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A unit commitment model for optimal vehicle-to-grid operation in a power system

Cited by 26 publications

References 38 publications

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Stochastic Security-constrained Unit Commitment Considering Electric Vehicles, Energy Storage Systems, and Flexible Loads with Renewable Energy Resources

Scenario-Based Distributionally Robust Unit Commitment Optimization Involving Cooperative Interaction with Robots

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