Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

Rasouli, Behnam; Salehpour, Mohammad Javad; Wang, Jin; Kim, Gwang-jun

doi:10.3390/app9224872

Cited by 23 publications

(12 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model assumes that the EV relevant parameters obey the above distribution, e.g., leaving home in the morning to stop charging and returning home at night to start charging. Reference [65] takes load samples by MCS and estimates the total average weight by a discrete probability formula, while [66] first selects whether the EV is V2G through roulette, then generates random numbers, and finally uses MCS to produce massive random scenarios. Reference [67] discretizes EV charging duration and charging-start time, and obtains joint uncertainty by Cartesian product.…”

Section: Radial Basis Function [45]mentioning

confidence: 99%

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

View full text Add to dashboard Cite

The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

show abstract

Section: Radial Basis Function [45]mentioning

confidence: 99%

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

View full text Add to dashboard Cite

show abstract

“…In [25], the uncertainty of EV charging station load is taken into account, and a microgrid day-ahead scheduling optimization scheme based on Monte Carlo simulation is proposed. In [26], a robust scheduling strategy considering the uncertainties of the realtime charging price and EV user charging demand is proposed. In [27], an optimization model for microgrid operation considering the EV charging and energy storage integration stations is proposed.…”

Section: Orderly Charging Scheduling Strategiesmentioning

confidence: 99%

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Jiang

Xiong

2021

Advances in Civil Engineering

View full text Add to dashboard Cite

The rapid increase of the number of electric vehicles (EVs) has posed great challenges to the safe operation of the distribution network. Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. Correspondingly, the residential distribution points, EV charging stations, and renewable plants are regarded as the edge nodes. Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. Finally, the charging station formulates the personalized charging scheduling strategies according to EV’s charging plans and the charging demands in order to follow the supply plan. The simulation results show that the load peak-to-valley difference and standard deviation of the proposed algorithm are reduced by 30.13% and 16.94%, respectively, compared with the disorderly charging and discharging behavior, which has verified the effectiveness and feasibility of the proposed method.

show abstract

“…Other authors have focused their effort on developing models for the EV integration in systems with thermal plants [22] or renewable energy sources, such as wind turbines [23]. Likewise, the EVs integration in micro-grids also have been studied [24,25]. In [26,27], charging methodologies are proposed based on EV users' preferences.…”

Section: Related Workmentioning

confidence: 99%

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

et al. 2020

View full text Add to dashboard Cite

The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

show abstract

Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

Cited by 23 publications

References 51 publications

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Participation of an EV Aggregator in the Reserve Market through Chance-Constrained Optimization

Contact Info

Product

Resources

About