Dynamic Pricing Strategy of Electric Vehicle Aggregators Based on DDPG Reinforcement Learning Algorithm

Liu, Dunnan; Wang, Weiye; Wang, Lingxiang; Jia, Heping; Shi, Minjun

doi:10.1109/access.2021.3055517

Cited by 44 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where the interval [a i , d i ] is non-empty under Assumption 2, meaning that we allow p k,i = 0 for at least one time-slot. Given the (tight) upper and lower bounds found for p k,i , we can also construct upper and lower bounds for N i=1 p k,i (which are also tight due to independence across vehicles) and derive the robust counterpart of constraint (7) as…”

Section: And [ω K ]mentioning

confidence: 99%

Towards a comprehensive framework for V2G optimal operation in presence of uncertainty

Vignali

Falsone

Ruiz

et al. 2022

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

Section: And [ω K ]mentioning

confidence: 99%

Towards a comprehensive framework for V2G optimal operation in presence of uncertainty

Vignali

Falsone

Ruiz

et al. 2022

Sustainable Energy, Grids and Networks

View full text Add to dashboard Cite

“…However, due to a large number of hyperparameters and the large search space of parameters in the DRL algorithm, it is difficult to find the optimal value. In this paper, we refer to the hyperparameters given by Liu D. et al [31] and obtain the final CDA algorithm-related parameters through preliminary experiments, as shown in Table 7. The discount coefficient of accumulative reward.…”

Section: Experimental Parameters Settingmentioning

confidence: 99%

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

Chen

Wang

2021

JMSE

View full text Add to dashboard Cite

In order to improve the horizontal transportation efficiency of the terminal Automated Guided Vehicles (AGVs), it is necessary to focus on coordinating the time and space synchronization operation of the loading and unloading of equipment, the transportation of equipment during the operation, and the reduction in the completion time of the task. Traditional scheduling methods limited dynamic response capabilities and were not suitable for handling dynamic terminal operating environments. Therefore, this paper discusses how to use delivery task information and AGVs spatiotemporal information to dynamically schedule AGVs, minimizes the delay time of tasks and AGVs travel time, and proposes a deep reinforcement learning algorithm framework. The framework combines the benefits of real-time response and flexibility of the Convolutional Neural Network (CNN) and the Deep Deterministic Policy Gradient (DDPG) algorithm, and can dynamically adjust AGVs scheduling strategies according to the input spatiotemporal state information. In the framework, firstly, the AGVs scheduling process is defined as a Markov decision process, which analyzes the system’s spatiotemporal state information in detail, introduces assignment heuristic rules, and rewards the reshaping mechanism in order to realize the decoupling of the model and the AGVs dynamic scheduling problem. Then, a multi-channel matrix is built to characterize space–time state information, the CNN is used to generalize and approximate the action value functions of different state information, and the DDPG algorithm is used to achieve the best AGV and container matching in the decision stage. The proposed model and algorithm frame are applied to experiments with different cases. The scheduling performance of the adaptive genetic algorithm and rolling horizon approach is compared. The results show that, compared with a single scheduling rule, the proposed algorithm improves the average performance of task completion time, task delay time, AGVs travel time and task delay rate by 15.63%, 56.16%, 16.36% and 30.22%, respectively; compared with AGA and RHPA, it reduces the tasks completion time by approximately 3.10% and 2.40%.

show abstract

“…Many nations are planning to phase out automobiles powered by internal combustion engines (ICEs) [2,3]. This has sparked a push to boost electric vehicle (EV) penetration into the modern vehicular fleet [4][5][6][7]. There are three main reasons for the recent adoption of EVs: affordability, increasing oil prices, and development sustainability [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The only downside of this model was its training and testing time of 1 to 4 hours, compared to 1 to 90 min for CNN and LSTM models. The study in [7] examined how adaptable and capable of regulation EV aggregators are in participating in electric power markets. Demand response and spot market dynamic pricing strategy were solved using the DDPG reinforcement learning algorithm to maximize transaction revenue.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model

2023

View full text Add to dashboard Cite

We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.

show abstract

Dynamic Pricing Strategy of Electric Vehicle Aggregators Based on DDPG Reinforcement Learning Algorithm

Cited by 44 publications

References 25 publications

Towards a comprehensive framework for V2G optimal operation in presence of uncertainty

Towards a comprehensive framework for V2G optimal operation in presence of uncertainty

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model

Contact Info

Product

Resources

About