Charging Scheduling Scheme Research Articles

A Semi-Decentralized Real-Time Charging Scheduling Scheme for Large EV Parking Lots Considering Uncertain EV Arrival and Departure

A Semi-Decentralized Real-Time Charging Scheduling Scheme for Large EV Parking Lots Considering Uncertain EV Arrival and Departure

Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes.

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node's Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely "the alternating backpack problem", and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes' energy requirements and MC's capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION

The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effective and optimal EV charging scheduling strategies are essential to avoid the adverse effects of large EV penetration in the power grid system. This paper proposes an optimal plug-in electric vehicle (PEV) charging scheduling in a distribution grid system using a hybrid algorithm approach that combines a multiverse optimizer (MVO) and also a barnacle mating optimizer (BMO) termed as HMVO-BMO. The optimization model is developed with the objective to minimize the grid power loss, considering overnight home charging. Random arrival times of PEVs are considered and charging is scheduled based on available power demand on the distribution grid. The proposed methodology is demonstrated on the IEEE 33-bus system with different PEV penetration levels. Comparisons are made between three optimization algorithm approaches, namely the standard MVO and BMO, and the proposed HMVO-BMO algorithms. The simulation results demonstrated that the proposed hybrid technique can achieve better and efficient results in terms of system power loss.

EV charging fairness protective management against charging demand uncertainty for a new “1 to N” automatic charging pile

Electric vehicles (EVs) have been popularly adopted and deployed over the past few years. However, the mismatch between EVs and charging infrastructure has become one of the major roadblocks to restricting EV promotion. Target at improve the temporal and spatial utilization rate of charging infrastructure, this paper presents a new “1 to N” automatic charging system with the combination of charging pile and special robotic arm. The connection between the charging pile and arrived EVs can be automatically switched by the robotic arm and the charging demand of EVs parking at the random parking spots could be satisfied. Based on the “1 to N” charging scenario a two-layer iterative charging scheduling strategy is proposed benefits of restraining the impact of the charging demand uncertainty while realizing the fairness in charging behavior. In addition, both the simulations and hardware in the loop experiments are implemented to verify the feasibility and real-time performance of the proposed “1 to N” charging system and charging schedule strategy.

Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

A hybrid metaheuristic to optimize electric first-mile feeder services with charging synchronization constraints and customer rejections

This paper addresses the on-demand meeting-point-based feeder electric bus routing and charging scheduling problem under charging synchronization constraints. The problem considered exhibits the structure of the location routing problem, which is more difficult to solve than many electric vehicle routing problems with capacitated charging stations. We propose to model the problem using a mixed-integer linear programming approach based on a layered graph structure. An efficient hybrid metaheuristic solution algorithm is proposed. A mixture of random and greedy partial charging scheduling strategies is used to find feasible charging schedules under the synchronization constraints. The algorithm is tested on instances with up to 100 customers and 49 bus stops/meeting points. The results show that the proposed algorithm provides near-optimal solutions within less one minute on average compared with the best solutions found by a mixed-integer linear programming solver set with a 4-hour computation time limit. A case study on a larger sized case with 1000 customers and 111 meeting points shows the proposed method is applicable to real-world situations.

A novel navigation and charging strategy for electric vehicles based on customer classification in power-traffic network

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles.

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

双碳目标下考虑居民不同意愿下充电的优化调度策略

双碳目标下考虑居民不同意愿下充电的优化调度策略

Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system

The implementation of an optimal power scheduling strategy is vital for the optimal design of the integrated electric vehicle (EV) charging station with photovoltaic (PV) and battery energy storage system (BESS). However, traditional design methods always neglect accurate PV power modeling and adopt overly simplistic EV charging strategies, which might result in suboptimal and infeasible designs. This study proposes a novel simultaneous capacity configuration and scheduling optimization model for PV/BESS integrated EV charging stations, which combines hybrid modeling for PV power prediction and optimal scheduling method for charging piles. The original model is then converted to a mixed integer linear programming problem by the Big-M method to greatly reduce the computational complexity. A typical scenario of commercial region is employed to demonstrate the effectiveness of the proposed approach. The results indicate that the approach could improve economic benefits by 15.67 % and reduce carbon emissions by 37.14 % compared with the entire grid-based mode. Besides, integrating the PV hybrid modeling method and optimal charging scheduling strategy could achieve superior capacity configurations and greatly improve the comprehensive performance of the system. It is thus illustrated that the approach could provide a promising solution for the optimal design and scheduling of PV/BESS integrated EV charging stations.

Mobile Charging Scheduling Approach for Wireless Rechargeable Sensor Networks Based on Multiple Discrete-Action Space Deep Q-Network

Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WRSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where MC is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches.

A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid

With the growing popularity of electric vehicles (EVs), it is a new challenge for the residential microgrid system to conduct charging scheduling to meet the charging demands of EVs while maximizing its profit. In this work, a safe reinforcement learning (RL)-based charging scheduling strategy is proposed to meet this challenge. We construct a complete microgrid system equipped with a large charging station and consider different types of EVs, as well as the vehicle-to-grid (V2G) mode and nonlinear charging characteristics of EVs. Subsequently, the charging scheduling problem is formulated as a constrained Markov decision process (CMDP) due to the various limitations of power and demands. To effectively capture the uncertainties of the supply side and demand side of the microgrid, a model-free RL framework is employed. However, the curse of dimensionality of the action space is inevitable as EVs increase. To solve this problem, a charging and discharging strategy based on a general ladder electricity pricing scheme is designed. Different EVs are divided into different sets according to their states under this strategy, and the agent gives control signals of different sets instead of controlling each EV individually, which effectively reduces the dimension of the action space. Subsequently, a constrained soft actor-critic (CSAC) algorithm is designed to solve the established CMDP, and a safety filter is introduced to ensure safety. In the end, a numerical case is conducted to verify the effectiveness of the proposed method.

Optimal Scheduling of Electric Vehicles Charging and Discharging Strategy Based on Real‐Time Price Considering Multi‐party Interest

In order to mitigate the negative impact brought by the large‐scale grid connection of EVs, it is highly significant to study the optimal charging scheduling strategy of EVs. In this paper, a multiobjective optimization model of ordered charging and discharging with the participation of distribution network operator (DNO), charging station operator (CSO) and EV users is established, which maximize the interest of three parties. At the same time, the real‐time price (RTP) based on the change in load rate is designed and compared with the time‐of‐use (TOU) price model to analyze the impact of both on the interests of all parties. At last, the sparrow search algorithm (SSA) is introduced and three improvements are made. The results demonstrate that different charging modes of EVs and charging price models will affect the balance of interests of the three parties, while the improved SSA performs better under different users' response rates. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Electric vehicle charging scheduling considering infrastructure constraints

The impacts of large-scale electric vehicles (EVs) charging on the power grid and the lack of charging infrastructure may directly hinder the promotion of EVs. With the limited number of charging piles and maximum instantaneous power at the charging station, how to effectively charge scheduling for EVs and reduce the charging cost for users becomes an important issue. To address this problem, we propose an EV charging scheduling strategy in response to time-of-use price. Here, the least cost of charging is set as the objective function and the limitations of charging piles number and instantaneous power of the stations are constraints. EV charging behavior characteristic is simulated using the Monte Carlo method based on 876,012 sets of historical charging data. Then, after solving the optimization problem by the adaptive genetic algorithm, each EV is assigned a specific charging pile that can meet its charging demand. The experimental results show that the proposed method can achieve better results than the comparative methods while ensuring the safe operation of charging stations. The effect of peak and valley reduction on the grid side is also realized.

Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview

The increasing electric vehicle (EV) adoption in the context of transportation electrification and carbon neutrality pursuit brings both new challenges and opportunities for all the stakeholders in EV-grid integration. To fully unleash the potentials of EVs as flexible distributed energy storage to facilitate efficient EV-grid interactions, it is imperative to predicting spatio-temporal distributions of EV charging demand, optimizing charging infrastructure layout and implementing smart charging scheduling schemes. Appropriate EV behavior modeling plays a fundamental role to realize these targets. This paper aims to provide a comprehensive review on EV behavior modeling and its applications in EV-grid integration algorithm development. Various models have been developed to describe EV usage patterns, charging decision making process and response to smart charging schemes. In particular, the existing usage pattern models including temporal, spatial and energy sub-models are expounded, and different sub-models of charging choice and response to smart charging are also presented. An EV behavior modeling paradigm is proposed to provide guidance for EV behavior model selection in different application scenarios by developing different portfolios of temporal, spatial, energy usage, charging choice and response models. Accordingly, enabling EV behavior modeling for EV charging demands prediction and smart charging scheduling is covered in details. This study provides in-depth behavioural insights and viable approaches to developing efficient EV behavior models for advancing EV-grid integration and provides perspectives towards future research directions.

Photovoltaics literature survey (no. 182)

Photovoltaics literature survey (no. 182)

Development of Smart Charging Scheduling and Power Management Strategy of a PV-ESS based Scalable EV Charging Station

This paper describes smart power management and charging scheduling strategy for a multiple port electric vehicle (EV) charging station, connected to battery storage systems and renewable energy sources. The station can charge scooters and passenger cars at different power levels. The energy management optimizes the power sources by analyzing the overall load demand, the electricity tariff, and the information provided by the EV user. The station's control system is set up to satisfy the charging demand primarily with a solar photovoltaic (PV) array and an energy storage system (ESS) as a battery. In the case of PV power generation and battery power shortage, it draws power from the grid to fulfill the charging demand. Additionally, a charging scheduling strategy is described in which the charging current setpoint is estimated by an optimization process in the local charger controller.

Electric Vehicle Charging Scheduling Strategy for Supporting Load Flattening Under Uncertain Electric Vehicle Departures

Electric Vehicle Charging Scheduling Strategy for Supporting Load Flattening Under Uncertain Electric Vehicle Departures

A cooperative EV charging scheduling strategy based on double deep Q-network and Prioritized experience replay

The rapid growth of the number of electric vehicles (EVs) has significantly increased the demand for electricity of residents, which may lead to transformer overload of distribution networks if there is no reasonable power consumption planning. In view of the coordinated charging of EVs under the photovoltaic (PV) energy connected to the power grid, a collaborative charging control strategy is proposed based on a double deep q-network with Prioritized experience replay (DDQN-PER). Specifically, Long–Short-Term Memory (LSTM) neural network is utilized to capture the uncertainties caused by power requirement, PV power, and real-time electricity price. Then, a modified DDQN approach and the corresponding reward function are applied to solve the cooperative charging problem. In addition, the introduction of a PER mechanism based on TD bias sampling in reinforcement learning alleviates the problem of reward sparsity in learning scenarios and improves the training stability and efficiency. The proposed approach can achieve collaborative scheduling of EVs, which helps reduce the charging cost, promote the consumption of PV energy and avoid transformer overload. Simulation case studies based on real data demonstrate that the proposed algorithm can reduce the charging cost by 30% and increase the PV power utilization by 10% compared to the DDQN algorithm.