Scheduling Of Electric Vehicles Research Articles

Fast frequency response constrained electric vehicle scheduling for low inertia power systems

Reduction in System Inertia (SI) and Primary Frequency Response (PFR) driven by the large-scale integration of uncertain and variable Renewable Energy (RE) sources disturbs the post-fault frequency dynamics. This necessitates the identification of potential solutions that can restrict high Rate of Change of Frequency (RoCoF). Electric vehicles (EVs), due to their fast response capability and instantaneous power delivery, are a viable source for the Fast Frequency Response (FFR) service. Potential assessment of EVs for FFR service is required to restrict high RoCoF and maintain secure and reliable system operation in real-time. The main contribution of this work is to assess the potential of EVs for FFR support through both Grid-to-vehicle (G2V) and Vehicle-to-grid (V2G) modes. In this work challenges like EVs aggregation, related netload uncertainty characterization, and post-fault frequency dynamics are modelled in a stochastic security-constrained scheduling framework. Further, a case study is carried out to investigate the role of EVs and their techno-economic impact on the overall system: (a) only in dumb charging mode and (b) participating in FFR service by scheduling aggregated EVs for both G2V and V2G with different level of RE integration. Numerical results indicate that the aggregated EVs scheduling can provide FFR support of around 71%, by simultaneously reducing system operating cost by 62% and 87% reduction in RE curtailment.

Technology balancing for reliable EV uptake in distribution grids: An Australian case study

The emergence of new technologies, such as photovoltaic (PV) solar systems, household Battery Energy Storage Systems (BESS) and electric vehicles (EVs), has introduced challenges for the Distribution Network Operators (DNOs) in the operation of power distribution networks. Uncontrolled renewable energy generation and storage, and EV charging can cause over/under voltage problems. Recently, there has been much effort, from control and scheduling of BESSs and EVs to PV curtailment, to address these issues. However, the knowledge about how much penetration of these technologies can be well-tolerated by the grid when all charging/discharging are uncontrolled, would still be of interest to DNOs. There are many depending factors such as customer consumption profiles, grid conditions and network constraints. In this paper, we propose a framework to determine the critical balance between PV, BESS and EV in a distribution grid. Using real data from 4000 customers in Victoria, Australia, we find that in the absence of advanced control and scheduling algorithms, the best approach to prevent voltage violation in the grid is to keep the installation of PV and BESS balanced with the EV penetration. We also show that power export to the grid by EVs using the vehicle-to-home (V2H) technology may contribute to keeping the grid voltage in the standard range if 40%–80% of EVs are in the discharge mode in critical peak times. While complete coordination of PV, EV and BESS has not yet been fully realized in many networks, our methodology provides a framework for DNOs to perform optimised planning.

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

SummaryIn modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time‐consuming process. In this article, an effective charge scheduling model is devised using the fractional social sea lion optimization (Fr‐SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator‐based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr‐SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr‐SSLO model is designed by integrating fractional calculus (FC) and sea lion optimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr‐SSLO approach. Moreover, various fitness functions are also considered for computing better performance.

Electric vehicle scheduling based on stochastic trip time and energy consumption

The vehicle scheduling problem (VSP) is profound in public transit planning. Especially electric VSP (EVSP) occupies a hot topic, as electric vehicles have enjoyed a fast-expanding market share in the bus market in recent years. It used to be solved regardless of the dreadful variability of traffic by setting the fixed trip times and further deterministic energy consumption, then the robustness of resulting schedules is compromised. Therefore, EVSP based on stochastic trip time and energy consumption is studied. We propose a probabilistic model for EVSP based on the probability density function (PDF) of trip time to minimize the fleet size and operating cost and maximize on-time performance. For modeling, we define the time compatibility and energy compatibility of trips by PDF. Based on the time compatibility, the lower bound of the fleet size is derived. As energy compatibility is a non-inherent attribute of trips, we next develop an adaptive large neighborhood search (ALNS) heuristic for EVSP. Experiments demonstrate that ALNS may considerably surpass the large neighborhood search algorithm for solving EVSP, and the probabilistic model may lead to more robust schedules without increasing fleet size.

Deep Reinforcement Learning for Charging Scheduling of Electric Vehicles Considering Distribution Network Voltage Stability.

The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization.

Energy-efficient driving scheduling for heterogeneous electric vehicles with consideration of overtaking

Applying individual eco-driving methods within a group of electric vehicles (EVs) is usually restricted by a preceding vehicle because the optimal velocity is different by type of EV. Driving with the same velocity for different types of vehicles is not a fundamental solution because of inefficiency. Overtaking a preceding vehicle allows enhancement of the driving efficiency, though it should be used carefully because energy consumption by the overtaking depends greatly on given conditions: time constraint for overtaking, the distance between intersections, vehicle type, and overtaking scheme. This paper introduces a system-level framework for vehicle overtaking velocity planning and scheduling to maximize the total driving efficiency of all vehicles. The framework starts with an accurate EV powertrain modeling of various vehicle types and derives the optimal velocity and acceleration for overtaking. Simulation results show the energy-efficient velocity planning for overtaking that reduces a driving cost function related to energy and delay. The proposed overtaking framework achieves 15% driving cost savings compared with the conventional group driving.

Integration of solar based charging station in power distribution network and charging scheduling of EVs

The rapid development of electric vehicles (EVs) such as Easy Bike, Auto-Rickshaw, and Electric Bike is a major contributor to global energy concerns. Although electric vehicles are bringing a new dimension to the transportation sector, with advantages such as being the cheapest method of transportation and emitting fewer greenhouse gases (GHGs), the massive amounts of energy required to charge the electric vehicles is a challenging issue. Pakistan is also moving toward the use of electric vehicles however the absence of charging facilities in Pakistan slows down the charging process and increases the prices for electric vehicle users. Finding the requisite charging without threatening the current power infrastructure is one of the most challenging tasks of the present era. Renewable energy-based charging is required to fulfill the charging demand of electric vehicles. To find the best configuration to meet the necessary daily charging demand, this proposed work undertakes a techno-economic assessment for a novel renewables-based grid-tied charging station. The technical, economic and environmental impacts of Solar based grid-tied charging stations are taken into account. Moreover, the results are justified by considering the losses and building the system model. The suggested strategy decreases energy costs from $.200/kWh to $.016/kWh while reducing grid load by 254,030 kWh/yr. Furthermore, the system completes 7.7 charging sessions every day, using 13% of the electricity generated. The remaining 87% of the electricity is sold back to the grid, which generates significant revenue.

Coordinated energy management for commercial prosumers integrated with distributed stationary storages and EV fleets

The integration of intermittent renewable energy and stochastic electric vehicle (EV) loads in buildings and power systems poses new challenges such as increased peak demands and grid instability. Regarding multi-directional interactions among heterogeneous loads, generation, storages, and power grid, the synergy between prosumers, stationary storages and electromobility is crucial for future energy systems. In this study, coordinated energy management was investigated for a neighborhood involving commercial buildings with distributed renewables and stationary batteries, and plug-in EVs. Compared to the collaborative control of EV and stationary storages at single prosumers, the energy sharing system with centralized control of mobile and stationary storages achieved superior techno-economic performance. Case studies demonstrated that the renewables-storage sharing with centralized storage scheduling decreased annual energy costs for both buildings and EV owners by over 15%, and the cycle aging cost of stationary storage by 25%. While the performance under day-ahead scheduling of EV and stationary storages depends more on the prediction accuracy, the real-time renewables-storage sharing strategy integrated with pre-scheduled EV charging provides an effective alternative solution for the stakeholders. The study explores potential coordinated approaches for communities with low-carbon buildings, flexible storages, and intelligent infrastructure to maximize the operational value and improve the grid stability.

Hybrid Algorithm for Large Scale in Electric Vehicle Routing and Scheduling Optimization

This study examines the optimal scheduling and routing of electric vehicles across multiple time periods to reduce battery consumption and commute times. The objective is to convey products to consumers using a fleet of electric vehicles as efficiently as feasible. This hybrid solution addresses this issue and combines the most advantageous aspects of the Local Search 2-Opt and Particle Swarm Optimization (PSO) metaheuristics. Local Search 2-Opt is used to enhance the quality of the original solutions, whereas PSO is used to search through all possible solutions and identify the best one. Considering constraints such as limited vehicle capacity, limited charging station availability, and limited time, the proposed method optimizes the coupling of consumers with electric vehicles. The evaluation results indicate that the hybrid algorithm accomplishes more significant energy savings, travel time savings, and overall solution quality than the alternatives currently considered to be state-of-the-art. This research's findings can be used to optimize EVRS operations and improve the administration of electric vehicle fleets.

ESS Scheduling and Control Approach for Factory-based EV Charging Station to Participate in Ancillary Services

Due to the global trend towards net zero emissions and increasing environmental awareness, some renewable energy sources (RES) and electric vehicles (EVs) have been integrated into the power system. An energy management system (EMS) can effectively integrate and coordinate distributed energy resource (DER) control in response to grid changes and impacts. Proper regulation of renewable energy and electric vehicles can reduce microgrid operating costs, which are critical for improving the safety and stability of power systems. Therefore, this paper proposes a three-stage optimal scheduling strategy from the perspective of a microgrid operator (MGO) to efficiently utilize resources. Based on an electric energy storage system (ESS), the traditional centralized optimal control method is compared with the method proposed to calculate the time and operating cost. It is verified that the method proposed can indeed execute an ancillary service (AS). In addition, the model predictive control (MPC) employed by the ESS can significantly improve the robustness of the system and reduce the occurrence of exceeding contract capacity.

Informed Prosumer Aggregator Bidding Strategy via Incorporating Distribution Grid Outage Risk Predictions

FERC Order 2222 paves the way for aggregated distributed energy resources (DERs) participation in the wholesale electricity market. A particular DER assumed to be widely available in the future is the distributed prosumer (DP), also called virtual power plant (VPP), which may host PV generation, and stationary and mobile battery energy storage systems, in addition to the on-site passive load. DP aggregation and participation in the day-ahead market ancillary service products (ASPs) require managing uncertainties associated with load consumption and photovoltaic generation, electric vehicle (EV) scheduling, market-clearance prices, etc. Outages in the distribution grid may distort these energy-limited resources from their optimal operating point, potentially impacting their ability to deliver the committed ASPs in real-time. To address these challenges, first, we develop a machine learning algorithm to predict the risk of outages in distribution feeders. Next, we incorporate the distribution feeder State of Risk (SoR) predictions with the bidding model of the DP aggregator to provide an informed decision-making tool for optimal participation in the energy and ASP markets. The simulation results demonstrate the efficacy and scalability of the proposed model in improving the aggregator profitability and preventing penalties for the inability to deliver ASPs due to unexpected energy capacity limits of DP assets.

Integrated solution for electric bus timetabling and vehicle scheduling combined with choices of charging locations

This paper presents a novel mathematical model, integrating timetabling and vehicle scheduling problems for electric buses. The objective is to minimize the number of buses while satisfying constraints concerning routing and charging, including design choices for where to install charging stations. The aim of the paper is to illustrate and discuss the effects of solving the timetabling and vehicle scheduling of electric buses (including where to install charging infrastructure) separately, compared to solving them jointly in one single step. For that purpose, we perform tests with: i) given timetable, that is, solving only the vehicle scheduling problem, ii) fixed headways for each line, and iii) variable headways. A small test case based on actual bus lines from Västra Frölunda, Gothenburg, Sweden, is used. From the numerical experiments, we verify that combining the two planning steps can significantly reduce the number of vehicles needed.

Optimal Scheduling of Electric Vehicle Integrated Energy Station Using a Novel Many-Objective Stochastic Competitive Optimization Algorithm

Optimal Scheduling of Electric Vehicle Integrated Energy Station Using a Novel Many-Objective Stochastic Competitive Optimization Algorithm

Electric Vehicle Aggregator as an Automatic Reserves Provider Under Uncertain Balancing Energy Procurement

Shift of the power system generation from the fossil to the variable renewables prompted the system operators to search for new sources of flexibility, i.e., new reserve providers. With the introduction of electric vehicles, smart charging emerged as one of the promising solutions. However, electric vehicle aggregators face the uncertainty both on the reserve activation and the electric vehicle availability. These uncertainties can have a detrimental effect on both the aggregators’ profitability and users’ comfort. State-of-the art literature mostly neglects the reserve activation or it’s uncertainty. On top of that, they rarely model European markets which are different that those commonly addressed in the literature. This paper introduces a new method for modeling the reserve activation uncertainty, also termed as balancing energy procurement in the European context, based on the real historic data from the European power system. Three electric vehicle scheduling models were designed and tested: the deterministic, the stochastic and the robust. The results demonstrate that the current deterministic approaches inaccurately represent the activation uncertainty and that the proposed models that consider uncertainty, both the stochastic and the robust, substantially improve the results. Additionally, the sensitivity analysis for the robust model was performed and it demonstrates how a decision-maker can choose its level of conservativeness, portraying its risk-awareness.

A novel modified tunicate swarm algorithm for optimal charge scheduling and pricing of electric vehicles with user priority‐based charging station selection

SummaryIn recent days, electric vehicles (EVs) are seen as a viable transportation alternative by industry people to minimize the greenhouse gas emissions problems of regular fuel vehicles. While EVs' performance is limited due to their restricted battery power, charging wait for service, and high resource costs, they are still a viable option. To enhance the EV's performance, this work introduced an optimized priority‐based charge scheduling with dynamic pricing model. For this, a novel modified tunicate swarm algorithm method, which is the enhanced form of the standard tunicate swarm algorithm, is proposed. When an EV arrives at the charging station, the EV user is permitted to choose the type of charging based on his/her requirements. In general, when charging EVs in public stations or during peak hours, customers must be patient and wait for significant periods of time. The introduced model ignores this negative effect. EV users have also mentioned the issue of controlled charging rates as a key barrier. This is a difficult task, but it is accomplished in this study, which optimizes total charge costs. Finally, the results acquired are discussed under overall cost and energy consumption.

Optimal semi‐dynamic traffic and power flow assignment of coupled transportation and power distribution systems for electric vehicles

AbstractAs the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated.

Scheduling of Electric Vehicle for Performance Improvement of Electricity Distribution Network: A Case Study in Indian Scenario

Scheduling of Electric Vehicle for Performance Improvement of Electricity Distribution Network: A Case Study in Indian Scenario

New technologies for optimal scheduling of electric vehicles in renewable energy‐oriented power systems: A review of deep learning, deep reinforcement learning and blockchain technology

AbstractWith global concerns about carbon emissions, the proportion of renewable energy generation worldwide is increasing, and the demand for flexible resources in power systems is growing. In recent years, as a clean means of transportation, the number of electric vehicles has increased, and the optimal scheduling of electric vehicles has become a research hotspot. The rise of artificial intelligence, blockchain, and other innovative technologies has enriched research on optimal scheduling of electric vehicles. To reveal the latest developments in electric vehicle optimal scheduling studies, this paper summarises the application of state‐of‐the‐art technologies, including deep learning, deep reinforcement learning, and blockchain technology in the optimal scheduling of electric vehicles. Moreover, the advantages and disadvantages of various technical applications are highlighted. Finally, considering the shortcomings and developmental status of applications of the above three technologies, some suggestions for future research directions are proposed.

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.

Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition

In responseto low-carbon requirements, a large amount of renewable energy resources (RESs) have been deployed in power systems; nevertheless, the intermittency of RESs raises the system vulnerability and even causes severe damage under extreme events. Electric vehicles (EVs), owing to their mobility and flexibility characteristics, can provide various ancillary services meanwhile enhancing system resilience. The distributed control of EVs under such scenarios in power-transportation network becomes a complex decision-making problem with enormous dynamics and uncertainties. To this end, a multiagent reinforcement learning method is proposed to compute both discrete and continuous actions simultaneously that aligns with the nature of EV routing and scheduling problems. Furthermore, the proposed method can enhance the learning stability and scalability with privacy perseverance in the multiagent setting. Simulation results based on IEEE 6- and 33-bus power networks integrated with transportation systems validate its effectiveness in providing system resilience and carbon intensity service.