Day‐ahead unit commitment model for microgrids

Assuming that all electric vehicles (EVs) integrated to the grid are managed by aggregators, it is possible to make full use of the controllability of the charging EVs. Based on this, the charging load of each EV aggregator is defined as one of the optimal scheduling variables in the unit commitment (UC) model. This can be accomplished by assessing the upper and lower limit of charging load of each EV aggregator in different scenarios, which is simulated by Monte Carlo method. According to this, a UC model is proposed for optimal coordination of EV aggregators and wind generation, considering the EV owner's requirement, the range of charging load and the unit constraints. Using the10-units system as the test system, the results of UC models with different EVs charging strategies and the wind power integration indicators such as utilization rate are investigated. Simulation results show that both the abandoned wind power and the unit operating costs can be decreased more effectively in the proposed UC model.

Current research about the application of large scale electric vehicles (EVs) is carried out in two fields: (1) From the aspect of the traffic system, charging navigation technologies were proposed, to improve traffic efficiency and charging convenience of the EV driver. (2) From the aspect of the distribution system, the “smart” charging strategies were developed to optimize charging profile, where power loss of distribution system, voltage limits and load variance were taken into account. However, few studies focused on simultaneous improvement of EV owner’s convenience, traffic system performance, charging station performance and distribution system performance after the application of large scale EVs. In this paper, a multi-objective function considering the performance indices of traffic system and distribution system including road travel speed, traffic flow, charging waiting time, power loss of distribution system and voltage of distribution node is developed to schedule large scale EVs’ charging behaviour and obtain optimal performance of the whole system. Constraints including load capacity of charging stations, charging requirements and endurance mileage of EVs are overall considered, and a method for determining weights of the multi-objective function is discussed. A simulation system is built for verifying the effectiveness of proposed strategy. Simulation results shows that, compared with the usual charging scheduling strategy, average heavy congestion ratio of the district around the charging station in the evening rush hours is reduced from 0.52 to 0.48, the percentage of EVs waiting for charging is reduced from 7.5% to 0.5%, the maximal power loss rate of distribution system is decreased by 3.5%, and the maximal voltage deviation of distribution system is decreased by 3.4% due to the proposed strategy.

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers’ safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered.

An inevitable replacement of electric vehicle (EV)s over fossil fuelled vehicles is imposing a threat to the electric grids as the power demand of these vehicles may cause overloading of power lines and frequency instability. This necessitates the estimation and scheduling of charging EV. This article focuses on the development of a novel charging schedule with an aggregator based EV controller using genetic algorithm for optimization. The study also helps to minimize the charging cost of both the utility and the EV owner to have a win-win situation. This is done by charging EV battery in minimum possible time and also provides the utility with active power support considering EV batteries and power converter of the charging station. In this way the bidirectional power of vehicle to grid (V2G) and grid to vehicle (G2V) can maintain nominal voltage and frequency for the load. This will help in leveling the residential load by supplying power during off-peak hours to charge the EV batteries and to enable the utility to receive power from EV battery for peak power shaving. Further, performance and cost comparison of the proposed charging schedule is done with an unscheduled one.

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

This paper addresses the optimal charging scheduling problem for Electric Vehicles (EVs) in an intelligent workplace parking lot powered by both the Photovoltaic Power (PV) System and the Power Grid. Due to the uncertain charging requirements of different EVs and time-varying available renewable energy, the charging load from the parking lot may bring a new challenge to the Power Grid. By minimizing total cost of the parking lot, we design a dynamic charging scheduling scheme to manage the charging processes of EVs based on the real- time information of EVs and renewable energy from the PV system. Numerical simulations are carried out to demonstrate the efficiency of the designed charging scheduling scheme.

A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system

With large-scale integration of intermittent renewable energy sources (RESs), power systems need more ancillary services to guarantee system stability and security. When properly managed, large-scale electric vehicles (EVs), whose markets have been growing rapidly in recently years, can provide frequency regulation service for power systems, benefiting both EVs and the grid. However, interests balance between different parties and regulation signals uncertainty are not well considered when deploying EVs to provide regulation services in previous studies. In this article, a leader–follower game is proposed for individual EVs and their aggregator to determine optimal day-ahead charging and frequency reserve scheduling considering regulation signals uncertainty. Acting as the leader in the game, the EV aggregator prices for its charging service and offers for regulation service to encourage EVs’ participation. EVs act as price-takers and aim at achieving a tradeoff between costs from electricity consumption and revenues from providing regulation service through an aggregative game. The proposed leader–follower game is then converted to a bilevel optimization problem, which can be solved by introducing an aggregate EV model. Furthermore, for the sake of privacy protection, a distributed approach is designed for EVs to optimize their scheduling. Case studies with the proposed scheduling model are carried out to demonstrate its effectiveness.

An agent-based negotiation scheme for the distribution of electric vehicles across a set of charging stations

Electric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a.

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.

Pricing mechanisms design for guiding electric vehicle charging to fill load valley

In this paper, we propose two decentralized Electric Vehicle (EV) charge scheduling schemes for shaping the load curve of residential communities connected to the electric grid. The first scheme is designed for Coordinated Valley-Filling (C-VF) of the load curve via only EV charging. The second scheme is designed for Coordinated Valley-Filling and Peak-Shaving (C-VF-PS) of the load curve via both EV charging and discharging. In both schemes, a set of grid-connected EVs referred to as an `EV Group' (EVG) coordinates their charge (and discharge) schedules by means of an iterative routine. Specifically, at each iteration of the respective routine, each EV in the EVG updates its charge (and discharge) schedule using a water-filling based algorithm that is specifically tailored for load curve shaping. To accommodate heterogeneous EV arrival times, which are often non-deterministic, each of C-VF and C-VF-PS is implemented in two methods, which differ in the way the EVG is formed. The first method requires all the grid-connected EVs to reschedule at designated time intervals, whereas the second method requires each EV to schedule only once, yielding lower computation and communication overheads. Numerical simulation results confirm that, compared to uncoordinated EV charging, C-VF and C-VF-PS reduce the load variance (flattens the load curve) by 47% and 65%, respectively. Furthermore, Method 2 is shown to be more effective than Method 1, in terms of computation and communication overheads.

Electric Vehicles (EVs) are already an integral part of the smart grid framework. The problem of scheduling the charging of these plug-in vehicles in coordination with the dynamic electricity tariffs and load-changes throughout the day is important and must be considered for efficient integration of such units. Since EVs are active and distributed storage units, they have the capability to provide regulation services to the power grid and hence can be used in schemes where the aggregator can compensate EVs for this service. The EV charging must also be scheduled in such a way that the customer or the vehicle owner incurs the least cost and the aggregator also makes the maximum revenue. The work presented in this paper attempts to satisfy the economy of both the EV owner and the aggregator by mathematically formulating this scheduling problem and solving it by different optimization techniques to arrive at the optimal cost and revenue for the customer and the aggregator respectively. Different heuristic techniques based evolutionary optimization algorithms have been applied to solve the optimal charge scheduling problem and the results compared and analyzed for assessing practical feasibility.