Recharge Policies Research Articles

Goods Delivery with Electric Vehicles: Electric Vehicle Routing Optimization with Time Windows and Partial or Full Recharge

With the rise of the electric vehicle market share, many logistic companies have started to use electric vehicles for goods delivery. Compared to the vehicles with an internal combustion engine, electric vehicles are considered as a cleaner mode of transport that can reduce greenhouse gas emissions. As electric vehicles have a shorter driving range and have to visit charging stations to replenish their energy, the efficient routing plan is harder to achieve. In this paper, the Electric Vehicle Routing Problem with Time Windows (EVRPTW), which deals with the routing of electric vehicles for the purpose of goods delivery, is observed. Two recharge policies are considered: full recharge and partial recharge. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) metaheuristic based on the ruin-recreate strategy is coupled with a new initial solution heuristic, local search, route removal, and exact procedure for optimal charging station placement. The procedure for the O(1) evaluation in EVRPTW with partial and full recharge strategies is presented. The ALNS was able to find 38 new best solutions on benchmark EVRPTW instances. The results also indicate the benefits and drawbacks of using a partial recharge strategy compared to the full recharge strategy.

Mixed electric bus fleet scheduling problem with partial mixed-route and partial recharging

Given the goal of reducing emissions and saving energy, an increasing number of transit agencies have proposed electrification plans for public transport buses. The two fundamental challenges to adopting electric vehicles in transit operations are the purchase of appropriate electric vehicles to establish the bus fleet and the creation of an efficient schedule and recharging plan. This paper examines the multi-depot and multi-vehicle type electric vehicle scheduling problem with partial mixed-route strategy and partial recharging policy. The partial mixed-route strategy proposed in this paper allows multiple transit routes to operate in a more cost-efficient way. Moreover, it takes into account the bus allocation problem of the transit network fleet to meet the parking restrictions of each depot. The problem is formulated in a mixed-integer programming model, and an adaptive large neighborhood search (ALNS) algorithm with new mechanisms specific to the problem is proposed to apply the model in a more efficient manner. The dataset of a real transit network in Nanjing is used for case study, and the performance of ALNS is tested by using randomly generated instances from the dataset. The results show that the proposed method is effective in finding high quality solutions and adopting partial recharging policy can reduce the fleet size and the total cost while providing advantages depending on the operational parameters of the schedule. In addition, comparison of two schedules using different partial mixed-route strategies shows that there may be two sides of adopting mixed-route scheduling.

Energy and Distance Optimization in Rechargeable Wireless Sensor Networks

The aim of a mobile recharger operating in a wireless sensor network (WSN) is to keep the network’s average consumed energy and covered distance low. As shown in the literature, the covered distance is minimized when the mobile recharger’s base is located as per the solution of a median problem, while the network’s average energy consumption is minimized as per the solution of a different median problem. In this work, the first problem is analytically investigated, showing that its solution depends on the traffic load and the topology characteristics. Furthermore, it is shown that, under certain conditions, the solution for both problems is identical. These analytical results motivate the introduction of a new on-demand recharging policy , simple to be implemented and depending on local information. The simulation results confirm the analytical findings, showing that the solutions of both median problems are identical under certain conditions in WSN environments. Additionally, the proposed recharging policy is evaluated against a well-known policy that exploits global knowledge, demonstrating its advantage for prolonging network lifetime. For both recharging policies, it is shown that energy consumption and covered distance are minimized when the mobile recharger is initially located at the solution of the said median problems.

Analysis of the influence of groundwater on land subsidence in Beijing based on the geographical weighted regression (GWR) model

A global geological phenomenon caused by natural or human activities is described as land subsidence. Groundwater extraction plays a significant part in causing land subsidence. Due to economic development, urban expansion, and rapid population expansion, the unscientific exploitation of groundwater in Beijing has been accelerated, which makes it the region with the fastest land subsidence rate in China. To study the spatial heterogeneity of land subsidence caused by groundwater aquifers level changes, the monitoring results of land subsidence in 2003–2010 years were analyzed by using PS-InSAR, based on ENVISAT ASAR in Beijing plain area. The maximum value of accumulated land subsidence in the study area is 707 mm, and in this study area multiple subsidence center areas have been formed. A GWR model based on a regular grid has been established by exploring the effects of unconfined aquifer (UA), first confined aquifer (FCA), second confined aquifer (SCA), third confined aquifer (TCA) on land subsidence and their spatial non-stationarity. The change of subsidence in all subsidence areas is positively related to the change of SCA water level. Except the fact that the main control factors of Liyuan and Songzhuang are the change of UA layer, the change of SCA is the main control factor of land subsidence in most subsidence areas. Though the contribution rate of SCA to land subsidence is the highest, the contribution rate of TCA has been increasing. It is predicted that the impact on land subsidence will increase year by year. The results of this will not only help to understand the spatial impact patterns of aquifers on land subsidence zones, but also to formulate optimal groundwater regulation and recharge policies. There is a scarcity of the consideration of the compressible layer in the study and it will become more comprehensive if further datasets are obtained.

Earth fissure hazard prediction using machine learning models

Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations’ major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy (>86%) and precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution.

Combined Stochastic Optimization of Frequency Control and Self-Consumption With a Battery

Optimally combining frequency control with self-consumption can increase revenues from battery storage systems installed behind-the-meter. This work presents an optimized control strategy that allows a battery to be used simultaneously for self-consumption and primary frequency control. Therein, it addresses two stochastic problems: the delivery of primary frequency control with a battery and the use of the battery for self-consumption. We propose a linear recharging policy to regulate the state of charge of the battery while providing primary frequency control. Formulating this as a chance-constrained problem, we can ensure that the risk of battery constraint violations stays below a predefined probability. We use robust optimization as a safe approximation to the chance-constraints, which allows to make the risk of constraint violation arbitrarily low, while keeping the problem tractable and offering maximum reserve capacity. Simulations with real frequency measurements prove the effectiveness of the designed recharging strategy. We adopt a rule-based policy for self-consumption, which is optimized using stochastic programming. This policy allows to reserve more energy and power of the battery on moments when expected consumption or production is higher, while using other moments for recharging from primary frequency control. We show that optimally combining the two services increases value from batteries significantly.

Electric vehicle routing problem with single or multiple recharges

Abstract Nowadays, due to the new laws and policies related to the greenhouse gas emissions, and the rise of social and ecological awareness of transport sustainability, logistic companies started to incorporate green technologies in their distribution activities. Here, electric vehicles, as a cleaner mode of transport than conventional vehicles come to the fore and many companies are already integrating electric vehicles in their delivery fleets. Compared to the conventional vehicles, electric vehicles have a shorter driving range due to the limited battery capacity, and they need to recharge at charging stations more frequently. To efficiently manage the fleet of electric vehicles, new algorithms that take into account visits to charging stations have to be developed. In this paper, we observed the Electric Vehicle Routing Problem with Time Windows (E-VRPTW) and multiple or single recharge policies during the route. The homogeneous fleet of battery electric vehicles with limited load and battery capacity, customer time windows and full linear recharge at charging stations are considered. The objective is to minimize total traveled distance while operating a minimal number of vehicles. To find the solution of the problem, on larger instances we applied the metaheuristic based on the ruin-recreate principle and on the small instances we solved the mixed integer program with commercial software.

Optimal Recharging Policies for Electric Vehicles

Recharging decisions for electric vehicles require many special considerations because of battery dynamics. Battery longevity is prolonged by recharging less frequently and at slower rates, and also by not charging the battery too close to its maximum capacity. In this paper, we address the problem of finding an optimal recharging policy for an electric vehicle along a given path. The path consists of a sequence of nodes, each representing a charging station, and the driver must decide where to stop and how much to recharge at each stop. We present efficient algorithms for finding an optimal policy in general instances with deterministic travel costs and homogeneous charging stations, and also for two specialized cases—one where the vehicle can stop anywhere along the path to recharge and another with equidistant charging stations along the path. In addition, we develop two heuristic procedures that we characterize analytically and explore empirically. We further analyze and test our solution methods on model variations that include stochastic travel costs and nonhomogeneous charging stations.

A Preliminary Analysis of Grid Impact of Electric Vehicles via Inertial Measurements

AbstractThis paper proposes a method to estimate the mechanical energy at the wheel of electric vehicles through inertial measurements. Simulations based on experimental data have been carried out to analyze the grid impact of electric vehicles and the size of their battery pack according to different recharge policies and the presence of a system to accumulate energy from a regenerative brake. The experimental campaign is carried out with different classes of cars traveling on urban and extra-urban roads.

Conjuctive Use of Groundwater and Surface Water in the Burdekin Delta Area

One of the key economic issues faced by the managers of the two water boards in the Burdekin River Delta is the allocation of surface water between immediate use on farm for irrigation and storage in the aquifer for future use. Because of the significance of the interaction between surface water and groundwater and the return flow externalities within the delta, policies on surface water and groundwater need to be determined simultaneously. A model is formulated of the dynamic system of surface water and groundwater in the delta with water demand, groundwater extraction cost and stochastic recharge and surface water availability. The optimal pumping/artificial recharge policies for each state of the joint surface water and groundwater system is derived for the southern part of the delta and this policy is applied over a large number of years to derive the expected development over time of extraction/artificial recharge and the state of the groundwater system. The implications of the optimal pumping/artificial recharge policy for any review of existing allocations of surface water and groundwater are discussed.