Models and Algorithms to Solve Electric Vehicle Charging Stations Designing and Managing Problem Under Uncertainty PDF Download

Author: Md Abdul Quddus
Publisher:
ISBN:
Category :
Languages : en
Pages : 217

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Book Description
This dissertation studies a framework in support electric vehicle (EV) charging station expansion and management decisions. In the first part of the dissertation, we present mathematical model for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions (e.g., number of batteries charged, discharged through Battery-to-Grid (B2G), stored, Vehicle-to-Grid (V2G), renewable, grid power usage) over a pre-specified planning horizon and under stochastic power demand. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on a road network of Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. The second part of the dissertation is dedicated to investigate the performance of a collaborative decision model to optimize electricity flow among commercial buildings, electric vehicle charging stations, and power grid under power demand uncertainty. A two-stage stochastic programming model is proposed to incorporate energy sharing and collaborative decisions among network entities with the aim of overall energy network cost minimization. We use San Francisco, California as a testing ground to visualize and validate the modeling results. Computational experiments draw managerial insights into how different key input parameters (e.g., grid power unavailability, power collaboration restriction) affect the overall energy network design and cost. Finally, a novel disruption prevention model is proposed for designing and managing EV charging stations with respect to both long-term planning and short-term operational decisions, over a pre-determined planning horizon and under a stochastic power demand. Long-term planning decisions determine the type, location, and time of established charging stations, while short-term operational decisions manage power resource utilization. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under stochastic exogenous factors such as outside temperature and air velocity. Since the research problem is NP-hard, a Sample Average Approximation method enhanced with a Scenario Decomposition algorithm on the basis of Lagrangian Decomposition scheme is proposed to obtain a good-quality solution within a reasonable computational time. As a testing ground, the road network of Washington, D.C. is considered to visualize and validate the modeling results. The results of the analysis provide a number of managerial insights to help decision makers achieving a more reliable and cost-effective electricity supply network.

Author: Ran Wang
Publisher: Springer
ISBN: 9811042500
Category : Technology & Engineering
Languages : en
Pages : 150

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Book Description
This book, discusses the latest research on the intelligent control of two important components in smart grids, namely microgrids (MGs) and electric vehicles (EVs). It focuses on developing theoretical frameworks and proposing corresponding algorithms, to optimally schedule virtualized elements under different uncertainties so that the total cost of operating the microgrid or the EV charging system can be minimized and the systems maintain stabilized. With random factors in the problem formulation and corresponding designed algorithms, it provides insights into how to handle uncertainties and develop rational strategies in the operation of smart grid systems. Written by leading experts, it is a valuable resource for researchers, scientists and engineers in the field of intelligent management of future power grids.

Author: Jingzi Tan
Publisher:
ISBN:
Category :
Languages : en
Pages : 292

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Book Description
We consider a facility location problem with uncertainty flow customers' demands, which we refer to as stochastic flow capturing location allocation problem (SFCLAP). Potential applications include siting farmers' market, emergency shelters, convenience stores, advertising boards and so on. For this dissertation, electric vehicle charging stations siting with maximum accessibility at lowest cost would be studied. We start with placing charging stations under the assumptions of pre-determined demands and uniform candidate facilities. After this model fails to deal with different scenarios of customers' demands, a two stage flow capturing location allocation programming framework is constructed to incorporate demand uncertainty as SFCLAP. Several extensions are built for various situations, such as secondary coverage and viewing facility's capacity as variables. And then, more capacitated stochastic programming models are considered as systems optimal and user oriented optimal cases. Systems optimal models are introduced with variations which include outsourcing the overflow and alliance within the system. User oriented optimal models incorporate users' choices with system's objectives. After the introduction of various models, an approximation method for the boundary of the problem and also the exact solution method, the L-Shaped method, are presented. As the computation time in the user oriented case surges with the expansion of the network, scenario reduction method is introduced to get similar optimal results within a reasonable time. And then, several cases including testing with different number of scenarios and different sample generating methods are operated for model validation. In the last part, simulation method is operated on the authentic network of the state of Arizona to evaluate the performance of this proposed framework.

Author: Ukesh Chawal
Publisher:
ISBN:
Category : Battery charging stations (Electric vehicles)
Languages : en
Pages : 91

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Book Description
There has been a significant increase in the number of electric vehicles (EVs) mainly because of the need to have a greener living. Thus, ease of access to charging facilities is a prerequisite for large scale deployment for EV. The first component of this dissertation research seeks to formulate a deterministic mixed-integer linear programming (MILP) model to optimize the system of EV charging stations, the locations of the stations and the number of slots to be opened to maximize the profit based on the user-specified cost of opening a station. Despite giving the optimal solution, the drawback of MILP formulation is its extremely high computational time (as much as 5 days). The other limit of this deterministic model is that it does not take uncertainty in to consideration. The second component of this dissertation is to overcome the first drawback of the MILP model by implementing a two-stage framework developed by (Chawal et al. 2018), which integrates the first-stage system design problem and second-stage control problem of an EV charging stations using a design and analysis of computer experiments (DACE) based system design optimization approach. The first stage specifies the design of the system that maximizes expected profit. Profit incorporates costs for building stations and revenue evaluated by solving a system control problem in the second stage. The results obtained from the DACE based system design optimization approach, when compared to the MILP, provide near optimal solutions. Moreover, the computation time with the DACE approach is significantly lower, making it a more suitable option for practical use. The third component of this dissertation is to overcome the second drawback of the MILP model by introducing stochasticity in our model. A two-stage framework is developed to address the design of a system of electric vehicle (EV) charging stations. The first stage specifies the design of the system that maximizes expected profit. Profit incorporates costs for building stations and revenue evaluated by solving a system control problem in the second stage. The control problem is formulated as an infinite horizon, continuous-state stochastic dynamic programming problem. To reduce computational demands, a numerical solution is obtained using approximate dynamic programming (ADP) to approximate the optimal value function. To obtain a system design solution using our two-stage framework, we propose an approach based on DACE. DACE is employed in two ways. First, for the control problem, a DACE-based ADP method for continuous-state spaces is used. Second, we introduce a new DACE approach specifically for our two-stage EV charging stations system design problem. This second version of DACE is the focus of this paper. The "design" part of the DACE approach uses experimental design to organize a set of feasible first-stage system designs. For each of these system designs, the second-stage control problem is executed, and the corresponding expected revenue is obtained. The "analysis" part of the DACE approach uses the expected revenue data to build a metamodel that approximates the expected revenue as a function of the first-stage system design. Finally, this expected revenue approximation is employed in the profit objective of the first stage to enable a more computationally-efficient method to optimize the system design. To our knowledge, this is the only two-stage stochastic problem which uses infinite horizon dynamic programming approach to optimize the second stage dynamic control problem and the first stage system design problem. Moreover, when the designs obtained from our DACE approach and MILP design are solved using DACE-based ADP method (simulation), an improvement of approximately 8% is observed in the simulated profit obtained from ADP design compared to that of MILP design indicating that when uncertainty is considered, DACE ADP design provides the better solution.

Author: Ottorino Veneri
Publisher: Springer
ISBN: 3319436511
Category : Technology & Engineering
Languages : en
Pages : 323

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Book Description
This book outlines issues related to massive integration of electric and plug-in hybrid electric vehicles into power grids. Electricity is becoming the preferred energy vector for the next new generation of road vehicles. It is widely acknowledged that road vehicles based on full electric or hybrid drives can mitigate problems related to fossil fuel dependence. This book explains the emerging and understanding of storage systems for electric and plug-in hybrid vehicles. The recharging stations for these types of vehicles might represent a great advantage for the electric grid by facilitating integration of renewable and distributed energy production. This book presents a broad review from analyzing current literature to on-going research projects about the new power technologies related to the various charging architectures for electric and plug-in hybrid vehicles. Specifically focusing on DC fast charging operations, as well as, grid-connected power converters and the full range of energy storage systems. These key components are analyzed for distributed generation and charging system integration into micro-grids. The authors demonstrate that these storage systems represent effective interfaces for the control and management of renewable and sustainable distributed energy resources. New standards and applications are emerging from micro-grid pilot projects around the world and case studies demonstrate the convenience and feasibility of distributed energy management. The material in this unique volume discusses potential avenues for further research toward achieving more reliable, more secure and cleaner energy.

Author: Jiuping Xu
Publisher: Springer
ISBN: 3319933515
Category : Technology & Engineering
Languages : en
Pages : 1752

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Book Description
This proceedings book is divided in 2 Volumes and 8 Parts. Part I is dedicated to Decision Support System, which is about the information system that supports business or organizational decision-making activities; Part II is on Computing Methodology, which is always used to provide the most effective algorithm for numerical solutions of various modeling problems; Part III presents Information Technology, which is the application of computers to store, study, retrieve, transmit and manipulate data, or information in the context of a business or other enterprise; Part IV is dedicated to Data Analysis, which is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making; Part V presents papers on Operational Management, which is about the plan, organization, implementation and control of the operation process; Part VI is on Project Management, which is about the initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria at the specified time in the field of engineering; Part VII presents Green Supply Chain, which is about the management of the flow of goods and services based on the concept of “low-carbon”; Part VIII is focused on Industry Strategy Management, which refers to the decision-making and management art of an industry or organization in a long-term and long-term development direction, objectives, tasks and policies, as well as resource allocation.

Author: Darweesh Ehssan A Salamah
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Electric cars are projected to become the vehicles of the future. A major barrier for their expansion is range anxiety stemming from the limited range a typical EV can travel. EV batteries' performance and capacity are affected by many factors. In particular, the decrease in ambient temperature below a certain threshold will adversely affect the battery's efficiency. This research develops deterministic and two-stage stochastic program model for charging stations' optimal location to facilitate the routing decisions of delivery services that use EVs while considering the variability inherent in climate and customer demand. To evaluate the proposed formulation and solution approach's performance, Fargo city in North Dakota is selected as a tested. For the first chapter, we formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC's in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations. For the second chapter, a novel solution approach based on the progressive hedging algorithm is presented to solve the resulting mathematical model and to provide high-quality solutions within reasonable running times for problems with many scenarios. We observe that the location-routing decisions are susceptible to the EV logistic's underlying climate, signifying that decision-makers of the DCFC EV logistic network for cities that suffer from high-temperature fluctuations would not overlook the effect of climate to design and manage the respective logistic network efficiently.

Author: Mukesh Saraswat
Publisher: Springer Nature
ISBN: 9811966346
Category : Technology & Engineering
Languages : en
Pages : 908

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Book Description
This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2022), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from 26 to 27 March 2022. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.

Author: Yang Song
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
The global shift towards electric vehicles (EVs) holds immense promise for mitigating greenhouse gas (GHG) emissions and advancing sustainable development goals. Nonetheless, the limited market penetration of EVs persists, primarily due to challenges in meeting the demand for replenishment compared to conventional internal combustion engine vehicles (ICEVs). The overarching goal of this dissertation is to develop mathematical models and a management framework for EV drivers' travel behaviors in a demand-supply-coupled transportation system, with the ultimate aim of facilitating the widespread adoption of EVs. By gaining deeper insights into and effectively managing various aspects of EV driver behaviors, such as charging preferences and route choices, the following benefits can be achieved: meeting the charging demands of EV drivers, optimizing the utilization of charging facility supply, and promoting the adoption of EVs as a preferred mode of travel. Firstly, the charging behavior of EV drivers is modeled based on the given charging facility supply. The existing research efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To fill the gap, an ensemble learning model based on gradient boosting is developed. A total of 18 features are defined and extracted from the multisource data, which cover information on drivers, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5 years of charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with an R square value of 0.54 and root mean square error of 0.14, both better than the two benchmark models, the multiple linear regression model and the random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users, or that are further from residential areas. Secondly, the provision of charging infrastructure is formulated as a demand management mechanism accounting for the underlying demand-supply coupled relationship. The existing studies treat each charging station as an independent entity and naively select the candidate locations with the highest individual usage rates. To address this issue, a two-stage learning-based demand-supply-coupled optimization model for the charging station location problem (CSLP) is proposed, aiming to incorporate the concept of EV charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station (CS) based on 15 defined features. Next, in stage two, a demand-supply-coupled CSLP model is developed with the objective of maximizing the total charging usage rates of both existing and newly selected charging stations. The proposed model is solved using a gradient-based stochastic spatial search algorithm. A case study using the same data as the first chapter is performed to test the effectiveness of the proposed model and algorithm. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches, namely the Greedy-Based Method and Neighbor-Swap-Based Method. Lastly, the routing behavior, as another aspect of EV driver travel behaviors, is modeled in a community charging setting. The existing research focuses on the EV traffic assignment under the scenario of corridor charging in a small-scale road network, ignoring the link interactions in community charging and path deviations in large-scale road networks. To tackle these challenges, an EV traffic assignment model for large-scale road networks with link interaction in community charging and with path deviations is proposed. First, the mathematical formulation for the EV traffic assignment model considering the interaction among road links connecting to the same CS is proposed, which is further proven to be equivalent to the user equilibrium (UE) condition. Then, a column-generation-based solution algorithm is developed to solve the model, facilitating the complex EV path deviations in a large-scale road network. The result of numerical examples shows that the proposed algorithm could converge in 0.025, 1.71, 4.73 and 91 seconds with a relative gap of no more than 0.0008 on the four testing networks, being the most accurate and fastest compared with the three benchmark algorithms, Frank-Wolfe algorithm, Interaction-Ignored algorithm, and Commercial-Solver-Based algorithm. The sensitivity analysis results show that the total travel cost and the total system dwelling time exhibit a negative correlation with charging supply while displaying a positive correlation with charging demand.

Author: Francisco G. Montoya
Publisher: MDPI
ISBN: 3039211560
Category : Technology & Engineering
Languages : en
Pages : 306

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Book Description
This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programming to the most modern methods based on bio-inspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.