Sustainable Energy Grids & Networks最新文献_第7页

Electric vehicle management in multi-energy systems

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-24 DOI: 10.1016/j.segan.2024.101608

Furkan Ahmad, Bijaya Ketan Panigrahi, Michela Longo, Luluwah Al-Fagih, Mohammad Saad Alam, Hossam A. Gaber

The rapid advancement of Electric Vehicles (EVs) has significantly transformed the landscape of transportation and energy systems, with global sales projected to reach 46.8 million by 2030. This Editorial introduces the special issue, "Integration of EVs within Multi-Energy Systems," which explores the pivotal role of EVs in promoting sustainable mobility and addressing modern energy demands. It highlights the ongoing shift from traditional centralized energy systems to decentralized multi-energy frameworks, incorporating microgrids, renewable energy sources, energy storage, and cutting-edge technologies. The special issue also discusses the challenges and opportunities of this transition, including the need for smart energy management, control strategies, and self-sustaining EV-integrated infrastructures. By examining the interaction between EVs and diverse energy resources, this special issue provides insights into optimizing energy utilization, enhancing grid stability, and fostering a low-carbon future. The findings emphasize the importance of collaborative efforts in designing resilient, efficient, and sustainable energy systems that can support the increasing electrification of transportation.

{"title":"Electric vehicle management in multi-energy systems","authors":"Furkan Ahmad, Bijaya Ketan Panigrahi, Michela Longo, Luluwah Al-Fagih, Mohammad Saad Alam, Hossam A. Gaber","doi":"10.1016/j.segan.2024.101608","DOIUrl":"10.1016/j.segan.2024.101608","url":null,"abstract":"<div><div>The rapid advancement of Electric Vehicles (EVs) has significantly transformed the landscape of transportation and energy systems, with global sales projected to reach 46.8 million by 2030. This Editorial introduces the special issue, \"Integration of EVs within Multi-Energy Systems,\" which explores the pivotal role of EVs in promoting sustainable mobility and addressing modern energy demands. It highlights the ongoing shift from traditional centralized energy systems to decentralized multi-energy frameworks, incorporating microgrids, renewable energy sources, energy storage, and cutting-edge technologies. The special issue also discusses the challenges and opportunities of this transition, including the need for smart energy management, control strategies, and self-sustaining EV-integrated infrastructures. By examining the interaction between EVs and diverse energy resources, this special issue provides insights into optimizing energy utilization, enhancing grid stability, and fostering a low-carbon future. The findings emphasize the importance of collaborative efforts in designing resilient, efficient, and sustainable energy systems that can support the increasing electrification of transportation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101608"},"PeriodicalIF":4.8,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transmission expansion planning: A deep learning approach

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-20 DOI: 10.1016/j.segan.2024.101585

Jizhe Dong , Jianshe Cao , Yu Lu , Yuexin Zhang , Jiulong Li , Chongshan Xu , Danchen Zheng , Shunjie Han

This paper proposes a transmission expansion planning (TEP) method based on deep learning (DL) to address the increasing complexity and excessive reliance on mathematical formulas in current TEP models. First, we utilize a traditional mathematical programming model to obtain unit outputs and line construction decisions by varying loads, thereby generating the dataset required for DL training. Next, we build a convolutional neural network (CNN) based DL model, which includes convolutional layers, pooling layers and fully connected layers, and whose inputs consist of load data and unit output data, while output is line construction data. We use Bayesian optimization (BO) to select the best hyperparameters for the model. We conducted both single and multiple training experiments on the Garver’s 6-bus, IEEE 24-bus and IEEE 118-bus systems. In the single training experiments, the R² values achieved by our proposed method on these three systems were 0.99471, 0.99594 and 0.99676, respectively, with K-fold cross-validation showing stable results. In the multiple training experiments, we repeated the CNN training 50 times and obtained confidence intervals for each metric to further validate the model’s effectiveness. Additionally, we performed significance testing on the BO results, showing that among the three comparative experiments, two had P-values less than 0.001, indicating a significant difference. The remaining one has a P-value is larger than 0.05 indicating a difference but not significant.

{"title":"Transmission expansion planning: A deep learning approach","authors":"Jizhe Dong , Jianshe Cao , Yu Lu , Yuexin Zhang , Jiulong Li , Chongshan Xu , Danchen Zheng , Shunjie Han","doi":"10.1016/j.segan.2024.101585","DOIUrl":"10.1016/j.segan.2024.101585","url":null,"abstract":"<div><div>This paper proposes a transmission expansion planning (TEP) method based on deep learning (DL) to address the increasing complexity and excessive reliance on mathematical formulas in current TEP models. First, we utilize a traditional mathematical programming model to obtain unit outputs and line construction decisions by varying loads, thereby generating the dataset required for DL training. Next, we build a convolutional neural network (CNN) based DL model, which includes convolutional layers, pooling layers and fully connected layers, and whose inputs consist of load data and unit output data, while output is line construction data. We use Bayesian optimization (BO) to select the best hyperparameters for the model. We conducted both single and multiple training experiments on the Garver’s 6-bus, IEEE 24-bus and IEEE 118-bus systems. In the single training experiments, the R<sup>2</sup> values achieved by our proposed method on these three systems were 0.99471, 0.99594 and 0.99676, respectively, with K-fold cross-validation showing stable results. In the multiple training experiments, we repeated the CNN training 50 times and obtained confidence intervals for each metric to further validate the model’s effectiveness. Additionally, we performed significance testing on the BO results, showing that among the three comparative experiments, two had P-values less than 0.001, indicating a significant difference. The remaining one has a P-value is larger than 0.05 indicating a difference but not significant.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101585"},"PeriodicalIF":4.8,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-layer game optimization strategy for an integrated energy system considering multiple responses and renewable energy uncertainty

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-20 DOI: 10.1016/j.segan.2024.101605

Hui Xiao , Yongxiao Wu , Linjun Zeng , Yonglin Cui , Huidong Guo , Buwei Ou , Yutian Lei

The integrated energy system (IES) is one of the most important developments in the field of multi-energy coupling, where the conflict of interests between different market players poses significant challenges to the economic, stable and efficient operation. To address this problem, this study proposes a two-layer game optimization strategy for an IES considering multiple responses and renewable energy uncertainty. First, on the energy supply side, a flexible response model for the heat and electricity output of a combined heat and power unit is constructed by introducing the Kalina cycle and an electric boiler. Based on the principle of electricity-heat-cooling calorific value equivalence, an integrated demand response model containing energy use conversion is established on the energy demand side. Second, the uncertainty in renewable energy output is addressed by constructing a robust model based on a polyhedral uncertainty set. Then, using the energy retailer (ER) as the leader and the energy producer (EP) and user agent (UA) as the followers, a one-master-multiple-slaves Stackelberg game model is established. Finally, the model is simulated and analyzed using the distributed method of the improved Dual-Mutation Differential Evolution (DMDE) algorithm nested CPLEX solver. The results indicate that the proposed optimal strategy can optimize the multiple parties' conflict of interests, which makes the benefits of EP, ER, and UA increase by 19.68 %, 38.63 %, and 9.36 %, respectively, and effectively balances the robustness and economy of the system. Compared with the traditional algorithms, the DMDE algorithm has significant advantages in terms of solution time and iteration number.

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引用次数: 0

Enhancement of short-term prediction capabilities of inter-area grid oscillations with a multi-variate ensemble-based method

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-19 DOI: 10.1016/j.segan.2024.101604

Carlo Olivieri , Francesco de Paulis , Lino Di Leonardo , Antonio Orlandi , Cosimo Pisani , Giorgio Giannuzzi

The actual and future even higher penetration of renewable energy sources into the power grid sets challenging issues for transmission system operators, especially concerning the hard-to-solve problem of inter-area electromechanical oscillations. Despite the useful existing monitoring systems, the possibility of having predictive monitoring features for such phenomena could be an appealing tool. The work presented in this paper aims to assess the possibility of enhancing the predictive monitoring features offered by machine learning techniques based on the combination of ensemble methods and Long-Short-Term Memory units using multi-variate methods. The development steps of a multi-variate prediction strategy are presented together with the assessment of its performance versus uni-variate solutions. The assessment takes into account different kinds of datasets, taken from real grid measurements, and strategy configurations. Either transient low frequency oscillation phenomena or normal grid operation are considered as representative cases of real-world scenarios. Finally, some preliminary considerations about improving prediction performance and the limitations are outlined.

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引用次数: 0

Generation method of wind power and photovoltaic output scenarios based on LHS-GRU

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-19 DOI: 10.1016/j.segan.2024.101602

Shuang Wang , Siwei Wu , Bo Tang , Ling Liu , Long Cheng

With the continuous increase in the installed capacity of new energy sources, the proportion of new energy in the power system is gradually increasing. However, due to the strong stochasticity and volatility of new energy sources, the task of constructing a New Power System has become extremely daunting, posing difficulties for power dispatch. Traditional scenario generation methods cannot accurately describe the characteristics of new energy sources under typical and extreme scenarios. Therefore, this paper proposes a method for generating scenarios based on Latin Hypercube Sampling (LHS) and Gated Recurrent Unit (GRU). The kernel density estimation theory is used to model the probability density of wind and photovoltaic output, and the optimal bandwidth is found by the cross-validation. On this basis, the initial scenario sets are generated using the LHS. The GRU model is used to learn the trend of wind power and photovoltaic output at different times respectively, and generate scenario sets that can accurately reflect the typical and extreme output of wind power and photovoltaic. Three kinds of indicators were used to evaluate the generated scenario sets in this paper. The ADO indicators of the generated wind power and photovoltaic scenario sets are 2415 and 1462, respectively, ranking second and first among the four methods. The ES indicators of the generated wind power and photovoltaic scenario set are 1.3005 and 1.0286, respectively, both ranking first. Compared with the real scenario sets in 2022, the ES indicators of 2023 are 1.5361 and 2.2826, respectively, ranking first, and second. The BS indicator diagram also shows that the generated scenario set can reflect the real scenario set.

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引用次数: 0

BESTS: Blockchain-enabled electric vehicles scheduling and coordination scheme at charging station

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-15 DOI: 10.1016/j.segan.2024.101596

Rajesh Gupta , Riya Kakkar , Sachi Chaudhary , Sudeep Tanwar , Zdzislaw Polkowski , Fayez Alqahtani , Amr Tolba

The proliferation of electric vehicles (EVs) in the automobile industry has become a prominent alternative to fossil-fuel vehicles by facilitating energy sustainability for a greener environment. Nevertheless, secure and efficient EV charging coordination is still one of the critical aspects that can bring various privacy challenges during the communication between EVs and charging station (CS) for charging. Towards this goal, we have proposed a blockchain-based EV scheduling scheme for coordinated charging at the CS, i.e., BESTS. To attain this, we formulate various EV scheduling scenarios based on their energy demands and different type of EVs (emergency, high authority, and regular) to allocate EVs at a CS over the fifth generation (5G) communication network and InterPlanetary file system (IPFS) protocol. Both 5G and IPFS offers cost-efficient, low latency, and highly reliable transactions for EV charging allocation at a CS. Moreover, we highlighted various smart contract functionalities of the proposed BESTS scheme executed in the remix integrated development environment (IDE) for EV charging allocation based on the available charging slots at the CS. The security aspect of the proposed BESTS scheme is evaluated and verified with the Echidna fuzzy property-based tool, which reflects that the smart contract is not having any vulnerability. Finally, the performance evaluation of the blockchain-based EV scheduling scheme is simulated considering various performance metrics such as gas consumption and cost analysis for the type of EVs and smart contract functions, gas consumption analysis for the number of EVs, and bit error rate.

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引用次数: 0

Demand response strategy for microgrid energy management integrating electric vehicles, battery energy storage system, and distributed generators considering uncertainties

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-14 DOI: 10.1016/j.segan.2024.101594

Annu Ahlawat Bhatia, Debapriya Das

The growing adoption of electric vehicles (EVs) in microgrids (MGs) necessitates effective energy scheduling while introducing several operational challenges for MG operators. The presented work integrates demand response (DR) programs into the operational framework of microgrids to address these challenges. The first phase of the proposed work estimates the optimal capacity of renewable distributed generators and the sizing and scheduling of battery energy storage systems (BESS) based on system load demand. For electric vehicle charging station (EVCS) modeling,

M_{1} / M_{2} / c

queuing theory-based approach is utilized to estimate the need for minimum charging plugs to reduce waiting time for EV owners. This second stage introduces a mathematical model for the optimal energy scheduling of MG by implementing incentive and price-based DR schemes. The primary objective is to maximize the economic benefits for MG operators and potential DR participants. The two DR participants explored are EVCS and DR aggregators. The EVCS aggregators optimize charging schedules for EVs and charging/discharging schedules for BESS based on hourly electricity prices, while the DR aggregators encourage non-EV consumers to adjust their load demand according to hourly incentive rates. The uncertain behavior of RE sources, load demand, and electricity market price is analyzed using Hong’s

(2 m + 1)

point estimation method. Furthermore, the energy management strategy optimally configures the MG with minimal power losses by imposing a network reconfiguration method. A day-ahead analysis of the proposed model leads to a 9.96% reduction in energy imported from the primary grid, resulting in an energy cost savings of 8.37%.

{"title":"Demand response strategy for microgrid energy management integrating electric vehicles, battery energy storage system, and distributed generators considering uncertainties","authors":"Annu Ahlawat Bhatia, Debapriya Das","doi":"10.1016/j.segan.2024.101594","DOIUrl":"10.1016/j.segan.2024.101594","url":null,"abstract":"<div><div>The growing adoption of electric vehicles (EVs) in microgrids (MGs) necessitates effective energy scheduling while introducing several operational challenges for MG operators. The presented work integrates demand response (DR) programs into the operational framework of microgrids to address these challenges. The first phase of the proposed work estimates the optimal capacity of renewable distributed generators and the sizing and scheduling of battery energy storage systems (BESS) based on system load demand. For electric vehicle charging station (EVCS) modeling, <span><math><mrow><msub><mrow><mi>M</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>/</mo><msub><mrow><mi>M</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>/</mo><mi>c</mi></mrow></math></span> queuing theory-based approach is utilized to estimate the need for minimum charging plugs to reduce waiting time for EV owners. This second stage introduces a mathematical model for the optimal energy scheduling of MG by implementing incentive and price-based DR schemes. The primary objective is to maximize the economic benefits for MG operators and potential DR participants. The two DR participants explored are EVCS and DR aggregators. The EVCS aggregators optimize charging schedules for EVs and charging/discharging schedules for BESS based on hourly electricity prices, while the DR aggregators encourage non-EV consumers to adjust their load demand according to hourly incentive rates. The uncertain behavior of RE sources, load demand, and electricity market price is analyzed using Hong’s <span><math><mrow><mo>(</mo><mn>2</mn><mi>m</mi><mo>+</mo><mn>1</mn><mo>)</mo></mrow></math></span> point estimation method. Furthermore, the energy management strategy optimally configures the MG with minimal power losses by imposing a network reconfiguration method. A day-ahead analysis of the proposed model leads to a 9.96% reduction in energy imported from the primary grid, resulting in an energy cost savings of 8.37%.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101594"},"PeriodicalIF":4.8,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A chance-constrained programming approach to optimal management of car-rental fleets of electric vehicles

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-14 DOI: 10.1016/j.segan.2024.101587

Giovanni Gino Zanvettor, Marco Casini, Antonio Giannitrapani, Simone Paoletti, Antonio Vicino

In the current context of growing electrification of the transport sector, offering rental and sharing programs for electric vehicles is considered one of the strategies to achieve decarbonization targets. Such programs should be supported by suitable optimization tools to manage the vehicle fleet, and make rental provision profitable for its operator. In this paper, we consider a rental system having a single station for electric vehicle pickup and delivery. For this system, we address the operational problem of simultaneously assigning rental requests to vehicles and determining the charging policies during inactivity intervals. The objective is to maximize the profit for the operator by minimizing the costs for electricity. The considered problem is complicated by uncertainty regarding the battery energy level when a vehicle returns to the station. This leads to a chance-constrained programming formulation, where the request-to-vehicle assignment and charging policies are determined by minimizing electricity costs while ensuring that the energy demand of the served requests is met with a prescribed high probability. Since the formulated mixed-integer problem with probabilistic constraints is hard to solve, a suboptimal approach is proposed, consisting of two sequential steps. In the first step, request-to-vehicle assignment is accomplished via a suitably designed heuristic procedure. Then, for a given assignment, the charging policy of each vehicle is determined by solving a relaxed chance-constrained problem. Numerical results are presented to assess the performance of both the assignment procedure and the optimization problem which determines the electric vehicle charging policies.

{"title":"A chance-constrained programming approach to optimal management of car-rental fleets of electric vehicles","authors":"Giovanni Gino Zanvettor, Marco Casini, Antonio Giannitrapani, Simone Paoletti, Antonio Vicino","doi":"10.1016/j.segan.2024.101587","DOIUrl":"10.1016/j.segan.2024.101587","url":null,"abstract":"<div><div>In the current context of growing electrification of the transport sector, offering rental and sharing programs for electric vehicles is considered one of the strategies to achieve decarbonization targets. Such programs should be supported by suitable optimization tools to manage the vehicle fleet, and make rental provision profitable for its operator. In this paper, we consider a rental system having a single station for electric vehicle pickup and delivery. For this system, we address the operational problem of simultaneously assigning rental requests to vehicles and determining the charging policies during inactivity intervals. The objective is to maximize the profit for the operator by minimizing the costs for electricity. The considered problem is complicated by uncertainty regarding the battery energy level when a vehicle returns to the station. This leads to a chance-constrained programming formulation, where the request-to-vehicle assignment and charging policies are determined by minimizing electricity costs while ensuring that the energy demand of the served requests is met with a prescribed high probability. Since the formulated mixed-integer problem with probabilistic constraints is hard to solve, a suboptimal approach is proposed, consisting of two sequential steps. In the first step, request-to-vehicle assignment is accomplished via a suitably designed heuristic procedure. Then, for a given assignment, the charging policy of each vehicle is determined by solving a relaxed chance-constrained problem. Numerical results are presented to assess the performance of both the assignment procedure and the optimization problem which determines the electric vehicle charging policies.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"41 ","pages":"Article 101587"},"PeriodicalIF":4.8,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of loop flow in electrical power systems with embedded HVDC using a modified Dijkstra algorithm

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-13 DOI: 10.1016/j.segan.2024.101597

Gabriel F. Alvarenga , A.C. Zambroni de Souza , Glauco N. Taranto , Bala Venkatesh

This paper introduces a new method for identifying and analyzing loop flow scenarios that occur due to energy transmission through embedded High Voltage Direct Current (HVDC) systems in electrical power grids. However, analyzing these systems with large-scale meshed networks involving interconnected buses could be challenging for system operators, requiring significant experience and an in-depth understanding of the system. To reduce this complexity, a modified Dijkstra algorithm was proposed to quickly and efficiently identify key loop paths. The main advantage of the modified Dijkstra algorithm is that it not only accelerates the analysis, but also simplifies the task. Overall, this study provides significant contributions to the field of electrical power systems by offering a valuable tool for system operators to analyze and manage loop flows in power transmission systems. Lastly, the loop flow analysis algorithm utilizing the modified Dijkstra methodology has the potential to improve the analysis capabilities of system operators by enhancing their ability to predict, mitigate, and efficiently manage loop flow issues, leading to more reliable, economical, and stable power grid operations.

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引用次数: 0

Modeling Battery Swapping Stations for sustainable urban mobility

IF 4.8 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2024-12-13 DOI: 10.1016/j.segan.2024.101592

Daniela Renga, Michela Meo

With the road transportation alone being responsible of almost half the total oil demand over all sectors, electric vehicles (EVs) represent a promising solution to address sustainability concerns raised by urban mobility. However, a sustainable and pollution-free EV charging process cannot be enabled without an extensive penetration of Renewable Energy (RE) sources and a pervasive deployment of smart charging scheduling approaches. In a similar scenario, renewable powered Battery Swapping Stations (BSSs) can play a key role to enable sustainable and feasible electric mobility (e-mobility). Considering an on-grid BSS, additionally powered by photovoltaic panels, we analyze the proper dimensioning of its capacity in terms of number of sockets and the proper sizing of the RE supply to satisfy the battery swapping demand, trading off cost, Quality of Service (QoS) and feasibility constraints. We propose an analytical model to represent the BSS operation and limit the complexity of system investigation, exploring its potentiality to dimension the BSS system based on the actual battery swapping demand. Our findings highlight how integrating a local RE supply allows to considerably decrease cost by almost 40%. Furthermore, in the planning and deployment of BSS systems, the model results effective in finding good tradeoffs among QoS requirements, capital expenditures, and operational cost.

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引用次数: 0