Reservation Scheme Research Articles

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Game-Based Demand Feedback Reservation Parking Pricing

Reservation parking is a new type of parking that allows users to obtain a parking space for an appropriate time slot by reservation. This paper pioneers a high degree-of-freedom reservation strategy and rules for commercial districts in which users can arbitrarily select parking time slots. The nested logit is applied to build a parking revenue model by analyzing the cost, demand, and price factors sequentially according to the user’s choice behavior. The parking lot price game in the region is dissected and a nonlinear pricing model is proposed for maximizing regional returns. A model validation of a parking lot in an actual commercial area shows that compared to the traditional parking method, the parking lot revenue increased by 69.63% upon the application of this reservation strategy and pricing scheme, while the parking lot traffic increased by 22.4%. This model can provide strong support for reservation parking pricing in commercial areas. In the comparison of scenarios, the scientific validity of this model using dynamic pricing and regional game competition is verified. In the application, a reasonable price scheme can be formulated based on the parking lot traffic, cost, and surrounding parking strategies, which can result in higher parking revenue while providing users with great parking convenience.

Simulation and optimization of dynamic-hybrid parking reservation strategies for one-way vehicle-sharing systems

ABSTRACT In this paper, we explore the regulation of one-way station-based vehicle-sharing system (OSVS) through dynamic-hybrid parking reservation policies. We first propose a dynamic-hybrid parking reservation policy. This policy only requires trips with expected travel distances shorter than a specific threshold to make a parking reservation. The distance threshold varies with time. We develop a discrete event simulation model based on the O2DES (object-oriented discrete event simulation) framework to compare the dynamic-hybrid parking reservation (DHPR) strategy with the no-reservation (NR), static-hybrid parking reservation (SHPR) and complete parking reservation (CPR) strategies. Furthermore, we propose a simulation-optimization model and an Elitism-based Genetic algorithm with the optimal computation budget allocation to determine the fleet size, station capacity, and dynamic reservation distance threshold. The analysis of case studies of a real-world system indicates that DHPR is always superior to NR, SHPR and CPR.

Accurate and Efficient Surrogate Model-Assisted Optimal Design of Flux Reversal Permanent Magnet Arc Motor

An accurate and efficient surrogate model plays a significant role in the optimal design of electrical motors. This paper presents an accurate and efficient surrogate modeling method for a flux reversal permanent magnet arc motor (FR-PMAM) based on machine learning algorithm. Specifically, the motor topology and the optimized design of the FR-PMAM are firstly illustrated. Then, the PM magnetic motive force-permeance model is developed to select structural parameters of FR-PMAM. After that, a finite-element analysis (FEA) model of FR-PMAM is calculated to obtain sample data. A machine learning algorithm random forest is introduced into the field of motor optimal design for the first time to evaluate the contribution of each parameter to the design objectives. Based on the sample data, an efficient machine learning algorithm CatBoost is innovatively employed to establish the accurate and efficient surrogate model of the FR-PMAM that can capture the complex function relationship between output objectives and input parameters. Subsequently, an improved genetic algorithm with the elitist reservation strategy is adopted to obtain the optimal combination of the structural parameters of FR-PMAM. Finally, a prototype of the FR-PMAM is manufactured. The FEA and experimental results validate the effectiveness of the investigated surrogate modeling method.

Demand Prediction of Emergency Supplies in Campus Public Health Emergencies Based on GM-GA-BP Neural Network

Medical alcohol is the most widely used and highly demanded emergency supply in both campus public environments and medical settings. It has a sharp contradiction between supply and demand in the event of a public health event. For this reason, forecasting the demand for medical alcohol is crucial for averting and managing unexpected public health issues on campus. An enhanced GM-GA-BP neural network prediction model was put out in this article. The benefits of the equal dimension and new information complement GM(1,1) model's new and old information replacement and small sample data prediction are combined. Following data fitting and modification, a BP neural network based on conjugate gradient algorithm is built to carry out training and prediction. Meanwhile, the connection weight and threshold were globally optimized through genetic algorithm.To forecast the demand for medical alcohol at a certain university in Yibin City from 2022 to 2023, a thorough comparison of a combination model vs the conventional BP neural network model was made. The results showed that the MAPE had been decreased by roughly 3% and the absolute percentage error was as low as 0.12686%. These findings have provided a scientific reference for the university to improve its emergency material reserve strategy represented by medical alcohol.

Resistance-minimising strategies for introducing a novel antibiotic for gonorrhoea treatment: a mathematical modelling study

Gonorrhoea is a highly prevalent sexually transmitted infection and an urgent public health concern because of increasing antibiotic resistance in Neisseria gonorrhoeae. Only ceftriaxone remains as the recommended treatment in the USA. With the prospect of new anti-gonococcal antibiotics being approved, we aimed to evaluate how to deploy a new drug to maximise its clinically useful lifespan. We used a compartmental model of gonorrhoea transmission in a US population of men who have sex with men (MSM) to compare strategies for introducing a new antibiotic for gonorrhoea treatment. The MSM population was stratified into three sexual activity groups (low, intermediate, and high) characterised by annual rates of partner change. The four introduction strategies tested were: (1) random 50-50 allocation, where each treatment-seeking infected individual had a 50% probability of receiving either drug A (current drug; a ceftriaxone-like antibiotic) or drug B (a new antibiotic), effective at time 0; (2) combination therapy of both the current drug and the new antibiotic; (3) reserve strategy, by which the new antibiotic was held in reserve until the current therapy reached a 5% threshold prevalence of resistance; and (4) gradual switch, or the gradual introduction of the new drug until random 50-50 allocation was reached. The primary outcome of interest was the time until 5% prevalence of resistance to each of the drugs (the new drug and the current ceftriaxone-like antibiotic); sensitivity of the primary outcome to the properties of the new antibiotic, specifically the probability of resistance emergence after treatment and the fitness costs of resistance, was explored. Secondary outcomes included the time to a 1% resistance threshold for each drug, as well as population-level prevalence, mean and range annual incidence, and the cumulative number of incident gonococcal infections. Under baseline model conditions, a 5% prevalence of resistance to each of drugs A and B was reached within 13·9 years with the reserve strategy, 18·2 years with the gradual switch strategy, 19·2 years with the random 50-50 allocation strategy, and 19·9 years with the combination therapy strategy. The reserve strategy was consistently inferior for mitigating antibiotic resistance under the parameter space explored and was increasingly outperformed by the other strategies as the probability of de novo resistance emergence decreased and as the fitness costs associated with resistance increased. Combination therapy tended to prolong the development of antibiotic resistance and minimise the number of annual gonococcal infections (under baseline model conditions, mean number of incident infections per year 178 641 [range 177 998-181 731] with combination therapy, 180 084 [178 011-184 405] with the reserve strategy). Our study argues for rapid introduction of new anti-gonococcal antibiotics, recognising that the feasibility of each strategy must incorporate cost, safety, and other practical concerns. The analyses should be revisited once robust estimates of key parameters-ie, the likelihood of emergence of resistance and fitness costs of resistance for the new antibiotic-are available. US Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases.

A stochastic formulation of the two-echelon vehicle routing and loading bay reservation problem

This paper studies a goods delivery problem for multiple courier companies in central city areas integrated with a loading bay scheduling problem. The goods delivery problem is formulated as a stochastic two-echelon vehicle routing problem, while the loading bays are managed through a central reservation system, which assigns loading bays to courier vehicles taking into account their delivery demands. A stochastic programming model with chance constraints is formulated to find the optimal vehicle routing and loading bay reservation strategy, and a hybrid genetic algorithm is developed to solve the model. Numerical results suggest that the reservation system can help coordinate deliveries from different courier companies and substantially improve the overall delivery efficiency and loading space utilisation.

Offloading Demand Prediction-Driven Latency-Aware Resource Reservation in Edge Networks

The wide applications of edge computing has brought dawn to terminals with limited computing resources and energy supply. Terminal completes its task through computing offloading to reduce energy consumption and improve performance. In order to improve the efficiency of resource scheduling and utilization, this paper proposes a delay-aware resource reservation strategy based on the prediction of spatialtemporal correlation task offloading demands. Due to the strong time-varying characteristics of terminal task offloading demand, this paper designs a Spatiotemporal Task Offloading Demand prediction model (STOD). It divides the region into multiple subregions and models them as a graph structure so as to predict the task offloading demand of each region separately by considering the complex spatial and temporal dependencies of regional task offloading demands. With this model, we propose a regional Edge Server Resource Reservation (ESRR) algorithm to minimize the terminal task offloading delay. The experimental results based on the real data sets show that ESRR can reduce the average offloading time consumption by 30% under different scenarios and verify its feasibility and effectiveness.

Per Sub-band Tone Reservation Scheme for Universal Filtered Multi-Carrier Signal

Per Sub-band Tone Reservation Scheme for Universal Filtered Multi-Carrier Signal

Optimal reservation control strategies in shared parking systems considering two types of customers

The operational challenges of shared parking platforms include heterogeneous sharing time intervals and two types of customers randomly providing demand information. Thus, the operation of shared parking platforms necessitates a more intricate reservation strategy than those of the hotels and leasing industries. To address the problem, this study proposes reservation control strategies in a shared parking system with two types of customers and then allocates capacity among customers. Firstly, we propose a dynamic programming (DP) model with dynamic characteristics of demand information and prove that the boundary condition of the model is NP-hard. Secondly, period- and product-based decomposition models have been proposed to approach the DP model. The objective functions of the period- and product-based decomposition models are concave and supermodular, respectively. We also propose three approximation algorithms to obtain reservation strategies based on decomposition models. Finally, several numerical experiments are conducted to verify the effectiveness of the proposed models and algorithms. Extended experiments are conducted to test the robustness of the method for real-world applications. The results support reservation control in shared parking systems.

Yield potential and selection of off-season maize for silage and grain using GT Biplot under limited water and frost

Maize silage has been used as a forage reserve strategy for critical periods or continuous use in animal feed. However, new genotypes and their potential must be identified. Thus, this study aimed to evaluate the potential of maize genotypes for silage and grain in one off-season in the midwest region of Brazil, under limited water and frost, and select them for this dual purpose (silage and grain) using the GT Biplot tool. The experiment was performed at Embrapa Western Agriculture in the autumn-winter season of 2021 in Dourados, Mato Grosso do Sul, Brazil. The experimental design consisted of randomized blocks of six maize genotypes (BRS1010, KWS9606, 1P2224, 1Q2383, BRS3046, and CAPO) with five replications under no-tillage. Silage points were evaluated at harvest when the grain milk line was at ¾ and maize grains at the maturation stage (dry plant). The 1P2224 and 1Q2383 maize genotypes present silage (high green and dry biomass) and grain yield potential. The GT Biplot tool identified the 1P2224 genotype as superior and suitable for cultivation or as a parent in a breeding program in the midwest region of Brazil for silage and grain yield evaluations of one off-season under limited water and frost.

Secure Long-Range Autonomous Valet Parking: A Reservation Scheme With Three-Factor Authentication and Key Agreement

Long-range autonomous valet parking (LAVP) is a current trend, partly due to traffic congestion and parking headache. For large-scale parking demands, reservation is introduced to effectively manage valet parking. However, existing schemes focus on parking request verification and parking check-in, which aren't applicable to LAVP because they ignore identity legitimacy and communication security in the phase of picking up as well as dropping off passengers. One viable solution is authentication and key agreement (AKA) protocol. Generally, due to low entropy of passwords and dictionary attacks, three-factor (i.e. passwords, biometrics, and smart card) AKA is more secure than single- and two-factor AKA. Unfortunately, known attacks and high overheads hinder the application of three-factor AKA in real-world environments. Hence, one of the most tough tasks is to balance security and availability, especially how to address the potential threats introduced by each factor while taking full advantage of three factors. Inspired by the above challenges, we propose a provably secure three-factor AKA protocol for reservation services in LAVP, namely SecLAVP. Specifically, the passenger and the autonomous vehicle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AV$</tex-math></inline-formula> ) complete mutual authentication with the assistance of drop-off/pick-up point ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DP$</tex-math></inline-formula> ). After successful authentication, the session key is generated between the passenger, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DP$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$AV$</tex-math></inline-formula> for secure communication. In the Real-Or-Random (ROR) model, we formally prove SecLAVP satisfies session-key security. Then, we apply AVISPA to simulate our proposed protocol, to demonstrate that SecLAVP can resist man-in-the-middle attacks. Additionally, informal security analysis indicates that SecLAVP satisfies our defined 16 design goals concerning security. Finally, we evaluate performance of SecLAVP in terms of communication overheads, computational overheads, and scheduling, to manifest the feasibility.

A Novel Low Complexity Two-Stage Tone Reservation Scheme for PAPR Reduction in OFDM Systems.

Orthogonal frequency division multiplexing (OFDM) has the characteristics of high spectrum efficiency and excellent anti-multipath interference ability. It is the most popular and mature technology currently in wireless communication. However, OFDM is a multi-carrier system, which inevitably has the problem of a high peak-to-average power ratio (PAPR), and s signal with too high PAPR is prone to distortion when passing through an amplifier due to nonlinearity. To address the troubles caused by high PAPR, we proposed an improved tone reservation (I-TR) algorithm to alleviate the above native phenomenon, which will pay some modest pre-calculations to estimate the rough proportion of peak reduction tone (PRT) to determine the appropriate output power allocation threshold then utilize a few iterations to converge to the near-optimal PAPR. Furthermore, our proposed scheme significantly outperforms previous works in terms of PAPR performance and computational complexity, such as selective mapping (SLM), partial transmission sequence (PTS), TR, tone injection (TI), etc. The simulation results show that in our proposed scheme, the PAPR is appreciably reduced by about 6.44 dB compared with the original OFDM technique at complementary cumulative distribution function (CCDF) equal to 10-3, and the complexity of I-TR has reduced by approximately 96% compared to TR. Besides, as for bit error rate (BER), our proposed method always outperforms the original OFDM without any sacrifice.

A combined day-ahead and intraday optimal scheduling strategy considering a joint frequency regulation reserve scheme among wind, photovoltaic, and thermal power

Under the premise of establishing a certain reserve power for frequency regulation, a new energy power plant (NEPP) transformed by frequency regulation control can participate in system frequency regulation. Considering the problem of cooperation between multiple NEPPs for reserve power for frequency regulation, this article presents a joint frequency regulation reserve scheme spanning wind, photovoltaic, and thermal power. A group of NEPPs composed of all NEPPs in the system works in the state of dynamic stepped output derating and participates in system frequency regulation together with thermal power units. Based on the joint frequency regulation reserve scheme and considering that the accuracy of new energy forecast directly affects the frequency regulation effect of NEPPs, we propose a combined day-ahead and intraday scheduling strategy considering the joint frequency regulation reserve scheme and derive the relevant scheduling model. The improved IEEE RTS 24-bus system is used as the test system for calculation. The results of the calculations show that the proposed strategy can optimize the participation scheme of frequency regulation of new energy, reduce the power and economic losses of new energy caused by participation in frequency regulation, and improve the ability of the power system to accommodate the output power of new energy under the premise of ensuring that the steady-state frequency deviation does not exceed the allowable frequency limits.

Secure Battery Swapping: A Reservation Scheme with Conditional Privacy-Preserving Bidirectional Heterogeneous Aggregate Signcryption and Incentive Mechanism

For large-scale battery-swapping demands, reserva- tions are utilized to effectively manage battery swapping. To achieve data security, one promising solution is heterogeneous signcryption (i.e. signature + encryption). However, existing heterogeneous signcryption cannot simultaneously support aggre- gation, batch verification, conditional privacy-preserving, bidirec- tional heterogeneous communication, and availability. Besides, to reduce the end-to-end delays, mobile edge computing (MEC) is introduced to reservations, so other challenge is to design an incentive mechanism to encourage mobile edge nodes (MENs) to transmit reservation messages. Seeking to address these chal- lenges, we propose a reservation scheme with conditional privacy- preserving bidirectional heterogeneous aggregate signcryption and incentive mechanism, namely SecBS. More precisely, in status publication (i.e. SecBS-SP), electric vehicles (EVs) obtain status messages released by battery swapping stations (BSSs) via MENs, which is realized via heterogeneous signcryption from certificateless cryptosystem (CLC) to identity-based cryptosystem (IBC). In the reservation (i.e. SecBS-R), under the incentive, MENs transmit reservations sent by EVs to BSSs, which is realized via heterogeneous signcryption from IBC to CLC. Finally, formally security proof and extensive simulations are carried out to manifest the security and feasibility of our SecBS.

A Bi-Objective Lane Reservation Problem Considering Dynamic Traffic Flow

Lane reservation strategies are widely used to ensure the right of way for eco-friendly vehicles and encourage people to green their commute. In most lane reservation problems (LRPs), the parameters underlying the system traffic conditions (e.g., vehicle speed and traffic flow) cannot be effectively specified. This paper addresses a new dynamic lane reservation problem (DLRP), which aims to optimize lanes that need to be reserved in different time periods based on trajectory data for existing transit network optimization. For passengers in reserved lanes, their travel time is minimized to guarantee traffic priority. Considering that the reserved lanes cause travel time growth on regular lanes, an improved multiobjective mixed integer nonlinear programming (MINLP) is established to minimize the delay. The problem complexity of this paper is NP-hard. This paper applies a preprocessing method for the actual traffic flow data analysis for link travel time calculation. We developed a hybrid evolutionary algorithm decomposing a multiobjective optimization problem (MOP) to a collection of simple MOPs. These subproblems are collaboratively solved. The hybrid crossover strategy exploits the advantages of different crossover operators for a better performance. The experimental results of MOEA/D and NSGA-II with standard test functions show that the proposed algorithm can improve the convergence and distribution of the results. Through numerous analyses and calculations of instances, the proposed algorithm is proven to be effective.

Load-Aware Channel Allocation for IEEE 802.11ah-Based Networks

Most wireless communication technologies addressed to the Internet of Things (IoT) applications face the bottleneck of dense and large-scale use cases. One solution to this problem is a periodic channel reservation strategy, in which only a small group of stations can compete for channel access during a given period. The IEEE 802.11ah standard, a.k.a. Wi-Fi HaLow, deploys this idea in its channel access protocol, named Restricted Access Window (RAW). A single RAW consists of one or more RAW slots during which only designated stations can contend for channel access. This paper considers an IEEE 802.11ah-based network with randomly distributed stations around the Access Point (AP), operating under a Rayleigh-fading channel with capture enabled. We develop an analytical model to evaluate the contention of a group of stations and propose a Load-Aware Channel Allocation (LACA) algorithm for the RAW slot period. The LACA algorithm ensures the delivery of all packets that designated stations carry, allowing the allocation of load-aware RAW slots, which is effective in enhancing the Age of Information (AoI). We evaluate the Packet Delivery Ratio (PDR) and channel usage within a pre-allocated RAW slot to prove the effectiveness of our proposal. We further study the impact of the spatial distribution of the stations around the AP and the capture effect under a Rayleigh channel on the performance of the proposed LACA algorithm. Extensive simulations are used to validate our analytical results. Our proposal provides a load-aware and adaptive channel allocation scheme based on the dynamic conditions of the network. Our model can be implemented in a global configuration scheme for the RAW mechanism in heterogeneous networks or for alternative communication technologies that address dense scenarios with the integration of periodic channel reservations.

Failure Prediction and Replacement Strategies for Smart Electricity Meters Based on Field Failure Observation

It is helpful to have a replacement strategy by predicting the number of failures of in-service electricity meters. This paper presents a failure number prediction method for smart electricity meters based on on-site fault data. The prediction model was constructed by combining Weibull distribution with odds ratios, then the distribution parameters, failure prediction number, and confidence intervals of prediction number were calculated. A strategy of meter replacement and reserve were developed according to the prediction results. To avoid the uncertainty of prediction results due to the small amount of field data information, a Bayesian failure number prediction method was developed. The research results have value for making operation plans and reserve strategies for electricity meters.

Proactivity in content delivery networks resource management: The stock options case

This paper focuses on the management and pricing of resources in Content Delivery Networks (CDNs), which are extensively adopted nowadays as a very efficient mechanism for Internet information provision. We elaborate on a resource management model that is aligned with the concepts and mechanisms in capital markets. We use the concept of Stock Options (SOs) to address the scarcity and potential unavailability of CDN resources. Using a Predictive Reservation Scheme (PRS), network resources (traffic volume) are being monitored through kernel estimators in a given time frame. A Secondary Market (SM) significantly improves the efficiency and robustness of the PRS by allowing the fast exchange of unused resources (stocks) and SOs between the Origin Servers (OSs). This exchange can happen by allowing automatic electronic double auctions at the end of each day or at shorter time intervals. As a result, OSs may acquire resources (if needed) at standard prices, avoiding penalizing tariffs for last-minute requests. The efficiency of our prediction reservation scheme further improves.

Research on a mobile terminal-based control platform for the delivery and management of power material transportation

Some of the power material transportation delivery control platforms have the defect of too small throughput in the actual application scenarios, therefore, a mobile terminal-based power material transportation delivery control platform is designed. The hardware part: the mobile terminal is used to arrange the necessary equipment for operation monitoring, such as monitoring screens and servers, and is connected to the industrial control mainframe through a USB connection cable and to GPS devices; the software part is on the basis of the established reserve strategy, the process of power material transport delivery control is improved, the number of profitable stored within the cross item is calculated for each demand entry and the corresponding inventory entry, and the balance profitability constraints are extracted, isolating the base stations and optimizing the system’s intelligent inventory function. Test results: The power material transport delivery control platform in the paper has a higher average throughput than the other two power material transport delivery control platforms: 26.344req/s and 24.495req/s respectively, indicating a higher practical value of the designed power material transport delivery control platform after making full use of mobile terminals.