Load Allocation Research Articles

Team Teaching Load using Linear Programming

Assignment of teaching loads refers to the allocation of teaching hours among academic staff. It will be the ideal way to assign the right courses to the right staff based on their expertise, preference and experience. The common practice in this department to allocate the teaching load is done manually through trial-and-error using Microsoft Excel which is inefficient and time consuming. Moreover, the manual allocation may lead to bias judgement and get unfavourable courses among academic staff. Thus, this study aims to propose a teaching load allocation model which able to optimize the teaching quality. A primary data has been collected using google form among 25 lecturers with 13 courses considered. Then, a linear programming model was applied based on department policies as constraints in order to find an optimal solution. A feasible solution will be solved using LINGO optimization software and the model serves as a best tool to assist head of department to allocate teaching loads. It found that the model proposed is suitable to be employed for teaching load allocation in this department .

DLMNN: A Deep Learning Modified Neural Network for Balancing the Load of Cloudlets on Cloud

Cloud Computing a revolution in the computing world, has enabled the users to utilize the services on the Cloud platform from anywhere at any time. As there is an increase in the demand for the utilization of a cloud environment, there are several challenges to be addressed by the companies or organizations to provide uninterrupted cloud services. To make the cloud services available without interruption, the challenge of balancing the load on cloud servers is a must. Proper allocation of load on the servers optimize the performance of the cloud and improves the efficiency to offer uninterrupted services. Recent studies have shown, cloud always needs to have a capable algorithm to distribute the load on servers of cloud architecture to be available to process cloudlets submitted by the customers. Our paper looks for a new load balancing algorithm that uses the concepts of neural network and is used to allocate the tasks in the cloud. The proposed algorithm consists of two steps. First, Features of tasks and cloud servers are extracted, and the necessary features are selected. The feature selection can be done by using MPCA. In the second step, the selected features are sent as input to the DLMNN algorithm to schedule the task in the cloud. Finally, the experimental results of the proposed DLMNN are compared with some existing algorithms.

Distributed Control With Virtual Capacitance for the Voltage Restorations, State of Charge Balancing, and Load Allocations of Heterogeneous Energy Storages in a DC Datacenter Microgrid

This paper proposes a distributed coordination control strategy for load sharing and energy balancing between heterogeneous energy storages. These control objectives are satisfied through a two-level control structure. At the primary level, the decentralized virtual impedance control, without the requirement of communication links, allocates the low frequency component of the loads to batteries, while the high frequency component is allocated to ultracapacitors. Distributed control strategy, introduced at the secondary level over a sparse communication network, achieves battery state of charge balancing and regulation of the local bus voltages. Two sets of data are exchanged via the communication links, the local bus voltages, and state of charges of batteries. The distributed controller for the restoration of local bus voltages implements an average consensus protocol, while the controller for energy balancing uses a cooperative protocol. In addition, the ultracapacitor voltages are locally restored at a slower time-scale. The proposed control strategy is resilient to communication link failures and features plug-and-play capability. Presented results demonstrate performance of the proposed control strategy for an islanded 380 VDC datacenter with variable loads. Different operating conditions are verified through the RTDS Technologies real-time digital power system simulator using switching converter models and nonlinear battery models.

PRÁTICA COMO COMPONENTE CURRICULAR: HORIZONTES DE COMPREENSÃO DOS FORMADORES DE PROFESSORES DE QUÍMICA

A proposta de implementação da Prática como Componente Curricular (PCC) nos cursos de formação de professores, prestes a completar dezessete (17) anos, ainda se constitui por entendimentos multifacetados, aspecto que tem repercutido em uma pluralidade de formatos para sua materialização nas matrizes curriculares. Diante desse cenário, nossa investigação buscou compreender os sentidos atribuídos pelos professores, formadores de professores de Química, sobre a PCC e identificar suas possíveis configurações em distintos cursos de Licenciatura. O material empírico contempla as respostas de vinte e um (21) professores, vinculados a nove (9) Instituições Públicas de Ensino Superior, a um questionário divulgado de forma on line, composto por nove (9) questões. Neste texto apresentamos a análise de três (3) questões, que tratam especificamente da PCC, a análise foi organizada em três (3) dimensões. A primeira estruturada a partir dos princípios teórico-metodológicos da Análise Textual Discursiva (ATD), desenvolvida por Moraes e Galiazzi (2016). A segunda e a terceira se orientaram por uma proposta de cunho qualitativo descritivo, tendo como base a investigação desenvolvida por Kasseboehmer e Farias (2012). Por meio do processo de compreensão das respostas ao questionário, podemos inferir que a PCC é percebida como espaço que permite a aproximação entre teoria e prática e valorização dos saberes de interface, ou seja, aqueles relacionados a aspectos específicos da aprendizagem, como os correlatos ao Ensino de Química. Suas possibilidades de implementação, em ampla maioria, remetem a alocação da carga de PCC em componentes curriculares que trabalham saberes de interface. Os princípios que orientam as estratégias didáticas nesses componentes se vinculam, majoritariamente, ao desenvolvimento de ações direcionadas ao microensino, relação teoria e prática, articulação a realidade escolar e análise e produção de materiais didáticos.

Optimal placement of data concentrators for expansion of the smart grid communications network

Evolving power systems with increasing renewables penetration, along with the development of the smart grid, calls for improved communication networks to support these distributed generation sources. Automatic and optimal placement of communication resources within the advanced metering infrastructure is critical to provide a high-performing, reliable, and resilient power system. Three network design formulations based on mixed-integer linear and non-linear programming approaches are proposed to minimise network congestion by optimising residual buffer capacity through the placement of data concentrators and network routeing. Results on a case study show that the proposed models improve network connectivity and robustness, and increase average residual buffer capacity. Maximising average residual capacity alone, however, results in both oversaturated and underutilised nodes, while maximising either minimum residual capacity or total reciprocal residual capacity can yield much-improved network load allocation. Consideration of connection redundancy improves network reliability further by ensuring quality-of-service in the event of an outage. Analysis of multi-period network expansion shows that the models do not deviate significantly from optimal when used progressively (within 5% deviation), and are effective for utility planners to use for smart grid expansion.

Load allocation improvement for chiller system in an institutional building using logistic regression

Chiller systems are an essential part of central air conditioning systems to provide cooling energy in buildings. This study discusses how to improve the load allocation of chillers to reduce the system electricity consumption. An analysis of comprehensive operating data from an existing system with 5 chillers of 2 different capacities identified 101 types of component combination. The variation of system coefficient of performance depended significantly on the chiller part load ratio, followed by the temperature of cooling water leaving the condensers and the type of component combination. Chiller sequencing took place for only 30.1% of the total operating time. There was 61.73% of the total operating time in underload conditions with an average part load ratio of 0.65. Implementing chiller sequencing at all times increased the average part load ratio to 0.78 with a 15.81% reduction in the electricity consumption. Based on the results of multi-nominal logistic regression, the underload conditions could be predicted by an increase in the temperature of supply chilled water and the temperature of cooling water entering the condensers. On the other hand, an increase in the temperature of returned chilled water and the temperature of cooling water leaving the condensers reflected a higher chance of overload conditions. Controlling system components with the trend of significant temperature variables was proposed to enhance chiller sequencing.

Joined Probabilistic Load Flow and Sensitivity Analysis of Distribution Networks Based on Polynomial Chaos Method

Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this paper, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized polynomial Chaos algorithm and on a regionwise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads.

Survivability Evaluation Method for Cascading Failure of Electric Cyber Physical System Considering Load Optimal Allocation

The scale of the electric cyber physical system (ECPS) is continuously extending, and the existing cascade failure models ignore both the information flow and power flow transferring characteristics and also lack effective survivability analysis. In this paper, the quantitative evaluation method for cascading failure of ECPS survivability considering optimal load allocation is proposed. Firstly, according to the system topological structure and correlation, the degree-betweenness weighted correlation matrix of ECPS is established by defining the degree function as well as the electric betweenness, and the formal representation of coupled ECPS network model is realized. Secondly, based on the structural connectivity change and risk propagation range of ECPS cascade failure, the survivability evaluation model is designed by taking into account the constraints such as node load capacity limitation, information flow optimal allocation strategy, power flow optimization equation, and system safety operation. Finally, the firefly algorithm with chaotic Lévy flight is proposed to solve the evaluation model efficiently. The case study vividly shows that the evaluation method can effectively quantify the survivability of ECPS and thus enhance the evaluation efficiency of large-scale coupled systems.

Reliability-constrained optimal sizing and rechargeable battery selection for improved load distribution in a fuel-cell hybrid railway propulsion system

Limiting the increase of global temperature requires electrification of the railway transport, which is relatively a distinct mode of transport in terms of economy, business and socialization of a country. However, battery complemented railway transport systems such as fuel cell hybrid railway propulsion systems are constrained by the high cost of the battery, and suboptimal performance of the fuel cell under varying loads. Optimized selection of battery can reduce its costs associated with sizing, life and utilization. Similarly, managing the load distribution between the battery and fuel cell can reduce the load variations concern for fuel cell. In this study, model-based selective characterization of the battery and fuel cell is done to identify the optimal features and constraints. Following characterization, the load allocation to battery and fuel cell is determined with the targets of collaborative utilization of optimal features and mitigation of constraints. Subsequently, considering this load allocation and results of characterization, conditions are proposed for reliable sizing and optimized selection of battery. The significance of the proposed conditions in reliable sizing is explored. Based on these conditions, certain commercially developed batteries are evaluated for optimized selection of battery. The results signify this study as a foundation for research on the reliable sizing and optimized selection of battery in fuel cell hybrid railway propulsion systems.

Coded Computation Over Heterogeneous Clusters

In large-scale distributed computing clusters, such as Amazon EC2, there are several types of “system noise” that can result in major degradation of performance: system failures, bottlenecks due to limited communication bandwidth, latency due to straggler nodes, and so on. There have been recent results that demonstrate the impact of coding for efficient utilization of computation and storage redundancy to alleviate the effect of stragglers and communication bottlenecks in homogeneous clusters. In this paper, we focus on general heterogeneous distributed computing clusters consist of a variety of computing machines with different capabilities. We propose a coding framework for speeding up distributed computing in heterogeneous clusters by trading redundancy for reducing the latency of computation. In particular, we propose heterogeneous coded matrix multiplication (HCMM) algorithm for performing distributed matrix multiplication over heterogeneous clusters that are provably asymptotically optimal for a broad class of processing time distributions. Moreover, we show that HCMM is unboundedly faster than any uncoded scheme that partitions the total workload among the workers. To demonstrate how the proposed HCMM scheme can be applied in practice, we provide results from numerical studies and Amazon EC2 experiments comparing HCMM with three benchmark load allocation schemes—uniform uncoded, load-balanced uncoded, and uniform coded. In particular, in our numerical studies, HCMM achieves speedups of up to 73%, 56%, and 42%, respectively, over the three benchmark schemes mentioned earlier. Furthermore, we carry out experiments over Amazon EC2 clusters and demonstrate how HCMM can be combined with rateless codes with nearly linear decoding complexity. In particular, we show that HCMM combined with the Luby transform codes can significantly reduce the overall execution time. HCMM is found to be up to 61%, 46%, and 36% faster than the aforementioned three benchmark schemes, respectively. Additionally, we provide a generalization to the problem of optimal load allocation in heterogeneous settings, where we take into account the monetary costs associated with distributed computing clusters. We argue that HCMM is asymptotically optimal for budget-constrained scenarios as well. In particular, we characterize the minimum possible expected cost associated with a computation task over a given cluster of machines. Furthermore, we develop a heuristic algorithm for (HCMM) load allocation for the distributed implementation of budget-limited computation tasks.

Load following of SMR based on a flexible load

When the small modular reactor (SMR) operates off-grid, the load following demand increases. To solve this problem, a flexible load is used to compensate the electricity load. The flexible load comprises power-consuming systems that can change their load actively, such as district heating, desalination, hydrogen production, and other industrial heat supply systems. Based on the modeling of the nuclear steam supply system, turbine, and flexible load, a simulation platform is established in the MATLAB/Simulink environment. For the load following to be compensated by the flexible load, the key problem is dynamic load allocation, in which the electricity load is divided into a moderate part and fluctuating part. The two parts of the load are undertaken by the SMR and flexible load, respectively. Based on the characteristics of different flexible loads, the changeable flexible load and fluctuating flexible load are presented. Two load following control methods are presented for the two types of flexible loads. The simulation results show that the present method can not only relieve the load following burden of the reactor core but also provide steam with stable quality to the flexible load, thereby improving the flexibility and efficiency of the SMR.

Curriculum Implementation Integral Education Based on Tauhid (Case Study of Curriculum Implementation in SMA Luqman Al Hakim Surabaya)

This study aims to explore how the design of Integral based Tauhid Education curriculum in SMA Luqman Al Hakim Surabaya, how to implement it, and how to improve the quality of education. The type of approach in this study is descriptive qualitative, as the direction of the type of qualitative research, then the data collected were analyzed to obtain various interpretative descriptions according to the logical and rational scientific research rules on the data collected. The results of this study can be summarized as follows. 1) The curriculum of Tauhid-Based Integral Education in SMA Luqman Al Hakim Surabaya is a blend curriculum of curriculum 2013. 2) The implementation of Tauhid-Based Integral Education Curriculum using three approaches that serve as references, first, Tilawah Method, second, Tazkiyah Method, and Third, Ta'limah Method. Curriculum development contains three characteristics, namely politeness priority before science, thorough and aligned, and back to nature. The implementation of the Tauhid-Based Integral Curriculum in SMA Luqman Al Hakim Surabaya is described in the structure and content of the curriculum, setting the learning load and time allocation, learning completeness, appraisal system, increase, graduation, and life skills education. 3. The implementation of Tauhid-Based Integral Education Curriculum in improving the quality of education in SMA luqman Al Hakim Surabaya, through the preparation of human resources who have qualifications in their field and have the spirit to preach through education. The three characters of the students’ output from the curriculum of Tauhid-Based Integral Education are, first Taqwa, second Intelligent, and third independent. Keywords : Curriculum Implementation, Integral Education, Tauhid-Based

Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode

A charging load allocation strategy for Electric Vehicles (EVs) considering charging mode is proposed in this paper in order to solve the challenge and opportunity of large-scale grid-connected charging under the background of booming EV industry in recent years. Based on the peak-to-valley Time-of-Use (TOU) price, this strategy studies the grid load, charging cost and charging station revenue variation of EVs connected to the grid in different charging modes. In addition, this paper proposes an additional charging mechanism for charging stations to encourage EV owners to participate in the peak and valley reduction of the grid through coordinated charging. According to the example analysis, under the same charging demand conditions, the larger EV charging power will have a greater impact on the grid than the conventional charging power. This article collects additional service fees for car owners who are not involved in the coordinated charging. When the response charging ratio is less, the more total service charges are charged, which can compensate for the decline in the sales revenue of the charging station during the valley period. While having good economy, it can also encourage the majority of car owners to participate in the coordinated charging from the perspective of charging cost.

Managing Municipal Wastewater Treatment to Control Nitrous Oxide Emissions from Tidal Rivers

Waste load allocation management models were developed for controlling nitrous oxide emissions from a tidal river. The decision variables were treatment levels at wastewater discharging stations and the rate of upstream water release. The simulation model for N2O emissions from the river was embedded in the optimization model and the problem was solved using the simulated annealing technique. In two of the models, the total cost was minimized, while in the third model, emissions from the river were minimized for a specified constraint on the available money. Proof-of-concept studies, with hypothetical scenarios for contaminant loading but realistic flow conditions corresponding to the Tyne River, UK, were carried out. It was found that the treatment cost could be reduced by 36% by treating wastewater discharges in the upper reaches more during the high tide as compared to during low tide. For the same level of N2O emissions, approximately 16.7% lesser costs could be achieved by not only treating the wastewater but also inducing dilution by releasing more water from the upstream side. It was also found that beyond a limit, N2O emissions cannot be reduced significantly by spending more money on treatment and water release.

Characterization of Street Sweeping Material for Addressing Total Maximum Daily Waste Load Allocations

AbstractMunicipal separate storm sewer system (MS4) permittees are increasingly required to reduce pollutant of concern (POC) loadings from their service areas to achieve waste load allocations (WL...

(Network-service value)-based admission decision mechanism for ONU’s virtual access under distributed control in virtual passive optical network

Abstract In this paper, a (network-service value)-based admission decision mechanism for ONU’s virtual access under distributed control in virtual passive optical network (VPON) is proposed. By introducing distributed control into VPON, a more efficient VPON architecture is proposed. By designing corresponding information flow, the virtual connections between ONU and OLT can be constructed. By proposing a comprehensive weight ratio allocation (CWRA) algorithm, an admission decision mechanism is constructed. By this mechanism, the load and bandwidth allocation of VPON can be adjusted. By simulation, the effectiveness of the proposed mechanism is demonstrated.

Variable fleet size and mix VRP with fleet heterogeneity in Integrated Solid Waste Management

The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran.

A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation.

This study proposes a fuzzy multi-stakeholder socio-optimal methodology for joint water and waste load allocation (WWLA) in river systems while addressing upstream flow uncertainty and different social choice rules (SCRs). QUAL2Kw, as the numerical river water quality model, is executed for various scenarios of water and waste loads to construct a comprehensive dataset of plausible settings, which is in turn used to train a meta-model in the form of multivariate linear regressions. The river upstream flow as the main uncertain parameter is assessed by fuzzy transformation method (FTM). Then, for different confidence levels of fuzzy uncertain input, the meta-model is linked with the non-dominated sorting genetic algorithm (NSGA-II) multi-objective optimization model to generate trade-off curves among the stakeholders' utility functions. Subsequently, five SCRs are utilized at each confidence level to determine the fuzzy interval solutions for each objective. Next, the possibility degree method is applied to rank the fuzzy interval solutions in each α-cut level. Finally, considering the priorities of all stakeholders, the fallback bargaining method is used to specify the most appropriate SCR in each confidence level. Application of the proposed methodology in Kor River, Iran, shows its efficacy to realize the socio-optimal WWLA scenario(s) among different stakeholders.

Multitimescale Coordinated Adaptive Robust Operation for Industrial Multienergy Microgrids With Load Allocation

Manufactory load allocation can be used as an effective industrial demand response scheme to reduce operating costs for industrial multienergy microgrids (iMEMGs). In addition, combined cooling, heat, and power (CCHP) plants with auxiliary devices can provide low-cost multiple energies for industrial plants. However, uncertain power generation from renewable energy sources impairs the iMEMG's operation, leading to challenges such as increased operating costs and energy supply deficiency. To conquer these challenges, this paper proposes a multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated on different timescales. In the weekly scheduling stage, industrial loads and CCHP units are scheduled for the following week and the hourly iMEMG operation is optimized within the week. Besides, this paper applies an adaptive robust optimization method where the uncertain renewable power generation is fully addressed. The proposed approach is tested on an iMEMG with various industrial manufactories, and it is compared with conventional methods. The simulation results indicate that compared to the conventional ones, the proposed approach can guarantee a robustly optimal operation solution for the iMEMG against any uncertainty realization.

Impairment Aware Routing, Bit Loading, and Spectrum Allocation in Elastic Optical Networks

The exponential growth of Internet traffic due to bandwidth hungry applications has led to the design of spectrum efficient, flexible, and scalable elastic optical networks. The scope of high spectral-efficient modulation formats and optical orthogonal frequency-division multiplexing (OFDM) in coherent optical communication systems appears to be significant in catering to the heterogeneous bandwidth demands. Furthermore, OFDM enables each subcarrier to be modulated independently to enhance the spectrum efficiency known as bit loading in OFDM. On the other hand, spectral efficiency and the optical reach of a signal are limited by the transmission impairments such as shot noise, amplified spontaneous emission noise, beat noises due to coherent detection, crosstalk in cross-connect, nonlinear and filter narrowing effects, etc. In this paper, we propose a novel impairment-aware routing, bit loading, and spectrum allocation scheme through a mixed-integer linear programming optimization framework. Within our optimization framework, we ensure that a minimum end-to-end bit error rate for each demand is provisioned in the presence of impairments. We also propose a novel heuristic for realistic networks and compare the performance with existing schemes. Through simulation, we demonstrate the benefits of our approach in terms of achieving lower blocking probability and the spectral fragmentation compared to other existing studies.