Load Allocation Research Articles

Research on power distribution in multiple-input multiple-output magnetic coupling resonance wireless power transfer system

Aimed at different demands of power of multiple receiver loads in multiple-input multiple-output wireless power transfer (MIMO-WPT) system, in the paper, a method of calculating the excitation source at the transmitter by general circuit model is put forward to realize the power allocation of the receiver loads. First, the magnetic coupling resonance (MCR) MIMO-WPT system is made to be equivalent to a linear circuit model, and the general circuit model of MIMO-WPT system is established, which describes output transfer characteristics, transmission efficiency and output power of the MIMO-WPT system with parametric state matrix. Then, based on the power demand of each receiver load, the vector expression of the excitation source at the transmitter of MIMO-WPT system is derived by the general circuit model. At last, the simulation experiment of WPT system with 3 transmitting and 2 receiving coils is designed to verify the accuracy of the general circuit model estimation and the feasibility of the power distribution of the receiver load. Meanwhile, the application of the general model provides a feasible solution to solve different power demands of multiple receiving loads in the MIMO-MCRWPT system.

KWh Demand Assessment and Forecasting of a Low Voltage Private Academic Institution

Transformer is one of the crucial components in an electrical system. They are used to convert energy to a utilization level. Load allocation is vital to sustaining the needed energy demand for a growing population, and it is an essential consideration for continuous consumer connectivity of electricity. In this study, the researchers are conducting assessment and forecast of the transformer requirement for a Low Voltage Private Academic Institution. It assessed the kWh demands and kWh allocation from 2005 to 2019 of the three transformers used of the locale. The kWh demand was used to forecast the per semiannual for the next five years. Six forecasted models were applied and the result were verified by QM for Windows Version 5. Mean Absolute Error (MAE), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE) were used to select the best forecasting model with the least value. DT (Distribution Transformer) 1 used the 4th degree polynomial model because of the least value of MAE, MSE and MAPE of 5340.626, 42306100 and 17.12%, while DT 2 and DT 3 applied the 3 rd degree polynomial model though it has the second lowest value of MAE, MSE and MAPE of 3142.907, 16748570, 20.44% and 2576.398, 8740315, 11.21% respectively because the projected demand of the 4th degree gave a negative value.. The researcher finds that DT 1 and 3 were starting to exceeds the allotted kWh. On the other hand, DT 2 and 4 were plenty of kWh allocation. Relocation of load from DT 1 and 3 to DT 2 may take for considerations by the institution.

KWh Demand Assessment and Forecasting of a Low Voltage Private Academic Institution

Transformer is one of the crucial components in an electrical system. They are used to convert energy to a utilization level. Load allocation is vital to sustaining the needed energy demand for a growing population, and it is an essential consideration for continuous consumer connectivity of electricity. In this study, the researchers are conducting assessment and forecast of the transformer requirement for a Low Voltage Private Academic Institution. It assessed the kWh demands and kWh allocation from 2005 to 2019 of the three transformers used of the locale. The kWh demand was used to forecast the per semiannual for the next five years. Six forecasted models were applied and the result were verified by QM for Windows Version 5. Mean Absolute Error (MAE), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE) were used to select the best forecasting model with the least value. DT (Distribution Transformer) 1 used the 4th degree polynomial model because of the least value of MAE, MSE and MAPE of 5340.626, 42306100 and 17.12%, while DT 2 and DT 3 applied the 3 rd degree polynomial model though it has the second lowest value of MAE, MSE and MAPE of 3142.907, 16748570, 20.44% and 2576.398, 8740315, 11.21% respectively because the projected demand of the 4th degree gave a negative value.. The researcher finds that DT 1 and 3 were starting to exceeds the allotted kWh. On the other hand, DT 2 and 4 were plenty of kWh allocation. Relocation of load from DT 1 and 3 to DT 2 may take for considerations by the institution.

Impact of Stochastic Generation/Load Variations on Distributed Optimal Energy Management in DC Microgrids for Transportation Electrification

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results.

Throughput-Efficient Lagrange Coded Private Blockchain for Secured IoT Systems

We develop a new Lagrange coded blockchain model for Internet-of-Things (IoT) systems based on Lagrange coded computing (LCC). In the model, a mining task assigned to a blockchain node (BN) is encoded with a specific encoding function. Thus, the final result, i.e., newly generated block or block verification result, can be decoded even when only some mining outputs returned by BNs are correct, while other outputs are erroneous or discarded due to delays. To be decoded correctly, the number of mining outputs returned prior to decoding must be at least a given decoding threshold. Then, security against malicious BNs and resilience against stragglers can be guaranteed if the number of mining tasks allocated to BNs is not less than the sum of decoding threshold, number of stragglers, and double of the number of malicious BNs. Unlike other IoT blockchains and LCC-based methods showing enhanced throughput but yielding poor security, our model can improve throughput without compromising on security. This is achieved through optimized load allocations when the higher loads (two or more mining tasks) are allocated to the fastest BNs leading to: 1) increased number of mining outputs returned prior to decoding required to meet the decoding threshold and 2) increased number of allocated mining tasks to strengthen security and resilience. To overcome the limitation of our model related to higher loads and, hence, higher mining costs to BNs, we develop a contract-theoretic mechanism that incentivizes each BN to complete its mining task through joint load and transaction fee allocations.

Cascading failures of overload behaviors on interdependent networks

The increasing dependence between infrastructure networks and the threat of cascading failures on network security motivated the analysis of the overload behaviors on interdependent networks. Here, considering the node load redistribution mechanism, we study the robustness of interdependent networks with different attack strategies, connection strategies and load distribution mechanisms considering attack strength (r), coupling strength (q) and different network topologies (Erdős–Rényi network model and Albert–László Barabási network model). Compared to the different connection strategies, with q and qc, we conclude that the response of the cascade failure is more evident in the assorted connections which keep more concentrated load nodes. Meanwhile, by the proportional allocation of load in interdependent networks, we observe that the BA–BA networks with DC strategy (connect nodes with high load in network A and nodes with low load in network B) have the worst robust level against cascading failures in the case of the intentional attack. In addition, by considering the charge distribution mechanisms, we demonstrate that interdependent networks have the greatest capacity to withstand risk when the charge distribution preference (β) is closed at the initial charge of the nodes (τ). Theoretical solutions are useful for revealing cascading failures of overload behaviors in large-scale networks composed of multiple networks in the real world.

Investigation of physiological stress shielding within lumbar spinal tissue as a contributor to unilateral low back pain: A finite element study

IntroductionThe pathomechanism of low back pain (LBP) remains unknown. Unilateral LBP patients have demonstrated ipsilateral morphological and material property changes within the lumbar soft tissues, potentially leading to asymmetric tissue loading. Through the comparison of healthy and unilateral LBP validated finite element models (FEMs), this study investigates potential stress shielding consequential of spinal tissue property augmentation. MethodsTwo FEMs of the musculoskeletal system – one demonstrating healthy and unilateral LBP conditions – were developed undergoing 30-degree flexion. FEMs included the vertebrae, intervertebral discs, and soft tissues from L1-S1. Material properties selected for the soft tissues were retrieved from published literature. To reflect unilateral LBP, the paraspinal morphology was atrophied, while the tissue moduli were increased. The symptomatic thoracolumbar fascia (TLF) was uniformly increased. Validation of the models preceded testing. ResultsModel validation in spinal flexion was accomplished through comparison to literature. Compared to the healthy model, the unilateral LBP multifidus (MF), longissimus thoracis (LT), and TLF exhibited average tension changes of +7.9, −5.1, and +9.3%, respectively. Likewise, the symptomatic MF, LT, and TLF exhibited tension changes of +19.0, −10.4, and +16.1% respectively, whereas the asymptomatic MF, LT, and TLF exhibited −4.0, −2.0, and +0.4% changes in tension, respectively. ConclusionRelative to the healthy tissues, the symptomatic LBP soft tissues demonstrated a 19.5 kPa increase in stress, with 99.8% of this increase distributed towards the TLF, suggesting a load allocation bias within the symptomatic unilateral LBP tissues. Consequentially, symptomatic paraspinal muscles may be unable to withstand loading, leading to stress shielding.

A HYBRID ALGORITHM TO SOLVE THE FIXED CHARGE SOLID LOCATION AND TRANSPORTATION PROBLEM

In this paper, we propose a Hybrid Algorithm (HA) to solve the Fixed Charge Solid Location and Transportation problem (FCSLTP). The FCSLTP considers the cost of facility location and route fixed costs during transportation planning or load consolidation. The HA integrates two heuristics into the Genetic Algorithm framework to solve the FCSLTP. Genetic operations are used to select the best combination of facility locations while a greedy heuristic which uses some cost relaxations are used for the initial load allocation. An improvement heuristic, a modified stepping stone method, is then used to consolidate load allocations to realize further possible cost savings. Parameters used for the genetic operations were decided through preliminary studies. Computational studies using randomly generated data were performed to compare the HA solutions with the solutions obtained using CPLEX, a commercial solver. Performance comparison was done based on the quality of solution and computing time. The results suggest the solution approach is competitive.

Exploring the Non-significant Difference on Students’ Cognitive Load Imposed by Robotics Tasks in Pair Learning

Currently, the cognitive load imposed by tasks with different complexity during collaborative learning or pair learning has not been well studied. This study examined the effect of three types of tasks with different complexity on students’ cognitive load during pair learning. We randomly selected a total of 76 pupils to participate in a one-semester robotics course for the experiment. The results showed that the difference in students’ cognitive load imposed by pair learning and robotics tasks with different complexity was not significant. However, it is complicated to explain the results. From one side of the coin, pair learning may be a key factor. The more complex the task is, the more possible that an individuals would collaborate with partners to promote the equal allocation of intrinsic cognitive load. In contrast, the simple tasks would cause free-riding and thus impede the equal allocation of cognitive load. From another side, the essence of germane cognitive load might play an important role too, since students’ schemas were not sufficiently automated when they encountered the easy tasks at the early learning stage, but automated to some extent when they complete complex tasks during the later learning period. Implications for practitioners are also discussed in this study.

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

Multi-objective economic dispatch of distributed generation using differential evolution algorithm

Economic Dispatch (ED) deals with the allocation of the essential load amid the existing power plants such that the operating cost is reduced. The ED problem is framed as a nonlinear optimization problem with subjected constraints. In this study, a distributed generation (DG) optimization using a multi objective differential evolution (MODE) algorithm is explored. This study aims to find out the minimum fuel cost, environmental pollution penalty cost and minimum system real power loss. MODE algorithm is implemented on 33-bus distribution system (DS) to obtain the ideal real power dispatch of DGs. Four DGs are considered which having two diesel generator, one wind generator and one fuel cell. The result shows the minimized value of Fuel/Emission penalty cost and power loss in the DG units using MODE algorithm when compared with Multi Objective Particle Swarm Optimization (MOPSO) algorithm.

Functional Analysis of an Unexplored Brain Region: the Claustrum

The claustrum is a thin, sheet-like neural structure located beneath the cerebral neocortex, and has reciprocal connections with nearly all neocortical areas. It has been hypothesized to play roles in higher brain functions, such as consciousness, multisensory integration, salience detection, and attentional load allocation. However, its roles in brain physiology have not been precisely elucidated, as only a few experimental studies on claustrum function exist. We established a transgenic mouse line expressing Cre recombinase specifically in a population of claustral excitatory neurons that received inputs from and sent outputs to widespread neocortical areas. The claustral neuronal firing was mostly correlated with the cortical slow-wave activity. In vitro optogenetic stimulation of the claustrum induced excitatory postsynaptic responses in most of the neocortical neurons, however, action potentials were primarily elicited in the inhibitory interneurons. In vivo optogenetic stimulation induced a synchronized Down-state featuring prolonged silencing of neural activity in all layers across widespread cortical areas, followed by Down-to-Up state transition. In contrast, genetic ablation of the claustral neurons led to an attenuation of slow-wave activity in the frontal cortex. These results indicate a crucial role of the claustum in synchronizing inhibitory interneurons across the wide cortical areas for spatiotemporal coordination of slow-wave activity.

Solution of the compromise optimization problem of network graphics on the criteria of uniform personnel loading and distribution of funds

The object of research is a model network schedule for performing a complex of operations. One of the most problematic areas is the lack of a unified procedure that allows finding a solution to the problem of compromise optimization, for which the optimization criteria can have a different nature of the influence of input variables on them. In this study, such criteria are the criteria for the uniformity of the workload of personnel and the distribution of funds. Two alternative cases are considered: with monthly planning and with quarterly planning of allocation of funds and staff load. The methods of mathematical planning of the experiment and the ridge analysis of the response surface are used. The peculiarities of the proposed procedure for solving the problem of compromise optimization are its versatility and the possibility of visualization in one-dimensional form – the dependence of each of the alternative criteria on one parameter describing the constraints. The solution itself is found as the point of intersection of equally labeled ridge lines, which are curves that describe the locally optimal values of the output variables. The proposed procedure, despite the fact that it is performed only on a model network diagram, can be used to solve the trade-off optimization problem on arbitrary network graphs. This is due to the fact that the combination of locally optimal solutions in a parametric form on one graph allows visualizing all solutions to the problem. The results obtained at the same time make it possible to select early dates for the start of operations in such a way that, as much as possible, take into account possible difficulties due to the formation of bottlenecks at certain stages of the project. The latter may be due to the fact that for the timely execution of some operation, it may be necessary to combine two criteria, despite the fact that the possible costs may turn out to be more calculated and estimated as optimal.

Impact of Communication Packet Delivery Ratio on Reliability of Optimal Load Tracking and Allocation in DC Microgrids

Communication systems introduce uncertainties that directly impact power system management. Quantitative analysis of such impact is essential for power system reliability and resilience. This paper establishes rigorous relationships between communication packet delivery ratio and errors on optimal load tracking and allocation (OLTA) in DC microgrids (MGs). By modeling channel uncertainties using packet delivery ratio and communication network topologies, impact of communication packet loss on the distributed OLTA algorithm for DC MGs is studied. It is shown that communication packet loss directly affects the convergence rate of the distributed OLTA algorithm under intermittent renewable generations. The results of this paper quantitatively characterize a practical criterion for securing reliability of OLTA solutions under communication uncertainty. Simulation studies using a real-world DC MG are conducted to validate the theoretical findings.

Complex Energy Networks Optimization: Part I—Development and Validation of a Software for Optimal Load Allocation

Abstract The growing diffusion of the distributed generation systems, due to the European and national legislations which impose the fossil fuel and greenhouse gas emissions reduction and the renewable sources exploitation, have led to an increase in the complexity of the existing energy networks. The main issue of the complex energy grids is their management, which consists in the resolution and optimization of the load allocation problem by minimizing the primary energy consumption and, thus, improving the overall efficiency. In this context, the aim of this paper is to develop and validate a nonlinear algorithm suitable for the resolution of the load allocation problem. In detail, the software combo, which has been developed by the University of Bologna, is based on a nonheuristic algorithm and allows to optimize a complex energy network—characterized by electrical, thermal, cooling and fuel fluxes—by evaluating all the possible combinations of solutions. The objective function of the software consists in the minimization of the total cost of energy production, including not only the variable costs, but also the costs related to the environmental impact of the energy systems. In this paper the mathematical model of the algorithm at the basis of the software combo is presented and described in detail. Furthermore, the software has been validated by its application to a case study and comparing the results with the ones obtained with a previously developed software based on a genetic algorithm (heuristic nonlinear method).

Dynamic Load Balancing in Parallel Execution of Cellular Automata

The allocation of the computational load across different processing elements is an important issue in parallel computing. Indeed, an unbalanced load distribution can strongly affect the performances of a parallel system caused by an excess of synchronization idle times due to less loaded processes waiting for more loaded ones. In this article, we focus on the load balancing issues in the context of the parallel execution of spatial-related applications where the domain space is partitioned in regions that are assigned to different processing elements. In particular, without loss of generality, we consider the well-known spatial-related Cellular Automata computational paradigm for evaluating the proposed dynamic load balancing approach. The main contribution of this article is the derivation of simple closed-form expressions that allow to compute the optimal workload assignment in a dynamic fashion, with the goal of guaranteeing a fully balanced workload distribution during the parallel execution. Based on these expressions, an algorithm for balanced execution of cellular automata is presented and implemented using the MPI technology. Eventually, an experimental section practically shows the behaviour of the proposed dynamic load balancing approach and proves its performance improvement, compared to the not-balanced version, as witnessed by the appreciable reduction of execution times.

Pollution Load Allocation on Water Pollution Control in the Citarum River

The Citarum River is a strategic river in Indonesia, but has poor water quality.  The existing pollution control program has no impact on the river’s water quality, because it uses a regulatory approach and prioritizes industrial sources. To improve the quality of the river, every pollutant source needs to reduce the pollution load discharged into the river based on the ability of the river to receive pollution. The purpose of this study was to measure pollution load allocation based on the total maximum daily load (TMDL) of the river. The results show that non-point sources contribute the highest pollution load (85%) compared with point sources (15%). The results of the measurement of pollution load allocation showed that the highest contribution of pollution load was from domestic waste (83.5%), followed by industrial waste (6.6%). The target of pollution load reduction for domestic sources is 81.8%, and for industries is 79.5%. The improvement of river water quality should be started at tributaries and domestic sources. This study can be used by local governments to develop water pollution control programs, for instance as a basis for determining the wastewater discharge permit of pollutant sources and permit trading.

River water quality management using a fuzzy optimization model and the NSFWQI Index

In this study, a novel multiple-pollutant waste load allocation (WLA) model for a river system is presented based on the National Sanitation Foundation Water Quality Index (NSFWQI). This study aims to determine the value of the quality index as the objective function integrated into the fuzzy set theory so that it could decrease the uncertainties associated with water quality goals as well as specify the river's water quality status rapidly. The simulation-optimization (S-O) approach is used for solving the proposed model. The QUAL2K model is used for simulating water quality in different parts of the river system and ant colony optimization (ACO) algorithm is applied as an optimizer of the model. The model performance was examined on a hypothetical river system with a length of 30 km and 17 checkpoints. The results show that for a given number of both the simulator model runs and the artificial ants, the maximum objective function will be obtained when the regulatory parameter of the ACO algorithm (i.e., q0) is considered equal to 0.6 and 0.7 (instead of 0.8 and 0.9). Also, the results do not depend on the exponent of the membership function (i.e., γ). Furthermore, the proposed methodology can find optimum solutions in a shorter time.

Smart load management for a stand-alone wind-solar hybrid system

ABSTRACT This Paper presents an energy management strategy of a wind-solar hybrid system installed at a remote rural village for residential users based on a Modified Genetic Algorithm (MGA). In this stand-alone wind-PV system no grid connection is needed, therefore we developed a suitable load management control in order to ensure and satisfy the electric load demand. An energy audit has been established for a typical residential house, then a scheduled hourly load is defined to the 24 hours of the day; MGA was applied after that to optimise the load allocation in real time by taking into consideration the priority of the loads and the available energy for each hour. Therefore, provide a better energy management efficiency and guarantee the end-user satisfaction. The developed algorithm allows testing the ability of the proposed MGA under different scenarios to approve the proposed strategy. The results have shown a good performance of the proposed algorithm to meet the aim of being efficient to use to any load profile. The different data used in this paper are taken from a work of Ibadan University, Nigeria [Ogunjuyigbe, A. S. O., T. R. Ayodele, and C. G. Monyei. 2015. “An Intelligent Load Manager for PV Powered Off-grid Residential Houses.” Energy for Sustainable Development 26: 34–42. doi:10.1016/j.esd] from which this current work was inspired.

Evaluation of the risk and benefit of the complementary operation of the large wind-photovoltaic-hydropower system considering forecast uncertainty

The complementary operation of wind, photovoltaic and hydropower systems has the potential to increase the integration of renewable energy sources into an existing grid. However, the high variability and forecast uncertainty of wind and photovoltaic power may pose potential risks to the grid and cascade reservoirs. In this study, the benefit and risk of the complementary operation of the wind-photovoltaic-hydropower hybrid system are evaluated. First, an optimal day-ahead complementary operation model is proposed to guide the daily operation of the hybrid system. Then, an optimal real-time load allocation model is developed to deal with power generation derivation resulting from the uncertainty of wind and photovoltaic power forecasting. Finally, the benefit and risk of the complementary operation of the wind-photovoltaic-hydropower system are evaluated. A case study is performed with the wind-photovoltaic-hydropower system in the Yalong River basin of China. The results show that (1) the total power generation, the power generation profit, and the utilization efficiency of electricity transmission lines are significantly increased, but the profit of hydropower stations is sacrificed to compensate for wind and photovoltaic power generation; and (2) the negative effect of the variability of wind and photovoltaic power generation on the power grid can be alleviated; (3) reliable power supply can be achieved, and power loss occurs mainly in the flood season and the maximum load loss is acceptable; and (4) it may lead to an increase in release variation, especially in the dry season, and thus flood control gates and hydropower generating units need to be operated more flexibly.