Load Imbalance Research Articles

Electricity Pricing and Its Role in Modern Smart Energy System Design: A Review

Energy is the foundation for human survival and socio-economic development, and electricity is a key form of energy. Electricity prices are a key factor affecting the interests of various stakeholders in the electricity market, playing a significant role in the sustainable development of energy and the environment. As the number of distributed energy resources (DERs) increases, today’s power systems no longer rely on a vertical market model and fixed electricity pricing scheme but instead depend on power dispatch and dynamic pricing to match supply and demand. This can help prevent significant fluctuations in supply–load imbalance and maintain system stability. Modern power grids have evolved by integrating information, communication, and intelligent control technologies with traditional power systems, giving rise to the concept of smart electric grids. Choosing an appropriate pricing scheme to manage large-scale DERs and controllable loads in today’s power grid become very important. However, the existing literature lacks a comprehensive review of electricity pricing in power systems and its transformative impact on shaping the energy landscape. To fill this void, this paper provides a survey on the developments, methods, and frameworks related to electricity pricing and energy trading. The review mainly considers the development of pricing in a centralized power grid, peer-to-peer (P2P) and microgrid-to-microgrid (M2M) energy trading and sharing, and various pricing methods. The review will cover the pricing schemes in modern power systems, particularly with respect to renewable energy sources (RESs) and batteries, as well as controllable load applications, and the impact of pricing schemes based on demand-side ancillary services (DSAS) for grid frequency support. Lastly, this review article describes the current frameworks and limitations of electricity pricing in the current energy market, as well as future research directions. This review should offer a great overview and deep insights into today’s electricity market and how pricing methods will drive and facilitate the future establishment of smart energy systems.

Gain Fault Detection Method for 3-phase AC Current Sensors

This paper proposes a novel method for detecting the gain fault of 3-phase AC current sensors in power electronics systems. In conventional methods, the sensor gain fault is detected by comparing the RMS values of the detection current of each phase. However, for an unbalanced three-phase load, a higher current will flow in the phase with the lower impedance, which causes misdiagnoses. In contrast, the proposed method utilizes the product of the detection current in each phase and sum of the detection current as part of the abnormality calculation (gain error calculation) of each current sensor, which can reduce calculation errors caused by an imbalance in the three-phase load. Simulation results show that the method can calculate the abnormality of the sensors with small error even if there is an imbalance in the three-phase load. Moreover, simulation results under conditions of varying current amplitude and frequency indicates that the method can detect sensor faults under transient conditions.

Game-Based Task Offloading and Resource Allocation for Vehicular Edge Computing With Edge-Edge Cooperation

Vehicular Edge Computing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC scenario, the uneven geographical distribution of the vehicles naturally causes the load imbalance among the edge servers and leads to the overload and performance degradation problems of the edge servers in hot areas. To this end, in this paper, we propose a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further offloaded to the other low-load edge servers. Our objective is to minimize the total task processing delay of all the vehicles while guaranteeing the task processing delay tolerance and the holding time of each vehicle. An M/M/1 queue is used to model the task queuing and task computing processes on each RSU. An exact potential game is adopted to model the competition process for the task offloading among the RSUs. A two-stage iterative algorithm is designed to decompose the optimization problem into two stages and solve them iteratively. We analyze the computational complexity of the algorithm and conduct extensive simulations by varying different crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other reference schemes.

To improve the performance on disk load balancing in a cloud environment using improved Lion optimization with min-max algorithm

A key challenge was optimizing the speed of the network while maintaining impartiality. Power monitoring solutions have been developed to reduce these load imbalances. These techniques frequently need unique hardware or software on the participants' end. To accomplish the cloud computing system of load balancing, this study created an intelligent virtual machine programming scheme using a machine learning technique. The researchers introduced two methods that identify optimal probabilistic solutions to the issue after conducting an in-depth investigation. A min-max load balancing solution is generated by the second method, while the first approach reduces the load on the network's crowded Access Points (APs). Let's focus on the mapping of links in particular because the mapping of nodes was established a priori. This study provides an innovative and effective Improved Lion Optimization (ILO) with Min-Max Algorithm for enabling VNE in real systems consisting, breaking new ground in the research to employ Genetic Operators for parallelization. Load balancing & power conservation were two crucial goals that must be taken into account, and also the findings reduce the cost of processing time, the proposed system outperforms the sequentially one in terms of both goals. In addition, the adaptive capacity of the proposed algorithm was assessed across various substrate structural configurations. The load balancing goal was achieved; the typical data center usage was higher than that of other methods, reaching nearly 80%; the largest amount of virtual machine migration was reduced by 94.5%; the data center's maximum energy consumption was reduced by 49.13%.

Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach

The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks.

Coupled dynamic characteristics of 10 MW semi-submersible offshore wind turbine under typical operation, extreme, and fault conditions

The Technical University of Denmark (DTU) 10 MW wind turbine is selected as the object of this study. The braceless semi-submersible platform for the National Renewable Energy Laboratory 5 MW wind turbine is upscaled to support the DTU 10 MW wind turbine. The servo control system of the 10 MW semi-submersible wind turbine is redesigned. Furthermore, torque control is performed below the rated wind speed to achieve maximum power capture, and the gain scheduling proportional integral (GSPI) control method is performed above the rated wind speed to realize the design of the variable propeller control system. This ensures the safe operation of the floating wind turbine above the rated wind speed. The reliability of the designed servo control system is verified by a full coupling dynamic response analysis with the designed servo control system, aerodynamics, and hydrodynamics under the combined action of wind and waves. The coupled dynamic characteristics of the 10 MW semi-submersible wind turbine under different conditions are analyzed, including operational, extreme, and fault situations. The results show that the servo control system effectively controls the motion response of a 10 MW floating offshore wind turbine (FOWT). Turbulent wind has an obvious excitation effect on the surge and pitch of the FOWT, whereas the heave motion is primarily excited by wave loads. Different fault conditions can cause different motion and structural responses. Furthermore, the tower top bending moment is greatly affected by the imbalance of aerodynamic load, and the surge, pitch motion, tower base bending moment, and mooring tension of the FOWT are affected by different fault conditions to varying degrees. In addition, the heave motion is generally barely affected by the fault conditions.

Multi-level correlation analysis method for abnormal operating indexes of smart distribution network

This article focuses on the interaction between multiple levels of smart distribution network operation, and designs a multi-level correlation analysis framework by considering several related factors, such as network structure, distributed power supply and energy storage access, voltage regulation and reactive power compensation, and three-phase load imbalance. Furthermore, the multi-level correlation analysis method and process of abnormal voltage, load rate and power factor of smart distribution network are proposed, and the multi-level correlation analysis of key operation indicators at each level is realized. It is found that there is coupling influence relationship at different levels of high, medium and low voltage of smart distribution network. This article accurately locates the causes of abnormal problems of key operation indicators and forms a targeted governance decision plan. The practice shows that this method can effectively ensure the safe, reliable and efficient operation of large-scale distributed power and energy storage connected to smart distribution network.

Warp-Aware Adaptive Energy Efficiency Calibration for Multi-GPU Systems

Massive GPU acceleration processors have been used in high-performance computing systems. The Dennard-scaling has led to power and thermal constraints limiting the performance of such systems. The demand for both increased performance and energy-efficiency is highly desired. This paper presents a multi-layer low-power optimisation method for warps and tasks parallelisms. We present a dynamic frequency regulation scheme for performance parameters in terms of load balance and load imbalance. The method monitors the energy parameters in runtime and adjusts adaptively the voltage level to ensure the performance efficiency with energy reduction. The experimental results show that the multi-layer low-power optimisation with dynamic frequency regulation can achieve 40% energy consumption reduction with only 1.6% performance degradation, thus reducing 59% maximum energy consumption. It can further save about 30% energy consumption in comparison with the single-layer energy optimisation.

Power Losses Caused by Load Imbalance in the Central Building of PGRI Semarang University

The use of energy is basically very much needed in everyday life. Energy that is widely used is electrical energy, because electricity is one of the important factors in human life. Some electrical energy producers, in the country use fossil fuel sources such as coal and petroleum, and fossil energy is not renewable. Therefore, the government through the Regulation of the Minister of Energy and Mineral Resources number 14 of 2012 regulates energy management to control energy consumption in order to achieve effective and efficient energy utilization. PGRI Semarang University [L1] as an academic institution uses a lot of non-linear loads for administrative, teaching, laboratory and other supporting activities. Based on the analysis of data obtained at the Center Building of PGRI Semarang University experiencing an imbalance, the condition is not in accordance with the standards and risks increasing power losses. Seeing these conditions, recommendations were obtained that can be implemented at the Center Building of PGRI Semarang University, namely, balancing the load of phases R, S, T to reduce current imbalances. From the load imbalance, there were power losses on the neutral wire of 889,363 kW and power usage for one day in the R, S, T phases of 15,497,991 kW

Load balancing techniques in cloud platform: A systematic study

In the current scenario, researchers have made a new invention and added to the computing paradigm every next second. Cloud computing is one of the most demanding, practical, accessible and extended technologies based on ‘pay as per use model’ and works on virtualisation via internet. Data sharing and accessing have become easy by properly organising various resources, such as storage, servers, development tools, software, etc, in cloud. Handling these resources has faced many challenges, such as cost management, system performance, migration, load imbalance, reliability and privacy etc. Load imbalance is one of the most important factors which are solved by load balancing techniques. This paper introduced the detailed classification of load balancing methods and techniques that are taken as a solution to overcome such problems and also helps future researchers. Also given is a proposed model for load balancing and some comparative studies of the heuristics methods based on platforms and simulator tools.

Load balancing scheduling mechanism for OpenStack and Docker integration

With the development of cloud-edge collaborative computing, cloud computing has become crucial in data analysis and data processing. OpenStack and Docker are important components of cloud computing, and the integration of the two has always attracted widespread attention in industry. The scheduling mechanism adopted by the existing fusion solution uses a uniform resource weight for all containers, and the computing nodes resources on the cloud platform is unbalanced under differentiated resource requirements of the containers. Therefore, considering different network communication qualities, a load-balancing Docker scheduling mechanism based on OpenStack is proposed to meet the needs of various resources (CPU, memory, disk, and bandwidth) of containers. This mechanism uses the precise limitation strategy for container resources and a centralized strategy for container resources as the scheduling basis, and it generates exclusive weights for containers through a filtering stage, a weighing stage based on weight adaptation, and a non-uniform memory access (NUMA) lean stage. The experimental results show that, compared with Nova-docker and Yun, the proposed mechanism reduces the resource load imbalance within a node by 57.35% and 59.00% on average, and the average imbalance between nodes is reduced by 53.53% and 50.90%. This mechanism can also achieve better load balancing without regard to bandwidth.

An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers generates resource fragmentation, leading to inefficient resource utilization and energy wastage in data centers. The inefficient utilization of resources makes high energy consumption and low quality of service (QoS) in resource management an urgent problem to be solved. We propose a load balancing strategy based on virtual machine consolidation (LBVMC), which aims to reduce the energy consumption and service level agreement (SLA) violation of data centers by balancing the multi-dimensional resource utilization in physical machines (PMs). First, we present a load state classification algorithm for PMs with load abnormality considering current and future loads to reduce unnecessary virtual machine (VM) migrations caused by occasional load fluctuations. Then, we propose a resource-weight based selection model for migratable VMs, which selects appropriate VMs to be migrated based on multi-dimensional resource utilization, and reduces resource fragmentation caused by load imbalance. Finally, we design a VM placement algorithm based on resource fitness and load correlation to deploy VMs on the optimal destination PMs to assure load balancing of the destination PMs after VM placement. We perform simulated experiments under homogeneous, heterogeneous, and bottleneck resource environments. Experimental results show that LBVMC is superior to other strategies in reducing energy consumption and SLA violations and achieves better overall performance.

Numerical investigation of using a field of shallow boreholes with latent heat storage and solar injection in cold climates

Modeling heat transfer within a field of boreholes (borefield) is essential to correctly assess the long-term performance of a solar-assisted ground source heat pump, which uses the ground to store the injected solar thermal heat. A novel model was developed that uses a hybrid approach, combining a numerical thermal resistance and capacitance model for the borehole with an analytical g-function for the ground. The developed model has the capability to simulate the long-term performance of a borefield with the phase change that is occurring in the water content of the grout. The model is used to assess the benefit of phase change within a saturated sand mixture, acting as filling material in the borehole and thermal energy storage for solar heat injected in the ground. The injected solar heat compensates the ground thermal load imbalance caused by substantially higher heating demand than cooling demand in cold climate regions. Through long-term hourly simulations, results show that water content in the borehole helps keep the temperature constant at the vicinity of the borehole, particularly during peak hours, and that solar injection permits the design of a more compact borefield by concentrating the stored heat in the vicinity of the boreholes.

Penelitian Efek Penggunaan Grid Tie Inverter pada Sistem Hibrid dengan Solar Panel dan Diesel

The distribution system of electrical power begins from generation and is transmitted to the load, namely consumers. In this distribution, it must be effective, efficient, and reliable. The amount of electrical power reaching the load is not the same as the amount of power generated due to voltage drop or power losses. This research aims to determine the magnitude of power losses in the medium-voltage distribution network located in Pasar Aceh. The research method employed is the analysis and simulation calculation method using ETAP 12.6 software. The study analyzes power losses due to load imbalance in the JTR distribution substation of Pasar Aceh. Based on the analysis results, the load imbalance in the Pasar Aceh substation has led to power losses. The power losses in the Pasar Aceh substation, specifically at the Side Transformer of Pasar Aceh, are 0.77% with a load imbalance of 16.33%. For the second transformer in the BNI 46 area, the power losses are 0.98% with a load imbalance of 30.33%, and for the transformer in the BLKG KOTTY area, the power losses are 1.17% with a load imbalance of 50.33%. Therefore, the larger the percentage of load imbalance in an electrical distribution network, the greater the resulting power losses.

A Survey on Rerouting Techniques with P4 Programmable Data Plane Switches

Traditionally, the networking industry has been dominated by closed and proprietary hardware and software. Vendors have been controlling the network by hard-coding how packets should be processed and providing the network operators with a set of predefined protocols. Recently, the industry, operators, and the research community have started to pay special attention to data plane programmability, which allows the user to define the packet processing behavior. Allowing the network operators and programmers to define, deploy, and test new forwarding behaviors in a relatively short time paved the way for a significant wave of innovation and experimentation. With the emergence of programmable data planes, traffic rerouting has been used by the research community. Rerouting approaches are deployed to mitigate various network issues. Despite the considerable number of works that deploy innovative rerouting mechanisms using programmable switches, the literature lacks a comprehensive survey. To this end, this paper provides an in-depth overview, detailed analysis, and unique categorization of the recent programmable data plane-based rerouting approaches. The survey explains the need for rerouting by highlighting the promising results while dealing with link/node failures, load imbalance, and congestion. It then discusses the challenges and considerations and presents future perspectives and open research issues.

Efficient spectral implementation of ODE wall model and the extension of integral wall model to unstructured LES solvers

While wall modeling has become an indispensable tool for scale-resolving simulation of high Reynolds numbers wall-bounded turbulent flows, discussion of efficient implementation of wall models in parallel unstructured-grid solvers is lacking. In this paper, we discuss physical and numerical improvements to two zonal wall models based on integral form of the boundary layer differential equations within an unstructured-grid cell-centered finite-volume LES solver. The first model is a novel implementation of the ODE equilibrium wall model, where the velocity profile is expressed in the integral form using the constant shear-stress layer assumption and the integral is evaluated using a spectral quadrature method, resulting in a local and algebraic (grid-free) formulation. The second model is the modified form of the integral wall model of Yang et al. (2015) [14], which is based on the vertically-integrated thin-boundary-layer PDE along with a prescribed composite velocity profile in the wall-modeled region. A modification to the inner layer velocity profile which fixes the coordinate invariance issue of the original model is proposed. Additionally, several numerical challenges unique to the implementation of these integral models in unstructured mesh environments, such as the exchange of wall quantities between wall faces and LES cells, and the computation of surface gradients, are identified and possible remedies are proposed. The computational performance of the wall models is assessed both in a priori and a posteriori settings against the traditional finite-volume based ODE equilibrium wall model, showing a comparable computational cost for the integral wall model, and superior cost scaling for the spectral implementation over the finite-volume based approach. Load imbalance among the processors in parallel simulations seems to severely degrade the parallel efficiency of finite-volume based ODE wall model, whereas the spectral implementation is remarkably agnostic to these effects. The integral wall model, however, is even more prone to parallel efficiency degradation than the finite-volume based ODE wall model due to communication overhead among processors in highly parallelized settings.

Adaptation of Parallel SaaS to Heterogeneous Co-Located Cloud Resources

Cloud computing has received increasing attention due to its promise of delivering on-demand, scalable, and virtually unlimited resources. However, heterogeneity or co-location of virtual cloud resources can cause severe degradation of the efficiency of parallel computations because of a priori unknown application-specific performance metrics, load imbalance, and limitations of memory bandwidth. This paper presents the runtime adaptation of parallel discrete element method (DEM) Software as a Service (SaaS) to heterogeneous or co-located resources of the OpenStack cloud. The computational workload is adapted by using weighted repartitioning and runtime measured performance of parallel computations on Docker containers. The high improvement in performance up to 48.7% of the execution time is achieved, applying the runtime adapted repartitioning when the load imbalance is high enough. The low load imbalance leads to the close values of computational load, when small variations in the system load and performance can cause oscillations in subsets of particles. Memory stress tests cause heterogeneity of non-isolated containers, which reduces the performance of memory bandwidth bound DEM SaaS on the co-located resources. The runtime adapted repartitioning handles the constant and periodically variable performance of non-isolated containers and decreases the total execution time of DEM SaaS.

Load balanced locality-aware parallel SGD on multicore architectures for latent factor based collaborative filtering

We investigate the parallelization of Stochastic Gradient Descent (SGD) for matrix completion on multicore architectures. We provide an experimental analysis of current SGD algorithms to find out their bottlenecks and limitations. Grid-based methods suffer from load imbalance among 2D blocks of the rating matrix, especially when datasets are skewed and sparse. Asynchronous methods, on the other hand, can face cache issues due to their memory access pattern. We propose bin-packing-based block balancing methods that are alternative to the recently proposed BaPa method. We then introduce Locality Aware SGD (LASGD), a grid-based asynchronous parallel SGD algorithm that efficiently utilizes cache by changing nonzero update sequence without affecting factor update order and carefully arranging latent factor matrices in the memory. Combined with our proposed load balancing methods, our experiments show that LASGD performs significantly better than alternative approaches in parallel shared-memory systems.

Coordinated power-water optimization for precision irrigation among distribution network and agricultural parks

With more and more agricultural parks are integrated to the electrical distribution network, the disordered characteristics of farmers’ irrigation water consumption will not only increase the cost of water and electricity, but also cause security problems in traditional term of rural distribution network, such as large short-term load fluctuation, overload and transformer load imbalance. In this context, this paper proposes a double-layer master–slave game model of power-water interaction and coordination between distribution network and agricultural irrigation park under photovoltaic (PV) assisted agricultural precision irrigation. The upper layer (electrical distribution network) plays an interactive game with the lower layer (agricultural irrigation parks) by establishing effective time-of-use (TOU) price. On the one hand, the electrical distribution network takes the operation security as the optimization objective, it could optimize line loss by network configuration and guides agricultural irrigation parks to consume electricity orderly by formulating electricity prices. On the other hand, agricultural irrigation parks take satisfaction and cost as the optimization objective after receiving TOU price from upper layer, and regulates the operation time and intensity of irrigation pumps to realize precise irrigation. Finally, an example is given to verify the model on the modified 148-node system. It is determined that the model can effectively support the safe and reliable operation of rural distribution network while realizing the transformation of agricultural irrigation water use from low efficiency to economical and high efficiency, so as to achieve win–win between agricultural irrigation park and distribution network.

Method of Calculation the Load Rates and the Unbalance Rates of the Current of the Phases in the MV/LV Network of the Sales and Service Center Lemba in Kinshasa

In this article, we discuss the calculation of load and current imbalance rates, which is a steady-state study of the distribution network that consists of determining, for each subscriber station, the operating rate of the installed power transformer and the current difference between two phases of the MV/LV distribution station. Knowing the allowable currents of each low-voltage phase of the subscriber substations from the busbars as well as the rated current of the substations transformer, we can calculate, secondly, the average currents and load factors of the substations including the differences between the currents and their unbalance factors in the low-voltage distribution power lines. The mathematical equations of the electrical quantities of the power system facilitate the evaluation of the performance of the power transformer. These equations confirm that the variation in load ratio and current imbalance of three phases are closely related to the current draw of the allowable loads at each phase.