Grid Resource Management Research Articles

Cloud-Edge-End Collaborative Multi-Service Resource Management for IoT-Based Distribution Grid

Cloud-Edge-End Collaborative Multi-Service Resource Management for IoT-Based Distribution Grid

A new approach to seasonal energy consumption forecasting using temporal convolutional networks

There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature. It is well known that energy forecasting plays a crucial role in several applications in smart grids, including demand-side management, optimum dispatch, and load shedding. A significant challenge in smart grid models is managing forecasts efficiently while ensuring the slightest feasible prediction error. A type of artificial neural networks such as recurrent neural networks, are frequently used to forecast time series data. However, due to certain limitations like vanishing gradients and lack of memory retention of recurrent neural networks, sequential data should be modeled using convolutional networks. The reason is that they have strong capabilities to solve complex problems better than recurrent neural networks. In this research, a temporal convolutional network is proposed to handle seasonal short-term energy forecasting. The proposed temporal convolutional network computes outputs in parallel, reducing the computation time compared to the recurrent neural networks. Further performance comparison with the traditional long short-term memory in terms of MAD and sMAPE has proved that the proposed model has outperformed the recurrent neural network.

Adaptive distributed demand side management with weighted dimension reduction for energy resource management in smart grid

AbstractDemand side management (DSM) consists of planning, executing, and controlling activities to reduce electricity consumption. By using demand response (DR), customers help reduce demand during peak times. Consumer participation in DSM depends on his behaviour. Consumer behaviour is determined by factors such as lifestyle, electricity price, electricity consumption tariff, contract type etc. Thus, factors affecting the consumer's behaviour should be considered in order to determine more accurately the amount of participation in DSM programs. This article presents a model for the optimal scheduling of distributed energy resources by taking into account factors related to consumer behaviour. To reduce the volume of the DR data while maintaining the main features, distributed principal component analysis (D_PCA) was used to reduce the volume of DR data. Also, by integrating this method with the accelerated hybrid alternating direction method of multipliers (AHADMM) algorithm, an adapted and accelerated method is achieved to realize the reliability and cyber security of the system. The case study was conducted on the IEEE 118 bus power system at different levels of demand, which verified proposed meta‐algorithm improved at least between 4 to 13 iterations of energy resource convergence speed compared to similar methods and while DR is also intended.

Optimum operation management of microgrids with cost and environment pollution reduction approach considering uncertainty using multi‐objective NSGAII algorithm

AbstractGenerating electricity near the consumption can provide more flexibility to supply various services to consumers as well as reduce system losses. The limited fossil fuels and air pollution are among the main incentives for the expansion of this technology. One of the perspectives proposed for effective increase in these resources’ involvement is to combine these resources with the objectives of the visibility of the relationship between distributed generation resources and power network, as well as control of these resources more efficiently. One of the methods of combining distributed generation sources is a new concept called micro grid; a topic that is of great importance in this regard is the proper management of distributed generation resources in micro grid aimed to reduce the costs of generating electricity and polluting the environment. The resource management in micro grid is a completely non‐linear and top‐order problem. In this paper, micro grid optimum programming has been studied considering the capacities available in the electricity market. In order to realize this, as well as reduce pollution and cost simultaneously, NSGAII multi‐objective genetic algorithm is used. Studies have been conducted in the form of three scenarios on a sample micro grid consisting of solar, wind, micro turbine, fuel cell and battery resources, considering the uncertainty of load and solar and wind generations. The probabilistic distribution function (PDF) and Roulette Wheel were used to create variables with uncertainty. The first scenario objective was to reduce costs by PSO, GA, and ABC algorithms’ optimization. In the second scenario, pollution was reduced and the studies were repeated using these three algorithms. Finally, in the third scenario, there was a simultaneous reduction in pollution and cost by these three algorithms, taking into account weight coefficients as well as NSGAII algorithm.

Distribution Grid Resource Business Center Integrating Dynamic Migration Algorithm and Lightweight System

The existing power system does not have a unified grid resource management model. The basic data of equipment assets cannot be stored, managed, and maintained in a unified manner. The production systems of different departments are developed by different manufacturers at different times, which leads to the problem of production data silos and high maintenance costs. To solve this problem, a grid resource business middle platform based on State grid-corporation public data model (SG-CIM, CIM) model is designed in this paper. Firstly, the concept and core technology of the middle platform in the power grid resource business are introduced. Then a dynamic data migration strategy based on the FA algorithm is proposed. Finally, the model based on SG-CIM and the application scenario of power transmission and distribution are built and its effectiveness is analyzed.

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

The grid environment is a dynamic, heterogeneous, and changeable computing system that distributes various services amongst different clients. To attain the benefits of collaborative resource sharing in Grid computing, a novel and proficient grid resource management system (RMS) is essential. Therefore, detection of an appropriate resource for the presented task is a difficult task. Several scientists have presented algorithms for mapping tasks to the resource. Few of them focus on fault tolerance, user fulfillment, and load balancing. With this motivation, this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm (DHOA), called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform. The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer. It also derives an objective function with candidate solution (schedule) as input and the outcome is the makespan value denoting the quality of the candidate solution. The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

A cloud framework for problem-based learning on grid computing

Training on Grid technologies has traditionally used existing Grid infrastructures to implement the hands-on education activities. However, these infrastructures are insufficient to develop all training skills as they can only be employed for the development of Grid applications, and they are limited for learning the management and configuration of Grid resources. The paper presents a set of educational activities grouped on a Project Based Learning (PBL) framework for training on Grid technologies. A Cloud-based tool has been implemented to provide Grid infrastructures as a Service on the cloud, with enhanced scalability and administration capabilities. The PBL has achieved a high impact in the teaching-learning process, addressing the training in all the necessary skills and efficiently providing Grid infrastructure resources on public clouds at a moderate cost. Finally, we evaluated the students' opinion on the activities achieving a very satisfactory result and a reasonable balance on the complexity of the PBL stages.

Using Continous Deployment techniques to manage software change at a WLCG Tier-2

Continuous Integration (CI) and Continuous Development (CD) are common techniques in software development. Continuous Integration is the practice of bringing together code from multiple developers into a single repository, while Continuous Development is the process by which new releases are automatically created and tested. CI/CD pipelines are available in popular automation tools such as GitLab, and act to enhance and accelerate the software development process. Continuous Deployment, in which automation is employed to push new software releases into the production environment, follows naturally from CI/CD, but is not as well established due to business and legal requirements. Such requirements do not exist in the Worldwide LHC Compute Gird (WLCG), making the use of continuous deployment to simplify the management of grid resources an attractive proposition. We have developed work presented previously on containerised worker node environments by introducing continuous deployment techniques and tooling, and show how these, in conjunction with CI/CD, can reduce the management burden at a WLCG Tier-2 resource. In particular, benefits include reduced downtime as a result of code changes and middleware updates.

A hybrid heuristic of Variable Neighbourhood Descent and Great Deluge algorithm for efficient task scheduling in Grid computing

To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. Scheduling tasks to resources in Grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND.

Bilateral Electricity Market in a Distribution System Environment

Increasing penetration of proactive agents, including distributed energy resources (DERs) and responsive loads (RLs), in distribution systems motivates methods to incentivize market participation from demand-side resources. The complexity resulting from large-scale integration of proactive agents has challenged the viability of centrally managing grid resources in a distribution system environment and motivated decentralized frameworks to coordinate the transactions. Existing decentralized frameworks are primarily focused on designing transaction mechanisms. They neither include a formal market construct nor a computationally feasible approach for market settlement for a large volume of transactions while ensuring the security of grid operation. This paper presents a distributed computational approach for a distribution system market where the coordination framework is based on agreement on bilateral energy transactions reached by market participants. Using the proposed distributed computational method, participants play a role in determining the set of bilateral transactions that maximizes the social welfare. The distribution system operator (DSO) ensures a secure operating condition of the distribution grid by validating each transaction that is agreed upon. Under a potential violation of security constraints by a proposed bilateral transaction, the DSO runs an optimal power flow (OPF) and recommends a new set of transactions (closest to those previously proposed) while maintaining power system security. The proposed framework is validated using IEEE 123-node test system. The results confirm the feasibility of the proposed approach for distribution systems.

IRMM: An Integrated Resource Management Model for Grid Computing

Grid computing is a collection of heterogeneous systems or heterogeneous objects that are geographically distributed over a network. Resource management is a process in which various activities like allocation of resources and scheduling are performed for handling issues like load balancing, reliability, scalability, maximum, throughput, minimum expectation time and security. There are several factors that make resource management difficult as different system may have different requirements, properties, conditions and different access and cost models. Resource management in Grid is the method of identifying requirements, finding corresponding resources to the applications, allocating those matching resources, scheduling and monitoring. In Grid resource management resource broker plays the very important role. Users communicate with a resource broker to access the grid information. Resource broker discover the resource that are available and negotiates with their owners or their agents to get the reservation of resources. Number of approaches exists through which one can develop grid resource management systems. In this paper a new architectural model has been implemented for grid resource management which is based on the characteristics of both the Economical model and Hierarchical model.

A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision.

Multi-users Coordinated Sharing Grid Resource Management System

The fundamental task of grid resource management system is to supply on-demand resources service for various users and applications by aggregate heterogeneous, dynamic and increase resource. Most current grid resource management system is exclusive which brings two sides of deficiency distinctively. We bring forward and realize a multi-user coordinated sharing resource management system, which is a utility oriented resource management system to support resources sharing by multi users or multi workload schedulers, thus extendable resource serving capability can be improved. When the system is used in an “intelligent building” and “intelligent community”, the IT cost of the community enterprises can be reduced significantly.

A model for resource management in computational grid using sequential auction and bargaining procurement

Resources in a computational grid system fall under the purview of different administrative domains of varying policies for their usages. Commercial grid offers their services (resources) on use-and-pay basis. Resource management in computational grid, offers a market place for the two prominent grid market players i.e. resource provider and resource consumer. It has been observed that, in the grid, the request for the resources may not be uniform throughout. It fluctuates from very high demand at peak time to low or negligible at off-peak time. This information may be used to fetch the resource utilization and cost benefits out of the grid. The provider would prefer to charge extra for its resources at peak time, whereas users may shift their resources usage preference to off-peak time. This work proposes a model in which the resource provider and consumer play a non-cooperative game at different time-zones and act independently to choose their actions. Some characteristic parameters such as cost, execution time and reliability have been considered to facilitate the job execution. Based on the outcome of the game, the grid cluster offering maximum reliability within the desired execution time and/or cost for the job execution. The model has been simulated for performance evaluation with quite encouraging results.

A micro-distributed ESS-based smart LED streetlight system for intelligent demand management of the micro grid

Currently, many researchers worldwide are focusing on creating a novel service model by integrating IoT (Internet of Things) technologies with ICT (Information and Communications Technologies) to increase energy efficiency by reducing power consumption and greenhouse gas emissions. This paper examines the problems related to the current integrated ESS (Energy Storage System) in a smart grid: high installation costs and low management efficiency. To solve these problems, this paper studies the development of a micro-distributed ESS in an intelligent LED (Light Emitting Diode) streetlight system, and its low-cost installation and high management efficiency in a micro grid. Through the application of this system, the initial installation costs can be reduced by using micro-distributed ESS and IoT-based intelligent energy management, facilitating power monitoring of the streetlights and energy efficient demand resource management in the micro grid. This provides a more intelligent and efficient environment, such as in a smart-city.

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

Ad Hoc Grid Resource Management: Grid Security

The purpose of security in ad hoc grid environments is to support secure execution of tasks on shared resources and to protect the resources from malicious user actions. The concept of trust management is capable to solve the security issues by incorporating trust into the process of decision making. The quality of the decisions is dependent on a correct assessment and representation of trustworthiness assigned to the potentially collaborating parties. In most cases the value of trustworthiness is derived at least from direct trust and recommendations, but other factors as risk, uncertainty, context dependent information and attributes characterizing the task and the shared resource should be included in the derived value as well. This paper presents a specification of parameters relevant for an accurate trust evaluation.

Weighted Sum-Rate Maximization for MIMO Downlink Systems Powered by Renewables

Optimal resource management for smart grid powered multi-input multi-output (MIMO) systems is of great importance for future green wireless communications. A novel framework is put forth to account for the stochastic renewable energy sources (RES), dynamic energy prices, as well as random wireless channels. Based on practical models, the resource allocation task is formulated as an optimization problem that aims at maximizing the weighted sum-rate of the MIMO broadcast channels. A two-way transaction mechanism and storage units are introduced to accommodate the RES variability. In addition to system operating constraints, a budget threshold is imposed on the worst-case energy transaction cost due to the possibly adversarial nature. Capitalizing on the uplink-downlink duality and the Lagrangian relaxation-based subgradient method, an efficient algorithm is developed to obtain the optimal strategy. Generalizations to the setups of time-varying channels and ON–OFF transmissions are also discussed. Numerical results are provided to corroborate the merits of the novel approaches.

Self Organisation in Multi-Agent Systems: A Survey of State-of-the-Art and Future Challenges

Background/Objectives: To analyze the performance of modern information technology using multi agent system. The complexity problems arise in economics, ecology, global computer, ensuring the safety of society and in many other fields. Methods/Statistical Analysis: A systematization and description of the self organization models and mechanisms implemented in the framework of the Multi-Agent is given, networks and biological self-organization mechanisms. By using techniques such as Decentralized Swarm Agents with behaviour - based intelligence, Environment and Agent model and the formulae for each particle is defined as a solution for the problem in particular task. Findings: In this research, an approach based on solving two problems in stochastic game such as coordination team by moving anywhere and finding the joint action. For dynamic partitioning we build a coordinate tree with coordinate agent and by choosing the individual agent with a low cost and by using the techniques such as checking the group among agents based on same type of trees to evaluate the same team for each agent and to calculate the fixed solution for the joint of muti agent cooperation. Applications/Improvements: Applications of self organizing in Multi-Agent systems are telecommunications, grid resource management and routing in computer. Some of the merits are each an individual agent in an environment will find the agent with low price and it is suitable for Markov or stochastic games and group of robots cooperation and group of robot playing football game. Multi agent system used to evaluate the complex problems and it will have more number of agents which are very intelligent and interactive to overcome any kind of problems.

Job Migration Policies for Grid Environment

For efficient resource management in Grid, the resources overloading must be prevented, which can be obtained by efficient Load Balancing and Job Migration (JM) mechanisms. JM addresses the problem of resource overloading through various techniques in a Grid environment. In this paper, JM model has been designed. The proposed JM model offers three policies based on the scheduling, checkpointing and replication for Grid environment decisions. The JM problem has been formulated as an optimization problem and resource scheduling algorithm minimizes the average finish time and makespan. For all the three proposed techniques, JM policies have been designed and the policy rules have been specified in Extensible Markup Language schema for decision making. Each of the above techniques has been executed according to its specific condition at run time. Finally, the performances of the proposed model, algorithms and techniques have been rigorously examined over the GridSim simulator using various parameters, such as makespan, hit-ratio etc. Experimental results establish the superiority of the proposed techniques over the existing techniques.