Interaction of Energy-Grid-Load Based on Demand Side Response of Full-cost Electricity Price

In the research of intelligent grids, people usually pay attention to the interaction between power source and load to absorb more new energy. In contrast, the utilization of power source and load on grid resources is less considered, and energy-grid-load is not fully explored. The profound benefits and value of the three interactions. To this end, the paper proposes the interactive mode and method of power supply, power grid, and load based on full-cost electricity price. The power source side needs to consider the impact of transmission costs when optimizing economic dispatch; the load side needs to calculate the full-cost electricity price based on the power flow tracking method, clarify the load’s use of power and grid resources, and reflect the “who benefits, who bears the cost” Principles and introduce demand-side response to reflect the meaning and value of full-cost electricity prices. The research found that the calculation example analysis based on the IEEE30-node system verifies that the method in this paper can reasonably allocate the cost of the power grid according to the user’s utilization of power grid resources, thereby improving the efficiency, fairness, orderliness and safety of power grid investment and operation.

Improved Access Control Mechanisms Using Action Weighted Grid Authorization Graph for Faster Decision Making

Access control mechanisms are the way to guarantee secure access to grid resources. Recent research works were focused on how to improve the representation of the resources' security policies for faster decisions making. PCM, HCM, GAG, and WGAG are all different ways to represent these security policies. This paper presents an enhancement to WGAG, the action-weighted grid authorization graph (Action-WGAG). A security policy-parser (SP-Parser) has been developed to implement the Action-WGAG. The evaluation results of the proposed model showed that it assures a smaller number of security rule checking in some cases and a reduction of the answer time to an access control request.

Implementasi Algoritma Load Balancing PLBA Komputasi Grid pada Lab Environment Menggunakan PVM3

Load balancing is one of the main parts of scheduling Grid resources. One of the load balancing models on Grid resources is the hierarchical model. This model has the advantage that it requires minimal communication costs between one resource and another. The PLBA load balancing algorithm uses a hierarchical model with dynamically obtained threshold values, so that it can adjust conditions at a time, both the state of the resource, the state of the computer network, and the state of the recipient or client. PVM3 is a software system capable of optimizing heterogeneous resources, so that resources can work in parallel. Resources can also complete tasks well, even though they are very large and complex tasks. This research has implemented the PLBA load balancing algorithm, with the aim of optimizing Grid resources. This research has also developed the PLBA load balancing algorithm by changing the arguments for NPEList, so that resources can be grouped more optimally. The PLBA load balancing algorithm has been successfully developed by modifying the arguments for NPEList, so that the running time required to complete the given tasks is shorter, because resources can be grouped more optimally. This has been shown by the shorter average running time when using the modified NPEList argument (0.75 * threshold1 <= ALCi <= 1.25 * threshold1) is shorter, than using the NPEList argument in previous research (ALCi = threshold1). Comparison of the average running time has been obtained as follows : (82513.63740 : 67837.71720); (63869.92450 : 50722.17210); (858,96710 : 207,33680); (321.88000 : 126.89100); (768.54560 : 468.27190); (780.22770 : 279.43730).

Implementasi Algoritma Improvised Prioritized Deadline Scheduling Algorithm (IPDSA) pada Grid Environment Menggunakan PVM3

Resource Scheduling is one of the most challenging parts of grid computing. A number of algorithms have been designed and developed to create effective resource scheduling. In this research, the algorithms that have been used are the improvised prioritized deadline scheduling algorithm (IPDSA), and the parallel virtual machine version 3 (PVM3) has been used for efficient task execution, with a deadline limit for each task. PVM3 is a software library that optimizes resources flexibly and heterogeneously on a computer. These resources have been connected to various architectures in parallel, so that they can complete tasks well, even though they are very large and complex. This research has implemented the IPDSA resource scheduling algorithm to optimize scheduling and Grid resources in a computer laboratory as a grid environment, where the computers (hosts) are the Grid resource. This research has also developed an IPDSA resource scheduling algorithm by giving priority to each task and implemented using PVM3. The IPDSA resource scheduling algorithm has been successfully implemented using PVM3, with average Tardiness showing a stable value and getting a Non-Delayed Task value above 97.3%, because the resources and tasks that are carried out can be distributed evenly according to the number of hosts used.

A REVIEW OF GRID COMPUTING

Grid computing is a combination of interconnected resources which can be spread all over the world having higher computing capabilities. The benefit of grid computing includes higher computation and memory capacity because of grid resources spread all over the world. The grid computing is managed by intra-grid scope which refers to the methodologies and the algorithms used for managing the grid network related issues such as task scheduling, resource balancing and security of the network. The advantages of grid computing include access to inaccessible resources, resource utilization and balancing, reliability, and parallel computing and scalability. The limitations of the grid computing include application in limited fields and suitability with applications running in batch mode only based on parallel processing

Integration of Smart Grid Resources into Generation and Transmission Planning Using an Interval-Stochastic Model

In the power industry, the deployment of smart grid resources in power systems has become an issue of major interest. The deployment of smart grid resources represents an additional uncertainty in the integrated generation and transmission planning that raises uncertainties in investment-related decision making. This paper presents a new power system planning method for the integration of electric vehicles (EVs) and wind power generators into power systems. An interval-stochastic programming method is used to account for the heterogeneous uncertainties attributable to natural variability and lack of knowledge. The numerical results compare the multiple integration scenarios and verifies the effectiveness of the proposed method in terms of cost distribution and regret cost.