Local Balancing of the Electricity Grid in a Renewable Municipality; Analyzing the Effectiveness and Cost of Decentralized Load Balancing Looking at Multiple Combinations of Technologies

With the integration of Intermitted Renewables Energy (I-RE) electricity production, capacity is shifting from central to decentral. So, the question is if it is also necessary to adjust the current load balancing system from a central to more decentral system. Therefore, an assessment is made on the overall effectiveness and costs of decentralized load balancing, using Flexible Renewable Energy (F-RE) in the shape of biogas, Demand Side Management (DSM), Power Curtailment (PC), and electricity Storage (ST) compared to increased grid capacity (GC). As a case, an average municipality in The Netherlands is supplied by 100% I-RE (wind and solar energy), which is dynamically modeled in the PowerPlan model using multiple scenarios including several combinations of balancing technologies. Results are expressed in yearly production mix, self-consumption, grid strain, Net Load Demand Signal, and added cost. Results indicate that in an optimized scenario, self-consumption of the municipality reaches a level of around 95%, the total hours per year production matches demand to over 90%, and overproduction can be curtailed without substantial losses lowering grid strain. In addition, the combination of balancing technologies also lowers the peak load to 60% of the current peak load in the municipality, thereby freeing up capacity for increased demand (e.g., electric heat pumps, electric cars) or additional I-RE production. The correct combination of F-RE and lowering I-RE production to 60%, ST, and PC are shown to be crucial. However, the direct use of DSM has proven ineffective without a larger flexible demand present in the municipality. In addition, the optimized scenario will require a substantial investment in installations and will increase the energy cost with 75% in the municipality (e.g., from 0.20€ to 0.35€ per kWh) compared to 50% (0.30€ per kWh) for GC. Within this context, solutions are also required on other levels of scale (e.g., on middle or high voltage side or meso and macro level) to ensure security of supply and/or to reduce overall costs.

Load balancing for smart grid: centralized and distributed approaches

As one of the greatest concerns in the context of smart grid, the load balancing problem is addressed by improving the electrical power efficiency and stability via scheduling power loads, thereby shaping the power demand into the desired pattern. The research explores the load balancing strategies to reduce the demand fluctuations in the smart grid systems. Centralized and decentralized load balancing methodologies are discussed. For centralized approaches, offline and online exact power allocation methods are investigated by utilizing the geometric water-filling (GWF) approach. Furthermore, decentralized load balancing problem is discussed at power distribution sub-network level. Electrical vehicle (EV) fleeting among the neighbouring charging stations is considered. Load balancing for the whole grid is achieved by local optimization processes via Proximal Jacobian Alternating Direction Method of Multipliers (ADMM) technique. Overall, facilitated by our proposed strategies, the reliability of the electric grid can be enhanced.

Load balancing for smart grid: centralized and distributed approaches

As one of the greatest concerns in the context of smart grid, the load balancing problem is addressed by improving the electrical power efficiency and stability via scheduling power loads, thereby shaping the power demand into the desired pattern. The research explores the load balancing strategies to reduce the demand fluctuations in the smart grid systems. Centralized and decentralized load balancing methodologies are discussed. For centralized approaches, offline and online exact power allocation methods are investigated by utilizing the geometric water-filling (GWF) approach. Furthermore, decentralized load balancing problem is discussed at power distribution sub-network level. Electrical vehicle (EV) fleeting among the neighbouring charging stations is considered. Load balancing for the whole grid is achieved by local optimization processes via Proximal Jacobian Alternating Direction Method of Multipliers (ADMM) technique. Overall, facilitated by our proposed strategies, the reliability of the electric grid can be enhanced.

Static Load Balancing Technique for Geographically Partitioned Public Cloud

Large number of users are shifting to the cloud system for their different kind of needs. Hence the number of applications on public cloud are increasing day by day. Handling public cloud is becoming unmanageable in comparison to other counterparts. Though fog technology has reduced the load on centralized cloud resources to a remarkable extent, still load handled at cloud end is significantly high. Geographic partitioning of public cloud can resolve these issues by adding manageability and efficiency in this situation. Dividing public cloud in smaller partitions opens ways to manage resources and requests in a better way. But, partitioned clouds introduce different ends for submission and operations of tasks and virtual machines. We have tried to handle all these complexities in this paper. Proposed work is focused upon load balancing in the partitioned public cloud by combining centralized and decentralized approaches, assuming the presence of fog layer. A load balancer entity is used for decentralized load balancing at partitions and a controller entity is used for centralized level to balance the overall load at various partitions. In the proposed approach, it is assumed that jobs are segregated first. All the jobs which can be handled locally by fog resources are not forwarded to the cloud layer directly. Those are processed locally by decentralized fog resources. Selection of an appropriate Virtual Machine (VM) for filtered set of job, which are forwarded to cloud environment, is done in three steps. Initially, selecting the partition with a maximum available capacity of resources. Then finding the appropriate node with maximum available resources, within a selected partition. And finally, the VM with minimum execution time for a task is chosen. Results are compared with the results produced in this work with First Come First Serve (FCFS) and Shortest Job First (SJF) algorithms, implemented in same setup i.e. partitioned cloud. This paper compares the Waiting Time, Finish Time and Actual Run Time of tasks using these algorithms. After initial experimentation, it is found that in most of the cases, while comparing above parameters, the proposed approach is producing better results than FCFS algorithm. But results produced by SJF algorithm produce better results. To reduce the number of unhandled jobs, a new load state is introduced which checks load beyond conventional load states. Major objective of this approach is to reduce the need of runtime virtual machine migration and to reduce the wastage of resources, which may be occurring due to predefined values of threshold. The implementation is done using CloudSim.

A Software Agent Based Technique for Load Balancing in Partitioned Cloud

Large number of users are shifting to the cloud system for their different kind of needs. Hence the number of applications on public cloud is increasing day by day. Public clouds considered and is the most convenient platform for common cloud users with generic needs and lesser security concerns. Public cloud can cater to the needs of a large group of users and provide a variety of services. Lower cost and timely availability are the other advantages one expects from public clouds. These features make it very much convenient and attractive choice. But on the other hand, handling public cloud become unmanageable in comparison to other counterparts. Monitoring so many users, tasks and resources are difficult task. Sometimes public clouds are divided on geographically. Geographic partitioning of public cloud can resolve these issues by adding manageability and efficiency in this situation. But, partitioned clouds introduce different ends for submission and operations of cloudlets and virtual machines. This ends for task submission and resource allocation adds complexities also. A concrete mechanism is to be designed for handling the load allocation and processing of the nodes. The proposed work is addressing the same issue by advising a combination of centralized and decentralized load balancing. The main objective of this work is to fix a VM for a cloudlet, which can process it in minimum time and without overloading or underloading the datacenters. Another objective under consideration is to reduce the number of jobs left unhandled due to threshold constraints.

Time Complexity of an Distributed Algorithm for Load Balancing of Microservice-oriented Applications in the Cloud

Microservice architecture is a relatively new cloud application design pattern. Each microservice has a single responsibility in terms of functional requirement, and that can be managed independently from other microservices. This is done using automated cloud orchestration systems. In this paper we analyze the time complexity of an simple swarm-like decentralized load balancing algorithm for microservices running inside OpenVZ virtualization containers. We show that it can offer performance improvements with respect to the existing centralized container orchestration systems.